Geotechnical risk assessment of landslides on natural slopes

Abstract

Landslides are one of the most frequent natural hazards that affect humans, causing significant damage to properties and resulting in fatalities and injuries. Rainfall has been identified as the major triggering factor for landslides, and the tropical region is severely affected due to heavy rainfalls and favourable geological and hydrological conditions prevailing in these regions. Moreover, the landslide impact tends to be severe due to the high economic, political and social vulnerability in the region. However, limited studies have delved into the complex conditions that prevail in these regions. In this thesis, an extensive review was conducted on landslide risk assessment and management. Special emphasis was placed on Sri Lanka as a representative tropical country where landslides are prevalent due to rainfall, complex subsurface conditions, and it is still developing, where many challenges exist, and limited studies have been conducted. This study aimed to assess the landslide susceptibility and hazard for regions with limited data, integrating heuristic methods, process-based methods and machine learning (ML) for landslide prediction, zonation and threshold development. Initially, the applicability of well-established process-based methods was investigated using case studies to predict landslides. Then, using these methods, the critical slopes within a catchment area were identified, and process-based slope stability analysis was conducted, identifying the failure initiation area with extreme event rainfall conditions. Then the runout area for these critical slopes was determined using process-based runout assessment with the initiation area identified. Finally, a framework was developed based on process-based parametric analysis integrated with ML for slope scale prediction of failure and zonation as a quick tool for predicting the landslide hazards. This research delivers robust, scalable, and data-efficient regional landslide risk assessment tools, particularly valuable in data-scarce or resource- constrained settings. Its strong predictive capability and practical adaptability enhance its potential for integration into landslide risk management.