The recent developments in measurement technology have led to the installation of efficient monitoring systems on many bridges and other structures all over the world. Nowadays, more and more structures have been built and instrumented with sensors. However, calibration and installation of sensors remain challenging tasks. In this paper, we use a case study, Adige Bridge, in order to present a low-cost method for the calibration and installation of elasto-magnetic sensors on cable-stayed bridges. Elasto-magnetic sensors enable monitoring of cable stress. The sensor installation took place two years after the bridge construction. The calibration was conducted in two phases: one in the laboratory and the other one on site. In the laboratory, a sensor was built around a segment of cable that was identical to those of the cable-stayed bridge. Then, the sample was subjected to a defined tension force. The sensor response was compared with the applied load. Experimental results showed that the relationship between load and magnetic permeability does not depend on the sensor fabrication process except for an offset. The determination of this offset required in situ calibration after installation. In order to perform the in situ calibration without removing the cables from the bridge, vibration tests were carried out for the estimation of the cables’ tensions. At the end of the paper, we show and discuss one year of data from the elasto-magnetic sensors. Calibration results demonstrate the simplicity of the installation of these sensors on existing bridges and new structures.

The main purpose of structural health monitoring (SHM) is to obtain information about the state of a structure in order to guide bridge management decisions. Nevertheless, in practice, once a rigorous estimate of the structural state is available, decisions are usually made based on the decision-maker's intuition or experience. In this paper, we present the implementation of expected utility theory (EUT) in those civil engineering decision problems in which decision-makers have to act based on the output of SHM. EUT is an analytical quantitative framework that allows the identification of the financially most convenient decisions, based on the possible outcomes of each action and on the probabilities of each structural state occurring. The advantage of the presented implementation is the optimization of decision strategies in SHM. In the paper, we first formalize the solution of single-stage decision processes, in which the decision-maker has to take only one action. Then, we formalize the solution of multistage decision processes, in which multiple actions may be taken over time. Finally, using an example based on a case study, we describe the variables involved in the analysis of SHM decision problems, discuss the possible results, and address the issues that may arise in the application of EUT in real-life settings.

This paper outlines a logical process based on Bayesian inference to exploit monitoring observations to improve the accuracy in the reliability estimates of civil structures that are retrofitted during their service lives. The proposed approach is particularly effective for complex structures, whose response prediction is affected by structural parameters with significant uncertainty; typical examples are prestressed concrete bridges, heavily influenced by the inherent uncertainties of the material’s creep and shrinkage. The methodology is applied to a real-life complex bridge in Italy, as opposed to academic problems and laboratory tests; the structural model developed for this study includes the original creep law of the Model B3 proposed by Bazant rather than linear approximation or response surfaces. The Bayesian inference is performed using a Markov Chain Monte Carlo (MCMC) method, while the reliability assessment utilises importance sampling (IS) for computational efficiency. The results show the extent to which structural health monitoring (SHM) and Bayesian inference reduce the uncertainty in structural parameters and the prediction of the structural demand. At the same time, they increase structural reliability. Particular attention is given to computational effort, structural modelling and algorithm optimisation.

Journal of Civil Structural Health Monitoring, 10.1007/s13349-015-0120-0 This article is part of the special issue on Probabilistic SHM. It was supposed to have been published with the rest of the articles in issue number 3 of Vol. 5 of JCSHM. It was left out by error

Decisions concerning bridge management often rely on experience, engineering judgement and heuristics. In this contribution, with the aid of a real-life case study, we show how a bridge management strategy can be optimized scientifically by applying expected utility theory, a framework already used in econometrics and medicine in order to identify optimal actions in agreement with a risk profile. We show how the engineering decision problem is modelled and how expected utility theory is implemented in order to obtain a set of decision rules that enables the decision maker to identify the optimal action based on structural reliability and costs. The results show that the implementation of expected utility theory in ordinary civil engineering problems is feasible and provides results based on the risk profile taken into account during the calibration of the model.