SUMMARYStructural health monitoring (SHM) is a process aimed at providing accurate and in‐time information concerning structural health condition and performance, which can serve as an objective basis for decision‐making regarding operation, maintenance, and repair. However, at the current state of practice, SHM is less used on real structures, and one reason for this is the lack of understanding of the Value of Information obtained from SHM. Consequently, even when SHM is implemented, bridge managers often make decisions based on experience or common sense, frequently considering with reserve and sometimes disregarding the suggestions arising from SHM. Managers weigh the SHM results based on their prior perception of the state of the structure and the confidence that they have in the specific applied SHM system and then make decisions considering the perceived effects of the actions they can undertake. In order to address and overcome the aforementioned identified limitations in the use of the SHM, a rational framework for assessment of the impact of the SHM on decision‐making is researched and proposed in this paper. The framework is based on the concept of Value of Information and demonstrated on the case study of the Streicker Bridge, a new pedestrian bridge on Princeton University campus. Copyright © 2013 John Wiley & Sons, Ltd.

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Exploiting wind energy in complex sites like mountain terrains implies the necessity for remote structural health monitoring of the wind towers. In fact, such slender vertical structures exposed to wind may experience large vibrations and repeated stress cycles leading to fatigue cracking. Possible strategies for remote fatigue damage detection are investigated. Specifically, this paper is focused on the use of suitable strain sensors for crack detection in critical sites of the structure, suggesting several strategies taking into account the possibility of wind direction changes and/or wind calm phases. They are based on a radial arrangement of strain sensors around the tower periphery in the vicinity of the base weld joint. The most promising strategy uses the strain difference between adjacent strain sensors as an index of the presence of a crack. The number of sensors to be installed is dictated by the minimum crack size to be detected, which in turn depends on the expected extreme wind conditions and programmed inspection/repair schedule for the structure.

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.