Integrating Remote Sensing Technique with 3D Numerical Modelling for Enhanced Maintenance of Critical Infrastructure in Landslide-Prone Areas

Landslides, alongside earthquakes and floods, are among primary natural phenomena that are responsible for significant social and economic losses. Their impact poses an increasing threat worldwide, particularly in marginal and degraded contexts, affecting urban areas, infrastructures, environmental, historical, and cultural heritage, and, in severe cases, resulting in human casualties. In recent years, the number of infrastructure collapses or severe structural damages due to landslide movements has risen significantly, hindering the functionality of infrastructures, and highlighting the urgent need to deeply understand their interactions. Landslides can endanger roads, bridges, and railways, compromising the accessibility and inclusivity, and exacerbating social and economic exclusion in affected areas. Critical infrastructures are often located in challenging areas, where the susceptibility to landslides and natural hazards is significantly elevated. These sites demand advanced monitoring technologies to ensure infrastructure safety and mitigate the social and economic impacts of landslides. This study explores an innovative approach that integrates Interferometric Synthetic Aperture Radar (InSAR) data with numerical Finite Element Modelling (FEM) to address these challenges. The proposed method was applied to a case study involving a partial interaction between a slow-kinematic landslide, documented in the Inventory of Landslide Phenomena in Italy (IFFI), and a bridge along a highway section in the Liguria Region. Leveraging high-resolution satellite-based data from the Copernicus European Ground Motion Service (EGMS), the InSAR analysis provided spatial and temporal monitoring of ground displacements. Satellite remote sensing offers a wide spatial and temporal coverage over multiple regions, enabling for the detection of extensive or hard-to-access areas with millimetric precision in deformation velocity, ensuring high efficiency at a favourable cost-benefit ratio. However, while InSAR analysis can precisely measure ground motions, it lacks the ability to provide insights into the physical mechanisms under varying loading conditions. To address this limitation, FEM modelling was used to simulate the three-dimensional landslide mechanical behaviour under hydraulic loading, offering a deeper understanding of the slope stability and infrastructure deformations. InSAR data post-processing enabled the estimation of transverse and vertical components of the actual displacement vector, aligning with the observed landslide deformations and facilitating the numerical model validation. Simultaneously, FEM results highlighted significant displacements downstream of the landslide area, indicating a slope stability close to the limit equilibrium condition. Quantitative analysis also revealed relevant deformations at the base of bridge piers located within the landslide, caused by horizontal forces impacting the foundations. The integration of InSAR observations and FEM calculations demonstrated consistency in the identified movement, validating the efficacy of the combined method in identifying critical zones in landslide-prone regions. This study highlights how advanced remote sensing technologies, when coupled with numerical simulations, can enhance the monitoring and maintenance of critical infrastructure, particularly in marginal or extensive contexts. By identifying vulnerable areas and supporting the maintenance strategies, this methodology can contribute to hydrogeological risk management and promote inclusivity in regions where social and economic disparities exacerbate natural hazards impacts.