Optimization of site exploration effort to improve the accuracy of tunneling-induced ground settlement prediction in soft clays

Uncertainty of soil parameters can lead to either an overestimation or underestimation of tunneling-induced ground settlement in soft clays. Thus, both site exploration and soil testing are important in the prediction of tunneling-induced ground settlement. In this study, the Loganathan and Poulos model, a closed-form analytical model, was used to predict the tunneling-induced settlement in clays, and the effect of the uncertainty of input soil parameters on the variation of predicted tunneling-induced settlement in clays was investigated. A series of parametric analyses were performed to investigate the relationship between the extent of site exploration and the accuracy of the tunneling-induced ground settlement prediction. To this end, soil properties at a site were first randomly generated using Monte Carlo simulation (MCS) with predefined sample statistics and spatial variability. Next, the maximum likelihood approach was used to evaluate the statistics of soil parameters, followed by a probabilistic analysis of the tunneling-induced ground settlement. Analysis with different sample sizes was carried out to simulate different levels of site exploration effort. The level of site exploration effort was found to have a limited influence on the mean of the predicted ground settlement; however, it exhibited a great influence on the variation of the predicted ground settlement. Furthermore, a nondominant optimization technique was used to determine the number of tests needed to develop a robust prediction of tunneling-induced ground settlement. The effectiveness and significance of the proposed approach was demonstrated through an illustrative example.