On the use of stochastic spectral methods in deep excavation inverse problems. (15th October 2015) - Cañavate-Grimal, Antonio; Falcó, Antonio; Calderón, Pedro; Payá-Zaforteza, Ignacio
Highlights: We use stochastic surrogate models and Bayesian methods to solve inverse problems. We develop an analytical formulation to determine the parameter influence in response. We propose an algorithm to bound the truncation error in surrogate models. We propose an iterative strategy that converge to the inverse problem solution. We avoid Monte Carlo Markov Chains to solve the inverse problem. Abstract: The back analysis or inverse analysis of the field instrumentation data is a common technique to ascertain the design parameter validity in deep excavation projects. That analysis is a process full of uncertainties and relies greatly on the expert judgement. Furthermore, deep excavation geotechnical models tend to be computationally very expensive making the inverse analysis a very lengthy process. In this paper, a Bayesian-type methodology to solve inverse problems which relies on the reduction of the numerical cost of the forward simulation through stochastic spectral surrogate models is presented. The proposed methodology is validated with three calibration examples.