This study proposed a methodology for optimal design of protection structures against rockfall based on minimization of the total cost (the construction cost of protection structures + the risk). The risk is defined as the product of the probability of damage due to rockfall and the consequence. The probability of damage was calculated rockfall hazard assessment method based on collision energy. The consequence was evaluated based on human life loss and economic loss due to traffic interruption. It is not practical to determine an optimal placement only from total cost because usually there are some budget constraints. Therefore, the proposed method determinines an optimal placement of protection structure solving optimization problem based on linear pragramming with constaraint, so that it can satisfy a given budget construction cost. We applied the proposed method to the actual slope shape and show examples of optimal placement of rockfall protection structures.

Estimation of urban seismic damage using simulation needs automatical modeling method of subsurface soil layers and buildings. This study focuses on subsurface soil layers and shows the method of constructing models by interporating urban borehole data. Important property of the modeling method is robustness, which means that the method works for borehole data with unconsistent layer orders. We developed the interporation method of soil data based on a robust determination method of leyer orders. We applied the method to a test site and check its robustness. This test also showed the method gave the realistic models. Finally, we applied the method to the estimation of urban seismic damage and discussed the usefulness by comparing the result with the one by a conventional method.

Rail temperature management is important to prevent track buckling. In order to clarify the effect of the shade of the geographic features and on the temperature of rails and reflect it in rail temperature manage-ment, we proposed a system that predicts daily fluctuations in rail temperature distribution from geographic data (DSM; Digital Surface Model and railway track) and weather data (solar radiation, air temperature, wind speed and humidity). To verify the prediction accuracy, the rail temperature distribution was measured at intervals of about 20 m and 10 minutes at places where some of the rails were shaded by buildings, etc., and compared with the predicted values. As a result, it was confirmed that the system reproduces the rail temperature distribu-tion of the track, including the difference between the shady and the sunny, and the meteorological condi-tion in both winter and summer. The measured values and the predicted values for the daily fluctuations of the rail temperature agreed well. The daily maximum temperature is predicted with an error of 2°C or less. In addition, we calculated the maximum rail temperature of a track expected in summer using the proposed method. The results showed that the current management assumes that the rail temperature is uniform, but there are 16% sections where the maximum rail temperature drops by 2°C or more and 9% sections where the maximum rail temperature drops by 3°C or more.

With Nankai Trough Earthquake expected to strike in the near future while natural disasters are getting intensified lately, their risk evaluations are urgently being called for. Since it is, in general, very challenging to provide precise damage estimations to natural phenomena, which are often accompanied by countless uncertainties, probabilistic approaches are commonly adopted to account for them. Nowadays, numerical simulations are widely applied for disaster predictions. Although they support high accuracy in terms of anticipating, their computational costs per unit calculation are extremely large, and hence, not suitable for developing probabilistic discussions in practice. This study focuses on a singular value decomposition-based surrogate model, which expresses the numerical analysis approximately. This paper made a modification on the surrogate model by newly introducing kernel ridge regression. The accuracy and effectiveness of the proposed method were examined through an inundation simulation and a tsunami simulation.

One of the damages caused by the large earthquake is the damage to the houses caused by the outflow from the reservoir. The residents are forced to evacuate vertically to the upper floors of the house because the the down floor is flooded before evacuationg to another location. However, the risk of damage to houses due to the collapse of the reservoir is not clear in current hazard map, and there are few damage cases and survey data. In this study, the flow velocity and flood depth of the collapsed reservoir are predicted by 3D flood analysis, and the risk of damage to the house is evaluated. As the result, the velocity and flood depth depended on the topography, and the less damage under the new seismic standards was found.