Implementation of meta-ensembled algorithms via light gradient boosting model for predicting high-performance concrete main strength features

Shaoka Zhao , Hongwei Li , Jianfeng Li , Linbin Li , Yongjun Liu , Shuanglan Wu , Yongning Liang , Feilan Wang , Junbo Chen

Abstract

High-performance concrete compressive and tensile strengths are essential in terms of the assurance of structural performance and reliability. The research will describe the effective estimation of such properties through an artificial intelligence-based approach to overcome several limitations of experimental testing. For this purpose, a Light Gradient Boosting model has been developed and enhanced using four meta-heuristic optimization algorithms: Dandelion Optimization, Runge-Kutta Optimization, Seagull Optimization Algorithm, and Black Widow Optimization Algorithm. The LGRDSB was an ensemble model that combined the strengths of all four optimizers. Among them, the RUN optimizer with the LGRK model emerged as the best, giving R-squared values of 0.9928 and 0.9914 for CS and TS predictions, respectively. Thus, the LGRDSB model ensemble emerged as most robust and reliable to handle diverse datasets, securing R-squared values greater than 98% and less than 1% error rates. These results highlight the performance of the proposed models in predicting HPC properties and provide a realistic approach toward integrating AI techniques into performance evaluation for HPC.

Key Words

artificial intelligence; ensemble learning; high-performance concrete strengths; hybrid machine learning; light gradient boosting

Address

Shaoka Zhao — School of Big Data and Artificial Intelligence, Fujian Polytechnic Normal University, Fuqing 350300, China
Hongwei Li — Zhejiang Industry &Trade Vocational College, Wenzhou 325000, Zhejiang, China
Jianfeng Li — 1) Faculty of Engineering, China University of Geosciences (Wu han), Wuhan 430000, China, 2) Xing Yun Chen (Hong Kong) Technology Limited, Hong Kong 999077, China, 3) Hainan Cloud Spacetime Information Technology Co., Ltd., Danzhou 571700, China
Linbin Li — Fuzhou Immigration Management Office, Fuzhou 350005, China
Yongjun Liu — Fujian Dongchen Construction Engineering Group Co., LTD, Fuzhou 350005, China
Shuanglan Wu — College of Transportation Engineering, Nanjing Tech University, Nanjing 211816, China
Yongning Liang — School of Civil Engineering, Fuzhou University, Fuzhou 350108, China
Feilan Wang — School of International Business and Economics, Fujian Business University, Fujian 350012, China
Junbo Chen — 1) Zhejiang Industry &Trade Vocational College, Wenzhou 325000, Zhejiang, China, 2) Cavite State University, Indang 4100, Cavite, Philippines

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