Model selection in geological and geotechnical engineering in the face of uncertainty - Does a complex model always outperform a simple model?

Abstract

In order to achieve higher fidelity (i.e., higher accuracy) in the model predictions, the solution models employed in geological and/or geotechnical engineering are becoming complex and sophisticated. However, simple and robust models are preferred by engineers in practice. Thus, a dilemma exists between the choice of a complex model for fidelity and that of a simple model for high robustness (i.e., lower variation in the discrepancy between model prediction and observation). This issue becomes more profound when model parameters exhibit uncertainty, which is quite common in geological and geotechnical problems. In this paper, we examine the issue of model selection in the face of uncertainty with three problems: the selection of the order of polynomial fit (i.e., lower order vs. higher order) in the development of data-driven empirical model, the selection of the level of sophistication (i.e., random variable vs. random field) in the probabilistic characterization of the detrended soil property, and the selection of the level of complexity (i.e., simple vs. complex) of the soil constitutive model in numerical modelling. The results illustrate that although the complex and sophisticated models could yield predictions that are more accurate, the simple models might yield predictions that are more robust. This paper provides an insight regarding the question, “Does a complex model always outperform a simple model?”