# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 1 Introduction  
A structure's seismic vulnerability is a quantity correlated with its failure in the event of earthquakes of previously known intensity. This quantity and seismic hazard experience's importance helps us determine the potential risk from future earthquakes [1]. Many destructive seismic activitieshave caused immense disruption in human history, resulting in substantial loss of life, severe economic consequences, and severe property damage. There has been a remarkable increase in working personnel's migration from rural to urban metropolitan regions due to career prospects and lifestyle. As a consequence, this imparts the responsibility of protecting the high occupancy of urban infrastructure. Old buildings still in service, historical structures with heritage, high importance buildings, and buildings not compliant with the latest seismic codes are the buildings with the highest seismic vulnerability. This proves the necessity of manifesting a seismic structural prioritization scheme, which shall prevent damage or adapt to post-disaster management regulations. Rapid Visual Screening (RVS) is a rapid and reliable method for determining the damage index for various buildings [2; 3]. [PERSON] and [PERSON] [4] have a laconic discussion as motivation for a layman. The United States first recognized the demand for a fast, reliable, and computationally easy method; therefore, the first RVS method was mentioned in 1988 as \"Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook\" [5]. This RVS approach (1988) was subsequently updated in 2002 with new developments in seismic engineering. However, an in-depth study and detailed analysis bring challenges in the urban sector where buildings are complex and built on the absence of non-standard norms of safety. The research focuses on one of the latest techniques for enhanced vulnerability assessment of reinforced structures. Eventually, many other countries followed this strategy by changing their RVS approaches concerning their local circumstances, improvements, etc. [6]; for instance, Indian RVS (IITK-GSDMA) [7] or the Philippine RVS [8]. The approach of RVS usually begins with a walk down evaluation to perform a visual inspection physically for recording the seismic parameters. For this purpose, the collection of datasheets needs an evaluator. RVS is a highly effective mechanism to prioritize the structures with high vulnerability due to the faster computational time and non-tedious methodology, thereby helping analyze more massive building stock in a short time. The RVS method is based on a scoring system, wherein the final performance score shall be obtained by performing elementary computations. Every singular approach has the respective cut-off score predefined, for instance, the Federal Emergency Management Agency (FEMA). The structures that do not reach the cut-off score shall be subjected to comprehensive second and third assessment stages. We shall note that seismic vulnerability assessment is performed mainly in three stages [9]: walk down an evaluation, preliminary evaluation, and detailed evaluation. The first stage is defined by a physical walk down evaluation with fundamental computations for the screening of vulnerable structures. Structures that struggle to fulfill expectations at this stage will be addressed with further action. The second step consists of a comprehensive analysis conducted by the detailed study of several structure components, such as the actual ground conditions, the quality of the material used, the state of the structural elements, etc. The third stage is brought into action when the structures require a more detailed analysis. The concept of structural activity in seismic activities, defined as non-linear dynamic structural research, is performed here [10].