Abstract
Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain.
Highlights
IntroductionAn optimal infrastructure management strategy is needed
While infrastructure is ageing, available economic and environmental resources are decreasing.an optimal infrastructure management strategy is needed
This study presents a measurement-system design methodology to identify the best sensor locations and sensor types using information from several static load tests
Summary
An optimal infrastructure management strategy is needed. Due to the justifiably conservative nature of design and construction of large civil structures, most structures have a significant amount of reserve capacity. This reserve is largely unquantified, resulting in sub-optimal asset-management decisions. Knowledge of load capacity of bridges can be exploited to extend lifetimes of existing structures, optimize retrofit designs and prioritize inspection and maintenance activities. Interpretation of the data provided by sensors is critical to identify accurate structural models and subsequently, to estimate bridge reserve capacity. Such interpretation is a type of inverse engineering where causes
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