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Spatial random fields-based Bayesian method for calibrating geotechnical parameters with ground surface settlements induced by shield tunneling
The precise mechanics simulation of shallow shield tunneling through soft soil is an engineering challenge. Elastic–plastic deformation causes progressive failure for the surrounding soil mass and affects excavation safety. In this study, the Bayesian method is used to validate the spatial variability of the geotechnical parameters and the uncertainty of the mechanics model. Key soil properties including the cohesion, internal friction angle, Young’s modulus, and mechanics model factors are calibrated with Bayes’ theorem. The site investigation of the soil properties always involves multi-source observations such as borehole testing data and tunnel monitoring data. The spatial random fields-based Bayesian method can assimilate prior knowledge and multi-source data in the posterior distributions of the key geotechnical parameters. Three basic components, multivariate random fields, the likelihood function, and a fast sampling method, are introduced in this study. The above approach is used in a complicated shield tunneling project that consists of the fifth and the sixth metro lines that intersect at the Huanhu West Road station in Tianjin, China. The results indicate that the posterior distributions of the key geotechnical parameters and the mechanics model factors precisely support the reliability analysis.
Spatial random fields-based Bayesian method for calibrating geotechnical parameters with ground surface settlements induced by shield tunneling
The precise mechanics simulation of shallow shield tunneling through soft soil is an engineering challenge. Elastic–plastic deformation causes progressive failure for the surrounding soil mass and affects excavation safety. In this study, the Bayesian method is used to validate the spatial variability of the geotechnical parameters and the uncertainty of the mechanics model. Key soil properties including the cohesion, internal friction angle, Young’s modulus, and mechanics model factors are calibrated with Bayes’ theorem. The site investigation of the soil properties always involves multi-source observations such as borehole testing data and tunnel monitoring data. The spatial random fields-based Bayesian method can assimilate prior knowledge and multi-source data in the posterior distributions of the key geotechnical parameters. Three basic components, multivariate random fields, the likelihood function, and a fast sampling method, are introduced in this study. The above approach is used in a complicated shield tunneling project that consists of the fifth and the sixth metro lines that intersect at the Huanhu West Road station in Tianjin, China. The results indicate that the posterior distributions of the key geotechnical parameters and the mechanics model factors precisely support the reliability analysis.
Spatial random fields-based Bayesian method for calibrating geotechnical parameters with ground surface settlements induced by shield tunneling
Acta Geotech.
Wang, Changhong (author) / Wang, Kun (author) / Tang, Daofei (author) / Hu, Baolin (author) / Kelata, Yonas (author)
Acta Geotechnica ; 17 ; 1503-1519
2022-04-01
17 pages
Article (Journal)
Electronic Resource
English
Bayesian method , Geotechnical parameters , Ground surface settlements , Mechanics model factors , Shield tunneling excavation , Spatial variability Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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