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

• Applicant: Professorin Dr.-Ing. Tamara Nestorovic
• Subject Area: Applied Mechanics, Statics and Dynamics
• Term: from 2020 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 448696650

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.

• DFG Programme: Research Grants