Bayesian Framework for Assessing Effectiveness of Geotechnical Site Investigation Programs
Jinzheng Hu, Jianguo Zheng, Jie Zhang, Hongwei Huang
Abstract
Site investigation data can play an important role in not only site characterization but also the improvement of engineering design. It is tempting to predict the potential benefit of a site investigation before it is actually conducted. In this study, a Bayesian framework to assess the effectiveness of a site investigation program is proposed. The expected reduced variance (ERV) is employed to measure the uncertainty reduction owing to site investigation data. The uncertain outcomes of all possible site investigation data are considered through their probabilistic distributions. Then a framework to calculate the ERV is established. Monte Carlo simulation is adopted and the statistical uncertainty can be analyzed through the bootstrap method. Two examples of a shallow foundation on sand and an undrained slope are illustrated in this paper. Based on the examples studied, the optimal sampling layout is also discussed. The proposed method is promising to be applied in optimization of the sampling layout for various geotechnical practices.