Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system  

作　　者：Dinh-Nhat TRUONG Van-Lan TO Gia Toai TRUONG Hyoun-Seung JANG 

机构地区：[1]Department of Civil Engineering,University of Architecture Ho Chi Minh City,Ho Chi Minh City 700000,Vietnam [2]Faculty of Civil Engineering and Technology,Dong A University,Da Nang City 550000,Vietnam [3]School of Architecture,Seoul National University of Science and Technology,Seoul 01811,Republic of Korea

出　　处：《Frontiers of Structural and Civil Engineering》2024年第4期551-567,共17页结构与土木工程前沿（英文版）

基　　金：Acknowledgements This research was supported by the Research Program funded by Seoul National University of Science and Technology(SeoulTech).

摘　　要：Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear failure.Existing design methods often exhibit significant bias and variability.Accurate estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and management.This study introduces a novel computation method,the jellyfish-least square support vector machine(JS-LSSVR)hybrid model,to predict punching shear strength.By combining machine learning(LSSVR)with jellyfish swarm(JS)intelligence,this hybrid model ensures precise and reliable predictions.The model’s development utilizes a real-world experimental data set.Comparison with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the JS-LSSVR hybrid model.This innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.

关 键 词：punching shear strength reinforced concrete flat slabs machine learning jellyfish search support vector machine 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TU528.2[自动化与计算机技术—控制科学与工程]