A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures

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 b...

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Main Authors: Li, HuaDong, Zeng, Jie, Almadhor, Ahmad, Riahi, Anis, Almujibah, Hamad, Abbas, Mohamed, Ponnore, Joffin Jose, Assilzadeh, Hamid
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Published: Elsevier Ltd 2024
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spelling my.um.eprints.447862024-07-12T04:15:13Z http://eprints.um.edu.my/44786/ A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures Li, HuaDong Zeng, Jie Almadhor, Ahmad Riahi, Anis Almujibah, Hamad Abbas, Mohamed Ponnore, Joffin Jose Assilzadeh, Hamid TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering 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 Elsevier Ltd 2024 Article PeerReviewed 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 <https://doi.org/10.1016/j.engstruct.2023.117051>. 10.1016/j.engstruct.2023.117051
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Li, HuaDong
Zeng, Jie
Almadhor, Ahmad
Riahi, Anis
Almujibah, Hamad
Abbas, Mohamed
Ponnore, Joffin Jose
Assilzadeh, Hamid
A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
description 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
format Article
author Li, HuaDong
Zeng, Jie
Almadhor, Ahmad
Riahi, Anis
Almujibah, Hamad
Abbas, Mohamed
Ponnore, Joffin Jose
Assilzadeh, Hamid
author_facet Li, HuaDong
Zeng, Jie
Almadhor, Ahmad
Riahi, Anis
Almujibah, Hamad
Abbas, Mohamed
Ponnore, Joffin Jose
Assilzadeh, Hamid
author_sort Li, HuaDong
title A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
title_short A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
title_full A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
title_fullStr A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
title_full_unstemmed A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
title_sort study on improving energy flexibility in building engineering through generalized prediction models: enhancing local bearing capacity of concrete for engineering structures
publisher Elsevier Ltd
publishDate 2024
url http://eprints.um.edu.my/44786/
_version_ 1805881166956331008
score 13.211869