The 2011 Great East Japan Tsunami exposed many hidden weaknesses in Japan’s tsunami countermeasures. Since then, many improvements have been made in both structural measures (numerical simulations, coastal defense structures, building damage assessment and control forests) and nonstructural measures (warning/observation and evacuation). This review summarizes the lessons and improvements in the five-year time period after the 2011 event. After five years, most of the lessons from the 2011 tsunami have been applied, including more realistic tsunami simulations using very fine grids, methods to strengthen coastal defense structures, building evacuations and coastal forests, improved warning content and key points to improve evacuation measures. Nevertheless, large future challenges remain, such as an advanced simulation technique and system for real-time hazard and risk prediction, implementation of coastal defense structures/multilayer countermeasures and encouraging evacuation. In addition, among papers presented at the coastal engineering conference in Japan, the proportion of tsunami-related research in Japan increased from 15% to 35% because of the 2011 tsunami, and approximately 65–70% of tsunami-related studies involve numerical simulation, coastal structures and building damage. These results show the impact of the 2011 tsunami on coastal engineering related to academic institutions and consulting industries in Japan as well as the interest in each tsunami countermeasure.

The estimation of seismic damage to buildings is complicated due to the many sources of uncertainties. This study aims to develop a new approach using an artificial intelligence system called adaptive neuro-fuzzy inference system (ANFIS) model to predict the damage of buildings at urban scale considering input uncertainties. First, the study performed seismic damage evaluation of buildings utilizing the capacity spectrum method (CSM) to obtain a set of 57,648 training data from a combination of three main parameters, i.e., 6 earthquake magnitudes, 8 structural types, and 1,201 distances. Next, the data was used to develop a practical ANFIS model for the seismic damage prediction. The variables of the fuzzy system are earthquake magnitudes, structural types, and distance between epicenter and building. To validate the applicability of the proposed model, analyses of spatial seismic building damage under five possible earthquakes in Chiang Mai Municipality were performed by using the proposed methodology. From the comparison of the damaged urban area, small discrepancies between the CSM and the ANFIS results could be observed. It should be noted that the proposed ANFIS model can predict the seismic building damage reasonably well compared with the CSM. Using the method proposed herein, it is possible to create damage scenarios for earthquake-prone areas where only a few seismic data are available, such as developing countries.