Kjhh
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Lei Wang, Nadarajah Ravichandran, C. In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: 1 prior estimate of the maximum ground settlement and model bias factor, 2 establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and 3 development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating. T1 - Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data. N1 - Funding Information: The anonymous reviewers are thanked for their constructive comments that have helped sharpen the work presented in this paper. The authors also wish to thank Dr.
Kjhh
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Kjhh
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Frequently Bought Together. Cancelled: Your expired order will turn to "Cancelled". Get Code. Hsein Juang. Completed: Your order has been successfully paid. Forgot Password? Aiyana ft. Home Improvement. You might be interested in. Finally, the first author wishes to thank Glenn Department of Civil Engineering, Clemson University for its financial support. Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data. The Bayesian approach involves three steps: 1 prior estimate of the maximum ground settlement and model bias factor, 2 establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and 3 development of posterior distribution of the predicted maximum settlement.
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