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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TL;DR: In this paper, an approach for using structural health monitoring (SHM) data in the reliability analysis and damage detection in high-speed naval craft (HSNC) under uncertainty is presented.
Abstract: Current and future trends in naval craft design are leaning toward the development of high-speed and high-performance vessels. Lack of information on wave-induced loads for these vessels presents a challenge in ensuring their safety that is best tackled with monitoring operational loads and detecting damage via structural health monitoring (SHM) systems. These monitoring systems, however, require efficient statistical and probabilistic procedures that are able to effectively treat the uncertainties inherent in the massive volumes of collected data and provide interpretable information regarding the reliability and condition of the craft structure. In this article, an approach for using SHM data in the reliability analysis and damage detection in high-speed naval craft (HSNC) under uncertainty is presented. This statistical damage detection technique makes use of vector autoregressive modeling for detection and localization of damage in the ship structure. The methodology is illustrated on an HSNC, HSV-2. ...
56 citations
TL;DR: A new eight-node curved inverse-shell element, named as iCS8, is developed based on iFEM methodology, which accommodates a curvilinear isoparametric coordinate system and can be effectively utilized to model cylindrical/curved geometries with a coarse discretization.
56 citations
TL;DR: In this article, a numerical finite element model of a metro tunnel is built and different types of structural defects are introduced at multiple locations of the tunnel, and transmissibility function and cross correlation analysis are applied to perform structural damage detection and localization based on simulated structural vibration data.
56 citations
TL;DR: In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic, where traditional sensor data are correlated with computer images to extract unit influence lines (UILs).
Abstract: In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic. With this methodology, video images are analyzed by means of computer vision techniques to detect and track vehicles crossing the bridge. Traditional sensor data are correlated with computer images to extract unit influence lines (UILs). Based on laboratory studies, UILs can be extracted for a critical section with different vehicles by means of synchronized video and sensor data. The synchronized computer vision and strain measurements can be obtained for bridge load rating under operational traffic. For this, the following are presented: a real life bridge is instrumented and monitored, and the real-life data are processed under a moving load. A detailed finite-element model (FEM) of the bridge is also developed and presented along with the experimental measurements to support the applicability of the approach for load rating using UILs extracted from operating traffic. The load rating of the bridges using operational traffic in real life was validated with the FEM results of the bridge and the simulation of the operational traffic on the bridge. This approach is further proven with different vehicles captured with video and measurements. The UILs are used for load rating by multiplying the UIL vector of the critical section with the load vector from the HL-93 design truck. The load rating based on the UIL is compared with the FEM results and indicates good agreement. With this method, it is possible to extract UILs of bridges under regular traffic and obtain load rating efficiently.
56 citations
TL;DR: The results show that the bridge information modeling framework can potentially facilitate the integration of information involved in bridge monitoring applications, and effectively support and provide services to retrieve and utilize the information.
56 citations