Ji-Myong Kim

Loss Prediction Model for Building Construction Projects Using Insurance Claim Payout

The amount of risk and the occurrence of damage in construction projects have increased as construction projects are becoming larger and more complex, increasing the demands for an effective risk assessment model. Therefore, quantitative risk analysis is needed to develop a financial risk assessment model using the risk indicators for construction projects. To address this need, authors analyzed the damage records of actual construction sites in South Korea, in order to identify the critical risk factors of damage and investigate the relationships among the risk indicators to develop a loss prediction model. Authors used claim payout records from an insurance company to reflect real financial loss as the dependent variable. As independent variables, authors adopted four risk categories based on our literature review and data analysis: natural hazards, geographic information, construction method and ability, and construction information. Our loss prediction model for construction projects, along with our findings, offers fundamental guidelines for construction companies, construction project owners, and insurance companies hoping to model and predict financial loss for building construction projects.

Typhoon Path and Prediction Model Development for Building Damage Ratio Using Multiple Regression Analysis

Since typhoon is a critical meteorological disaster, some advanced countries have developed typhoon damage prediction models. However, although South Korea is vulnerable to typhoons, there is still shortage of study in typhoon damage prediction model reflecting the vulnerability of domestic building and features of disaster. Moreover, many studies have been only focused on the characteristics and typhoon and regional characteristics without various influencing factors. Therefore, the objective of this study is to analyze typhoon damage by path and develop to prediction model for building damage ratio by using multiple regression analysis. This study classifies the building damages by typhoon paths to identify influencing factors then the correlation analysis is conducted between building damage ratio and their factors. In addition, a multiple regression analysis is applied to develop a typhoon damage prediction model. Four categories; typhoon information, geography, construction environment, and socio-economy, are used as the independent variables. The results of this study will be used as fundamental material for the typhoon damage prediction model development of South Korea.

Development of a Maintenance and Repair Cost Estimation Model for Educational Buildings Using Regression Analysis

The aim of this study is to identify key performance indicators as well as the correlations among these indicators to develop a maintenance and repair cost estimation model for educational buildings based on actual payment records. The importance of financial estimation for facility management systems has increased. Especially, the estimation of maintenance and repair costs is essential to facility management for educational buildings considering the long-life cycle of a building. However, data regarding facility management is still limited. This study adopts payment records related to maintenance and repair work from educational institutions to develop a quantitative approach. Statistical analyses and a multiple regression analysis are conducted to examine the record and generate a cost estimation model. The findings and results of this study provide a guide for maintenance and repair cost estimation of integrated facility management and could be used as a guideline for budgeting for school maintenance.

Assessing regional typhoon risk of disaster management by clustering typhoon paths

Many countries are affected by typhoons and cyclones. Huge damages are caused by these natural disasters. Therefore, loss prediction models are required to ensure that society remains resilient to natural disasters, including typhoons. Although previous studies have focused on identifying strategies for mitigating typhoon damages, the relationships between the characteristics of the built environment and the extent of typhoon damages have not been sufficiently examined. The purpose of this study is to identify the features of typhoon damages and to develop a metric for estimating the impact of built environment vulnerability on financial loss using the data of types 1 and 2 typhoons. Typhoon damage data (loss ratios) recorded at the provincial level were used as the dependent variable. The independent variables were categorized into two types: typhoon characteristics and built environment indicators. The results obtained from the regression models showed that the loss ratios of type 1 typhoons were correlated with wind speed, rainfall, and number of cutting slopes, while the extent of financial loss caused by type 2 typhoons was mainly correlated with the meteorological indicators. The results obtained with the loss prediction models developed in this study would help public disaster managers develop different strategies for typhoon risk mitigation.

Revealing building vulnerability to windstorms through an insurance claim payout prediction model: a case study in South Korea

ABSTRACT The aim of this study is to develop regional vulnerability functions of buildings to estimate the loss from windstorms. Windstorms trigger critical financial damage to assets around the world. Insurance companies assess the financial risk of their exposures by employing windstorm risk assessment models. The vulnerability function in the risk assessment model is generally based on the analysis of actual damage records from insurance companies. However, the absence of detailed loss data is an obstacle to developing vulnerability functions. To fill this gap, this study provides a methodology to develop a function using an insurance companys loss data associated with windstorms. Vulnerability functions are generated based on the wind speed, line of business, and value of the property. The findings and methodology of this study offer a practical way of reflecting the real economic losses and regional vulnerability of buildings and help to develop vulnerability functions for insurance companies and emergency planners.