A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced concrete beam

• 主要な著者: Chen, Yang, Zeng, Jie, Jia, Jianping, Jabli, Mahjoub, Abdullah, Nermeen, Elattar, Samia, Khadimallah, Mohamed Amine, Marzouki, Riadh, Hashmi, Ahmed, Assilzadeh, Hamid
• フォーマット: 論文
• 出版事項: Elsevier 2024
• 主題: QD Chemistry; TA Engineering (General). Civil engineering (General); TP Chemical technology
• オンライン･アクセス: http://eprints.um.edu.my/45259/; https://doi.org/10.1016/j.powtec.2024.119680

This research explores lightweight foamed reinforced concrete beams, crucial in modern construction for their strength and reduced weight. It introduces a novel approach, integrating three machine learning models: Neural Networks (NNs), Genetic Algorithms (GAs), and Ensemble Techniques, especially Gradient Boosting Machines (GBM). The study evaluates a dataset of 100 tests under various stress conditions, leveraging NNs for deep learning, GAs for feature optimization, and the robustness of GBM. The results demonstrate NNs achieving 88.5% deflection accuracy, 87% load -bearing capacity, and 86% failure point accuracy. GAs show slightly lower performance, while GBM excels with 90.2%, 91%, and 89% in these areas, respectively. Notably, the combined model significantly improves accuracy, reaching 96.8% in deflection, 97.2% in load -bearing capacity, and 96.5% in failure point prediction. This fusion of diverse machine learning approaches marks a significant advancement in structural engineering, enhancing predictive modeling for concrete beams.