The statistical characteristics of design error rectification costs experienced in 139 Australian construction projects are analyzed. Theoretical probability distributions are fitted to the design error cost data. A generalized Pareto probability function was found to provide the best overall distribution fit for design error costs. The generalized Pareto distribution is used to calculate the probability of design error costs being experienced for the selected sample. A mean design error cost of 14.2% of a project’s contract value is reported. A significant difference between mean design error costs and project types was found for civil engineering (23.44%) and fit-out (22.50%) projects. Projects $>$Australian dollars (A$)101M were found to experience significantly higher mean design error costs (26.18%) than other projects. Being able to determine the likelihood of design error rectification costs from the derived empirical probability distribution will provide an ameliorated assessment of risk before the commencement of construction. Strategies to reduce design error rectification costs are also discussed.

Today, unpredictable damage can result from extreme weather such as heat waves and floods. This damage makes communities that cannot respond quickly to disasters more vulnerable than cities. Thus, people living in such communities can easily become isolated, which can cause unavoidable loss of life or property. In the meantime, many disaster management studies have been conducted, but studies on effective disaster response for areas surrounded by mountains or with weak transportation infrastructure are very rare. To fill the gap, this research aimed at developing an automated analysis tool that can be directly used for disaster response and recovery by identifying in real time the communities at risk of isolation using a web-based geographic information system (GIS) application. We first developed an algorithm to automatically detect communities at risk of isolation due to disaster. Next, we developed an analytics module to identify buildings and populations within the communities and efficiently place at-risk residents in shelters. In sum, the analysis tool developed in this study can be used to support disaster response decisions regarding, for example, rescue activities and supply of materials by accurately detecting isolated areas when a disaster occurs in a mountainous area where communication and transportation infrastructure is lacking.