Author
Listed:
- Chao Liu
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China
Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang 050031, China
Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang 050031, China)
- Hongrui Zhang
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China)
- Ying Yuan
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China
Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang 050031, China
Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang 050031, China)
- Aihong Zhou
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China
Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang 050031, China
Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang 050031, China)
- Weiwen Liu
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China)
- Wanying Guo
(School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China)
Abstract
Spatial variability of soil parameter distribution is crucial to calculating the pile foundation failure probability. Traditional reliability design methods describe the dispersion degree of soil parameters with their point variance without considering the influence of correlation distance. In this paper, static cone penetration test data of a project site are used, and random field theory is introduced to describe the average spatial characteristics of soil parameters. Then, the method of spatial average is used to calculate the correlation distance of soil parameters in each foundation soil layer. Given the influence of the correlation distance, a variance reduction function is determined to convert point variance to spatial mean-variance and further calculate the failure probability of pile foundation with the Monte Carlo method to study the influence of correlation distance on pile foundation failure probability. Results show that the spatial variability of parameters can be better reflected, and project cost can be reduced by considering the influence of correlation distance during the pile foundation design process. These results lay a foundation for further research on the pile foundation reliability design method.
Suggested Citation
Chao Liu & Hongrui Zhang & Ying Yuan & Aihong Zhou & Weiwen Liu & Wanying Guo, 2023.
"Influence of Correlation Distance of Soil Parameters on Pile Foundation Failure Probability,"
Sustainability, MDPI, vol. 15(5), pages 1-17, February.
Handle:
RePEc:gam:jsusta:v:15:y:2023:i:5:p:4298-:d:1083073
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