When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decision tree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.

The Department of Rural Roads (DRR) is one of the highway authorities in Thailand responsible for over 48 000 kilometres of rural roads and highway networks. One of its responsibilities is to provide better road safety management. In road safety procedures, black spots are usually identified by observing the frequency of accidents at a particular road section. This research aims to develop a model that includes levels of accident severity in the black spot identification process. The classification of severity levels includes fatalities, serious injuries, minor injuries, and damaged property only. The Analytic Hierarchy Process (AHP) is employed to derive the weight of each severity level. The identification model is developed using Equivalent Accident Number (EAN) and Upper Control Limit (UCL). The data applied in the model are obtained from the road accident investigation of DRR. Five roads — Nakhon Ratchasima 3052, Chonburi 1032, Nonthaburi 3021, Samutprakarn 2001 and Chiangmai 3029 — have been selected based on the top frequency accident recorded in the last three years. Based on the results of black spots identified in the study, most accidents occurred from frontal and rear-ended impacts due to exceeded speed limits. The article discusses recommendations.