Implementation of Surrogate Models for the Analysis of Slope Problems

Abstract

Numerical modeling is increasingly used to analyze practical rock engineering problems. The geological strength index (GSI) is a critical input for many rock engineering problems. However, no available method allows the quantification of GSI input parameters, and engineers must consider a range of values. As projects progress, these ranges can be narrowed down. Machine learning (ML) algorithms have been coupled with numerical modeling to create surrogate models. The concept of surrogate models aligns well with the deductive nature of data availability in rock engineering projects. In this paper, we demonstrated the use of surrogate models to analyze two common rock slope stability problems: (1) determining the maximum stable depth of a vertical excavation and (2) determining the allowable angle of a slope with a fixed height. Compared with support vector machines and K-nearest algorithms, the random forest model performs best on a data set of 800 numerical models for the problems discussed in the paper. For all these models, regression-type models outperform classification models. Once the surrogate model is confirmed to preform accurately, instantaneous predictions of maximum excavation depth and slope angle can be achieved according to any range of input parameters. This capability is used to investigate the impact of narrowing GSI range estimation.

Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools

“…Nonetheless, all types of RF models performed similarly. In terms of judging ML model performance, it is essential to bear in mind that ML model metrics can only be understood within the specific context of the problem being studied [11]. The objective of the ML model in the current study is not to build a surrogate model that accurately predicts displacements, as these can be regarded as meaningless, as discussed.…”

“…This finding is indeed specific to the current case study, considering its unique geometry, engineering assumptions, and ranges of input parameters defined for the FE models. This result greatly simplifies the remainder of the back-analysis process as it indicates that efforts can be focused solely on the calibration In terms of judging ML model performance, it is essential to bear in mind that ML model metrics can only be understood within the specific context of the problem being studied [11]. The objective of the ML model in the current study is not to build a surrogate model that accurately predicts displacements, as these can be regarded as meaningless, as discussed.…”

“…It has been demonstrated that ML can also be incorporated into numerical modelling tasks [9,10]. This can be achieved by generating models and analyzing the numerical data with ML tools [11]. The coupling of ML with engineering models is sometimes referred to as surrogate models.…”

A Comparative Study of Embedded Wall Displacements Using Small-Strain Hardening Soil Model

“…Another reason to generate evenly distributed inputs is for applying ML tools to interpret the numerical results. ML models perform better when trained on evenly distributed data [22]. Two Python scripts were written for the automation process, corresponding to the two arrows shown in Figure 3.…”

“…Another reason to generate evenly distributed inputs is for applying ML tools to interpret the numerical results. ML models perform better when trained on evenly distributed data [22].…”