An automated diagnosis methodology is necessary for the maintenance of superannuated social infrastructures. In this context, the hammering test is an efficient inspection method, and it has been widely used because of the resulting accuracy and efficiency of operation. While robotic automation of the hammering inspection method is highly desirable, the development of an automatic diagnostic algorithm that can operate at actual inspection sites is essential. Furthermore, portability of the diagnostic algorithm is also highly desirable. In this study, in order to construct reliable detectors and to improve their portability for the performance of the hammering test, we propose a boosting-based defect detector that is robust against variations in environmental conditions. In particular, we present the construction of a noise-robust classifier with a refinement of the feature values extracted from hammering sounds and an updating rule of template vectors of its evaluation function. Our experimental results in a concrete tunnel demonstrate the effectiveness of the proposed method; the accuracy of the classifier at an actual site and adaptivity to environmental noise are confirmed.
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