Geotechnical stability and optimization of foundation systems in landslide-prone urban areas using coupled numerical models and IoT sensors

Sanchez P.; Lopez M. · Minimax Theory and its Applications (2026)

Resumen

The expansion of urban areas in geotechnically complex regions has led to the imperative for more precise assessments of ground stability and the optimization of foundation design. To this end, a methodological framework was developed that integrates numerical modeling using the coupled Finite Element Method (MEF), real-time monitoring with Internet of Things (IoT) technologies, and stability analysis using the Shear Strength Reduction Method (SRM). This methodology was implemented in five urban sites with contrasting geotechnical conditions, characterized by different types of surface soil and risk levels defined by the probability of increased pore pressure (no). The advent of loT sensors has enabled the calibration of critical parameters, including permeability (k), and the detection of critical events that were previously unanticipated, such as earthquake-induced pressure peaks or extreme water variability. The findings indicated that sites A and E exhibited safety factor reductions exceeding 30% under maximum saturation, with critical values falling below 1.05. suggesting the imminent occurrence of a failure condition. In response, the pile length was optimized to achieve an FS > 1.30. implying increases of 42.00% and 48.33%, respectively. The other sites maintained critical FS above 1.40, without requiring geometric adjustments, allowing the design to focus on compliance with the Limit of Service State (ELS). The efficacy of coupled modeling in simulating dynamic scenarios and validating adaptive structural solutions was demonstrated, while the indispensability of loT monitoring in adjusting the design based on actual ground conditions was confirmed. The proposed methodological framework is a comprehensive strategy for assessing geotechnical stability in at-risk urban areas. It involves the optimization of deep foundations and the reduction of uncertainty through continuous observation. This approach promotes the development of safer, more efficient, and resilient designs in critical conditions. © (2026), (Heldermann Verlag). All rights reserved.

Instituciones: Universidad San Ignacio de Loyola