Bidirectional and nested model for numerical simulation and machine learning: a case study of long-term settlement prediction of tunnel

Abstract

Numerical simulation and machine learning are commonly adopted research methods in engineering. This paper proposes a bidirectional and nested model for numerical simulation and machine learning (BNNM). This model permits numerical simulation methods and machine learning methods to participate in each other’s calculation process. It helps overcome the obstruction of unclear mechanisms and inaccurate parameters in numerical simulation methods, and avoid overfitting problem caused by too many features in machine learning methods. Moreover, BNNM frees machine learning methods from the dependence on a specific set of labels. The BNNM helps train machine learning models using obtainable labels, and output results that cannot be easily obtained using field, experiment, and numerical simulations. To illustrate its construction method and performance, a representative BNNM model is constructed using BPNN, in addition to a simple numerical simulation model. This model predicts the long-term settlement of shield tunnel. The results show that the representative model effectively reduces the modelling difficulty associated with numerical simulation and improves prediction accuracy of BPNN model. The model also derives long-term constitutive models of various soils with only the tunnel settlement data set. Although a simplified constitutive model was used, the main advantages of the BNNM model have been highlighted.