Multi-Objective Optimization Design of FRP Reinforced Flat Slabs under Punching Shear by Using NGBoost-Based Surrogate Model

Multi-objective optimization problems (MOPs) in structural engineering arise as a significant challenge in achieving a balance between prediction accuracy and efficiency of the surrogate models, which are conventionally adopted as mechanics-driven models or numerical models. Data-driven models, such...

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Vydáno v:	Buildings (Basel) Ročník 13; číslo 11; s. 2727
Hlavní autoři:	Liang, Shixue, Cai, Yiqing, Fei, Zhengyu, Shen, Yuanxie
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2023
Témata:	Algorithms Case studies Concrete Concrete slabs Corrosion resistance Datasets Design Design optimization Design parameters Fiber reinforced plastics Fiber reinforced polymers FRP reinforced flat slabs Genetic algorithms Learning algorithms Machine learning Mathematical models Mechanics (physics) Model accuracy multi-objective optimization Multiple objective analysis NGBoost NSGA-II Numerical models Optimization algorithms Pareto optimization Polymers Probability distribution Punching shear punching shear resistance Reinforced concrete SHAP Shear strength Slabs Sorting algorithms Structural engineering China
ISSN:	2075-5309, 2075-5309
On-line přístup:	Získat plný text
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