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

• A Dynamic Time Warping (DTW)-based KMeans clustering algorithm to classify nonlinear apparent earth pressure (AEP) patterns. • Parametric analyses identify soft clay thickness ( T sc ) as the most critical factor. • Machine learning models reduce AEP prediction errors compared to Terzaghi-Peck and CIRIA methods. 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 interdependencies. 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.