Quantification of the Posterior Utilities of SHM Campaigns on an Orthotropic Steel Bridge Deck

This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression mod-els linking daily averaged pavement temperatures, daily aggregated heavy-traffic counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The re-sults, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management. (Less)

[1]  Sebastian Thöns,et al.  The effects of SHM system parameters on the value of damage detection information , 2018 .

[2]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[3]  H. Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[4]  Farreras-AlcoverIsaac,et al.  Assessing temporal requirements for SHM campaigns , 2016 .

[5]  P. H. Wirsching,et al.  Probabilistic Fatigue Analysis , 1995 .

[6]  Marios K. Chryssanthopoulos,et al.  Data-based Models for Fatigue Reliability of Orthotropic Steel Bridge Decks based on Temperature, Traffic and Strain Monitoring , 2017 .

[7]  Sebastian Thöns,et al.  On the Value of Monitoring Information for the Structural Integrity and Risk Management , 2018, Comput. Aided Civ. Infrastructure Eng..

[8]  Isaac Farreras Alcover,et al.  Data-based models for assessment and life prediction of monitored civil infrastructure assets , 2014 .

[9]  Daniel Straub,et al.  Value of information analysis with structural reliability methods , 2014 .

[10]  Sebastian Thöns,et al.  On the value of SHM in the context of service life integrity management , 2015 .

[11]  Sebastian Thöns,et al.  Quantification of the conditional value of SHM data for the fatigue safety evaluation of a road viaduct , 2019 .

[12]  Sebastian Thöns,et al.  On the value of structural health monitoring , 2014 .

[13]  Matteo Pozzi,et al.  Assessing the value of information for long-term structural health monitoring , 2011, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[14]  Sebastian Thöns,et al.  The effects of deterioration models on the value of damage detection information , 2018 .