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Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

This paper introduces a new application of the Electrical Resistivity Imaging (ERI) method within the realm of structural assessment, deviating from its conventional use in geology. The study presents an innovative inversion model that incorporates the Levenberg-Marquardt algorithm, representing a notable leap in seamlessly integrating ERI into structural analysis. Rigorous validation of the inversion methodology is conducted through extensive benchmarking against simulated reference data, focusing on 1D and 2D resistivity distributions within timber specimens. By utilizing known resistivity fields, the paper quantitatively validates the accuracy of reconstructed models obtained through numerical simulations. Notably, both longitudinal and transverse surveys exhibit exceptional outcomes, showcasing a high correlation with the actual resistivity profiles, achieved within a concise 10-13 iterations. This meticulous validation process conclusively underscores the effectiveness and precision of the proposed inversion approach. Beyond its scientific contribution, this research expands the conventional boundaries of ERI application and establishes it as an invaluable tool for structural monitoring. [ABSTRACT FROM AUTHOR]

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility's characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM. [ABSTRACT FROM AUTHOR]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) provides strong support for bridge safety evaluation by tracking the real-time structural performance. An accurate and efficient imputation method for missing data in the SHM system is of vital importance for bridge management. In this paper, an innovative vertical–horizontal combined (VHC) algorithm is proposed to estimate the missing SHM data by a more comprehensive consideration of different types of information reflected in different time dimensions of the monitoring data. In the vertical time dimension, the variation trends and changing ranges for the missing data are analyzed by the relational analysis from the global view of data. In the horizontal time dimension, the exact value of the missing data is estimated based on the adjacent data inside the missing data set from the local view of data. The proposed VHC algorithm is validated and compared with the current methods in the data imputation for the missing deflection values in the SHM system of an example tie-arch bridge. The results show that the proposed algorithm demonstrates the best imputation performance among all methods for both missing types with the highest imputation accuracy/similarity and satisfying the 95% confidence bound. The application of reward coefficient significantly increases the imputation accuracy for the continuous missing situation and the improvement grows with the missing step number. Loss ratio and sample size also have significant influences on the imputation performance with the proposed method. Data missing in the structural health monitoring (SHM) system affects the continuity of monitoring data and the structural conditions reflected by them. This paper proposed an innovative data imputation algorithm combining information from both vertical and horizontal time dimensions of the monitored data in the bridge SHM system. The proposed vertical–horizontal combined (VHC) algorithm shows better imputation performance when compared with the current methods widely used in engineering practice and shows a higher applicability in a small sample size situation. Thus the proposed algorithm provides an accurate and efficient tool for dealing with the data missing phenomenon in the SHM system and is of great importance to the safety evaluation and further management decision process for bridge structure. Moreover, the methodologies and approaches proposed in this paper are quite general and can be applied to the missing data imputation problems in many research fields with time series data. [ABSTRACT FROM AUTHOR]

Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved. [ABSTRACT FROM AUTHOR]

Cantilever beams can have various behaviour depending on the details of the connection. Partially-fixed connections have a different performance from conventionally-fixed connections, in which the exact information is not available. In this paper, using the finite element model updating (FEMU) of a steel cantilever beam, the support conditions of a partially-fixed connection and the behaviour of the beam are investigated. The FEMU is utilized as an appropriate technique for structural health monitoring, which requires the use of data acquisition methods from the structure. In this research, instead of using discrete sensors, displacement and strain information are continuously measured from the beam surface using the digital image correlation (DIC) method. The difference between the initial model and the data extracted from the DIC is determined by a cost function. A hybrid method called Powell particle swarm optimization is used to minimize the cost function. This method includes particle swarm optimization (PSO) as a global search technique and Powell optimization as a local search technique; the hybrid method achieves better performance by combining the two methods. A dynamic inertia weight is presented to improve the performance of the PSO algorithm. Abaqus software and Python programming have been used to carry out the FEMU process. Finally, the amount of rotation and displacement of support are calculated, and the dependency of beam behaviour on each of the variables is investigated. This method yields acceptable convergence of the results and can be extended to study other structural components. [ABSTRACT FROM AUTHOR]

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform. [ABSTRACT FROM AUTHOR]

Continuous dynamic monitoring brings an important opportunity to evaluate the health and integrity of civil structures in a long-term manner. However, high dimensionality and sparsity of data caused by long-term monitoring and negative influences of environmental and/or operational variability are major challenges in this process. To address these important issues, this article proposes an innovative unsupervised data normalization method based on a novel hybrid feature weighting-selection algorithm and the idea of natural nearest neighbor (NN) searching emanated from the theory of mutual friendships in human societies. The proposed hybrid algorithm is a combination of global feature weighting with a new weighting measure and local feature selection. For this algorithm, this article leverages the natural NN searching that seeks to find adequate NNs automatically. The main objective of the proposed method is to remove the environmental and/or operational effects and provide normalized weighted features for reliable continuous dynamic monitoring. Using such features, an anomaly detector based on the Mahalanobis-squared distance is developed to assess and detect structural damage. The key innovations of this paper contain proposing a fully nonparametric unsupervised learning technique in two parts of data normalization and anomaly detection and developing a novel hybrid algorithm for removing the environmental and/or operational variations. Long-term dynamic features (modal frequencies) of a three-span box-girder concrete bridge (Z24 Bridge) and a long-span concrete arch bridge (Infante Dom Henrique Bridge) are considered to verify the proposed technique with several comparisons. Results indicate that this technique is successful and reliable in mitigating the environmental and/or operational effects and notifying accurate structural states. [ABSTRACT FROM AUTHOR]

Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2–15.0%, and the average efficiency by 35.7–96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change. [ABSTRACT FROM AUTHOR]

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives. [ABSTRACT FROM AUTHOR]

Cracks and other damages generated during the service of bridges can reduce the load bearing capacity and threaten operational safety. Finite Element Model Updating (FEMU), as one of the important means of structural health diagnosis, identifies structural damage through changes in model parameters. The three key factors of FEMU are updating variables, objective functions, and optimization algorithms. The poor selection of the above three factors in existing research leads to high calculation errors in model updating, and inevitably lead to the inability of the finite element model to carry out structural health monitoring, affecting the normal operation of the structure. In order to solve the above problems, this paper combines previous research and establishes a model updating algorithm based on the combination of eigenvector difference approach and particle swarm optimization (ED-PSO). The validity and accuracy of this method are verified by finite element analysis of a simply supported beam. Compared with the existing model updating algorithms based on the combination of static and dynamic methods and particle swarm optimization (CSD-PSO), the results show that the proposed ED-PSO model updating algorithm has higher accuracy and is expected to be better applied to bridge finite element model updating research. [ABSTRACT FROM AUTHOR]

Featured Application: The authors have developed a spatial data-fusion algorithm to adjust and effectively integrate data sets from total station and GNSS surveys with a universal EGM geoid model. The solution's high effectiveness can be applied to structural monitoring, risk management, architectural designing, and building information modelling (BIM). During the dynamic development of modern technologies based on advanced algorithmic and instrumental solutions, it is essential to integrate geospatial data efficiently. Such an approach is applied in all geo-information services, especially mobile ones, and is helpful in, for example, precise navigation or effective risk management. One leading application is deformation monitoring (structural monitoring) and displacement control surveying. In addition, spatial data integration methods are used in modern accessibility analysis, Smart City ideas, tracing utility networks, and building information modelling (BIM). The last aforementioned technology plays a crucial role in architectural design and construction. In this context, it is crucial to develop efficient and accurate algorithms supporting data fusion, which do not strain the computing resources and operate efficiently online. This paper proposes an algorithm for real-time adjustment of integrated satellite GNSS (global navigation satellite system), total station, and Earth Gravitational Model (EGM) vertical direction data in a geocentric coordinate system based on a statistical general linear mixed model. A numerical example shows that the proposed algorithm of the online adjustment works correctly. The results of the online adjustment are the same as those of the offline adjustment. It is also shown that the GNSS measurements are necessary only at the total station points in the spatial total station traverse. There is no need to add additional merging points of the total station positions because the differences between the results of the online adjustment, including and excluding the merging points, are very small (around 1–2 mm in standard deviation). [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements. [ABSTRACT FROM AUTHOR]

Internal erosion is the most important failure mechanism of earth and rockfill dams. Since this type of erosion develops internally and silently, methodologies of data acquisition and processing for dam monitoring are crucial to guarantee a safe operation during the lifespan of these structures. In this context, artificial intelligence techniques show up as tools that can simplify the analysis and verification process not of the internal erosion itself, but of the effects that this pathology causes in the response of the dam to external stimuli. Therefore, within the scope of this paper, a methodological framework for monitoring internal erosion in the body of earth and rockfill dams will be proposed. For that, artificial intelligence methods, especially deep neural autoencoders, will be used to treat the acoustic data collected by geophones installed on a dam. The sensor data is processed to identify patterns and anomalies as well as to classify the dam's structural health status. In short, the acoustic dataset is preprocessed to reduce its dimensionality. In this process, for each second of acquired data, three parameters are calculated (Hjorth parameters). For each parameter, the data from all the available sensors are used to calibrate an autoencoder. Then, the reconstruction error of each autoencoder is used to monitor how far from the original (normal) state the acoustic signature of the dam is. The time series of reconstruction errors are combined with a cumulative sum (CUSUM) algorithm, which indicates changes in the sequential data collected. Additionally, the outputs of the CUSUM algorithms are treated by a fuzzy logic framework to predict the status of the structure. A scale model is built and monitored to check the effectiveness of the methodology hereby developed, showing that the existence of anomalies is promptly detected by the algorithm. The framework introduced in the present paper aims to detect internal erosion inside dams by combining different techniques in a novel context and methodological workflow. Therefore, this paper seeks to close gaps in prior studies, which mostly treated just parts of the data acquisition–processing workflow. [ABSTRACT FROM AUTHOR]

Introduction: The development of different optimization algorithms following the No Free-Lunch Theory has led the researchers to explore and develop new optimization algorithms which may be used to perform complicated mathematical solutions. Objectives: This paper proposes a novel Pigeon Nesting Algorithm (PNA), a single solution-based SI algorithm, which is capable of escaping the local optima due to the combination of nesting and homing behaviour of pigeon and provide accurate optimization results. Method: In order to check the validity of proposed PNA algorithm, it has been applied to the 29 benchmark functions and compared with the existing single solution and population-based meta-heuristic algorithms. In order to check the application PNA for SHM of a practical structure, its performance for damage detection of ASCE Benchmark structure using stiffness-based objective function has been compared with other well-known optimization algorithms. Results: The PNA gives good competition to the other algorithms when tested for the 29 benchmarks functions. PNA proves to be a robust algorithm for health monitoring of ASCE benchmark structure as it shows an approximate error of 1%. It has also been found that the algorithm can be used for detecting damage even in the presence of noise of 5%. Conclusion: The algorithm also outperforms other single solution-based algorithms when used for optimization of design problems. [ABSTRACT FROM AUTHOR]

In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33 % accuracy (P%), 91.18 % regression rate (R%) and a 90.54 % F1-score (F%). [ABSTRACT FROM AUTHOR]

It is a promising method to identify structural damage using bridge dynamic response under moving vehicle excitation, but the lack of accurate information about road roughness and vehicle parameters will lead to the failure of this method. The paper proposed a step-by-step EKF damage identification method, which transforms the inversion problem of unknown structural parameters under unknown loads (vehicle and road roughness) into two separate inversion problems: moving contact force identification and damage parameters identification. Firstly, the VBI model is converted into bridge vibration model under a moving contact force, and the moving contact force covering the information of road roughness and vehicle parameters can be calculated by EKF iteration. Secondly, the moving contact force identified in the first step is loaded on the bridge as a known condition, and the bridge damage problem is also solved by the EKF method. Numerical analyses of a simply-supported bridge under the moving vehicle are conducted to investigate the accuracy and efficiency of the proposed method. Effects of the vehicle speed, the damage cases, the measurement noise, and the roughness levels on the accuracy of the identification results are investigated. The results demonstrate the proposed algorithm is efficient and robust, and the algorithm can be developed into an effective tool for structural health monitoring of bridges. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a novel distance‐based slicing (DBS) algorithm is proposed for the visualization of inclusion defects within a thick solid structure. The algorithm is based on the propagation distance traveled by the ultrasonic wave signals and was integrated with the generation laser scanning ultrasonic propagation imaging (UPI) system in which the fixed sensor was positioned to detect the induced through‐the‐thickness ultrasonic signals. The advantages provided by the UPI system allow for an in‐situ non‐destructive evaluation for real world applications. An investigation of an isotropic solid specimen of large thickness with multiple internal inclusion defects of different shapes and depth location was performed using the proposed algorithm and system. The results in the form of ultrasonic propagation freeze frames show that the algorithm is much more effective in detecting each individual defect and is particularly useful for a thick‐walled specimen. Additionally, a relationship was determined between the visualized result of each defect and the source position during data acquisition which enabled us to estimate the depth location of the defect within the specimen. [ABSTRACT FROM AUTHOR]

Summary: Rapid health assessment of essential buildings such as hospitals, fire stations, and large residential complexes is crucial after damaging earthquakes. The use of advanced technologies such as wireless sensors, learning algorithms, and signal processing methods became more attractive in such fast applications due to their higher reliabilities and efficiencies compared to the conventional visual inspection methods. This paper presents a robust post‐earthquake damage detection framework for predicting the extent and location of damage occurrence in the braced‐frame structures after an earthquake. To do so, features derived from acceleration response of the structure were used along with a classification learner to determine the health condition of the structure. Decision tree classifiers are used for the purpose of damage classification where the Bayesian optimization algorithm is implemented to optimize the architecture of the mentioned classifier. A one‐story chevron steel‐braced frame, a three‐story X‐braced steel‐frame, and a five‐story three‐dimensional building are considered to validate the proposed method. The total number of 3774 and 1887 nonlinear response history analyses were respectively performed for 2D and 3D numerical models under scaled SAC motions, using the OpenSees simulation platform. Furthermore, in order to simulate the field condition, a maximum level of 10% white Gaussian noise is added to the output signals. Results obtained from the three case studies show that the proposed framework is robust and reliable in predicting the extent of damage level in the braced‐frame structures in a short time after an earthquake. [ABSTRACT FROM AUTHOR]

The present paper deals with structural health monitoring of trusses, space frame and plate structure utilizing the Bayesian data fusion approach. The application of the proposed approach has been demonstrated on a 25-member plane truss, a 42-member space frame, a cantilever plate and a 120-member space truss. Different damage indexes of interest have been calculated for the damaged structure utilizing the natural frequency and modeshapes as damage indicators. Damage indexes used are modal strain energy (DIMSE), frequency response function strain energy dissipation ratio (FRFSEDR), flexibility strain energy damage ratio (FSEDR) and residual force-based damage index (RFBDI). Next, the Bayesian data fusion approach has been applied to these four damage indexes to find out the accurate damage location. The proposed approach reduces the number of suspected damaged elements in the structure significantly, thus reducing the computational time of optimization algorithm. Proposed algorithm has also shown encouraging performance in noisy environments. Overall present approach is found to be robust and computationally efficient, and thus can be applied for damage detection involving field evaluation of various structures. [ABSTRACT FROM AUTHOR]

Featured Application: Acceleration based moving force identification is applied for weigh in motion and bridge damage detection purposes. This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level. [ABSTRACT FROM AUTHOR]

Application of massive data to structural health monitoring (SHM) may lead to serious problems such as difficulty, computational inefficiency, and low damage detectability. This paper proposes innovative hybrid methods in order to detect damage under massive data, ambient vibration, and environmental and/or operational variability. Each of the proposed methods consists of a three-stage algorithm including response modelling via a time series representation, Gaussian mixture model (GMM) for dimensionality reduction, and an outlier detector. Due to the importance of response modelling under ambient vibration, this article employs the combination of AR with ARX called the ARARX model. In the second stage, a GMM is individually fitted to residual samples extracted from ARARX regarding the undamaged and damaged conditions to obtain low-dimensional features from massive data. Eventually, outlier analyses are carried out by the Mahalanobis distance and an auto-associative neural network to make a decision about the occurrence of damage. Two different kinds of threshold limits are also considered to examine the performances of the proposed methods. A large-scale cable-stayed bridge is applied to validate the reliability and effectiveness of these methods with several comparative studies. Results demonstrate that both proposed methods are effective tools for damage detection. [ABSTRACT FROM AUTHOR]

The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the vision-based SHM of bridges along with their systematic procedures. The proposed procedures are implemented via a two-span accelerated bridge construction (ABC) system undergoing a large-scale shake-table test. The research objectives are to explore the effect of refined matching algorithms on blob-based features in improving their accuracies and to implement the proposed algorithms on large-scale bridges tested under seismic loads using vision-based SHM. The procedure begins by adopting blob-based feature detectors, i.e., the scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE algorithms, and their stability is compared. The least medium square (LMEDS), least trimmed square (LTS), random sample consensus (RANSAC), and its generalization maximum sample consensus (MSAC) algorithms are applied for model fitting, and their sensitivity for removing outliers is analyzed. The raw data are corrected using mathematical models and scaled to generate displacement data. Finally, seismic vibrations of the bridge are generated, and the seismic responses are compared. The data are validated using target-tracking methods and mechanical sensors, i.e., string potentiometers. The results show a good agreement between the proposed blob feature detection and matching algorithms and target-tracking data and reference data obtained using mechanical sensors. [ABSTRACT FROM AUTHOR]

Recently, advances in sensor technologies, data communication paradigms, and data processing algorithms all affect the feasibilities of the bridges structural health monitoring and deterioration detection, and other implementations of monitoring operations. The paper proposes a method to design an irregularity detection and monitoring system for road bridges that combines internet of things (IoT) and artificial intelligence (AI) technologies. Raspberry Pi 4 embedded computer integrating IoT and AI technology with convolutional neural network (CNN) is employed to simultaneously monitor remote bridges on websites and apps via Google Firebase cloud database. The first step of successful testing in the laboratory showed that the system can work stably and coincide with the proposed goals. [ABSTRACT FROM AUTHOR]

Purpose: In order to improve the computation efficiency of the four-point rainflow algorithm, a new fast four-point rainflow cycle counting algorithm (FFRA) using a novel loop iteration mode is proposed. Design/methodology/approach: In this new algorithm, the loop iteration mode is simplified by reducing the number of iterations, tests and deletions. The high efficiency of the new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems. Findings: The extensive simulation results show that the extracted cycles by the new FFRA are the same as those by the four-point rainflow cycle counting algorithm (FRA) and the three-point rainflow cycle counting algorithm (TRA). Especially, the simulation results indicate that the computation efficiency of the FFRA has improved an average of 12.4 times compared to the FRA and an average of 8.9 times compared to the TRA. Moreover, the equivalence of cycle extraction results between the FFRA and the FRA is proved mathematically by utilizing some fundamental properties of the rainflow algorithm. Theoretical proof of the efficiency improvement of the FFRA in comparison to the FRA is also given. Originality/value: This merit makes the FFRA preferable in online monitoring systems of structures where fatigue life estimation needs to be accomplished online based on massive measured data. It is noticeable that the high efficiency of the FFRA attributed to the simple loop iteration, which provides beneficial guidance to improve the efficiency of existing algorithms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is developing rapidly with wider application fields. IoT's main infrastructure is called a wireless sensor network (WSN). Hence, WSN must be able to operate on various network models. Multi-hop clustering is considered a solution for adapting to various network sizes. Multi-hop clustering must be designed to maintain the balance of energy consumption between nodes, and many algorithms have been proposed for this purpose. However, most clustering algorithms are designed with the assumption that the network is a two-dimensional plane. In many applications, WSN is more appropriately modeled as a three-dimensional (3D) network, for example, the WSN application for structural health monitoring or underwater wireless sensor networks. Here, a clustering algorithm for 3D-WSN is proposed. This algorithm is developed based on an analysis of the balance of energy consumption, such that the network lifetime is expected to be longer. The main novelty of our algorithm is the utilization of multi-hop layered transmission. From the simulation, the performance of the proposed algorithm exhibits a good energy balance compared to an un-balanced analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study. Design/methodology/approach: This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time. Findings: The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections. Originality/value: This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm. [ABSTRACT FROM AUTHOR]

High-rate systems are defined as engineering systems that undergo accelerations of amplitudes typically greater than 100 g n over less than 100 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. The use of feedback mechanisms in these high-rate applications is often critical in ensuring their continuous operations and safety. Of interest to this paper are algorithms needed to support high-rate structural health monitoring (HRSHM) to empower sub-millisecond decision systems. HRSHM is a complex task because high-rate systems are uniquely characterized by (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations that necessitate careful crafting of adaptive strategies. This paper studies benefits of integrating a data-driven predictive model with a physics-based state observer to reduce latency and convergence time estimating actionable information. The predictive model, constructed with long short-term memory (LSTM) cells, performs multi-step ahead signal prediction acting as the input to the physical model, a model reference adaptive system (MRAS). The MRAS then performs state estimation of the predicted signal rather than the true signal. A comparison study was done between the proposed hybrid algorithm and a physics-based MRAS on a testbed involving a fast-moving boundary condition. Results showed that the hybrid algorithm could perform state estimations with zero timing deadline overshoot and with up to 50% faster convergence time when compared to the MRAS under constant boundary conditions. However, the hybrid generally underperformed the MRAS algorithm in terms of convergence accuracy during motion of the boundary condition by increasing convergence time by 20%, attributable to the lag in learning the new dynamics used in predicting. The performance of the NSE algorithm was also examined on a true high-rate system, where it was shown to be capable of qualitatively tracking actionable information. • A hybrid, deep learning algorithm for real-time state estimation. • Physics-integrated algorithm with state estimation time of less than the desired sampling rate. • An algorithm with sub-millisecond computation capabilities for applications in high-rate systems. • Demonstrated performance using three case studies on experimental datasets acquired on two different testbeds. [ABSTRACT FROM AUTHOR]

A mathematical morphological filter-based de-noising method is developed in this study for bridge cable force monitoring data. Structure elements, one of the most important parameters in the mathematical morphology, dominate de-noising effects. The de-noising effects subject to single structure element and multi-structure element filters are discussed based on the simulation signals. The results indicate that the de-noising effects by using the spherical structure element are better than using the straight line or rhombic structure element. Moreover, the multi-structure element filter outperforms the single one. Through simulation analysis, the de-noising performance of the low-pass filter, wavelet filter and morphological filter is compared. The results show that the performance of the wavelet and morphological filters is better than that of the low-pass filter. For low signal-to-noise signals, the performance of the wavelet filter is superior. With the increase of signal-to-noise ratio, the morphological filters show more advantages. Taking the cable force monitoring data of the 3rd Nanjing Yangtze River Bridge as an example, the de-noising performance of the wavelet and morphological filters is discussed. The results show that both the wavelet filters and morphological filters have satisfactory de-noising effects. The mathematical morphology method can provide an optional and effective de-nosing choice, which enriches the means of de-noising for bridge monitoring data. [ABSTRACT FROM AUTHOR]

In guided-wave-based damage-imaging algorithms, damage reconstruction typically involves comparing the signals with and without a defect. However, in many cases, defect-free data may not be available. Therefore, in this study, baseline and baseline-free approaches were used for damage imaging, exploiting not only the amplitude of the signal as the feature but also five additional features, namely, the amplitude of the sparse signal after deconvolution, the amplitude of the coefficients at the excitation frequency from the re-assigned short-time Fourier transform, the time of flight determined from cross-correlation, kurtosis in the time domain, and kurtosis in the frequency domain. For this study, three different plates with different types of defects were considered: a metallic plate with a notch-type artificial defect, a pultruded type of composite plate with an impact defect, and a laminate composite plate with plexiglass serving as an added mass damper artificial defect. The Reconstruction Algorithm for Probabilistic Inspection of Damage (the RAPID algorithm) was used to characterize the defects on the three plates, and the defect parameters were then quantified by creating an ellipse after thresholding. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address this problem. However, notwithstanding the wide recognition of the current algorithms, their constraints are commonly acknowledged. Hence, this article advocates for the adoption of innovative hunting-inspired algorithms, namely the Ant Lion Optimizer (ALO), African Vulture Optimization Algorithm (AVOA), Grey Wolf Optimizer (GWO), Marine Predator Algorithm (MPA), and Whale Optimization Algorithm (WOA), which emulate the behaviors of wildlife species, to discern the areas and magnitudes of deterioration in a suspension footbridge. Moreover, in order to reduce computational time, only natural frequencies are applied as objective functions. The obtained results indicate that all the utilized algorithms can accurately detect the damages in the considered structure. [ABSTRACT FROM AUTHOR]

Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic defect localization and sizing procedure for Structural Health Monitoring based on guided waves imaging. The procedure is applied to an aluminum plate equipped with active piezoelectric sensors. The defect localization and sizing strategy is obtained through to the use of a convolutional neural network trained exclusively on numerical simulations of guided wave signals and post-processed by the delay and sum imaging algorithm. The paper shows the effectiveness of the proposed approach to invert both synthetic and experimental data. [ABSTRACT FROM AUTHOR]

Time reversal (TR) techniques due to their spatial and temporal focusing characteristics, have been increasing applied in damage evaluation. Since probing waves strongly attenuate in concrete material, imaging of damage in concrete using conventional time reversal imaging (TRI) algorithm often requires a large number of deployed transducers to minimize unexpected artifacts. To relieve economic burden and fussy installation induced by massive transducers, this paper develops a small number of transducers-enabled concrete-adapted time reversal imaging (CATRI) algorithm using implantable technology. The CATRI algorithm adopts a compensation factor to overcome wave attenuation and eliminate artifacts during crack imaging process. The implantable technology is achieved by a designed concrete implantable module (CIM) device that can be easily implanted into concrete structures. The performances of the CIM device and CATRI algorithm were numerically and experimentally studied. The results show that the proposed techniques overcome the strong wave attenuation-induced problems and successfully identify crack regions. The comparison of imaging results by using conventional TRI algorithm highlights the superiority of the CATRI algorithm for concrete crack imaging. [ABSTRACT FROM AUTHOR]

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%. [ABSTRACT FROM AUTHOR]

Owing to the ill-conditioned nature of electrical resistivity tomography and the measurement error of the hardware equipment, the reconstructed resistivity distribution image often contains artifacts of varying degrees. Other soft-field imaging technologies, such as electrical impedance tomography and electrical capacitance tomography, also encounter artifacts. Artifacts interfere with the assessment of damaged areas. To eliminate the influence of artifacts on the reconstructed image, a novel artifact elimination algorithm called the fast artifact filtering (FAF) algorithm is proposed. Based on the calculation results of existing algorithms, such as the Newton's one-step error reconstructor (NOSER) algorithm, the FAF algorithm can remove the damaged areas with low confidence from the potentially damaged areas and only retain the damaged areas with high confidence for final imaging. Several simulation models were used to test the effectiveness of the artifact elimination algorithm proposed in this study. The test results show that the number of artifacts in the final reconstructed image is significantly reduced after the NOSER algorithm is combined with the FAF algorithm. In addition, when the number of finite element model division elements was 4802, the refresh time of a single image increased by approximately 1 ms. A structural health monitoring test for hollow structure is provided. The results show that the FAF also performs well on the measured voltage data. [ABSTRACT FROM AUTHOR]

• An approach for detection of damper malfunction is developed by implementing isolation forest algorithm on characterized cumulative displacement measurements. • Physics-informed correlation between the temperature variation and the displacement measurements are utilized for identifying measurement noise to ensure reliable datasets for malfunction detection. • Characterization with hourly cumulative displacement can significantly reduce the data complexity while capturing the intrinsic data characteristics that influence the bridge behavior. • The approach was verified with real monitoring data in a suspension bridge by detection of malfunction in the viscous damper. Long-span cable-supported bridges are flexible and prone to significant deformations under temperature, wind, and vehicle loads. Such large deformations induce frequent longitudinal movement at the end of the bridge girder, which can cause malfunction or failure in the components such as expansion joints, bearings, or longitudinal viscous dampers. This paper develops a condition assessment approach using the girder end displacement measurements, which primarily includes four steps, namely, data preprocessing, feature characterization, datasets preparation and splitting, and anomaly detection. The data preprocessing step considers the physics-based correlation between the temperature and displacement, which is used to pre-check noisy data issues in the displacement monitoring data. Subsequently, hourly cumulative displacement is calculated as the feature to feed into the machine learning model. The third step prepares the datasets and splits them into training and testing datasets. Then the isolation forest algorithm is implemented for anomaly detection with well-tuned parameters of the number of decision trees and the number of samples in each decision tree. Structural health monitoring data of a suspension bridge are collected to verify the proposed approach, and the detection targets are specified as viscous damper malfunction and a specific holiday period. The proposed approach demonstrates its ability to detect both events successfully, which can shed light on the predictive and preventive maintenance of the bridge. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid YUKI-ANN is implemented for Structural Health Monitoring (SHM) of laminated composite plates. A finite element model is constructed and used to identify damage at five randomly selected elements in a laminated composite plate. The process of damage identification is divided into two main steps, namely damage localization and quantification. During the localization step, damaged elements are identified using a damage indicator based on Modal Strain Energy change ratio (MSEcr). After excluding the healthy elements, the level of damage is predicted using ANN modified with four optimization algorithms: Arithmetic Optimizing Algorithm (AOA), Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO), and YUKI algorithm. The performance of these optimization algorithms is evaluated, and it is found that the YUKI algorithm (YA) outperforms AOA, BCMO, and PSO algorithms without exception. YA gives better predictions with lower errors when compared to PSO and BCMO algorithms with equivalent computational time. In some cases, AOA provides slightly better predictions than YA, but the computational time for these predictions is eight times more that of YA. If both performance and efficiency are considered, YA seems to be the best choice. Find the MATLAB code for YUKI-ANN at https://github.com/Brahim-Benaissa/YUKI_ANN. [ABSTRACT FROM AUTHOR]

Classification models are a fundamental component of physical-asset management technologies such as structural health monitoring (SHM) systems and digital twins. Previous work introduced risk-based active learning , an online approach for the development of statistical classifiers that takes into account the decision-support context in which they are applied. Decision-making is considered by preferentially querying data labels according to expected value of perfect information (EVPI). Although several benefits are gained by adopting a risk-based active learning approach, including improved decision-making performance, the algorithms suffer from issues relating to sampling bias as a result of the guided querying process. This sampling bias ultimately manifests as a decline in decision-making performance during the later stages of active learning, which in turn corresponds to lost resource/utility. The current paper proposes two novel approaches to counteract the effects of sampling bias: semi-supervised learning , and discriminative classification models. These approaches are first visualised using a synthetic dataset, then subsequently applied to an experimental case study, specifically, the Z24 Bridge dataset. The semi-supervised learning approach is shown to have variable performance; with robustness to sampling bias dependent on the suitability of the generative distributions selected for the model with respect to each dataset. In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias. Moreover, it was found that the number of inspections made during a monitoring campaign, and therefore resource expenditure, could be reduced with the careful selection of the statistical classifiers used within a decision-supporting monitoring system. [ABSTRACT FROM AUTHOR]

Purpose: In order to improve the computation efficiency of the four-point rainflow algorithm, a one-stage extraction four-point rainflow algorithm is proposed based on a novel data preprocessing method. Design/methodology/approach: In this new algorithm, the procedure of cycle counting is simplified by introducing the data preprocessing method. The high efficiency of new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems. Findings: According to the data preprocessing method, in the process of cycle extraction, all equivalent cycles can be extracted at just one stage instead of two stages in the four-point rainflow algorithm, where the cycle extraction has to be performed from the doubled residue. Besides, there are no residues in the new algorithm. The extensive numerical simulation results demonstrate that the accuracy of new algorithm is the same as that of the four-point rainflow algorithm. Moreover, a comparative study based on a long input data sequence shows that the computation efficiency of the new algorithm is 42% higher than that of the four-point rainflow algorithm. Originality/value: This merit of new algorithm makes it preferable in some application scenarios where fatigue life estimation needs to be accomplished online based on massive measured data. And it may attribute to preprocessing of input data sequence before data processing, which provides beneficial guidance to improve the efficiency of existing algorithms. [ABSTRACT FROM AUTHOR]

Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder–Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification. [ABSTRACT FROM AUTHOR]

Displacement is an important parameter in the assessment of the integrity of infrastructure; thus, its measurement is required in a multitude of guidelines or codes for structural health monitoring in most countries. To develop a low-cost and remote displacement measurement technique, a novel method based on an unmanned aerial vehicle (UAV) and digital image correlation (DIC) is presented in this study. First, an auxiliary reference image that meets the requirements is fabricated using the selected first image. Then, the speeded-up robust features (SURF) algorithm is introduced to track the feature points in the fixed areas. The least square algorithm is then employed to resolve the homography matrix of the auxiliary reference image and target images; then, the acquired homography matrices are utilized to calibrate the deviation caused by the UAV wobble. Finally, the integral pixel and sub-pixel matching of the DIC algorithm is employed to calculate the displacement of the target object. The numerical simulation results show that the proposed method has higher calculation accuracy and stability. The outdoor experiment results show that the proposed method has definite practicability. [ABSTRACT FROM AUTHOR]

Impact force is the most common form of load which acts on engineering structures and presents a great hidden risk to the healthy operation of machinery. Therefore, the identification or monitoring of impact forces is a significant issue in structural health monitoring. The conventional optimisation scheme based on inversion techniques requires a significant amount of time to identify random impact forces (impact force localisation and time history reconstruction) and is not suitable for engineering applications. Recently, a pattern recognition method combined with the similarity metric, PRMCSM, has been proposed, which exhibits rapidity in practical engineering applications. This study proposes a novel scheme for identifying unknown random impact forces which hybridises two existing methods and combines the advantages of both. The experimental results indicate that the localisation accuracy of the proposed algorithm (100%) is higher than that of PRMCSM (92%), and the calculation time of the hybrid algorithm (179 s) for 25 validation cases is approximately one nineteenth of the traditional optimisation strategy (3446 s). [ABSTRACT FROM AUTHOR]

Cracks are the main damages of concrete structures. Since cracks may occur in areas that are difficult to reach, non-contact measurement technology is required to accurately measure the width of cracks. This study presents an innovative computer vision system combining a camera and laser rangefinder to measure crack width from any angle and at a long distance. To solve the problem of pixel distortion caused by non-vertical photographing, geometric transformation formulas that can calculate the unit pixel length of the image captured at any angle are proposed. The complexity of crack edge calculation and the imbalance of data in the image are other problems that affect measurement accuracy, and a combination of the improved U-net convolutional networks algorithm and Canny edge detection method is adopted to accurately extract the cracks. The measurement results on the different concrete wall indicate that the proposed system can measure the crack in a non-vertical position, and the proposed algorithm can extract the crack from different background images. Although the proposed system cannot achieve fully automated measurement, the results also confirm the ability to obtain the crack width accurately and conveniently. [ABSTRACT FROM AUTHOR]

Nowadays, the research of digital geometry is gradually shifting from low-level geometric attributes to high-level semantic attributes. The symmetry of the 3D geometric model is an important bridge between the underlying geometric information of the related model and the high-level semantic information. Symmetry analysis is an important issue in geometric processing. Bridge safety is a major issue related to the national economy and people's livelihood. Therefore, the bridge structure health monitoring system is becoming a research focus in academic and technical circles. Wireless sensor network has been widely used in bridge health monitoring system due to its convenient installation, low maintenance cost, and flexible deployment. This article summarizes the existing bridge health monitoring system based on wireless sensor network. In the bridge control system of wireless sensor network, the basic principles and typical methods are introduced in turn, and the key technologies in the system are analyzed in combination with several specific examples. Finally, the existing problems and future research directions of the existing system are summarized. During the operation stage, vortex-induced resonance often occurs below the wind speed. The vortex excitation force of the main actuator along the axis of the bridge is related to the actual wind field and its own structural vibration. This article briefly introduces typical cases and control measures of vortex-induced vibration of long-span bridges, and summarizes the current research status of vortex-induced vibration of bridge main girder structures. At the same time, the vortex-induced vibration of bridge girder is experimentally studied by using symmetrical algorithm. [ABSTRACT FROM AUTHOR]

Summary: In this study, the Q‐learning algorithm is integrated into the binary water strider algorithm to adaptively control the search operators and repair strategies. The proposed algorithm (QWSA) is applied to optimal sensor placement in structures with numerous candidate locations. To handle the constraint related to the number of sensors, an efficient strategy based on the triaxial driving‐point residue (DPR3) coefficient is developed, in addition to the random solution repair strategy. The reinforcement learning algorithm with softmax policy determines which strategy should be adopted for the generated solutions. Since the utilized sensors are triaxial, the tridimensional modal assurance criterion (TMAC) is applied as the objective function. To verify the effectiveness of the proposed method, two large‐scale dome‐shaped trusses are investigated, and the results are compared with the basic water strider algorithm and several other well‐known and modern metaheuristics in the literature. The results demonstrate that placements obtained by QWSA have in average lower than half of the cost value obtained by compared methods. Additionally, average DPR3 (DPR3¯) of the QWSA is higher than the rest of the algorithms. The Wilcoxon test indicates that, at a 95% confidence level, the results of QWSA are superior to all algorithms. Additionally, the proposed method outperforms them in terms of convergence speed. [ABSTRACT FROM AUTHOR]