An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials  

作　　者：Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah 

机构地区：[1]Department of Civil Engineering,National Institute of Technology Patna,Patna,India [2]Department of Computer Engineering and Applications,GLA University,Mathura,India [3]Department of Computer Science,College of Sciences and Humanities-Aflaj,Prince Sattam Bin Abdulaziz University,Al-Kharj,Saudi Arabia [4]Department of Computer Science,College of Science and Arts,Qassim University,Unaizah,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2024年第5期1521-1555,共35页工程与科学中的计算机建模（英文）

基　　金：supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1445).

摘　　要：This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关 键 词：Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]