Data-driven apparent earth pressure prediction in braced excavations in stratified soft-stiff clay deposits

Abstract

The analysis of apparent earth pressure (AEP) in braced excavations in soft clay environments demands advanced methodologies to address complex soil-structure interactions and nonlinear parameter inter-dependencies. Traditional empirical approaches often oversimplify these critical factors, compromising design reliability. This study introduces a data-driven framework that merges machine learning (ML) techniques with finite element (FE) modeling to enhance AEP prediction and interpretation. A novel Dynamic Time Warping (DTW)-based KMeans clustering algorithm is employed to classify AEP distributions, validated against FE simulations and field-monitored data. By integrating FE modeling with data-driven clustering, the framework generates refined apparent pressure diagrams (APDs) tailored to T_sc-specific conditions, outperforming conventional Terzaghi-Peck and CIRIA diagrams. Results demonstrate that ML models reduce prediction errors compared to empirical approaches. This work underscores the transformative potential of ML in advancing geotechnical engineering, offering a paradigm for robust excavation design in heterogeneous soil strata.

Keywords

Soft clay / Braced excavation / Strut force / Apparent pressure diagrams (APD) / Machine learning / Dynamic time warping clustering

CRediT authorship contribution statement

Runhong Zhang: Writing - original draft, Methodology, Conceptualization. Haoran Chang: Software, Investigation, Data curation. Anthony Teck Chee Goh: Writing - review & editing, Supervision, Conceptualization. Weixin Sun: Writing - review & editing, Validation.

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.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52308392); the Fundamental Research Funds for the Central Universities (No. 2682024CX122).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102246.

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