Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers
In concrete construction, early-age thermal cracks in foundations, abutments, piers, and slabs can arise from non-uniform temperature distribution due to heat from cement hydration. These cracks negatively impact the integrity, load-bearing capacity, and service life of the concrete structures. This...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Forces in Mechanics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266635972400043X |
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| author | Tu Anh Do Ba-Anh Le |
| author_facet | Tu Anh Do Ba-Anh Le |
| author_sort | Tu Anh Do |
| collection | DOAJ |
| description | In concrete construction, early-age thermal cracks in foundations, abutments, piers, and slabs can arise from non-uniform temperature distribution due to heat from cement hydration. These cracks negatively impact the integrity, load-bearing capacity, and service life of the concrete structures. This paper investigates the application of machine learning (ML) models to predict early-age thermal cracking in concrete bridge piers. The study aims to develop models to forecast thermal cracking potential (ηmax) and estimate the timing of potential cracking (t) based on a dataset of various cross-sectional bridge piers and typical tropical temperatures. Four ML models—Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Genetic Programming (GP)—were trained on 759 samples. The dataset, prepared using the EACTSA program, included parameters like cross-sectional dimensions, ambient temperature, and initial concrete temperature, with ηmax and t as outputs. Results show that all the ML models achieved high prediction accuracy with R² scores over 0.96. The GP symbolic equations offer transparency and practical implementation. Compared to conventional methods, ML models provide a rapid, effective tool to optimize concrete member dimensions, formwork removal timing, and control concrete temperature, mitigating early-age thermal cracking risk. |
| format | Article |
| id | doaj-art-bee495d8ed8f4a5f84bf74fea3a10d5e |
| institution | Kabale University |
| issn | 2666-3597 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Forces in Mechanics |
| spelling | doaj-art-bee495d8ed8f4a5f84bf74fea3a10d5e2024-11-30T07:14:07ZengElsevierForces in Mechanics2666-35972024-12-0117100297Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piersTu Anh Do0Ba-Anh Le1Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay, Lang Thuong, Dong Da, Hanoi, VietnamCorresponding author.; Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay, Lang Thuong, Dong Da, Hanoi, VietnamIn concrete construction, early-age thermal cracks in foundations, abutments, piers, and slabs can arise from non-uniform temperature distribution due to heat from cement hydration. These cracks negatively impact the integrity, load-bearing capacity, and service life of the concrete structures. This paper investigates the application of machine learning (ML) models to predict early-age thermal cracking in concrete bridge piers. The study aims to develop models to forecast thermal cracking potential (ηmax) and estimate the timing of potential cracking (t) based on a dataset of various cross-sectional bridge piers and typical tropical temperatures. Four ML models—Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), and Genetic Programming (GP)—were trained on 759 samples. The dataset, prepared using the EACTSA program, included parameters like cross-sectional dimensions, ambient temperature, and initial concrete temperature, with ηmax and t as outputs. Results show that all the ML models achieved high prediction accuracy with R² scores over 0.96. The GP symbolic equations offer transparency and practical implementation. Compared to conventional methods, ML models provide a rapid, effective tool to optimize concrete member dimensions, formwork removal timing, and control concrete temperature, mitigating early-age thermal cracking risk.http://www.sciencedirect.com/science/article/pii/S266635972400043XBridge pierEarly-age thermal crackingTime of cracking occurrenceMachine learningGenetic programming |
| spellingShingle | Tu Anh Do Ba-Anh Le Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers Forces in Mechanics Bridge pier Early-age thermal cracking Time of cracking occurrence Machine learning Genetic programming |
| title | Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers |
| title_full | Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers |
| title_fullStr | Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers |
| title_full_unstemmed | Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers |
| title_short | Machine learning approach for predicting early-age thermal cracking potential in concrete bridge piers |
| title_sort | machine learning approach for predicting early age thermal cracking potential in concrete bridge piers |
| topic | Bridge pier Early-age thermal cracking Time of cracking occurrence Machine learning Genetic programming |
| url | http://www.sciencedirect.com/science/article/pii/S266635972400043X |
| work_keys_str_mv | AT tuanhdo machinelearningapproachforpredictingearlyagethermalcrackingpotentialinconcretebridgepiers AT baanhle machinelearningapproachforpredictingearlyagethermalcrackingpotentialinconcretebridgepiers |