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Dependability of a structural system is a comprehensive concept that – by definition – describes the quality of the system as its ability to perform as expected in a way that can justifiably be trusted. One of the attributes of dependability is integrity, which can be interpreted as the absence of improper alterations of the structural configuration. The assessment of the integrity during the whole life-cycle can be carried out efficiently by implementing a monitoring system able to detect and diagnose any fault at its onset. The essential feature of the monitoring system dealt with in the paper is the elaboration of data gathered on site by a combination of simulation and heuristics. In detail, the first part of the paper deals with the extension of the concept of dependability, as formulated in computer science, to structural engineering. The second part illustrates a two-step hierarchical strategy for the assessment of the integrity of a structure through monitoring of its response under ambient vibrations; Bayesian neural network models are used for fault detection and diagnosis from observable symptoms. In the first step, the occurrence of any fault is detected and the relevant portion of the structure identified; in the second step the specific element affected by the fault is recognised and the intensity of the alteration of the structural performance
evaluated. The strategy is applied to assess the integrity of a long-span suspension bridge subjected to wind action and traffic loading. As the bridge is under design, measured data are simulated by analysing the response of a detailed FE model of the whole structural system. The final objective of the study is the optimal design of the integrity monitoring system for the bridge.