Abstract
Regular borehole sampling and laboratory tests have a long process, many disturbances and the risk of large errors. Hence, it is difficult to quickly and accurately obtain the geotechnical parameters in scientific calculations. The Cone Penetration Test (CPT) is an in-situ testing technique with a wide range of applications and high data quality. However, CPT data (cone resistance, qc, and sleeve friction, fs) cannot be directly converted into geotechnical parameters. The fundamental reason is the lack of mathematical-physical methods that establish the relationship between geotechnical parameters and CPT data. This study proposes a stochastic discrete-continuum coupling method to calibrate the geotechnical parameters. The deterministic and stochastic relationship between CPT data and geotechnical parameters is established. Firstly, the macroscopic and microscopic constitutive models are introduced. Secondly, a Bayesian sampling algorithm based on the Markov chain Monte-Carlo (MCMC) method is combined with the coupling mechanism. Finally, two examples verify the rationality of the stochastic discrete-continuum coupling method. CPT data of Ottawa 20–30 sand is used as the research objective, the prediction results are compared with the real results through numerical calculation, and effectiveness of the deterministic discrete-continuum coupling method is verified. Then, based on the coupling numerical model, the shallow bearing stratum in Shanghai, namely ②1 silty clay, is taken as an example, and the geotechnical parameters are calibrated. The predicted values of CPT data based on the posterior distributions are closer to the measurements than the prior reconnaissance information. In the engineering practice, the mechanism and statistics between the geotechnical parameters and CPT data are revealed, it provides a new idea for calibrating geotechnical parameters.
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Acknowledgments
The above work is sponsored by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (TP2018042), and the Shanghai Pujiang Program (18PJ1403900). Research is also supported by the Key Project of Science and Technology of Shanghai (21DZ1204300).
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Tang, D., Wang, C., Hu, B. et al. Calibration of Geotechnical Parameters Based on the Stochastic Discrete-continuum Coupling Method and Bayesian Theory. KSCE J Civ Eng 27, 1054–1065 (2023). https://doi.org/10.1007/s12205-022-1449-7
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DOI: https://doi.org/10.1007/s12205-022-1449-7