Machine Learning-Based Geotechnical Modeling for Seismic Foundation Performance and Soil–Structure Interaction in Urban Infrastructure
DOI:
https://doi.org/10.63125/ayseh851Keywords:
Machine Learning, Geotechnical Engineering, Seismic Foundation Performance, Soil–Structure Interaction, Urban Infrastructure, Artificial Neural NetworksAbstract
This study investigated a machine learning-based geotechnical modeling framework for predicting seismic foundation performance and characterizing soil–structure interaction (SSI) effects in large-scale urban infrastructure. A quantitative, data-driven research design was applied to 142 documented foundation-structure case records spanning shallow spread footings, mat foundations, and deep pile foundations situated on rock, dense sand, stiff clay, soft clay, and liquefiable soil profiles; after data screening for incomplete geotechnical or seismic parameters, 131 cases were retained for final analysis. Structural, geotechnical, and ground-motion parameters, including shear wave velocity, foundation-to-structure mass ratio, peak ground acceleration, and epicentral distance, were compiled and used to characterize seismic foundation demand and observed damage outcomes. Multiple regression analysis indicated that geotechnical and ground-motion parameters significantly predicted structural damage severity, with the overall model explaining 58.1% of variance (R² = 0.581, p < .001). Shear wave velocity of the founding soil (β = -0.37, p < .001) and peak ground acceleration (β = 0.34, p < .001) emerged as the strongest predictors of elevated foundation damage classification. A deep artificial neural network (ANN) model achieved a foundation performance classification accuracy of 89.4%, outperforming random forest (80.7%), decision tree (66.4%), and multiple linear regression (69.8%) benchmarks, with statistically significant differences across paired comparisons (p < .001). Cumulative analysis revealed a positive association between predicted damage risk index and observed peak interstory drift ratio (r = 0.56, p < .001), confirming the influence of soil–structure interaction on superstructure demand. Reliability testing demonstrated strong internal consistency for the geotechnical risk exposure construct (α = 0.87) and the foundation resilience index (α = 0.82). Shallow foundations on liquefiable soil accumulated the highest proportion of high damage-risk classifications at 39.8%, compared to 9.6% for foundations on rock or stiff soil. Findings demonstrate that machine learning-based predictive geotechnical modeling provides a reliable, objective, and scalable complement to traditional finite element and elastic half-space SSI analysis methods, supporting its application as a decision-support tool for seismic foundation design and retrofit prioritization in urban infrastructure.
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