Chen, Yang and Zeng, Jie and Jia, Jianping and Jabli, Mahjoub and Abdullah, Nermeen and Elattar, Samia and Khadimallah, Mohamed Amine and Marzouki, Riadh and Hashmi, Ahmed and Assilzadeh, Hamid (2024) A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced concrete beam. Powder Technology, 440. p. 119680. ISSN 0032-5910, DOI https://doi.org/10.1016/j.powtec.2024.119680.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-K202204301) ; (KJZD-K202304302), Chongqing Construction Science and Technology Programme Project (5-9), Princess Nourah bint Abdulrahman University (PNURSP2024R730), Prince Sattam bin Abdulaziz University (PSAU/2024/R/1445) |
| Uncontrolled Keywords: | Lightweight foamed reinforced concrete beams; Neural networks (NNs); Genetic algorithms (GAs); Ensemble techniques; Gradient boosting machines (GBM); Predictive modeling |
| Subjects: | Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Sep 2024 04:48 |
| Last Modified: | 30 Sep 2024 04:48 |
| URI: | http://eprints.um.edu.my/id/eprint/45259 |
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