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| Reverse Deduction Model of Structural Stress and Quantitative Analysis of Its Uncertainty |
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| DOI：10.7643/issn.1672-9242.2023.03.009 |
| KeyWord:structural safety monitoring structural stress reverse deduction digital model correlation analysis neural network uncertainty |
| Author | Institution |
| ZHU Quan-hua |
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou, Guangdong Guangzhou , China;China Ship Scientific Research Center, Jiangsu Wuxi , China |
| ZHANG Tao |
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou, Guangdong Guangzhou , China;China Ship Scientific Research Center, Jiangsu Wuxi , China |
| WANG Xue-liang |
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou, Guangdong Guangzhou , China;China Ship Scientific Research Center, Jiangsu Wuxi , China |
| JIANG Zhen-tao |
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou, Guangdong Guangzhou , China;China Ship Scientific Research Center, Jiangsu Wuxi , China |
| YUE Ya-lin |
Southern Marine Science and Engineering Guangdong Laboratory Guangzhou, Guangdong Guangzhou , China;China Ship Scientific Research Center, Jiangsu Wuxi , China |
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| Abstract: |
| The work aims to explore an artificial intelligence method for solving the shortcomings of structural safety online monitoring and evaluation and propose a method for reversely deducing the stress distribution of the whole structure based on limited measuring points, so as to build a data-driven algorithm model based on neural network technology. Firstly, based on the finite element simulation data of the structure, a limited number of measuring points representing the structural response characteristics were obtained by the correlation analysis method. Then, neural network method was adopted to build the algorithm model of deducing stress distribution in the whole field of structure based on limited measuring points. With the connector structure of Scientific Research & Demonstration Platform (SRDP) as the object, the application of the algorithm model was studied, and the uncertainty analysis of the algorithm model in this application case was carried out. Relative uncertainty u95rel of the deduced results was 8.6%. The deduced results of this algorithm model correctly reflect the overall response characteristics of the structure. According to the modeling process, the uncertainty mainly results from correlation analysis method, neural network modeling and model convergence condition. |
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