Probability analysis of vertical drainage improvement for soft soil settlement prediction via a Bayesian back analysis framework and the simplified Hypothesis B method - Shijie Zhai; Guangyin Du; Tao Peng; Yuxiao Wang; Zhiheng Shang (Journal of Intelligent Construction)
The time-dependent settlement of soft soils is one of the key problems in geotechnical engineering. Using Bayesian back analysis, this study examined the probability of settlement of the Ballina embankment in Australia. As random variables, the primary compression index (Cc), swelling index (Cs), and secondary compression index (Cα) were examined for their influence on the settlement probability distribution. To generate compression index samples, Markov chain Monte Carlo simulation (MCMCS) was used, and the predicted settlement samples were derived from the compression index samples. Consequently, the predicted settlement samples can be used for probability analysis. A comparison between the field settlement data and the predicted settlement data reveals that the 90% confidence interval of the predicted settlement data is in reasonable agreement with the field settlement monitoring data. With the incorporation of more monitored settlement data into the Bayesian framework, the distribution of the predicted settlement shifts from the Weibull distribution to the normal distribution. In addition, the degree of uncertainty in the prediction of settlement decreases with the amount of data incorporated into the model. Additionally, the small amount of data used in the Bayesian framework can lead to underestimations of failure probability.