Development of rapid visual screening form for Nepal based on the data collected from - its 2015 earthquake

Abstract

An earthquake on the 25th April 2015 in Nepal caused more than 9,000 deaths and 22,000 causalities. The main reason for such huge casualties is the lack of earthquake awareness and poor construction practices. With a large number of vulnerable houses, Nepal faces a huge risk from future earthquakes, due to the recent construction in urban areas of poorly designed and constructed buildings. Such unplanned and haphazard growth will be potentially dangerous from natural events like earthquakes. A vulnerability map gives the exact location of sites where people, natural environments and or properties are at risk due to a potentially catastrophic event. This will allow them to decide on mitigating measures to prevent or reduce loss of life, injury and environmental consequences before a disaster occurs. While Rapid Visual screening is a classical method of preliminary vulnerability study, it is one of the most economical, reliable, simple and efficient methods to determine the vulnerability level of buildings. The most known rapid visual screening methods have been developed in countries of high seismic risk such as the USA, Japan, Indonesia, New Zealand, India and Canada and are briefly described in this paper. The main objective of this paper with the help of ordinal regression is to calculate variables of damage grade model in a rapid visual screening form. This is useful to identify vulnerable and non-vulnerable buildings very quickly, and help make a plan for the implementation of a disaster risk mitigation program. Based on SPSS Statistics Software, an ordinal regression method was used to model the relationship between outcome variables. The analysis was performed by employing a database after the 25th April Gorkha earthquake of 2015. Preparing RVS special features of buildings in Nepal, have been given due consideration, and were evaluated for adherence of age, plan configuration, position, land surface condition, plinth area, building height, floor count, foundation types, ground floor types, roof types, condition, are highly significant parameters in analysing the vulnerability of them during an earthquake.

Conventional RVS Methods for Seismic Risk Assessment for Estimating the Current Situation of Existing Buildings: A State-of-the-Art Review

“…It enables the immediate use of the data obtained via the NEA RVS smartphone application. Rupakheti and Apichayakul (2019) [192] suggested an RVS approach for structures in Nepal that utilized data from the 2015 Gorkha earthquake, and the ordinal regression approach in the Statistical Package for the Social Sciences (SPSS) [193], a statistical program. Various regression methods were employed in this study to select the best regression model.…”