Li, HuaDong and Zeng, Jie and Almadhor, Ahmad and Riahi, Anis and Almujibah, Hamad and Abbas, Mohamed and Ponnore, Joffin Jose and Assilzadeh, Hamid (2024) A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures. Engineering Structures, 303. ISSN 0141-0296, DOI https://doi.org/10.1016/j.engstruct.2023.117051.
Full text not available from this repository.Abstract
Load-bearing in structural engineering involves a structure's ability to support and distribute weight effectively. This research investigates innovative methods to enhance load-bearing capabilities while optimizing energy flexibility within structural systems. Accurately predicting the local bearing capacity of concrete is not only vital for ensuring structural stability in building engineering, especially in anchorage zones, but also for promoting environmental sustainability through optimized material use. Existing prediction models, primarily designed for ordinary-strength concrete, often overlook the nuanced influence of concrete strength and ducts. This oversight can lead to substantial inaccuracies when these models are applied to high-strength and ultra-high-strength concrete. To holistically address these challenges, this study introduces generalized prediction models that factor in crucial elements such as concrete strength, local area aspect ratio, and ducts. The results show that the Mean of the GB50010-2010 model, CECS104:99 model, and ACI318-19 model ranged from 0.845 to 0.937, which might overestimate the experimental data with high variation, while the AASHTO model might underestimate the local bearing capacity of concrete, with a mean value of 1.045. The SD, MAPE, RMSE, IAE, R2, and α20 index were approximately within the range of 0.12–0.19, 0.14–0.24, 227–373, 2.4–3.4, 0.7–0.9, 0.6–0.9 for the existing models, and 0.11–0.13, 0.09–0.1, 176–178 1.95–1.96, 0.93–0.94, 0.90–0.91 for FA model and ANN models. This indicated that the proposed FA model and ANN model outperformed all the existing normative models used for concrete local bearing capacity. © 2023
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education in Saudi Arabia [Grant no. 223202], Prince Sattam bin Abdulaziz University [Grant no. PSAU/2023/R/1445], Khon Kaen University [Grant no. R.G.P.2/555/44] |
| Uncontrolled Keywords: | Artificial neural network (ANN); Concrete; Fitting analysis (FA); Local bearing capacity; Prediction model |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Jul 2024 04:15 |
| Last Modified: | 12 Jul 2024 04:15 |
| URI: | http://eprints.um.edu.my/id/eprint/44786 |
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