A probabilistic analysis of subsoil parameters uncertainty impacts on tunnel-induced ground movements with a back-analysis study. (July 2015)

• Record Type: Journal Article
• Authors: Miro, S.; König, M.; Hartmann, D.; Schanz, T.
• Is Part Of: Computers and geotechnics. Volume 68(2015)
• Page Start: 38
• Page End: 53
• Publication Date: 2015-07
• Subjects: Mechanized tunneling -- Finite element simulation -- Surrogate modeling -- Global sensitivity analysis -- Reliability analysis -- Bayesian updating; Engineering geology -- Data processing -- Periodicals; Soil mechanics -- Data processing -- Periodicals; Rock mechanics -- Data processing -- Periodicals; 624.1510285
• DOI: 10.1016/j.compgeo.2015.03.012
• Languages: English
• ISSNs: 0266-352X

Abstract

Abstract: In this paper a probabilistic-based analysis is presented for evaluating the influences of subsoil parameter uncertainties on tunnel-induced ground movements in mechanized tunneling. The procedures of the tunneling process using Slurry Shield Tunnel Boring Machine are numerically modeled and simulated by utilizing a finite element code. To keep the computational cost of the presented simulation model low, an efficient and reliable surrogate modeling technique is used to substitute the original simulation model. The input parameter uncertainties are mathematically represented by adequately chosen probability density functions within their extreme lower and upper bounds. Subsequently, a variance-based global sensitivity analysis is conducted for quantifying the impact of each uncertain parameter on different system responses that are considered in this study. Afterwards, the propagation of parameter uncertainties are evaluated by performing a Monte Carlo-based simulation using the computationally inexpensive surrogate model. At this stage, the variations of system responses, which result from input parameters propagating uncertainties, are compared with predetermined threshold values and, based on that, failure criteria of the tunneling system are defined as well as probabilistically quantified. In a last step, a Bayesian updating procedure is employed for reducing subsoil parameter uncertainties by utilizing recorded synthetic measurements.