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Crack control optimization of basement concrete structures using the Mask-RCNN and temperature effect analysis
1Department of Architectural Arts, Xuancheng Vocational & Technical College, Xuancheng City, China.
Plos One
|October 5, 2023
Summary
This study optimizes basement concrete structures to reduce cracks using Mask-RCNN and temperature analysis. The method significantly decreases crack density, length, width, stress concentration, and deformation, enhancing structural stability.
Area of Science:
- Civil Engineering
- Materials Science
- Computational Mechanics
Background:
- Cracking in basement concrete structures compromises integrity.
- Temperature fluctuations induce stress and deformation, exacerbating crack propagation.
- Existing methods for crack mitigation require optimization.
Purpose of the Study:
- To propose an optimization methodology for mitigating cracks in basement concrete structures.
- To leverage Mask Region-based Convolutional Neural Network (Mask-RCNN) and temperature effect analysis.
- To enhance structural stability by minimizing stress and deformation.
Main Methods:
- Image segmentation of concrete structures using Mask-RCNN for crack identification.
- Finite element analysis to simulate stress and deformation under temperature variations.
- An optimization algorithm to adjust geometric and material parameters.
Main Results:
- Crack density reduced by 60.22%, average crack length by 40.24%, and width by 35.43%.
- Maximum stress concentration decreased by 25.22%, and maximum deformation by 30.32%.
- Real-time monitoring validated significant improvements in structural stability.
Conclusions:
- The proposed optimization methodology effectively reduces cracks in basement concrete structures.
- The approach enhances structural stability by mitigating stress concentration and deformation.
- The optimization algorithm demonstrates robustness and consistent performance in crack control.
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