2021
DOI: 10.1002/stc.2706
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Metaheuristics‐optimized ensemble system for predicting mechanical strength of reinforced concrete materials

Abstract: Summary This paper develops a novel artificial intelligence (AI)‐based approach, called the metaheuristics‐optimized ensemble system (MOES), to assist civil engineers significantly in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials. MOES integrates the advantages of hybrid and ensemble models by combining a metaheuristic optimization algorithm and efficient AI models. The metaheuristic algorithm finds the optimal hyperparameters of individual AI techniques and si… Show more

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Cited by 13 publications
(6 citation statements)
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“…us, this finding complies with results reported in recent studies that utilize hybrid computing models in civil engineering [119][120][121][122][123][124][125]. Although SSA has been demonstrated to be an effective swarm intelligent method used for solving various complex optimization tasks Houssein et al 2020; [76,126,127], its application in constructing sophisticated computer vision-based systems is still rare.…”
Section: Resultssupporting
confidence: 85%
“…us, this finding complies with results reported in recent studies that utilize hybrid computing models in civil engineering [119][120][121][122][123][124][125]. Although SSA has been demonstrated to be an effective swarm intelligent method used for solving various complex optimization tasks Houssein et al 2020; [76,126,127], its application in constructing sophisticated computer vision-based systems is still rare.…”
Section: Resultssupporting
confidence: 85%
“…Integrations of machine learning and metaheuristic approaches have rarely been investigated for the task at hand. In various fields, the successful utilization of metaheuristics in optimizing machine learning models has been demonstrated [43][44][45][46]. Nevertheless, the applications of such hybrid scheme for pothole recognition are still limited.…”
Section: Introductionmentioning
confidence: 99%
“…ese algorithms are used to optimize the performance of machine learning model to achieve a balance between model accuracy and model generalization. e employed metaheuristic approaches include symbiotic organisms search [42], particle swarm optimization [43,44], the forensic-based investigation optimization [45], equilibrium optimization [20], Harris hawks optimization [46], simulated annealing [47], social spider optimization [48,49], gray wolf optimization [38,50], teaching-learningbased algorithm [51], salp swarm algorithm [52,53], artificial bee colony [54], pigeon-inspired optimization [55], cuckoo search optimization [56], imperialist competitive algorithm [57], moth flame optimization [58], and cuckoo search algorithm [59]. ose previous works have demonstrated the effectiveness of metaheuristic algorithms in optimizing machine learning models and solving complex tasks in various application domains.…”
Section: Research Background and Motivationmentioning
confidence: 99%