Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods

Abstract

The application of steel strut force servo systems in deep excavation engineering is not widespread, and there is a notable scarcity of in-situ measured datasets. This presents a significant research gap in the field. Addressing this, our study introduces a valuable dataset and application scenarios, serving as a reference point for future research. The main objective of this study is to use machine learning (ML) methods for accurately predicting strut forces in steel supporting structures, a crucial aspect for the safety and stability of deep excavation projects. We employed five different ML methods: radial basis function neural network (RBFNN), back propagation neural network (BPNN), K-Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF), utilizing a dataset of 2208 measured points. These points included one output parameter (strut forces) and seven input parameters (vertical position of strut, plane position of strut, time, temperature, unit weight, cohesion, and internal frictional angle). The effectiveness of these methods was assessed using root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE). Our findings indicate that the BPNN method outperforms others, with RMSE, R, and MAE values of 72.1 kN, 0.9931, and 57.4 kN, respectively, on the testing dataset. This study underscores the potential of ML methods in precisely predicting strut forces in deep excavation engineering, contributing to enhanced safety measures and project planning.

Keywords

Deep excavation / Steel supporting structure / Strut forces / Machine learning / Time / Temperature

CRediT authorship contribution statement

Haibo Hu: Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Xunjian Hu: Writing - review & editing, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Xiaonan Gong: Writing - review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was supported by the National Natural Science Foundation of China (Grant No. 51778575).

Appendix A Supplementary material

Supplementary material to this article can be found online at https://doi.org/10.1016/j.undsp.2023.12.005.

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