Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures
High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML)...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Materials |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2023.1187094/full |
| _version_ | 1797840528298999808 |
|---|---|
| author | Gongmei Chen Salman Ali Suhail Alireza Bahrami Muhammad Sufian Marc Azab |
| author_facet | Gongmei Chen Salman Ali Suhail Alireza Bahrami Muhammad Sufian Marc Azab |
| author_sort | Gongmei Chen |
| collection | DOAJ |
| description | High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool for predicting concrete properties. Accurate prediction of the compressive strength of HSC is important as HSC can experience strength losses of up to 80% after exposure to temperatures of 800°C–1000°C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for predicting the compressive strength of HSC. The results of this study demonstrate that the RF model is the most efficient for predicting the compressive strength of HSC, exhibiting the R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, Shapley Additive Explanations analysis highlights temperature as the most significant factor influencing the compressive strength of HSC. This article provides valuable insights into the timely and effective determination of the compressive strength of HSC under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence the compressive strength of HSC, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure. |
| first_indexed | 2024-04-09T16:16:49Z |
| format | Article |
| id | doaj.art-34ccec6f4d4741c08aa76320533855a5 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-04-09T16:16:49Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-34ccec6f4d4741c08aa76320533855a52023-04-24T04:30:10ZengFrontiers Media S.A.Frontiers in Materials2296-80162023-04-011010.3389/fmats.2023.11870941187094Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperaturesGongmei Chen0Salman Ali Suhail1Alireza Bahrami2Muhammad Sufian3Marc Azab4School of Architecture and Civil Engineering, Changchun Sci-Tech University, Changchun, ChinaDepartment of Civil Engineering, University of Lahore (UOL), Lahore, PakistanDepartment of Building Engineering, Energy Systems and Sustainability Science, Faculty of Engineering and Sustainable Development, University of Gävle, Gävle, SwedenSchool of Civil Engineering, Southeast University, Nanjing, ChinaCollege of Engineering and Technology, American University of the Middle East, Egaila, KuwaitHigh-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool for predicting concrete properties. Accurate prediction of the compressive strength of HSC is important as HSC can experience strength losses of up to 80% after exposure to temperatures of 800°C–1000°C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for predicting the compressive strength of HSC. The results of this study demonstrate that the RF model is the most efficient for predicting the compressive strength of HSC, exhibiting the R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, Shapley Additive Explanations analysis highlights temperature as the most significant factor influencing the compressive strength of HSC. This article provides valuable insights into the timely and effective determination of the compressive strength of HSC under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence the compressive strength of HSC, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure.https://www.frontiersin.org/articles/10.3389/fmats.2023.1187094/fullcompressive strengthhigh-strength concretemachine learningraw material interactionfire resistance |
| spellingShingle | Gongmei Chen Salman Ali Suhail Alireza Bahrami Muhammad Sufian Marc Azab Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures Frontiers in Materials compressive strength high-strength concrete machine learning raw material interaction fire resistance |
| title | Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures |
| title_full | Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures |
| title_fullStr | Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures |
| title_full_unstemmed | Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures |
| title_short | Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures |
| title_sort | machine learning based evaluation of parameters of high strength concrete and raw material interaction at elevated temperatures |
| topic | compressive strength high-strength concrete machine learning raw material interaction fire resistance |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2023.1187094/full |
| work_keys_str_mv | AT gongmeichen machinelearningbasedevaluationofparametersofhighstrengthconcreteandrawmaterialinteractionatelevatedtemperatures AT salmanalisuhail machinelearningbasedevaluationofparametersofhighstrengthconcreteandrawmaterialinteractionatelevatedtemperatures AT alirezabahrami machinelearningbasedevaluationofparametersofhighstrengthconcreteandrawmaterialinteractionatelevatedtemperatures AT muhammadsufian machinelearningbasedevaluationofparametersofhighstrengthconcreteandrawmaterialinteractionatelevatedtemperatures AT marcazab machinelearningbasedevaluationofparametersofhighstrengthconcreteandrawmaterialinteractionatelevatedtemperatures |