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Civil infrastructure relies heavily on structural health monitoring systems. However, these systems often encounter challenges due to sensor failures and environmental damage. Consequently, numerous anomalous data points are generated, significantly distorting the accuracy of structural safety assessments. While deep neural networks have emerged as a promising tool for efficiently identifying abnormal data, the meticulous optimization of hyperparameters during training remains a challenge. To address this challenge, this paper introduces a novel approach termed multiple transfer learning, designed to continually enhance a model's classification performance without the need for meticulous hyperparameter configurations. This methodology achieves adaptive training by iteratively migrating across bridge anomaly datasets, bypassing the need for elaborate hyperparameter setting. In this study, five distinct hyperparameter working conditions are established and evaluated to validate the effectiveness of the multiple transfer learning method. The findings highlight the robustness of this approach, demonstrating that multiple transfer learning achieves satisfactory recognition accuracy levels irrespective of the initial hyperparameter setting during network model training. This method circumvents the need for continuous hyperparameters optimization, enabling the adaptive detection of abnormal bridge data. • A training method called multiple transfer learning based on transfer learning is proposed in this paper to solve setting hyperparameters. • The relationship between the number of multiple transfer learning times and the accuracy of anomaly data identification is displayed. • Comparison experiments with different working conditions were set up to verify the effectiveness of multiple transfer learning. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven model for predicting the code-based seismic vulnerability index calibrated on a dataset comprising almost 300 buildings. The vulnerability index, estimated following the Italian Seismic Code, involved rigorous investigations, including geometric surveys, experimental tests, and numerical modelling. Leveraging data from these investigations, encompassing approximately 15 categorical and numerical explanatory variables, the authors developed several regression and classification predictive models, such as Logistic Regression and Artificial Neural Networks (ANN). The optimal models perform binary classification to determine the categorization into two macro-classes, defined by an arbitrary vulnerability threshold. The ANN model stands out as the best performer. When adjusting the vulnerability threshold to obtain a balanced dataset, such a model achieves an accuracy higher than 85%. The paper also discusses the importance of each feature by calculating the SHapley Additive exPlanations (SHAP) values. The proposed model can aid decision-makers in allocating resources effectively to mitigate seismic risks of built environments. • Data-driven model for code-based seismic vulnerability assessment. • 15 explanatory variables are considered. • Regression models perform poorly. • Binary classification is the only reliable approach. • The ANN model has an 85% accuracy on binary classification with balanced dataset. [ABSTRACT FROM AUTHOR]

• Develops a deep Bayesian survival model that integrates deep neural network and Bayesian inference into the survival analysis for rail useful lifetime modelling. • Demonstrates survival analysis is an effective way to handle censored data problems in rail useful lifetime modelling. • Conducts extensive evaluation to verify the effectiveness of this model, using Concordance index, root mean square error, and a proposed CW-index. • The estimated lifetimes obtained by this model are the closest to real lifetimes compared to other commonly used models. • This model can provide the confidence interval of the estimated lifetime, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel monocular vision based 3-dimentional displacement measurement approach for structures. • A stationary monocular camera measurement system is designed to achieve 3-dimensional displacement measurement. • State-of-the-art deep learning approaches are adopted to realize the depth direction displacement measurement. • Two tests are conducted on steel cantilever beams in a laboratory to validate the proposed measurement system. Vibration displacement of civil structures are crucial information for structural health monitoring (SHM). However, the challenges and costs associated with traditional physical sensors make displacement measurement difficult. In recent years computer vision (CV) techniques have been employed for measuring vibration displacement in civil structures. There has been a growing interest in CV-based three-dimensional (3D) displacement measurement, as it provides comprehensive information for structural health assessment. Most existing methods use multi-view geometry, requiring multiple cameras for depth measurement. This paper proposes a new system for measuring the 3D vibration displacement utilising a single camera. Instead of using multi-view geometry, deep neural networks are utilised to learn the depth of scenes from monocular images. Compared with the multi-view methods, the proposed 3D measurement system with monocular vision is more cost-effective and much more convenient to set up and use in practice, avoiding the complicated calibration and object matching between multiple cameras. Experimental tests are conducted in the laboratory to investigate the feasibility of the proposed system. Physical displacement sensors are equipped with the testing structure to provide the ground truth data. The results demonstrate that the proposed monocular 3D displacement system is able to produce reasonable 3D full-field displacement measurement, which makes monocular image based CV system a promising approach to achieve 3D displacement measurement, with its obvious advantages in cost and convenience compared to the traditional sensor-based or multi view CV-based methods. [ABSTRACT FROM AUTHOR]

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied to the web of the section. These loads induce buckling and distortion in the web, ultimately leading to the member's collapse. It is a challenging task to accurately determine the web crippling capacity of a CFSL channel due to its complexity and various influencing factors. This paper presents hybrid soft computing techniques for accurately predicting the web crippling capacity of CFSL channels subjected to two flange load cases. The developed soft computing techniques combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to improve computational efficiency and accuracy. The finite element models of CFSL channels are developed and validated by experimental results and then employed to generate a database, which is used to train machine learning models, including ANN, GA-ANN, and PSO-ANN. Analysis is undertaken on the reliability of existing design formulas for determining the web crippling capacity of CFSL channels. It is shown that the PSO-ANN model outperforms the other models in terms of prediction accuracy. The existing design codes and formulas are not reliable in estimating the web crippling capacity of CFSL channels. However, the proposed model yields good correlation with finite element analysis results. A user- friendly graphical interface tool is developed for the practical design of cold-formed steel lipped channels. • FE model is developed that simulates the web crippling behavior of CFSL channels. • Hybrid ML models are developed for predicting web crippling capacity of CFSL channels. • Reliability of existing formulas for estimating web crippling capacity is investigated. • A user-friendly graphical interface tool is developed for design purpose. [ABSTRACT FROM AUTHOR]

In this paper, discrete element modelling and artificial neural networks (ANNs) were adopted to evaluate seismic performance of a masonry-infilled reinforced concrete (RC) frame. Discrete element models were developed to simulate quasi-static tests of infilled frames and were validated by using experimental data from the literature in terms of ultimate bearing capacities, initial stiffnesses and hysteresis curves. Parametric analysis was conducted based on the validated models to further investigate the influence factors of infilled RC frames and to collect a database for training ANN models. Subsequently, 440 datasets were divided into a training set (70 %), a validating set (15 %), and a testing set (15 %). Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were employed to predict the seismic behavior of a masonry-infilled RC frame. The BPNN model used the Levenberg-Marquardt algorithm exhibited the highest coefficient of determination, and it was better than the RBFNN model with more neurons. Eventually, a practical ANN model was proposed to evaluate the seismic performance of a masonry-infilled RC frame. • Seismic behavior of masonry-infilled RC frames was simulated by the DEM. • Influence factors of seismic capacity of infilled RC frames were investigated. • A BPNN-based model was proposed to predict seismic performance of infilled RC frames. [ABSTRACT FROM AUTHOR]

Recently, wide attention has been paid to develop deep learning-based models for structural damage identification. However, in most of the studies, the pure deep learning-based structural damage identification methods lack physical interpretability and scientific consistency for generalization. Therefore, although such methods can achieve good damage identification results when data with similar damage patterns used for training and testing the network model, they usually give poor predictions in the unseen domain, i.e., the network's extrapolation ability is limited. In this research, a physics-guided deep learning neural network (PGDLNN) is proposed by incorporating the physical loss constructed by structural modal parameters sensitivity analysis into the original Convolutional Neural Network (CNN) for structural damage identification. The model's physical interpretability is improved, which can lead to more accurate damage identification results, especially in the domain with unseen damage patterns in training the network model. Numerical and experimental studies on simply supported beams are carried out to demonstrate the feasibility and effectiveness of the proposed method. The influences of measurement noise and incomplete measurements are also investigated. The results show that the incorporation of physical knowledge to deep learning model can enhance the generalization of the network, and hence improve the accuracy of damage severity quantification. This study not only performs damage localization and quantification simultaneously using the physics-guided deep learning neural network, but also demonstrates the superiority of incorporating physical knowledge into data-driven deep learning models for structural health monitoring. • This paper proposes a physics-guided deep learning approach for damage identification. • Modal sensitivity is integrated into deep neural networks to formulate physics loss. • Structural damage identification with unobserved damage patterns is conducted. • Better identification results are obtained and a better physics generalization is achieved. • Numerical and experimental validations are conducted to validate the approach. [ABSTRACT FROM AUTHOR]

The fragility curve is defined as the conditional probability of failure of a structure, or its critical components, at given values of seismic intensity measures (IMs). The conditional probability of failure is usually computed adopting a log-normal assumption to reduce the computational cost. In this paper, an artificial neural network (ANN) is constructed to improve the computational efficiency for the calculation of structural outputs. The following aspects are addressed in this paper: (a) Implementation of an efficient algorithm to select IMs as inputs of the ANN. The most relevant IMs are selected with a forward selection approach based on semi-partial correlation coefficients; (b) quantification and investigation of the ANN prediction uncertainty computed with the delta method. It consists of an aleatory component from the simplification of the seismic inputs and an epistemic model uncertainty from the limited size of the training data. The aleatory component is integrated in the computation of fragility curves, whereas the epistemic component provides the confidence intervals; (c) computation of fragility curves with Monte Carlo method and verification of the validity of the log-normal assumption. This methodology is applied to estimate the probability of failure of an electrical cabinet in a reactor building studied in the framework of the KARISMA benchmark. [ABSTRACT FROM AUTHOR]

Constructing an interpretable model for the long-term deformation Structural Health Monitoring (SHM) of earth-rock dams is of great significance for improving the safety state evaluation and monitoring effect. In this paper, a physics-data-driven model for the deformation SHM of earth-rock dams is proposed based on deep mechanism knowledge distillation. Firstly, the deterministic model is established based on the Finite Element Model (FEM) and outputs the hydraulic load component curve and aging component curve. Then a regression prediction model (HTSGAN) between influencing factors and deformation measurements at multiple measurement points is established based on the Graph Convolutional Network (GCN) and attention mechanism. Finally, the Teacher-Hydraulic-Time-Seepage Graph Attention Networks (T-HTSGAN) model is established based on the feature-based multi-teacher knowledge distillation using the knowledge of hydraulic loading physics and soil-rock creep physics of the FEM for mechanism constraints. The model effectively solves the problems of poor model interpretability and lack of physics knowledge constraints in previous earth-rock dam SHM models. The research results are applied to a project of a 185.5-meter-high concrete-faced rockfill dam, and the predictive performance of the model is more effective and stable through the comparison of six baseline models. The comparative analysis of the component curves proves the effectiveness of the proposed knowledge distillation method for mechanism constraints and improves the interpretability of the neural network model. Therefore, the model is more suitable for engineering applications. • A new physics-data-driven deformation monitoring model for earth-rock dams is proposed. • Mechanical constraints of dam SHM model is further improved by a new neural network model mechanism constraint method. • The application results of a high CFRD show that the model has high accuracy and good engineering applicability. [ABSTRACT FROM AUTHOR]

To predict the dynamic response of high-speed trains (HSTs) passing through cable-stayed bridges (CSBs), this paper proposed a prediction framework based on the genetic algorithm (GA) and the neural network. Firstly, an HST-CSB coupled dynamic model was established using the rigid-flexible coupling method. Subsequently, a fused Temporal Convolutional Network and Long Short-Term Memory (TCN-LSTM) neural network model was developed, and its hyperparameters were optimized using the GA. The dataset was created based on the HST-CSB coupled dynamic model for training and testing the model. Additionally, the generalization capability of the model after adopting different track deformations was discussed. The main results were as follows: Comparative analysis with various neural network models revealed that the GA-TCN-LSTM model exhibited the most accurate prediction results across different train speeds. The significant temperature fluctuations of the bridge led to a drastic deterioration of the track smoothness, resulting in decreased predictive performance of the neural network models. The GA-TCN-LSTM model could optimize the hyperparameters, demonstrating good robustness and generalization capabilities. The predictive performance of the model improved when using the German low-interference spectrum, attributed to the greater irregularities in the German low-interference spectrum, resulting in larger train dynamic responses and stronger feature characteristics in the training data for the neural network. Different neural network models exhibited varying adaptability to track irregularities, with the GA-TCN-LSTM model demonstrating high accuracy in predicting dynamic responses under different train speeds. • A coupled dynamic model of high-speed train and cable-stayed bridges is established. • A model combining Temporal Convolutional Networks and Long Short-Term Memory network is established. • The genetic algorithm is employed to optimize the hyperparameters to mitigate the impact of them on model. • The generalization capability of the model under different conditions is discussed the performance. [ABSTRACT FROM AUTHOR]

This paper aims to develop a unique artificial neural network (ANN)-based equation as well as MATLAB- and Python-based graphical user interfaces (GUIs) using the most comprehensive and up-to-date database for predicting the behaviour of axially loaded concrete-filled double skin tubular (CFDST) short and slender columns with normal- and high-strength materials. Two machine learning (ML) methods, which are ANN and extreme gradient boosting (XGBoost), are trained and tested using 1721 sets of data, with 129 of them collected from experimental studies and 1592 generated by finite element (FE) simulations. The accuracy of the developed ML models is assessed through comparing their predictions with the experimental and FE results. To demonstrate the effect of each parameter on the predicted results, the SHapley Additive exPlanations (SHAP) method is used. The developed ML models are also used to conduct parametric studies to examine the effect of geometric and material parameters on the predicted results. The accuracy of the ML models and the proposed ANN-based equation in predicting the ultimate axial capacity of CFDST columns is compared with that of six design methods including two design code provisions and four design equations proposed by researchers. A numerical example is presented to illustrate the design procedure of the CFDST column using the proposed ANN-based equation. The results indicate that the ANN model performs better on unseen data than the XGBoost model with lower root mean square error for the test set. The results also show that the ML models and the proposed ANN-based equation are superior to the other design models in prediction accuracy. • 1721 data samples of CFDST columns are used to train and test the ML models. • Optimized ANN and XGBoost models are used to predict the behaviour of CFDST columns. • ANN-based equation, MATLAB- and Python-based GUIs are developed. • Parametric studies are conducted using the trained ML models. • The superiority of the proposed design models over six design equations is shown. [ABSTRACT FROM AUTHOR]

Dynamic analysis of forced-vibration systems in civil engineering could be computationally inefficient or even hard to converge if the systems are stiff or highly complicated. Rapid advances in machine learning make it possible to formulate surrogate models for forced-vibration systems using neural networks. The widely used neural networks such as the convolutional neural network (CNN), recurrent neural network (RNN), etc., usually require a constant sampling rate and data length, thus they are difficult to be implemented for real-time calculation of the dynamic system with varying sampling rates. Recently, the continuous-time state-space neural network (CSNN) has shown the capability to lift these restrictions and has been drawing growing attention from the community. In this paper, we propose a generalized CSNN model for various forced-vibration systems (linear and nonlinear). The CSNN model comprises two sets of independent neural networks aiming to compute the state derivative and system response, respectively. Both neural networks adopt linear and nonlinear layers in parallel, instead of only fully connected nonlinear layers as adopted in the literature. This configuration is aimed to enhance the CSNN model with its capability to recognize the linear and nonlinear behaviors of systems. Additionally, the bias options in the CSNN model are all turned off to improve the stability of the model in the long-term time-series forecast, premised on the assumption that the forced-vibration systems are dissipative systems without drift, which is the most common case in civil engineering. Integration on the state derivative at the current time step is executed to obtain the state at the next time step using the explicit 4th-order Runge–Kutta method. Both numerical and experimental illustrative examples are provided, demonstrating that the CSNN model can achieve high performance and training efficiency with a few hyper-parameters, and thus is highly promising for engineering applications. • A novel continuous-time state-space neural network (CSNN) model is proposed for data-driven modeling of forced-vibration linear/nonlinear systems. • The CSNN model does not require any prior knowledge of the system, and is not restricted to the pre-designated sampling rate and input data length. • The CSNN model has much fewer hyper-parameters than traditional neural network models and is unlikely to induce overfitting. • Numerical and experimental studies demonstrate that the CSNN model is superior to LSTM and PhyCNN in modeling both linear and nonlinear systems. [ABSTRACT FROM AUTHOR]

Several breakthroughs have appeared in different applications due to deep learning which can be applied in Bridge Weigh-In-Motion (BWIM) systems as well. Therefore, deep learning applications should be examined in BWIM systems. However, some deep learning-based solutions have already been proposed in the international literature, they cannot be compared to static methods because available datasets are not appropriate to train and test artificial neural networks. In addition, numerous other aspects have been already considered during tests. In the current paper, a numerically simulated and validated comprehensive dataset is provided. It is designed to meet requirements of incremental development. This dataset makes it possible to benchmark static algorithms and deep learning-based methods on the same dataset. In the second part of the paper, a deep learning-based solution is proposed. The developed solution shows promising results on the provided dataset and also demonstrates the applicability of the dataset. • Deep learning-based algorithm is developed for Bridge Weigh-In-Motion system. • Validated FEA-based influence surfaces and strain gauge data are used as input. • BME-Simulated 1 (BME-S1) dataset with more than 100000 vehicle models is provided. • A BWIM system is developed with class B+ in accordance with COST 323 standard. [ABSTRACT FROM AUTHOR]

[Display omitted] • An innovative two-stage machine learning framework (TMLF) was proposed. • The uncertainties of material and geometrics were considered. • The strengths of the RHS-CHS T-joint follow the gamma distribution. • ANN model shows the highest accuracy in predicting the RHS-CHS T-joints' strength. • The TMLF can accurately predict the distribution of structural component strength. Machine learning technics have been extensively used in the strength prediction of structural components in recent years, nevertheless, these established strength prediction models usually can not address the inherent uncertainties introduced by the material and geometrics of structural components. This paper intends to propose an innovative two-stage machine learning framework for developing probabilistic strength prediction models of structural components with the consideration of uncertainties of material and geometrics based on limited test results. Rectangular hollow section to circular hollow section (RHS-CHS) T-joints are used as an example for evaluating the proposed framework. To this end, 58,744 training and 19,870 validation datasets are first generated through numerical simulation. Three machine learning algorithms are used and evaluated in this paper to develop the best model for predicting the strength of the RHS-CHS T-joints. The analysis results show that the artificial neural networks (ANN) showed the best generalization performance. The combination of the uncertainties of material and geometrics is considered through the Latin hypercube sampling method. Then, a new strength database considering the randomness of the structural parameters (e.g., material and geometrics) are developed through the trained ANN model and the probabilistic distribution of the strength of the RHS-CHS T-joints is analyzed through the Anderson-Darling test method. The analysis results indicate that the random strengths of the RHS-CHS T-joints follow a gamma distribution while the material and geometrics follow the normal distribution. Finally, the machine learning models for probabilistic strength prediction of the RHS-CHS T-joints are developed and validated. The analysis results indicate that the developed machine learning model can accurately capture the distribution of the strength of RHS-CHS T-joints, confirming the efficiency of the proposed two-stage machine learning framework for developing probabilistic strength prediction models of structural components. A software named "Probabilistic strength prediction of RHS-CHS T-joint" is proposed based on the developed probabilistic strength prediction model for practical application. [ABSTRACT FROM AUTHOR]

• An optimization strategy was conducted to obtain a final set of ANN model. • The developed ANN model exhibited superior performance than current codes and empirical equations. • An explicit equation based on Artificial Neural Networks were derived for practical application. In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking into account load eccentricity. To this end, an experimental database of CFST specimens from the literature has been compiled, totaling 1224 individual tests, both under concentric and under eccentric loading. Except for eccentricity, other parameters taken into consideration include the cross section width, height and thickness, the steel yield limit, the concrete strength and the column length. Both short and long specimens were evaluated. The architecture of the proposed ANN model was optimally selected, according to predefined performance metrics. The developed model was then compared against available design codes. It was found that its accuracy was significantly improved while maintaining a stable numerical behavior. The explicit equation that describes mathematically the ANN is offered in the paper, for easier implementation and evaluation purposes. [ABSTRACT FROM AUTHOR]

Highlights • Structural damage prediction using ANN and reduced mode shapes. • The best location of the sensor in shell structures to ensure the best quality of modal information. • Improvement in modal tests, since it is only significant to acquire signals in a limited location. Abstract Delamination is one of the most common failure mode in laminated composites that leads the separation along the interfaces of the layers. The structural performance can be significantly affected by this degradation. Such damages are not always visible on the surface and could potentially lead to catastrophic structural failures. The existence of delamination alters the vibration characteristics of the laminated structures, so if they are detected and measured previously, they can be used as indicator for quantifying health and the potential risk of catastrophic failures. To ensure structural performance and integrity, accurate Structural Health Monitoring (SHM) is crucial. In this study, an optimized methodology for delamination identification on laminated composite plates involving the use of reduced mode shapes and computational tools, i.e., Genetic Algorithm (GA) and Artificial Neural Networks (ANN) is performed. In a first step, the sensor distribution on the surface of the structure was optimized using Fisher Information Matrix (FIM) criteria. After, GA and ANN were applied in order to identify and predict delamination location. A feed-forward based neural network is used in order to detect damage on the laminated plate using data obtained from Finite Element Analysis (FEA). The present methodology identifies damage localization in structures and also quantifies damage severity. The applicability of the technique is demonstrated on laminated plates and results are compared with numerical algorithms. This paper shows the effectiveness of GA and ANN as tools for delamination damage identification problem. The algorithms in their inverse formulations are capable of predicting accurately delamination position in plates-like structures. [ABSTRACT FROM AUTHOR]

Artificial neural networks are computational approaches based on machine learning to learn and make predictions based on data, and have been applied successfully in diverse applications including structural health monitoring in civil engineering. It is difficult to optimize the weights in the neural networks that have multiple hidden layers due to the vanishing gradient issue. This paper proposes an autoencoder based framework for structural damage identification, which can support deep neural networks and be utilized to obtain optimal solutions for pattern recognition problems of highly non-linear nature, such as learning a mapping between the vibration characteristics and structural damage. Two main components are defined in the proposed framework, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the original input vector while preserving the required necessary information, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. Vibration characteristics, such as natural frequencies and mode shapes, are used as the input and the structural damage are considered as the output vector. A pre-training scheme is performed to train the hidden layers in the autoencoders layer by layer, and fine tuning is conducted to optimize the whole network. Numerical and experimental investigations on steel frame structures are conducted to demonstrate the accuracy and efficiency of the proposed framework, comparing with the traditional ANN methods. [ABSTRACT FROM AUTHOR]

Abstract: Pattern recognition is a promising approach for the detection of structural damage using measured dynamic data. Much research of pattern recognition has employed artificial neural networks (ANNs) as a systematic way of matching pattern features. When such methods are used, the ANN design becomes the most fundamental factor affecting performance and effectiveness of the pattern recognition process. The Bayesian ANN design algorithm is proposed in Lam et al. [Lam HF, Yuen KV, Beck JL. Structural health monitoring via measured Ritz vectors utilizing artificial neural networks. Computer-Aided Civil and Infrastructure Engineering 2006;21:232–41] provides a mathematically rigorous way of determining the number of hidden neurons for a single-hidden-layer feedforward ANN. The first objective of this paper is to extend this Bayesian ANN design algorithm to cover the selection of activation (transfer) functions for neurons in the hidden layer. The proposed algorithm is found to be computationally efficient and is suitable for real-time design of an ANN. As most existing ANN design techniques require the ANN model class to be known before the training process, a technique that can automatically select an “optimal” ANN model class is essential. As modal parameters and Ritz vectors are commonly used pattern features in the literature, the second objective of this paper is to compare the performance of these two pattern features in structural damage detection using pattern recognition. To make a fair judgment between the features, the IASC–ASCE benchmark structure is employed in a case study. The results show that the performance of ANNs trained by modal parameters is slightly better than that of ANNs trained by Ritz vectors. [Copyright &y& Elsevier]

Abstract: Structural engineers heavily rely on computer software to perform structural analysis, and they increasingly computerize design procedures to avoid manual repetitions. To benefit fully from the computerization, it is necessary to utilize the domain knowledge contained in a database such as a concrete shear database used in this paper. A knowledge-based system uses a database of knowledge in combination with its retrieval mechanism such as artificial neural networks (ANN) to imitate problem-solving strategy of human. This paper presents an application of the knowledge-based approach, utilizing the shear database and retrieval of information using ANN. The database can be used more extensively than regression of shear strength that had been reported in other literature. As a demonstration, two models are developed and compared with design equations. The first model estimates shear strength, and the second model systematically provides conservative estimation. Although both models already outperform all existing design equations, they can be easily revised for further improvement whenever additional experimental data sets become available. [Copyright &y& Elsevier]

Previous studies have applied artificial neural networks (ANN) with the back-propagation learning algorithm for diagnosing pre-stressed concrete piles. Recent developments of ANN breed a new form of network architectures for modeling this specific type of classification problems: Probabilistic Neural Networks (PNN). This paper presents this probabilistic neural network architecture for diagnosing the causes of prestressed concrete pile damages. In this paper, the use of neural networks for construction is first presented and the various types of neural networks are introduced. Then, based upon a set of data collected from the previous study on prestressed concrete pile diagnosis, the common features of concrete pile damage and their causes are identified. The PNN model and its architecture are described. Using the set of data, the network is trained and the procedural steps are described. A random seed approach for cross validation is used to assess the reliability of the model. Lastly, the result of the network training is discussed and analyzed, which demonstrates the robustness of the model developed. [Copyright &y& Elsevier]

• A generative adversarial network (GAN) based method was proposed to establish a synthesized crack image dataset with pixel-wise annotations. • The generated crack annotations and crack images during the training epochs were demonstrated. • Comparative study was conducted to validate the performance of the synthesized crack image dataset for training the crack detection DNN. Crack detection is vital to maintain the structural safety of in-service bridges, which is an increasing demand in the industrial community. The deep learning-based crack detection is an emerging method that provides a novel way to deal with this problem. Although, the deep neural networks (DNN) can learn to detect cracks themselves, they require large numbers of crack images to learn the features of cracks in the real world. Besides, except for the crack forms, there always exist all kinds of noise motifs that will disturb the correct detection of crack regions. The lack of crack sample images has been an obstacle for the improvement of deep learning-based crack detection method. This paper proposes a generative adversarial network (GAN) based method to establish a synthesized crack image dataset with pixel-wise annotations, which provides a novel way aside from the traditional data augmentation method. The Deep Convolutional GAN (DCGAN) model was adopted for the generation of synthesized crack annotations while the Pixel2Pixel model was used to generate the corresponding synthesized crack images. The generated crack annotations and crack images during the training epochs were demonstrated to show how the GANs learn to generate the synthesized images. Moreover, comparative study was conducted to validate the performance of the synthesized crack image dataset for training the crack detection DNN. The results showed the DNN trained by the synthesized images can achieve 74.34% of the MeanIoU that was reached by the same DNN model trained with real images. As for the way of using the synthesized and real crack images for training a crack detection DNN, the way that use the synthesized crack images for pre-training and then use the real images for fine-tuning is better than the way that directly mix the synthesized and real crack images for training. This work provides reference for the GAN-based establishment of crack image dataset and the evaluation of the image quality for training crack detection DNNs. [ABSTRACT FROM AUTHOR]

Two vibration problems are discussed in the paper: i) identification of the fundamental (first) period of building vibration T1, ii) simulation of the maximal displacement of a selected building floor D. The problems are related to prefabricated, five-storey flat buildings. The analysis is based on measurements of paraseismic excitations and dynamic responses of buildings. The identification problem is formulated as the evaluation of a relation between structural and soil basement parameters, and the fundamental period T1. The simulation problem corresponds to the evaluation of a relation between inputs composed of structural, soil and excitation parameters and the displacement output D. Back-propagation neural networks (BPNNs) were successfully used in [9] to the analysis of identification problem. In the paper this problem is reanalized by BPNNs in which the soil basement parameter Cz is changed from the crisp variable into a linguistic variable Cl. The compression of discrete response spectrum values is additionally carried out to decrease the BPNN size for inputs in the simulation problem. [Copyright &y& Elsevier]

• Innovation application of arterial neural network (ANN) on predicting thermal behavior of concrete, steel reinforcement and fiber reinforced polymers exposed to fire. • The predicted and experimental results were in good agreement. • This paper presents developed software tool that implements proposed ANN for parametric study and designing purposes. • Unique parametric studies have been carried out on reinforced concrete columns through the proposed ANN. This paper presents a novel application of artificial neural networks (ANNs) for predicting complete temperature profiles with varying time for circular reinforced concrete (RC) columns exposed to fire. The application is valid for un-strengthened columns or columns strengthened with fibre reinforced polymers (FRP) and isolated with different types and thicknesses of insulation materials under varying fire exposure condition. In addition, prediction of the temperature variation with time for constituent materials of the column such as concrete, reinforcement steel and FRP sheets is conducted. To reach this end, extensive nonlinear finite element analysis was conducted using ANSYS software to build database of 1200 training samples with different column diameters, concrete cover thicknesses, thermal conductivity curves, specific heat curves, insulation types and thicknesses. The prediction results of ANN model is validated against published full-scale experimental studies which reveal good agreement between experimental and predicted results. The novelty of this technique is the ability of ANN model to predict rapidly and efficiently the temperature–time for any part of continent materials of reinforced concrete column many in case scenarios of parameters; this is especially useful in avoiding the time-consuming and costly full-scale experimental fire tests. Moreover, this study encourages designers to use FRP in design applications in spite of the threat of fire conditions, by utilizing ANN as backend calculation provider for software design tool with graphical user interface presented in this paper. [ABSTRACT FROM AUTHOR]

The present paper deals with the investigation of the ability of Artificial Neural Networks (ANN) to reliably predict the r/c buildings’ seismic damage state. In this investigation, the problem was formulated as a problem of approximation of an unknown function as well as a pattern recognition problem. In both cases, Multilayer Feedforward Perceptron networks were used. For the creation of the ANNs’ training data set, 30 r/c buildings with different structural characteristics, which were subjected to 65 actual ground motions, were selected. These buildings were subjected to Nonlinear Time History Analyses. These analyses led to the calculation of the buildings’ damage indices expressed in terms of the Maximum Interstorey Drift Ratio. The influence of several configuration parameters of ANNs to the level of the predictions’ reliability was also investigated. In order to investigate the generalization ability of the trained networks, three scenarios were considered. In the framework of these scenarios, the ANNs’ seismic damage state predictions were evaluated for buildings subjected to earthquakes, neither of which are included to the training data set. The most significant conclusion of the investigation is that the ANNs can reliably approach the seismic damage state of r/c buildings in real time after an earthquake. [ABSTRACT FROM AUTHOR]

Recent researches are directed towards the regional seismic risk assessment of structures based on a bridge inventory analysis. The framework for traditional regional risk assessments consists of grouping the bridge classes and generating fragility relationships for each bridge class. However, identifying the bridge attributes that dictate the statistically different performances of bridges is often challenging. These attributes also vary depending on the demand parameter under consideration. This paper suggests a multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes. The proposed methodology helps identify the relative importance of each uncertain parameter on the fragility curves. Results from the case study of skewed box-girder bridges reveal that the ground motion intensity measure, span length, and column longitudinal reinforcement ratio have a significant influence on the seismic fragility of this bridge class. [ABSTRACT FROM AUTHOR]

• A numerical database of 450 CFST columns is developed using ABAQUS. • The results of 531 CFST columns are used to train and test the optimised ANN model. • Predictive equations and Graphical User Interface are developed for CFST columns. • Strength reduction factors are proposed using Monte Carlo simulation. • The proposed ANN model and equations predict well the strengths of CFST columns. This paper presents an optimised Artificial Neural Network (ANN) model for predicting the ultimate axial strengths of concentrically loaded Concrete-Filled Steel Tubular (CFST) short and slender columns strengthened with Carbon Fibre-Reinforced Polymer (CFRP). Since experimental data on CFRP strengthened CFST columns is limited, an accurate Finite Element (FE) model is developed and used to provide additional numerical data. A multi-layered feed-forward back-propagation network is proposed, optimised, and trained using the results of 76 experimental tests and 450 generated FE models. The accuracy of the ANN model is assessed through comparing its computed results with experimental data. A reliability analysis is performed using Monte Carlo Simulation (MCS) to evaluate the safety of the solutions computed by the ANN model. ANN-based equations and Graphical User Interface (GUI) are developed based on the trained ANN model for the determination of the ultimate axial strengths of CFST columns. The results show that the developed ANN model is capable of accurately predicting the ultimate axial strengths of CFRP strengthened CFST columns with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

The estimation of the maximum displacements of RC beams subjected to impact loads is of pivotal importance to define demand models to be employed in structural design. The dynamic behavior is quite complex and the maximum displacements can be obtained through experimental tests or high-fidelity FEM models, while simplified analytical models accounting for few degrees of freedom have generally low accuracy. In this context, this paper presents a study on the training and interpretation of a machine-learning model that strategically combines several algorithms for the said purpose. To train the model, a comprehensive database of 342 samples of rectangular simply supported beams under rigid impact loads is adopted. Seven ML regression algorithms are adopted in this study to develop a predictive model: (1) Support Vector Regression (SV R), (2) Gaussian Process Regression (GPR), (3) Random Forest Regressor (RFR), (4) XGBoost , (5) CatBoost , (6) LightGBM , and (7) Artificial Neural Networks (ANN). Finally, a stacking model combining SV R , GPR , RFR , XGBoost , CatBoost and ANN is built to improve accuracy, wherein 75% and 25% of the data are used for training and validation, respectively, in combination with the 5 cross-validation technique. This effort resulted in approximately 90% validation accuracy exceeding current mechanics-based/semi-empirical models. Finally, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the input features; the most influential factors are impact velocity and impactor mass which are representing the initial kinetic energy. Moreover, a full parametric analysis of the proposed model is provided and the influence of each input is discussed. The model is finally applied to perform a structural check for three damage levels. The proposed model can be used in practical applications where a reliable estimation of the maximum displacements of RC beams under impact loading is required. • Dataset of 342 samples of rectangular RC beams subjected to impact loads. • Seven ML regression algorithms are compared to predict maximum displacements. • Stacking model combining SV R , GPR , RFR , XGBoost , CatBoost and ANN. • SHapley Additive exPlanations (SHAP) algorithm to estimate importance of the inputs. • A full parametric analysis of the proposed model is provided. [ABSTRACT FROM AUTHOR]

Nowadays, Fiber Reinforced Polymers are extensively applied in the field of civil engineering due to their advantageous proprieties such as high strength-to-weight ratio and high corrosion resistance in aggressive environments. It is well-known that the compressive strength of concrete significantly increases if a lateral confining pressure is provided. The present paper aims to present an analytical model, able to predict the strength of FRP-confined concrete, which is based on Artificial Neural Networks . The innovation of the proposed model consists of a formulation of an analytical relationship that does not consider the traditional effectiveness parameter commonly found in the models presented in the literature. An extensive experimental database was used to define the variables of the proposed equations. The proposed model is recommended for circular columns with continuous FRP wrapping. The validity of the predictions is indicated through a parametric study and the accuracy is tested by an experimental versus theoretical comparison. An additional comparison is shown by considering the theoretical predictions obtained from the proposed model and the outcomes of equations adopted by important international design codes. The results evidence that the proposed model is adapt for the design of FRP-confined concrete and guarantees an improved accuracy with respect the available competitors. [ABSTRACT FROM AUTHOR]

• Deep neural network is employed for wind pressure prediction for the first time. • Errors of all taps on roof are exhaustively analysed by multiple error metrics. • The accuracy in roof ridge and corner bay is evidently higher than the literature. • A nested DNNs model is proposed to improve the accuracy in the corner zone. Although the problems of wind pressure prediction on roofs have been studied extensively, the prediction accuracy is still unsatisfactory owing to the limited capacity of shallow learning of artificial neural networks (ANNs), especially in the areas characterized by flow separation. Moreover, the literatures have only conducted error analysis on part of the predicted taps instead of all taps, and have not made precision comparison with related studies. In this paper, the model of deep neural networks (DNNs) with the ability of deep learning are built to predict the mean and root-mean-square (RMS) wind pressure coefficients on low-rise buildings. For quantitatively comparing the presented results with those from the literature, the same buildings from the NIST-UWO database as studied by the literature are also predicted in this work. In order to improve the DNNs model for predicting the corner zone with higher pressure gradients, a nested DNNs model is further proposed by returning the mean coefficients predicted by one DNNs model as the input of another DNNs model for the RMS coefficients. The prediction results of the DNNs model are also compared with those from the methods of random forest (RF) and general regression neural network (GRNN). The study shows that the DNNs model obviously enhances the prediction accuracy around the roof ridge and the corner bay in comparison with the ANNs model. For the mean or RMS coefficients of all the taps on the whole roof, the correlation coefficient between predicted and experimental results exceeds 0.997, the mean-square-error (MSE) is less than 5 %, and the relative errors of>95 % predicted samples are lower than 10 %. The prediction accuracy in the corner zone is further improved by the nested network where the samples within errors less than 5 % are evidently increased and the errors > 20 % disappear. Also, the prediction results from the DNNs model are apparently better than those from RF and GRNN. This study could provide a benchmark for future studies on the prediction of mean or RMS wind pressure coefficients on the roofs. [ABSTRACT FROM AUTHOR]

This paper presents a novel data-driven approach for active structural control through the use of deep reinforcement learning, wherein, the control system learns to react in an optimal manner through a training process that utilizes deep neural networks within a reinforcement learning framework. The key advantage of this paradigm is the data-driven approach to active control which helps circumvent the need for high-fidelity modeling that typically requires extensive prior knowledge about the structure of interest. Furthermore, the proposed framework is applicable for designing a variety of active controllers, and different external load types, for example, wind and seismic loads for any desired building. The efficacy of the proposed framework is demonstrated in the context of seismic response control through three numerical case studies. The results confirm that the proposed approach yields significant structural response reductions in the linear and nonlinear regimes. Furthermore, implementation issues such as sensitivity to structural property variations, and time delay are thoroughly investigated. • Data-driven active control framework circumventing detailed modeling using deep RL • Customizable environment for various building geometries and control systems • Active controller trained for linear/nonlinear buildings and tested by unseen motions • Trainable using synthetic ground motion data and generalizable to real earthquakes • A robust data-driven control model to system property variations and time delay [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive study of mechanical characteristics of a reconfigurable origami-inspired structure using Finite Element and Artificial Neural Network (ANN) approaches. We introduce a design of crease section in the proposed reconfigurable structure, which undergoes enormous and complicated deformation in the folding and unfolding process. Although this crease design can make the deploying process more straightforward, it can jeopardize the stability and life cycle of the structure. We explore how the geometric parameters of the design affect the stability and fatigue failure, including length ratio, total height, story's height, thickness, crease indexes, and circumscribed circle's radius. In order to reduce the computational time, we develop an ANN employing the obtained Finite Element method (FEM) results. The ANN results demonstrate that decreasing the circumscribed circle's radius, the length ratio, and the total height enhance the stability of the origami-inspired structure. It is found that crease indexes affect stability based on the radius of the circumscribed circle. In addition, we investigate how these parameters simultaneously contribute to this design's buckling load and life cycle. The results provide a detailed design parameter characteristic map that can be used to optimize origami structure performance. • Study the stability and fatigue failure of a reconfigurable origami-inspired structure using Kresling pattern. • Develop an Artificial Neural Network and use the Finite Element Method available in ANSYS software. • The stability is increased with the decrease of the length ratio (b/a) and the radius of the circumscribed circle. • Increasing the thickness of the structure leads to an increase in the buckling load. • Decreasing the length ratio and total height can improve the life cycle and stability. [ABSTRACT FROM AUTHOR]

• This paper presents a database of 287 reinforced concrete slabs failing in shear. • Artificial neural networks are used to find a matrix-based expression. • The developed expression is proposed for the assessment of slab bridges. • As mechanical models are lacking, the presented expression can be used to predict the shear capacity of slabs. • The matrix-based expression gives insight in the sensitivity to certain parameters. According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, and thus leads to overly conservative values. While mechanics-based models have attempted to describe the problem of one-way shear in concrete slabs under concentrated loads, this problem is still not fully understood. Therefore, this paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load that are as good as possible based on 287 test results obtained from the literature. The methods used for this purpose are: (i) the development of the database with experimental results from the literature, and (ii) the development of the ANN-based matrix formulation. For the latter purpose, many thousands of ANN models were generated, based on 475 distinct combinations of fifteen typical ANN features. The proposed "optimal" model yields maximum and mean relative errors of 0.0% for the 287 datapoints. Moreover, it was illustrated to clearly outperform (mean V test / V ANN = 1.00) the Eurocode 2 provisions (mean V E,EC / V R,c = 1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed in this work, which results in an improvement of the current assessment procedures. [ABSTRACT FROM AUTHOR]

• Damage detection in structures by applying using artificial neural network (ANN) and cuckoo search (CS) algorithm. • Structural damage localization and quantification in bridges and beam-like structures. • Improve ANN training parameters using CS. • ANN combined with CS (ANN-CS) is accurate and cost effective. This paper presents a new approach for damage detection in structures by applying a flexible combination based on an artificial neural network (ANN) and cuckoo search (CS) algorithm. ANN has become one of the most powerful tools employing computational intelligence techniques to tackle complex problems in numerous fields. However, due to the application of backpropagation algorithms based on gradient descent, a major drawback of ANN is the common problem of local minima that acts as a great hindrance to the search for the best solution. To overcome this disadvantage, we propose to combine ANN with evolutionary algorithms based on global search techniques. This paper employs CS to improve ANN training parameters (weight and bias) by minimizing the difference between real and desired outputs and then using these parameters to generate the network. Two numerical models, comprising a steel beam calibrated using experimental measurements and a large-scale truss bridge, are used to assess the robustness of the proposed approach. The results demonstrate that ANN combined with CS (ANN-CS) is accurate and requires a lower computational time than ANN, and evolutionary algorithm (EA) alone in terms of structural damage localization and quantification. [ABSTRACT FROM AUTHOR]

A critical challenge in structural control is the design of controllers capable of rapid adaptation to uncertainties, including changes in local dynamics and unknown parameters. A solution is the use of data-based controllers, but these typically require pre-training and may be computationally expensive to utilize. In this paper, the authors proposed a fast neuro-controller capable of pure online learning. The controller is built using an ensemble of recurrent neural networks (ERNNs) based on long short-term memory (LSTM) cells running in parallel. A multi-rate sampler is used to sequentially construct the individual RNN input vectors using local measurements at sampling rates pre-determined from the structure's natural frequencies. Each RNN performs an individual estimation with the associated sampling rate, and these estimations are assembled into an attention and a dense layer to produce an estimated control force. Critical advantages of the proposed architecture are (1) rapid online adaptation and processing; (2) capability to adapt to local dynamics and non-stationarities; and (3) pure online learning architecture minimizing reliance on pre-training. First, the performance of the ERNNs control algorithm is validated on an actively controlled single-degree-of-freedom system, and stability of the controller is demonstrated. Second, the ERNNs is simulated on a full-scale 20-story building equipped with a semi-active tuned liquid wall damper (TLWD) system that consisted of distributed semi-active tuned liquid multiple column dampers. The ERNNs controller effectively improved the passive performance of the control strategy and performed similarly to that of a full state-feedback sliding mode controller under multi-hazard scenario. The evolution of attention weights under both the wind and seismic hazards demonstrated that the importance of each RNN follows the importance of the structural frequency responses, showing that the controller was capable of capturing and adapting fast-changing dynamics. • A novel pure data-driven neural controller for multi-hazard mitigation. • Fast online adapting capability to system dynamic uncertainties. • The proposed controller is robust to uncertainty in system dynamic properties. • The proposed controller provides great control with limited measurements. • Fast computational speed for preventing control latency. [ABSTRACT FROM AUTHOR]

• This work studied web crippling behaviour of aluminium alloy lipped channels. • Experimental tests, finite element models, and deep neural network were built. • The effects of related factors on web crippling strength were studied. • The prediction accuracy of current design guidelines was assessed. • New formulas for web crippling strength were proposed. This work presents a deep-learning model referred to as a deep belief network (DBN) to investigate the end-two-flange (ETF) web crippling behaviour of roll-formed aluminium alloy lipped channels (both unfastened and fastened to the supports) with centred and offset web circular holes. A total of 1,080 data points was generated for training the DBN, using an elasto-plastic finite element model that was validated using 30 new experimental results presented in this paper. When the DBN predictions were compared to the experimental results, they were found to be 5% conservative on an average. A parametric analysis was then carried out using the DBN predictions, to study the effects of hole size, hole position, section thickness, and the bearing plate on web crippling strength of perforated roll-formed aluminium alloy channels. The DBN predictions were utilised to evaluate the performance of the current design rules of American Iron and Steel Institute (AISI 2016), Australian and New Zealand Standards (i.e., AS/NZS 1664, AS/NZS 4600:2018), and Eurocode (CEN 2007). The current design standards were demonstrated to be over conservative in predicting the web crippling strength of roll-formed aluminium alloy perforated channels. From the parametric analysis, new web crippling strength and web crippling strength reduction factor formulae for roll-formed aluminium alloy perforated channels were proposed. Additionally, a reliability analysis revealed that the proposed equations accurately predicted the ETF web crippling strength of roll-formed aluminium alloy perforated lipped channels. [ABSTRACT FROM AUTHOR]

• A practical vibration-based CNN combined FE model and field-testing data is proposed for in-service steel railway bridges. • A data augmentation strategy is proposed to classify various acceleration responses related to damage scenarios. • t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient weighted Class Activation Mapping (Grad-CAM) are used for visualization of feature extraction and feature mapping. • The proposed method can be used to assess the health state of structure to fulfil the regulatory, such as Australian Standard, and safety aspects of steel railway bridges. • The proposed techniques can be utilized for rail infrastructure management without the need of involving specialized expertise and complex signal processing. Railway bridges exposed to extreme environmental conditions can gradually lose their effective cross-section at critical locations and cause catastrophic failure. This paper has proposed a practical vibration-based deep learning approach for damage classification of various extents and degrees of cross section losses due to damages like corrosion in operational railway bridges using vibration-based Convolutional Neural Networks (CNN)s. Firstly, field testing of an in-service railway bridge is conducted and the modal parameters of the bridge are obtained to validate the developed Finite Element (FE) model of the bridge. In the next phase, corrosion scenarios of the main bridge members are generated as various quantities of cross section losses of these members by the validated FE following the Australian Standard AS7636. In the deep learning part, a 1D CNN aligned with novel specific data augmentation strategies is developed to classify various acceleration responses related to each damage scenario simulated by the validated FE. Furthermore, a visualization of feature extraction and feature mapping using t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) is illustrated. The case studies on the simulated and field data-validated FE model results applying background noises and variations, and the real field testing data suggest that the proposed method can reach a perfect damage classification close to an accuracy of 100%. [ABSTRACT FROM AUTHOR]

Bond strength estimation plays an important role in structure engineering. This paper proposes to adopt machine learning approaches to conduct a data-driven analysis of bond strength between ultra-high performance concrete (UHPC) and reinforcing bars. To make up for the lack of experimental data, a new database is established by integrating 557 instances from several published works. A total of nine machine learning models which can be divided into three types are implemented to train the bond strength estimators based on the database, including linear models, tree models, and artificial neural networks. Four strong metrics, i.e. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R 2), and Ratio of Accurate Estimation (RACC), are used to evaluate the performance of models. Among them, Artificial Neural Network and Random Forest achieve great estimation performances in the top two, which far exceed the empirical formulas. They have 74% and 73% of estimated data to keep the relative error within 10%, respectively. The statistical relative importance of different factors from tree models consistently shows that the ratio of embedded depth to the diameter of reinforcing bars has a significant impact on the bond strength of UHPC, which is conformable with the observations in experiments. • The largest database of bond strength between UHPC and reinforcing bars was built. • Nine machine learning models were used to train the bond strength estimator. • The ratio of the embedded depth had a significant effect on the bond strength. [ABSTRACT FROM AUTHOR]

This paper explores the applicability of artificial neural networks (ANN) for predicting the spread of structural response under the presence of uncertain parameters described as random fields. The use of ANN is carried out in combination with Monte Carlo simulation (MCS) for calculating response statistics in stochastic analysis of structural systems using finite elements. To this extent, the ANNs are trained with a few samples, following a conventional MCS procedure and used henceforth to predict the stochastic response for the rest of samples. The basic idea is to achieve a dimensionality reduction of the input ANN training space by using as input vector the random phase angles of the spectral representation method instead of the random variables describing the uncertain input parameters. A further improvement of the efficiency of the proposed approach is achieved by exploiting the uniform distribution of the random phase angles, in order to span efficiently the training space using a latin hypercube sampling (LHS) technique. The advantage of this approach over conventional computation of stochastic response via a standard stochastic finite element-based MCS is the fast and reliable prediction of the required response sample space which can be accomplished at a fraction of computing time and is independent of the size of the finite element model. Numerical results are presented, demonstrating the efficiency and the applicability of the proposed methodology as well as its distinct advantages over existing ANN-based stochastic finite element methodologies (SFEM). [ABSTRACT FROM AUTHOR]

In vibration-based structural health monitoring, changes in the natural frequency of a structure are used to identify changes in the structural conditions due to damage and deterioration. However, natural frequency values also vary with changes in environmental factors such as temperature and wind. Therefore, it is important to differentiate between the effects due to environmental variations and those resulting from structural damage. In this paper, this task is accomplished by predicting the natural frequency of a structure using measurements of environmental conditions. Five methodologies – multiple linear regression, artificial neural networks, support vector regression, regression tree and random forest – are implemented to predict the natural frequencies of the Tamar Suspension Bridge (UK) using measurements taken from 3 years of continuous monitoring. The effects of environmental factors and traffic loading on natural frequencies are also evaluated by measuring the relative importance of input variables in regression analysis. Results show that support vector regression and random forest are the most suitable methods for predicting variations in natural frequencies. In addition, traffic loading and temperature are found to be two important parameters that need to be measured. Results show potential for application to continuously monitored structures that have complex relationships between natural frequencies and parameters such as loading and environmental factors. [ABSTRACT FROM AUTHOR]

• By conducting an extensive parametric study including 475 finite element model (FEM) of steel–concrete composite beams (SCCB), an attempt was made to provide a reliable database. • The Lateral distortional buckling (LDB) strength of SCCB as one of the failure modes has been investigated. • The MR, and ANN have been used to predict the formulae for better prediction of the LDB of SCCB by practical engineers. • The accuracy of the developed formula is verified versus the actual data using several suitable criteria. • The existing predictions given by the researchers were compared with the formula obtained from the MR, and ANN. Lateral distortional buckling (LDB) mode has been investigated as one of the failure modes in steel–concrete composite beams (SCCB). The LDB mode in such beams is known by lateral displacement accompanied by rotation of the compressed bottom flange that occurs due to the web distortion. However, many studies have been conducted to investigate the behavior of this failure mode, no equation has yet been found that can accurately calculate the ultimate LDB resistance of SCCB. Consequently, in the current paper, by conducting an extensive parametric study including 475 finite element models (FEM), an attempt was made to provide a reliable database. Then, based on the provided database, two methods of artificial neural network (ANN) and multiple regression (MR) were employed, and based on them, high-precision equations were proposed to predict the ultimate LDB strength of SCCB. Finally, the proposed formulas were compared with the existing formulas for estimating the LDB strength of SCCB. The results showed that the proposed formulas not only present a reasonable accuracy compared to the existing formulations but also can be used by engineers as practical equations in the design process of SCCB. [ABSTRACT FROM AUTHOR]

• Machine learning (ML)-based approach is developed for seismic failure mode and maximum bearing capacity prediction for corroded RC columns. • The best models for seismic failure model prediction is the RF and CatBoost model which has an accuracy of 89%. • The best model for bearing capacity prediction is the CatBoost model which has a R2 of 0.92. • The ML-based models can conveniently predict the life-cycle seismic performance of RC columns. Corrosion of steel reinforcements is a major factor that will adversely affect the seismic performance of the reinforced concrete (RC) columns. This paper investigates the application of machine learning (ML)-based approach for seismic failure mode and maximum bearing capacity prediction for corroded RC columns. A comprehensive database consisting of 180 cyclic tests of corroded RC columns are collected. Six ML algorithms including three single learning methods (k -Nearest neighbors, Decision tree, Artificial neural network) and three ensemble learning methods (Random forest, AdaBoost, CatBoost) are selected to develop the predictive model. The performance of the six models are evaluated and the application of ML-based approaches for life-cycle seismic performance assessment of RC column is demonstrated with a case-study column. The results show that the Random forest and CatBoost models have the best performance for seismic failure mode prediction with an accuracy of 89%. The best model for bearing capacity prediction is the CatBoost model which has a R2 of 0.92, and the CatBoost model is superior to the traditional mechanism-based code models for bearing capacity prediction. The ML-based models can conveniently predict the seismic failure mode and bearing capacity of RC columns in its life-cycle context without complicated numerical simulations or theoretical calculations. [ABSTRACT FROM AUTHOR]

• A fiber model is described for circular DCFST slender columns with ultra-high-strength concrete. • Constitutive laws for ultra-high-strength concrete with confinement effects are included. • New numerical solution algorithms based on King-Werner's approach are developed. • The behavior and design of slender DCFST columns loaded eccentrically are presented. • The fiber model and design equations developed by the ANN yield accurate solutions. The use of ultra-high-strength concrete in double-skin concrete-filled steel tubular (DCFST) composite columns has received considerable attention in recent years. However, no numerical study on the behavior of eccentrically loaded circular DCFST slender beam-columns made of ultra-high-strength concrete has been reported. This paper proposes computational modeling procedures for accurately predicting the axial load–deflection responses and axial load-moment interaction diagrams of circular DCFST slender beam-columns filled with ultra-high-strength concrete when subjected to the combined action of bending and axial compression. The computational procedures based on the fiber element methodology incorporate the effects of concrete confinement, second-order and initial geometric imperfection on the behavior of DCFST slender beam-columns. The enhanced King-Werner's algorithm is implemented in the fiber-based procedures to solve the nonlinear moment equilibrium equation. The accuracy of the present model is verified with experimental results published in the literature and finite element analysis results. The numerical results reveal that the confined concrete model can capture the response of ultra-high-strength concrete with a sudden drop after reaching the peak load and the ductility in the softening branch. The significance of several influencing factors on the structural behavior is investigated in the comprehensive parametric study including 154 specimens with a wide range of column geometric and material properties. The design of circular DCFST slender beam-columns with ultra-high-strength concrete in accordance with Eurocode 4 is discussed. The artificial neural network (ANN) model is developed to propose new empirical formulas for evaluating the member resistance of DCFST slender beam-columns. The proposed equations give accurate and reliable predictions of the column's resistance and can be employed in practical design. [ABSTRACT FROM AUTHOR]

• A recursive long short-term memory network approach for predicting nonlinear structural seismic response is proposed. • A recursive mechanism is adopted for predictions with arbitrary lengths and sampling rates. • The LSTM network is applicable to ground motions with different spectral characteristics and amplitudes. • The LSTM network achieves extremely low computational cost compared with the traditional methods. Artificial neural networks have been used to predict nonlinear structural time histories under seismic excitation because they have a significantly lower computational cost than the traditional time-step integration method. However, most existing techniques require simplification procedures such as downsampling to maintain identical length and sampling rates, and they lack sufficient accuracy, generality, or interpretability. In this paper, a recursive long short-term memory (LSTM) network was proposed for predicting nonlinear structural seismic responses for arbitrary lengths and sampling rates. Referring to the traditional integral solution method, the proposed LSTM model uses the recursive prediction principle and is therefore applicable to structures and earthquakes with different spectral characteristics and amplitudes. The measured ground motions and multilayer frame structures were used for model training and validation. The rules of hyperparameter selection for practical applications are herein discussed. The results showed that the proposed recursive LSTM model can adequately reproduce the global and local characteristics of the time history responses on four different structural response datasets, exhibiting good accuracy and generalization capability. [ABSTRACT FROM AUTHOR]

• Two soft computing models are developed for the estimation of masonry compressive strength. • The unit and mortar strengths and the height to thickness ratio have been found as main influencing parameters. • Proposed ANN model proves quite more accurate compared to existing codes and literature models. • A database of 410 individual specimens has been compiled for the development and evaluation of the models. Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature. [ABSTRACT FROM AUTHOR]

Abstract: The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry. [Copyright &y& Elsevier]

Abstract: This paper investigates the effectiveness of two adaptive control strategies for modulating the control force of variable friction dampers (VFDs) that are employed as semi-active devices in combination with laminated rubber bearings for the seismic protection of buildings. The first controller developed in this study is an adaptive fuzzy neural controller (AFNC). It consists of a direct fuzzy controller with self-tuning scaling factors based on neural networks. A simple neural network is implemented to adjust the input and output scaling factors such that the fuzzy controller effectively determines the command voltage of the damper according to current level of ground motion. A multi-objective genetic algorithm is used to learn the shape of the activation functions of the network. The second controller is based on the simple adaptive control (SAC) method, which is a type of direct adaptive control approach. The objective of the SAC method is to make the plant, the controlled system, track the behavior of the structure with the optimum performance. Here, SAC methodology is employed to obtain the required control force which results in the optimum performance of the structure. For comparison purposes, an optimal linear quadratic Gaussian (LQG) controller is also developed and considered in the simulations together with maximum passive operation of the friction damper. The results reveal that the developed adaptive controllers can successfully improve the seismic response of base-isolated buildings against various types of earthquake. [Copyright &y& Elsevier]

Abstract: Following the Eurocode 3 philosophy, it is expected that the design of elliptical hollow section (EHS) tubes will be based on the slenderness concept, which requires the calculation of the EHS critical stress. The critical stress of an EHS tube under compression may be associated with local buckling, distortional buckling or flexural buckling. The complexity in deriving analytical expressions for distortional critical stress from classical shell theories, led us to apply Artificial Neural Networks (ANN). This paper presents closed-form expressions to calculate the distortional critical stress and half-wave length of EHS tubes under compression, using ANN. Almost 400 EHS geometries are used and based solely on three parameters: the outer EHS dimensions (A and B) and its thickness (t). Two architectures are shown to be successful. They are tested for several statistical parameters and proven to be very well behaved. Finally, some simple illustrative examples are shown and final remarks are drawn concerning the accuracy of the closed-formed formulas. [Copyright &y& Elsevier]

Abstract: The punching shear strength of two way slabs without shear reinforcement and without unbalanced moment transfer is estimated using both neural networks and new simplified punching shear equations. An artificial neural network (ANN) was used to predict the punching shear strength of internal slab–column connections. Neural network analysis is conducted using 244 test data available in the literature and experiments conducted by the authors to evaluate the influence of concrete strength, reinforcement ratio and slab effective depth on punching shear strength. A wide range of slab thicknesses (up to 500 mm) and reinforcement ratios were used. In general, the results obtained from the neural network are very close to the experimental data available. The test results were used to develop two new simplified practical punching shear equations. The equations also showed a very good match with available experimental data. Four equations for the punching shear strength prescribed in well-known specifications were evaluated based on the available experimental results. This paper includes a discussion of the parameters of punching shear strength in the American, Canadian, British and European specifications. [Copyright &y& Elsevier]

Abstract: This paper presents a direct adaptive fault-tolerant neural control scheme for the active control of nonlinear hysteretic base-isolated buildings using the recently developed Extended Minimal Resource Allocation Network (EMRAN). EMRAN is a learning algorithm in which the structure of the neural controller is adapted on-line based on the input–output data. EMRAN starts with no hidden neurons and calculates the number of hidden neurons based on growing/pruning criteria. If the criteria are not met, then the parameters of the network are adjusted using an Extended Kalman Filter (EKF). The constants associated with the growing/pruning criteria and EKF are estimated using Genetic Algorithm (GA) optimization. The advantage of the proposed control architecture is its ability to learn on-line with no a priori training. Most of the existing studies in structural control using neural networks require computationally intensive off-line training. Consequently, once the network parameters are learnt, the parameters remain fixed. Such procedures require an accurate mathematical model of the system. These issues are addressed in the current controller scheme by utilizing the on-line adaptation capabilities of the neural networks. The advantages of on-line adaptation are demonstrated using the controller’s capability to handle actuator failures and system uncertainties. Performance of the proposed control scheme is evaluated using the recently developed nonlinear three-dimensional base-isolated benchmark structure incorporating lateral–torsional superstructure behavior and the biaxial interaction of the nonlinear bearings in the isolation layer. Results show that the proposed controller scheme can achieve the desired performance objectives under both partial actuator failure conditions and large uncertainties associated with the system’s parameters. [Copyright &y& Elsevier]

Abstract: In this paper, back-propagation neural networks (BPNN) and an adaptive neuro-fuzzy inference system (ANFIS) models developed to predict the moment capacity of ferrocement members are presented. A database from tests on ferrocement members is developed from the review of literature and some new tests. The selected input variables include the width and the depth of specimens, cube compressive strength of mortar, and tensile strength and volume fraction of wire mesh. A parametric study is carried out using BPNN to study the influence of each parameter affecting the moment capacity of the ferrocement member. The results of this study indicate that both BPNN and ANFIS provide good predictions which are better than those from other available methods. These models can serve as reliable and simple predictive tools for the prediction of moment capacity of ferrocement members. [Copyright &y& Elsevier]