Project Details
Projekt
Design methodology for cross-life structural health monitoring with unknown damage process – Optimized sensor networks
Applicant
Professor Dr.-Ing. Steffen Marx
Subject Area
Structural Engineering, Building Informatics and Construction Operation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 501808860
Structural Health Monitoring (SHM) has emerged as a pivotal methodology for the assessment of bridge condition in recent years. Nevertheless, the current state of SHM systems faces notable shortcomings: These systems are predominantly designed based on expert judgement and are often implemented reactively in response to existing damage. This limitation can be attributed to the inherent challenges in reliably predicting the mechanisms, locations, and timings of structural damage. This project addresses the aforementioned gap by conducting foundational research with the objective of developing monitoring systems capable of proactively observing critical points in prestressed concrete bridges before damage occurs. In the first phase of the project (2022–2025), the objective was to identify structural points with a high propensity for damage and to ascertain the pertinent physical parameters for their monitoring. Two complementary approaches were investigated: (1) a cluster-based approach that predicts damage by conducting a systematic analysis of damage data from similar bridge structures, and (2) an object-based approach that identifies critical points through a detailed examination of the specific structure. The proposed continuation project, “Optimized sensor networks,” builds upon the outcomes of the first phase and seeks to extend, refine, and validate its findings. The overarching goal of this second phase is to establish robust links between physical parameters and tailored monitoring concepts for reliable damage identification. Achieving this requires a comprehensive, holistic approach to the SHM process, encompassing the acquisition of sensor signals from the structure, the processing of these signals into interpretable physical parameters, and the extraction of meaningful damage indicators. A principal objective of this research is to provide a comprehensive and systematic approach to addressing the inevitable uncertainties that arise throughout the entire SHM process chain. These uncertainties, which originate from a multitude of sources including measurement noise, environmental factors, and modelling assumptions, significantly compromise the reliability of damage identification. The objective of this project is to identify, quantify, and model the aforementioned uncertainties. The findings will provide the basis for the development of a probabilistic framework for evaluating the impact of uncertainties on the reliability of sensor networks. Subsequently, the framework will be expanded to incorporate a cost-benefit optimization for sensor networks. The objective of this optimization is to achieve the greatest possible information gain while simultaneously minimizing the costs associated with the design, installation, and operation of SHM systems. Finally, the developed framework will be validated under real-world environmental and operational conditions using the openLAB research bridge.
DFG Programme
Priority Programmes
