Inverse calculation of in situ stress in rock mass using the Surrogate-Model Accelerated Random Search algorithm

A new approach is presented that incorporates the Surrogate Model Accelerated Random Search (SMARS) Algorithm to inversely determine the overall stress state in a rock mass based on sparse stress measurements. The SMARS algorithm relies on a random search component to maintain global search capabilities, while using the surrogate-model method to accelerate convergence to a solution estimate. Two examples sets are carried out in this presentation to display the utility of the proposed SMARS-based inverse characterization process. The first example set compares the SMARS algorithm with two other popular methods, namely a multiple linear regression analysis method and a neural network method, to estimate the in situ stress field for a simple numerically simulated test case. The results of the numerical testing verify that SMARS provides a relatively stable approach and gives rise to a relatively high accuracy and efficiency (i.e., with less computational expense) compared to the other contemporary approaches considered. Finally, an example is shown for utilizing the SMARS approach for in situ stress estimation based on an actual underground mine located in Pennsylvania. The SMARS results are shown to produce a realistic estimate of the distribution of stress within the area investigated, and overall, the approach has potential for practical use in realistic scenarios to efficiently and accurately estimate in situ stresses in rock mass. (C) 2014 Elsevier Ltd. All rights reserved.