Executive Summary : | The Indian Himalayan region, one of the world's most seismically active areas, has experienced significant damage and collapse cases due to earthquake shaking. The region's potential for great earthquakes in the near future necessitates extensive research and development to enhance earthquake disaster preparedness. A pre-earthquake vulnerability assessment, such as rapid visual screening (RVS), is crucial for initiating action. A proposed study aims to develop effective vulnerability exposure models using machine learning techniques, integrating them with AI-assisted tools for rapid visual screening. The research will involve field surveys, identifying irregular building features, AI-assisted identification of vulnerable features, and developing ML-based expected vulnerability score relationships and exposure maps. The cities selected for the study are Shimla, Dharamshala, Mandi, Kullu, Mussoorie, Nainital, and Gangtok. The proposed region-specific vulnerability models can be used to screen vulnerable buildings and initiate mitigation measures. The RVS guidelines will be less biased towards surveyor skill and experience and adaptable to AI-assisted tools for faster assessment and efficient data handling. The outcomes of these projects can be used by urban local bodies and other stakeholders in disaster preparedness. |