Sensitivity and reliability assessment of buckling restrained braces using machine learning assisted-simulation

Tamimi, Mohammad F.; Alshannaq, Ammar A.; Abu Qamar, Mu’ath I.

doi:10.1016/j.jcsr.2023.108187

Journal of Constructional Steel Research

2023

DOI: 10.1016/j.jcsr.2023.108187

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Sensitivity and reliability assessment of buckling restrained braces using machine learning assisted-simulation

Mohammad F. Tamimi,

Ammar A. Alshannaq,

Mu’ath I. Abu Qamar

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Cited by 3 publications

References 34 publications

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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Asgarkhani,

Kazemi,

Jakubczyk-Gałczyńska

et al. 2024

Engineering Applications of Artificial Intelligence

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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Asgarkhani,

Kazemi,

Jakubczyk-Gałczyńska

et al. 2024

Engineering Applications of Artificial Intelligence

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Data-driven design approaches for hollow section columns—Database analysis and implementation

Koh,

Blum

2025

Journal of Constructional Steel Research

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Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

Yan,

Xie,

Shen

2024

Buildings

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This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

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Sensitivity and reliability assessment of buckling restrained braces using machine learning assisted-simulation

Cited by 3 publications

References 34 publications

Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods

Data-driven design approaches for hollow section columns—Database analysis and implementation

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

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