Development and optimization of structural monitoring and damage detection in massive elements using piezoelectric transducers and smart aggregates

损伤存在的识别在结构健康监测中具有重要作用，而损伤的精确定位则是关键。损伤指数是损伤存在的一个定性指标，仅依赖于损伤指数的方法无法检测到损伤的准确位置。本研究计划的目标是开发有效的SHM方法，用于使用压电致动器和传感器对大型结构元件和结构进行损伤检测。结合混合方法的优点以提高其效果的混合方法是本研究的重点。申请人提出的基于波传播和采集的二维元件损伤检测的混合方法，使用压电陶瓷换能器和智能聚集体，应该进一步追求和理论上发展，以达到适用于三维问题的适当方法，即3D大型结构元件。一种新的三维DI应该在混合方法中开发、分析和实现。该方法将基于弹性声波传播的飞行时间法与三维动态动态分析相结合。基础研究的结果需要通过实验研究来验证，利用现有的适应性实验装置，利用超声激光进行高分辨率和高精度的结构响应采集。此外，特殊的压电换能器——智能聚集体将进一步开发和实现。通过一种系统的方法，我们将研究哪些执行器和传感器的配置和组合可以满足计算负担和设计成本的要求，同时提高损伤检测效率。这些标准需要多目标结构优化，这应该通过实现深度学习（DL）神经网络来解决优化问题。在前一阶段研究成果的基础上，需要识别有效分类结构健康状况的信号特征。此外，在数值模型的基础上，利用深度学习设计损伤检测的最优实验，并进一步进行实验研究。

Not only the identification of the damage presence plays an important role in structural health monitoring (SHM), but primarily its precise locating. Damage index (DI) represents a qualitative indicator of the damage presence, yet the methods that rely only on DI cannot detect exact location of the damage. The goal of this research proposal is to develop efficient SHM methods for damage detection in massive structural elements and strucutres using piezoelectric actuators and sensors. The hybrid methods which combine the advantages of the hybridized approaches in order to increase their effect are in the focus of this research. A hybrid approach for the damage detection in 2D elements based on wave propagation and acquisition using piezoceramic transducers and smart aggregates proposed by the applicant should be further pursued and theoretically developed in order to reach to an appropriate approach applicable to 3D problems i.e. to 3D massive structural elements. A new 3D DI should be developed, analyzed and implemented within a hybrid method. This hybrid method combines the 3D DI with the method based on time-of-flight of propagating elastic ultrasound waves. The findings from the fundamental research should be verified by experimental investigation using available adaptable experimental setup with an ultrasonic laser for structural response acquisition with high resolution and precision. In addition, special piezoelectric transducers – smart aggregates will be further developed and implemented. Through a systematic approach it will be investigated which configurations and constellations of actuators and sensors would fulfill the requirement that both the computational burden and the design costs can be reduced, by increasing at the same time the damage detection efficiency. These criteria require multi-objective structural optimization, which should be tackled by implementation of deep learning (DL) neural networks for optimization problems. Based on the results from the previous research phase the signal features for efficient classification of the structural health should be identified. In addition, the optimal experiments for damage detection should be designed using DL based on numerical models and further implemented for experimental investigation.