In this study, experimental investigations were conducted to measure the ground displacement through particle image velocimetry (PIV) using colored sand. The effects of the mixing ratio of the colored sand, the number of pixels of the specimen’s image captured via a single lens reflex camera, the minimum measured displacement, and the subset size of the PIV analysis on the measurement accuracy of the PIV were investigated by precision tests. The relationships between the mixing ratio of the colored sand, the condition of photogrammetry and analysis, and the PIV measurement error were clarified based on precision test results. Additionally, the method for setting the PIV measurement condition were proposed. Finally, an instance, in which the proposed method was applied to the bearing capacity model test was presented to clarify the validity of the proposed method.

 This study proposes a damage detection method for bridges using a damage indicator that is automatically derived from vehicle induced vibrations. To extract damage-sensitive features, the principal component analysis is applied to the characteristic polynomials of auto-regressive models estimated from measured acceleration. A stochastic distance between damage-sensitive features from the healthy bridge and unknown test samples evaluates anomalies involved in the modal properties. A statistical hypothesis testing based on the stochastic distance was performed for damage detection. A field experiment on an actual steel truss bridge whose truss members were artificially severed was conducted to investigate efficacy of the proposed method. Observations showed the proposed method enables to detect three different damage patterns. Efficacy of the proposed method was also demonstrated by comparing to the previously investigated damage-sensitive features.

 In order to maintain aged bridges, it needs to inspect each bridge efficiently. Thus, researchers try to develop new bridge inspection methods such as a health monitoring using sensors or visual inspection. From these topic, we focus on a study of the check point screening. It means that bridge inspectors check the point of damaged segment instead of all parts of infrastructure. These methods in many previous studies need experimental data before the damaged bridge for compared. And the inertial characteristics of mass and etc. don’t changing. As these are prerequisite, it can’t be useful method actually. And also many papers do not describe enough estimated precision. Therefore we studied the oscillating experiment method by the dual sine forces. It is an oscillating experiment, but can obtain the data which are equivalent to a static transformation experiment if we use this method. Because it is easy to estimate stiffness for a static transformation experiment. In this study, we modelled an actual bridge in spring mass point system and simulated the number that considered possible bad conditions. And we presumed the stiffness of all parts on account of the data which we obtained there. Furthermore, this study shows conditions when a high precision is estimated. It is apt to seem that a stiffness value before the damage needs the damage evaluation. However, it is thought that there is the damage point if we can find a stiffness low part in the set of the stiffness value conspicuously. Finally, the damage point identification and a degree of risk evaluation of the damage are possible without the experimental data before the damaged bridge.