Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming

This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously report...

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Published in:Materials Vol. 14; no. 23; p. 7134
Main Authors: Ilyas, Israr, Zafar, Adeel, Javed, Muhammad, Farooq, Furqan, Aslam, Fahid, Musarat, Muhammad, Vatin, Nikolai
Format: Journal Article
Language:English
Published: Basel MDPI AG 24.11.2021
MDPI
Subjects:
Algorithms
Artificial intelligence
Carbon fiber reinforced concretes
Carbon fiber reinforced plastics
Civil engineering
Compressive strength
Concrete
Construction materials
Diameters
Fiber reinforced polymers
Gene expression
Genetic algorithms
Genetic engineering
Machine learning
Mathematical functions
Mathematical models
Mechanical properties
Modulus of elasticity
Parametric analysis
Parametric statistics
Performance evaluation
Rehabilitation
Reliability analysis
Research methodology
Researchers
Retrofitting
Soft computing
Statistical analysis
Structural members
Thickness
Variables
ISSN:1996-1944, 1996-1944
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Abstract This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.
AbstractList This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.
This study provides the application of a machine learning-based algorithm approach names "Multi Expression Programming" (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.This study provides the application of a machine learning-based algorithm approach names "Multi Expression Programming" (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.
Author Zafar, Adeel
Vatin, Nikolai
Farooq, Furqan
Aslam, Fahid
Ilyas, Israr
Javed, Muhammad
Musarat, Muhammad
AuthorAffiliation 5 Department of Civil and Environmental Engineering, Bandar Seri Iskandar 32610, Perak, Malaysia; muhammad_19000316@utp.edu.my
1 Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan; israr.awaan@gmail.com (I.I.); adeel.zafar@mce.nust.edu.pk (A.Z.)
4 Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; f.aslam@psau.edu.sa
2 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
3 Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; furqan@cuiatd.edu.pk
6 Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia; vatin@mail.ru
AuthorAffiliation_xml – name: 1 Department of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan; israr.awaan@gmail.com (I.I.); adeel.zafar@mce.nust.edu.pk (A.Z.)
– name: 3 Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland; furqan@cuiatd.edu.pk
– name: 6 Peter the Great St. Petersburg Polytechnic University, 195291 St. Petersburg, Russia; vatin@mail.ru
– name: 4 Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; f.aslam@psau.edu.sa
– name: 2 Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
– name: 5 Department of Civil and Environmental Engineering, Bandar Seri Iskandar 32610, Perak, Malaysia; muhammad_19000316@utp.edu.my
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2021 by the authors. 2021
Copyright_xml – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive...
This study provides the application of a machine learning-based algorithm approach names "Multi Expression Programming" (MEP) to forecast the compressive...
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StartPage 7134
SubjectTerms Algorithms
Artificial intelligence
Carbon fiber reinforced concretes
Carbon fiber reinforced plastics
Civil engineering
Compressive strength
Concrete
Construction materials
Diameters
Fiber reinforced polymers
Gene expression
Genetic algorithms
Genetic engineering
Machine learning
Mathematical functions
Mathematical models
Mechanical properties
Modulus of elasticity
Parametric analysis
Parametric statistics
Performance evaluation
Rehabilitation
Reliability analysis
Research methodology
Researchers
Retrofitting
Soft computing
Statistical analysis
Structural members
Thickness
Variables
Title Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming
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https://pubmed.ncbi.nlm.nih.gov/PMC8658637
Volume 14
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