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

在结构健康监测中，损伤的识别起着重要作用，而损伤的精确定位更是关键。损伤指数（DI）代表了损伤存在的定性指标，然而仅依赖DI的方法不能检测损伤的确切位置。本研究的目标是开发有效的SHM方法，用于大规模结构元件和结构的损伤检测，使用压电致动器和传感器。混合方法结合了联合收割机的优点，以提高它们的效果是本研究的重点。由申请人提出的基于波传播和使用压电陶瓷换能器和智能聚集体的采集的2D元件中的损伤检测的混合方法应该被进一步追求和理论上开发，以便达到适用于3D问题的适当方法，即适用于3D大规模结构元件。新的3D DI应该在混合方法中开发、分析和实施。该混合方法将3D DI与基于传播弹性超声波的飞行时间的方法相结合。基础研究的结果应通过实验研究进行验证，使用可用的适应性强的实验装置与超声激光结构响应采集具有高分辨率和精度。此外，还将进一步开发和实施特殊的压电传感器-智能骨料。通过一个系统的方法，它将被调查的配置和星座的致动器和传感器将满足的要求，既可以减少计算负担和设计成本，通过增加在同一时间的损伤检测效率。这些标准需要多目标结构优化，这应该通过实施深度学习（DL）神经网络来解决优化问题。基于从以前的研究阶段的结果的信号特征的有效分类的结构健康应该被识别。此外，损伤检测的最佳实验应设计基于数值模型的DL和进一步实施的实验研究。

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.