Irwanda Laory

Evaluating two model-free data interpretation methods for measurements that are influenced by temperature

Interpreting measurement data to extract meaningful information for damage detection is a challenge for continuous monitoring of structures. This paper presents an evaluation of two model-free data interpretation methods that have previously been identified to be attractive for applications in structural engineering: moving principal component analysis (MPCA) and robust regression analysis (RRA). The effect of three factors are evaluated: (a) sensor-damage location, (b) traffic loading intensity and (c) damage level, using two criteria: damage detectability and the time to damage detection. In addition, the effects of these three factors are studied for the first time in situations with and without removing seasonal variations through use of a moving average filter and an ideal low-pass filter. For this purpose, a parametric study is performed using a numerical model of a railway truss bridge. Results show that MPCA has higher damage detectability than RRA. On the other hand, RRA detects damages faster than MPCA. Seasonal variation removal reduces the time to damage detection of MPCA in some cases while the benefits are consistently modest for RRA.

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.

Performance of two model-free data interpretation methods for continuous monitoring of structures under environmental variations

Interpreting measurement data from continuous monitoring of civil structures for structural health monitoring (SHM) is a challenging task. This task is even more difficult when measurement data are influenced by environmental variations, such as temperature, wind and humidity. This paper investigates for the first time the performance of two model-free data interpretation methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) for monitoring civil structures that are influenced by temperature. The performance of the two methods is evaluated through two criteria: (1) damage detectability and (2) time to detection with respect to two factors: sensor-damage location and traffic loading intensity. Furthermore, the performance is studied in situations with and without filtering seasonal temperature variations through the use of a moving average filter. The study demonstrates that MPCA has higher damage detectability than RRA. RRA, on the other hand, detects damages faster than MPCA. Filtering seasonal temperature variations may reduce the time to detection of MPCA while the benefits are modest for RRA. MPCA and RRA should be considered as complementary methods for continuous monitoring of civil structures.

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods.

Structural identification methods for full-scale bridges

Results of two doctoral theses, one using a model-based approach and the other using model-free methods, are summarized. Both included case studies of full-scale bridges. While model-based approaches are appropriate for individual infrastructure-management cases where the cost of modeling can be justified, model-free methods provide inexpensive anomaly detection for long-term monitoring of many structures. Both are compatible with civil-engineering contexts of modeling simplifications, incomplete knowledge, noise, outliers and missing data. They provide useful additions to an engineers data-interpretation toolbox.

A Hybrid Model-Free Data-Interpretation Approach For Damage Detection During Continuous Civil-Infrastructure Monitoring

A hard challenge associated with infrastructure monitoring is to extract useful information from large amounts of measurement data in order to detect changes in structures. This paper presents a hybrid model-free approach that combines two model-free methods - Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) - to detect damage during continuous monitoring of structures. While a merit of MPCA is the ability to detect small amount of damage, an advantage of RRA is fast damage detection. The objective of this paper is to exploit these two complementary advantages through an appropriate combination. The applicability of this hybrid approach is studied on a railway truss bridge in Zangenberg (Germany). Its performance is compared with that of individual methods in terms of damage detectability and time to detection. Results show that the hybrid approach has higher damage detectability and identifies damage faster than individual applications of MPCA and RRA.