Optimizing Landslide Monitoring and Alert Systems through Material Point Method Modeling: A Case Study of the Guanghua Landslide in Taiwan

Taiwan's precipitous landscape, susceptible geological composition, and high-intensity rainfall contribute significantly to the prevalence of slope instabilities. The amplification of extreme precipitation events, driven by climatic changes in recent years, has further escalated the risks associated with slope failures. In the context of large-scale landslide monitoring systems, the strategic positioning of monitoring instruments and the calibration of alert thresholds present critical challenges. Effective placement of these instruments is paramount, targeting zones of notable displacement and active slope dynamics to ensure the acquisition of timely and precise data necessary for managing emergent landslide risks. Additionally, the establishment of scientifically grounded warning thresholds can markedly improve the efficiency of disaster prevention mechanisms.

This study integrates a Material Point Method (MPM) numerical model with recorded monitoring data to construct a comprehensive model that accurately reflects the physical behaviors and existing conditions of the Guanghua landslide area. The MPM is adept at addressing large deformation scenarios and provides a detailed depiction of slope displacement behaviors, which are verified through comparisons with field monitoring data. Incorporating engineering reliability analysis into the model allows for the consideration of uncertainties, enhancing discussions on optimal monitoring strategies and the determination of effective warning thresholds. The outcomes of this research are instrumental in refining slope disaster monitoring systems, advancing early warning capabilities, and developing sophisticated risk management strategies.