An integrated physics-based and data-driven approach to structural condition identification

基础设施的性能对一个国家的经济和人民的生活质量都很重要。据估计，在维护和管理方面，基础设施不足每天要花费英国200万英镑。为了高效有效地管理和保护基础设施，拟议的项目旨在开发一种综合算法，以创建一种可靠和有效的结构健康监测方法，该方法可以找到不同的应用。金属结构广泛应用于交通和能源基础设施。作为荷载传递元件，这种结构中的连接由于应力集中而脆弱，即使在常规检查下，局部损伤也特别难检测到。因此，本研究的个案研究将集中于监测螺栓或铆接结构的连接状况。该项目将首先对端部螺栓连接的钢梁在不同情况下因松动/不匹配而造成的损伤进行实验研究。来自应变仪和加速计的监测数据将被处理，以确定梁的动态特征。还将建立有限元模型，并利用实验结果进行校准。最后，还将开发一种用于结构状态识别的集成深度学习算法。建议的方法有两个创新之处。首先，它融合了基于物理的方法和数据驱动的方法。其次，由于存在多个隐含层，利用深度学习能够识别和优化非线性特征，因此，该项目旨在为结构健康监测做出新的贡献，在不同的结构类型中有不同的应用。

Infrastructure performance is important for a nation's economy and its people's quality of life. Inadequate infrastructure is estimated to cost the UK £2 million a day, in terms of maintenance and management. To manage and protect infrastructure efficiently and effectively, the proposed project aims to develop an integrated algorithm to create a reliable and effective approach for structural health monitoring, which can find different applications.Metallic structures are widely employed in both transport and energy infrastructure. As load transferring elements, connections in such structures are vulnerable due to stress concentrations, with localised damage being particularly hard to detect even under regular inspections. Therefore, the case study of this research will focus on the monitoring of connection condition in bolted or riveted structures.The project will commence with an experimental investigation of a steel beam with end bolt connections under different damage scenarios due to loosening/lack-of-fit. Monitoring data from strain gauges and accelerometers will be processed to determine the beam's dynamic features. A finite element model will also be constructed and calibrated using the experimental results. Last but not least, an integrated deep learning algorithm will be developed for structural condition identification. There are two innovations in the suggested approach. Firstly, it integrates physics-based and data-driven methods. Secondly, the exploitation of deep learning enables the identification and optimisation of non-linear features, due to the existence of multiple hidden layers.Thus, the proposed project aims to make a novel contribution to structural health monitoring with diverse applications in different structural types.