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Research on multi-parameter Error model of Backcalculated Modulus Using Abaqus Finite Element Batch modeling Based on Python Language

引用

BUILDINGS 2024年第11期14卷 3454-3454页

作者： Xiong, Chunlong Yu, Jiangmiao Zhang, Xiaoning Luo, Chuanxi South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510640 Peoples R China Guangzhou Xiaoning Roadway Engn Technol Res Inst C Guangzhou 510641 Peoples R China

The error in modulus backcalculation is a crucial component in validating the rationality and reliability of results for engineering applications. The objective of this study is to identify the theoretical limitations associated with backcalculated modulus errors under typical parameter uncertainties and to determine the primary factors contributing to these errors. Firstly, using the actual measurements or data from the Long-Term Pavement Performance (LTPP) project, the statistical distributions of errors for typical parameters in the modulus backcalculation model were determined. Subsequently, a factor level table for orthogonal experimental design was developed, leading to the construction of 81 orthogonal design experimental schemes and their corresponding theoretical pavement structure models based on the actual error distributions. The deflection responses of 81 theoretical pavement structure models were then computed using an ABAQUS finite element batch analysis method devised in Python. Furthermore, a multi-parameter error model for modulus was established using multiple linear regression and variance analysis. Finally, the theoretical limitations of modulus errors under actual errors were analyzed. The results show that the errors of thickness, load amplitude and load frequency follow a normal distribution, while the distribution of backcalculated modulus errors follows an approximate mixed Gaussian distribution. When the errors of multiple parameters are combined randomly, the modulus errors range from -100% to 595%, and the probability of the modulus errors being less than 15% is highest in the asphalt surface layer, followed by the subgrade, and then the base and subbase layers. Within the same error range, the modulus error is random. However, with different error ranges, the overall level of modulus error increases in proportion to the size of those ranges. Compared to factors such as thickness, load amplitude, and load frequency, the errors in deflect

关键词： error in modulus backcalculation distribution characteristics ABAQUS finite element batch analysis Python language multi-parameter errors model

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