Measurement System Configuration for Damage Identification of Continuously Monitored Structures

Measurement system configuration is an important task in structural health monitoring in that decisions influence the performance of monitoring systems. This task is generally performed using only engineering judgment and experience. Such approach may result in either a large amount of redundant data and high data‐interpretation costs, or insufficient data leading to ambiguous interpretations. This paper presents a systematic approach to configure measurement systems where static measurement data are interpreted for damage detection using model‐free (non‐physics‐based) methods. The proposed approach provides decision support for two tasks: (1) determining the appropriate number of sensors to be employed and (2) placing the sensors at the most informative locations. The first task involves evaluating the performance of measurement systems in terms of the number of sensors. Using a given number of sensors, the second task involves configuring a measurement system by identifying the most informative sensor locations. The locations are identified based on three criteria: the number of non‐detectable damage scenarios, the average time to detection and the damage detectability. A multi‐objective optimization is thus carried out leading to a set of non‐dominated solutions. To select the best compromise solution in this set, two multi criteria decision making methods, Pareto‐Edgeworth‐Grierson multi‐criteria decision making (PEG‐MCDM) and Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE), are employed. A railway truss bridge in Zangenberg (Germany) is used as a case study to illustrate the applicability of the proposed approach. Measurement systems are configured for situations where measurement data are interpreted using two model‐free methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA). Results demonstrate that the proposed approach is able to provide engineers with decision support for configuring measurement systems based on the data‐interpretation methods used for damage detection. The approach is also able to accommodate the simultaneous use of several model‐free data‐interpretation methods. It is also concluded that the number of non‐detectable scenarios, the average time to detection and the damage detectability are useful metrics for evaluating the performance of measurement systems when data are interpreted using model‐free methods

Configuring and enhancing measurement systems for damage identification

Engineers often decide to measure structures upon signs of damage to determine its extent and its location. Measurement locations, sensor types and numbers of sensors are selected based on judgment and experience. Rational and systematic methods for evaluating structural performance can help make better decisions. This paper proposes strategies for supporting two measurement tasks related to structural health monitoring – (1) installing an initial measurement system and (2) enhancing measurement systems for subsequent measurements once data interpretation has occurred. The strategies are based on previous research into system identification using multiple models. A global optimization approach is used to design the initial measurement system. Then a greedy strategy is used to select measurement locations with maximum entropy among candidate model predictions. Two bridges are used to illustrate the proposed methodology. First, a railway truss bridge in Zangenberg, Germany, is examined. For illustration purposes, the model space is reduced by assuming only a few types of possible damage in the truss bridge. The approach is then applied to the Schwandbach bridge in Switzerland, where a broad set of damage scenarios is evaluated. For the truss bridge, the approach correctly identifies the damage that represents the behaviour of the structure. For the Schwandbach bridge, the approach is able to significantly reduce the number of candidate models. Values of candidate model parameters are also useful for planning inspection and eventual repair.Swiss National Science Foundatio

Performance-driven measurement system design for structural identification

Much progress has been achieved in the field of structural identification due to a better understanding of uncertainties, improvement in sensor technologies and cost reductions. However, data interpretation remains a bottleneck. Too often, too much data is acquired, thus hindering interpretation. In this paper, a methodology is described that explicitly indicates when instrumentation can decreases the ability to interpret data. The approach includes uncertainties along with dependencies that may affect model predictions. Two performance indices are used to optimize measurement system designs: monitoring costs and expected identification performance. A case-study shows that the approach is able to justify a reduction in monitoring costs of 50% compared with an initial measurement configuration

Configuring and enhancing measurement systems for damage identification

Engineers often decide to measure structures upon signs of damage to determine its extent and its location. Measurement locations, sensor types and numbers of sensors are selected based on judgment and experience. Rational and systematic methods for evaluating structural performance can help make better decisions. This paper proposes strategies for supporting two measurement tasks related to structural health monitoring – (1) installing an initial measurement system and (2) enhancing measurement systems for subsequent measurements once data interpretation has occurred. The strategies are based on previous research into system identification using multiple models. A global optimization approach is used to design the initial measurement system. Then a greedy strategy is used to select measurement locations with maximum entropy among candidate model predictions. Two bridges are used to illustrate the proposed methodology. First, a railway truss bridge in Zangenberg, Germany is examined. For illustration purposes, the model space is reduced by assuming only a few types of possible damage in the truss bridge. The approach is then applied to the Schwandbach bridge in Switzerland, where a broad set of damage scenarios is evaluated. For the truss bridge, the approach correctly identifies the damage that represents the behaviour of the structure. For the Schwandbach bridge, the approach is able to significantly reduce the number of candidate models. Values of candidate model parameters are also useful for planning inspection and eventual repair

Modular Bayesian uncertainty assessment for structural health monitoring

Civil infrastructure are critical elements to a society’s welfare and economic thriving. Understanding their behaviour and monitoring their serviceability are relevant challenges of Structural Health Monitoring (SHM). Despite the impressive improvement of miniaturisation, standardisation and diversity of monitoring systems, the ability to interpret data has registered a much slower progression across years. The underlying causes for such disparity are the overall complexity of the proposed challenge, and the inherent errors and lack of information associated with it. Overall, it is necessary to appropriately quantify the uncertainties which undermine the SHM concept. This thesis proposes an enhanced modular Bayesian framework (MBA) for structural identification (st-id) and measurement system design (MSD). The framework is hybrid, in the sense that it uses a physics-based model, and Gaussian processes (mrGp) which are trained against data, for uncertainty quantification. The mrGp act as emulators of the model response surface and its model discrepancy, also quantifying observation error, parametric and interpolation uncertainty. Finally, this framework has been enhanced with the Metropolis–Hastings for multiple parameters st-id. In contrast to other probabilistic frameworks, the MBA allows to estimate structural parameters (which reflect a performance of interest) consistently with their physical interpretation, while highlighting patterns of a model’s discrepancy. The MBA performance can be substantially improved by considering multiple responses which are sensitive to the structural parameters. An extension of the MBA for MSD has been validated on a reduced-scale aluminium bridge subject to thermal expansion (supported at one end with springs and instrumented with strain gauges and thermocouples). A finite element (FE) model of the structure was used to obtain a semi-optimal sensor configuration for stid. Results indicate that 1) measuring responses which are sensitive to the structural parameters and are more directly related to model discrepancy, provide the best results for st-id; 2) prior knowledge of the model discrepancy is essential to capture the latter type of responses. Subsequently, an extension of the MBA for st-id was also applied for identification of the springs stiffness, and results indicate relative errors five times less than other state of the art Bayesian/deterministic methodologies. Finally, a first application to field data was performed, to calibrate a detailed FE model of the Tamar suspension bridge using long-term monitored data. Measurements of temperature, traffic, mid-span displacement and natural frequencies of the bridge, were used to identify the bridge’s main/stay cables initial strain and friction of its bearings. Validation of results suggests that the identified parameters agree more closely with the true structural behaviour of the bridge, with an error that is several orders of magnitude smaller than other probabilistic st-id approaches. Additionally, the MBA allowed to predicted model discrepancy functions to assess the predictive ability of the Tamar bridge FE model. It was found, that the model predicts more accurately the bridge mid-span displacements than its natural frequencies, and that the adopted traffic model is less able to simulate the bridge behaviour during periods of traffic jams. Future developments of the MBA framework include its extension and application for damage detection and MSD with multiple parameter identification