Settlement estimation during foundation excavation using pattern analysis and explainable AI modeling.

With the rapid expansion of underground engineering, accurate settlement estimation during foundation excavation using monitoring data has gained prominence. Previous studies have typically overlooked data patterns, relying solely on time-series models, which yielded limited accuracy and short-term predictability. To address these issues, this paper performs a thorough pattern analysis covering both the temporal and spatial properties, and proposes a spatiotemporal modeling method grounded in explainable artificial intelligence technique. A real-world engineering case study is conducted to validate the effectiveness of the proposed method. Counterintuitively, the results reveal a weak temporal effect within the settlement data, with only the last step playing a dominant role, whereas the spatial correlation among measuring points is notably more significant. Compared to conventional models, the proposed method consistently outperforms, achieving significantly higher R2 scores and excelling in long-term estimation with reductions of at least 78% in RMSE, 80% in MAE, and 72% in MAPE. • Spatiotemporal pattern analysis and XAI modeling for estimation are performed. • Pattern analysis results shows weak temporal correlations of settlement. • Spatial correlations in measuring points persist across construction stages. • The method yields notably smaller RMSE, MAE, and MAPE than conventional models. • SHAP analysis results highly align with the patterns uncovered by data analysis. [ABSTRACT FROM AUTHOR]