Real measurement data-driven correlated hysteresis monitoring model for concrete arch dam displacement

Xu, Bo; Zhu, Zhenhao; Qiu, Xiaolong; Wang, Shida; Chen, Zeyuan; Zhang, Hu; Lu, Junyi

doi:10.1016/j.eswa.2023.121752

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2023.121752

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Real measurement data-driven correlated hysteresis monitoring model for concrete arch dam displacement

Bo Xu,

Zhenhao Zhu,

Xiaolong Qiu

et al.

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Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Zhang,

Liu,

Wang

et al. 2024

Advances in Structural Engineering

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Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and tw, lw and f ′c are the key parameters affecting the shear capacity of the flanged shear wall.

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Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Zhang,

Liu,

Wang

et al. 2024

Advances in Structural Engineering

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Real measurement data-driven correlated hysteresis monitoring model for concrete arch dam displacement

Cited by 1 publication

References 37 publications

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

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