Topic
Structural health monitoring
About: Structural health monitoring is a research topic. Over the lifetime, 11727 publications have been published within this topic receiving 186231 citations.
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Journal Article•
89 citations
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
89 citations
TL;DR: In this paper, a damage identification procedure based on dynamic response for fiber reinforced polymer (FRP) sandwich composites is evaluated, in which the damage magnitude is quantified based on the relationship between the changes of mechanical properties and the related changes of dynamic responses (i.e., the curvature mode shapes in this study).
89 citations
TL;DR: In this paper, a crack width wireless radio-frequency identification sensor was developed for applications on various materials (such as concrete and metal) and able to detect submillimeter deformations occurring on the object on which it is placed.
Abstract: All mechanical structures are subjected to deformation and cracks, due to fatigue, stress, and/or environmental factors. It is, therefore, of uttermost importance to monitor the mechanical condition of critical structures, in order to prevent catastrophic failures, but also to minimize maintenance costs, i.e., avoid unnecessary inspections. A number of technologies and systems can be used for this purpose: among them, the ones proposing the use of wireless passive crackmeters have a strong impact potential, in terms of simplicity of installation and measurement and low cost. This paper, hence, shows a crack width wireless radio-frequency identification sensor, developed for applications on various materials (such as concrete and metal) and able to detect submillimeter deformations occurring on the object, on which it is placed. A design method based on high-sensitivity phase detection is shown.
89 citations
TL;DR: In this article, a bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage.
Abstract: A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors. Sophisticated structural identification methods, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory small-scaled bridge section.
89 citations