BPNN-assisted control algorithm application to a super large cable-net structure of active FAST reflector system

Structures, Год журнала: 2025, Номер 73, С. 108467 - 108467

Опубликована: Фев. 17, 2025

Язык: Английский

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A novel concentration prediction technique of carbon monoxide (CO) based on beluga whale optimization-extreme gradient boosting (BWO-XGBoost)

Journal of the Taiwan Institute of Chemical Engineers, Год журнала: 2025, Номер 171, С. 106045 - 106045

Опубликована: Фев. 27, 2025

Язык: Английский

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Pushover-ML: A Machine Learning approach to predict a trilinear approximation of pushover curves for low-rise reinforced concrete frame buildings

SoftwareX, Год журнала: 2025, Номер 30, С. 102122 - 102122

Опубликована: Март 7, 2025

Язык: Английский

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A Systematic Review on Utilizing Artificial Intelligence in Lateral Resisting Systems of Buildings

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 27, 2025

Язык: Английский

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Information theory-guided machine learning to estimate seismic response of non-linear SDOF structures

Engineering Structures, Год журнала: 2025, Номер 336, С. 120448 - 120448

Опубликована: Май 1, 2025

Язык: Английский

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A machine learning framework for predicting seismic behavior in elevated reinforced concrete tanks

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 13, 2025

Язык: Английский

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Machine learning in earthquake engineering: A review on recent progress and future trends in seismic performance evaluation and design

Engineering Structures, Год журнала: 2025, Номер 340, С. 120721 - 120721

Опубликована: Июнь 5, 2025

Язык: Английский

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Efficiency and explainability of design‐oriented machine learning models to estimate seismic response, fragility, and loss of a steel building inventory

Earthquake Engineering & Structural Dynamics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 20, 2024

Abstract Machine learning (ML) has recently been used as an efficient surrogate to estimate different steps of performance‐based earthquake engineering (PBEE), from dynamic structural analysis fragility and loss assessments. However, due the varied data, models, features in existing literature, relative efficiency ML models across PBEE remains unclear. Additionally, black‐box nature advanced algorithms limits their ability provide design‐oriented insights, hindering broader application PBEE‐based design. This study provides a comprehensive comparison accuracy explainability using consistent database 621 steel moment frames with varying designs geometry. Eight were careful training workflow comprising feature selection, hyperparameter tuning, cross‐validation, model inference. The sensitivity representative outputs—maximum responses, median fragility, expected annual loss—was assessed statistical measures. In addition, best for each step was examined explore relationship between design parameters corresponding output. results show that while can reasonably map all outputs, higher drift fragilities, component‐based metrics. optimal algorithm remained same steps, where support vector machines random forests provided highest average R 2 0.93 0.91 over outputs on test set. Although selected sets algorithms, height, number stories, fundamental period, minimum beams’ inertia influential both notably affected outputs.

Язык: Английский

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Multi-target machine learning-assisted design of sustainable steel fibre-reinforced concrete

Structures, Год журнала: 2024, Номер 71, С. 108036 - 108036

Опубликована: Дек. 26, 2024

Язык: Английский

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Integrating structural and seismic properties for enhanced seismic response prediction of building structures via artificial neural network

Structures, Год журнала: 2024, Номер 70, С. 107716 - 107716

Опубликована: Ноя. 15, 2024

Язык: Английский

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