Data of AI Method on Hammering Sounds at Concrete Bridge

It is a popular to hammer sound test for visual inspection of deterioration in concrete structures. This convenient method is much effectiveness for expert engineers. However, it is difficult for young engineers and applicable to robotization to apply quantifying and to be systematic under consideraton to complicapable of relation of sound data and degree of deterioration. The factor of relation to hammering sound data and degree of degradation are not clearly. Development of quantifying and being systematic as engineering are the most important in practical business. In this study, it is expected to apply using AI technology. In this study AI is expressed by machine learning, in particular deep learning based on neural network. It makes a clearly for effectiveness of methods of machine learning and propose to apply to autoencoder.

本数据集系针对混凝土结构劣化状况进行锤击声检测的流行测试。此便捷方法对于资深工程师而言极具实效性。然而，对于年轻工程师而言，将其应用于机器人化并实现量化及系统化考量，以关联声数据与劣化程度之间的关系则显得颇具挑战。锤击声数据与劣化程度之间的关联因素尚不明确。在工程实践中，量化与系统化的发展至关重要。本研究旨在应用人工智能技术。在此研究中，人工智能技术以机器学习的形式呈现，特别是基于神经网络的深度学习。本研究旨在明确机器学习方法的有效性，并提出将自动编码器应用于此的建议。