Abstract
It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning. The proposed method uses the maximum response acceleration, standard deviation, logarithmic decay rate, and natural frequency to improve the accuracy of damage assessment. We propose a three-step Random Forest method to evaluate various damage types based on the results of these many measurements. Then, the accuracy of the proposed method is verified based on the results of a cross-validation and a vibration test of an actual damaged specimen.
Highlights
In recent years, the problem of bridge deterioration has become increasingly serious in various parts of the world
Gaussian used train the RandomNeural. Networks and it was [16], confirmed that the damage can[17,18,19,20], be identified withprocess a high regression. These studies propose machine learning accuracy by the leave-one-out cross-validation (LOOCV) method and the actual vibration test of methods based interesting experimental data, they basically remain at the level of damage detection, aluminum alloyon because it is very difficult to prepare test specimens with various damage geometries in the order of 102 or more in experiments
The results of the analysis obtained from a number of finite element models were used to train the Random Forest, and it was confirmed that the damage can be identified with a high accuracy by the leave-one-out cross-validation (LOOCV) method and the actual vibration test of aluminum alloy I-beams
Summary
The problem of bridge deterioration has become increasingly serious in various parts of the world. In Japan a large number of bridges that were built during the high economic growth period of the 1960s and 1970s are approaching the age of 50 years, which is generally considered the point at which deterioration begins [1,2,3]. When inspecting bridges at high locations, it is not easy to reach them to perform a proximate visual inspection and hammering test. There is the issue of large disparities in the results of proximate visual inspections depending inspection would be expensive and would require extensive effort. There is the issue of on the experience, knowledge, and competence of the inspector Such inspection methods large disparities in the results of proximate visual inspections depending on the experience, knowledge, lack reliability.
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