Shear strength predicted by mechanics—Based models: a) ACI 318–19; b) ASCE/SEI 43–05; c) Wood 1990; and d) XGBoost model according to different aspect ratios (h<sub>w</sub>/l<sub>w</sub> ratios >1.5, = 1.5, <1.5).

Shear strength predicted by mechanics—Based models: a) ACI 318–19; b) ASCE/SEI 43–05; c) Wood 1990; and d) XGBoost model according to different aspect ratios (h<sub>w</sub>/l<sub>w</sub> ratios >1.5, = 1.5, <1.5).

<p>Shear strength predicted by mechanics—Based models: a) ACI 318–19; b) ASCE/SEI 43–05; c) Wood 1990; and d) XGBoost model according to different aspect ratios (h<sub>w</sub>/l<sub>w</sub> ratios >1.5, = 1.5, <1.5).</p>

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Bibliographic Details
Main Author:	Hoa Thi Trinh (20347834) (author)
Other Authors:	Tuan Anh Pham (1346049) (author), Vu Dinh Tho (20347837) (author), Duy Hung Nguyen (20347840) (author)
Published:	2024
Subjects:	Biophysics Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shapley additive explanations entire dataset including efficient solution compared traditional models often available experimental results random search performed random search methods theoretical formulas often sectional shapes using gaussian process emerged div >< p 2 </ sup xgboost model architecture reinforced concrete walls rc walls based random search theoretical formulas sectional shapes reinforced concrete gaussian process xgboost model results show benchmark models rc walls verified experimentally validation set test sets test set significantly worse shear strength research explores rc wall predictive capacity predictive ability prediction r lateral load largest database large errors input variables flexural strength current standards comparative analysis bayesian optimization 969 respectively 27 %, 1057 samples
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