Ye Jiaxing, Kobayashi Takumi, Iwata Masaya, Tsuda Hiroshi, Murakawa Masahiro
National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan.
Artificial Intelligence Research Center (AIRC), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8561, Japan.
Sensors (Basel). 2018 Mar 9;18(3):833. doi: 10.3390/s18030833.
Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.
开发高效的人工智能（AI）系统以取代人类在无损检测中的作用是一个备受关注的新兴话题。在本研究中，我们提出了一种使用在线机器学习的新型锤击响应分析系统，旨在在混凝土结构评估中实现接近人类的性能。当前的计算机化锤击测深系统通常采用实验室规模的数据来验证模型。然而，在实际应用中，由于结构的几何形状和材料各不相同，响应信号模式可能会更加复杂。为了处理大量未见过的数据，我们提出了一种用于响应表征的顺序处理方法。更具体地说，所提出的系统可以自适应更新自身，以在锤击测深数据解释中接近人类性能水平。为此，引入了一个两阶段框架，包括特征提取和模型更新方案。我们对各种先进的在线学习算法进行了审查和评估，以完成这项任务。为了进行实验验证，我们从多个检测地点收集了10940个响应实例；每个样本都由人类专家标注了健康/缺陷状态标签。结果表明，所提出的方案以高效率和低计算负荷实现了良好的评估精度。
