A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation
The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/207 |
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| author | Chun Zhang Yinjie Zhao Guangyu Wu Han Wu Hongli Ding Jian Yu Ruoqing Wan |
| author_facet | Chun Zhang Yinjie Zhao Guangyu Wu Han Wu Hongli Ding Jian Yu Ruoqing Wan |
| author_sort | Chun Zhang |
| collection | DOAJ |
| description | The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%. |
| format | Article |
| id | doaj-art-8a84e2516f4647c6b8e04b0b6cce1367 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-8a84e2516f4647c6b8e04b0b6cce13672025-01-24T13:26:10ZengMDPI AGBuildings2075-53092025-01-0115220710.3390/buildings15020207A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical SimulationChun Zhang0Yinjie Zhao1Guangyu Wu2Han Wu3Hongli Ding4Jian Yu5Ruoqing Wan6School of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaDesign and Research Institute of Nanchang University, Nanchang 330047, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Infrastructure Engineering, Nanchang University, Nanchang 330031, ChinaThe correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%.https://www.mdpi.com/2075-5309/15/2/207structural assessmentmachine visiondeep learningsurrogate modelreinforced concrete beam |
| spellingShingle | Chun Zhang Yinjie Zhao Guangyu Wu Han Wu Hongli Ding Jian Yu Ruoqing Wan A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation Buildings structural assessment machine vision deep learning surrogate model reinforced concrete beam |
| title | A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation |
| title_full | A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation |
| title_fullStr | A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation |
| title_full_unstemmed | A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation |
| title_short | A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation |
| title_sort | correlation analysis based structural load estimation method for rc beams using machine vision and numerical simulation |
| topic | structural assessment machine vision deep learning surrogate model reinforced concrete beam |
| url | https://www.mdpi.com/2075-5309/15/2/207 |
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