Physics-informed data-driven loose bolt identification

大型建筑物的维护是一项极其昂贵且往往危险的工作，需要定期进行。在需要进行的许多检查任务中，螺栓松动的识别是一项特别艰巨的任务，特别是当该操作涉及位于难以接近或不安全环境（海上平台，桥梁，铁路轨道等）的大型结构时。目前还没有商业上可用的技术能够可靠、安全、快速地进行松螺栓识别，因此需要逐一测量每个螺栓的旋紧扭矩。该项目的日本合作伙伴申请了一项新专利技术，该技术使用激光烧蚀来触发结构大部分的高频振动。通过研究这些高频振动响应，可以确定是哪个螺栓松动了。到目前为止，这种技术已经成功地在带有有限数量螺栓的小型金属板上进行了演示。将该协议扩展到大规模结构需要解释从默认状态到振动模式的细微变化。该项目旨在开发一种机器学习技术，该技术可以从激光烧蚀测试中分析通用大型结构的模态前和模态后频率响应，并使用该数据准确识别松动螺栓。

Maintenance of large structures is an extremely costly and often hazardous endeavour which needs to be performed on a regular basis. Among the many inspection tasks which needs to be undertaken, the identification of loose bolts is an especially daunting one, in particular when this operation concerns large structures located in poorly accessible or unsafe environments (offshore platforms, bridges, rail tracks, etc.). At present there are no commercially-available technologies which enable to perform loose bolt identification in a reliable, safe and fast manner, thus requiring a one-by-one measurement of the screwing torque of each bolt. A newly patented technique filed by the Japanese partners of this project uses laser ablation to trigger high frequency vibrations in large portion of a structure. By studying these high frequencies vibrational responses, it is possible to identify which bolt have come loose. This technique has so far been successfully demonstrated in the case of a small-scale metallic plate with a limited number of bolts. Upscaling this protocol to large-scale structures requires interpreting the subtle changes in the vibrational modes from a default state. This project aims to develop a machine learning technique which analyses the pre and post modal frequency response of a generic, large-scale structure from laser ablation testing and uses this data to accurately identify the loose bolts.