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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]

• Twelve ML models are investigated to map out semi-rigid steel structural behaviors. • Distribution of load-carrying capacity responses is non-normal and heavy-tailed. • Linear regression methods are less efficient than tree-based ensemble algorithms. • XGBoost algorithm presents the best performance among all the methods investigated. The paper investigates the potential application of machine learning methods to estimate the load-carrying capacity of semi-rigid connected steel structures. The database is developed using the advanced analysis based on beam-column and zero-length elements. The input variables are member cross-sections and parameters of the high-order nonlinear functions describing semi-rigid connection behaviors. Twelve machine learning algorithms, including three linear regression models, support vector machines, deep learning and seven tree-based ensemble algorithms, were evaluated. Three practical semi-rigid connected steel (including two-dimensional planar and three-dimensional space) structures were studied. The numerical results revealed that the distribution of the load-carrying capacity responses of the structures was non-normal and heavy-tailed. The class of linear regression methods was less efficient than the tree-based ensemble algorithms. Among the seven tree-based ensemble approaches, the extreme gradient boosting method presented the best performance in determining not only the lowest (mean squared, mean absolute and mean absolute percentage) errors but also the highest coefficient of determination. It was followed by the light gradient boosting machines and categorical gradient boosting algorithms. Three linear regression methods (linear regression, Lasso and Ridge), light gradient boosting machines, categorical gradient boosting and extreme gradient boosting provided the competitive computing cost. The extreme gradient boosting algorithm was therefore recommended for the accurate prediction of load-carrying capacities of semi-rigid connected steel frames. [ABSTRACT FROM AUTHOR]

• Develop machine learning-based flexural and load capacities model for RC beams strengthened in flexure with FRCM. • Explain the output of the proposed xgBoost model and identify the most significant factors using the SHAP approach. • Propose a resistance reduction factor for the FRCM-strengthened RC beams in flexure based on a reliability analysis. • Compare the proposed model with the existing empirical equations for the flexural capacity of FRCM-strengthened beams. This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination ( R 2 = 99.3 %) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (β T = 3.5). [ABSTRACT FROM AUTHOR]