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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]

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]

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]