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.

BWIM aided damage detection in bridges using machine learning

Abstract: In this study, a new, model-free damage detection method is proposed and validated on a simple numerical experiment The proposed algorithm used vibration data (deck accelerations) and bridge weigh-in-motion data (load magnitude and position) to train a two-stage machine learning setup to classify the data into healthy or damaged The proposed method is composed in its first stage of an artificial neural network and on the second stage of a gaussian process The proposed method is applicable to railway bridges, since it takes advantage of the fact that vehicles of known axle configuration cross the bridge regularly, that normally only one train is on the bridge at a time and that the lateral positioning of the loads does not change The novelty of the proposed algorithm is that it makes use of the data on the load’s position, magnitude and speed that can be obtained from a Bridge Weigh-in-Motion system to improve the accuracy of the damage detection algorithm

Active sensing platform for wireless structural health monitoring

Abstract: This paper presents SHiMmer, a wireless platform for sensing and actuation that combines localized processing with energy harvesting to provide long-lived structural health monitoring. The life-cycle of the node is significantly extended by the use of super-capacitors for energy storage instead of batteries. During this period the node is expected to work completely maintenance-free. The node is capable of harvesting up to 780 J per day. This makes it completely self- sufficient while employed in real structural health monitoring applications. Unlike other sensor networks that periodically monitor a structure and route information to a base station, our device acquires the data and processes it locally after being radio-triggered by an external agent. The localized processing allows us to avoid issues due to network congestion. Our experiments show that its 32- bits computational core can run at 100 MIPS for 15 minutes daily.

Structural Health Monitoring Sensor Placement Optimization Under Uncertainty

Abstract: This paper develops a methodology for the optimum layout design of sensor arrays of structural health monitoring systems under uncertainty. This includes finite element analysis under transient mechanical and thermal loads and incorporation of uncertainty quantification methods. The finite element model is validated with experimental data, accounting for uncertainties in experimental measurements and model predictions. The structural health monitoring sensors need to be placed optimally in order to detect with high reliability any structural damage before it turns critical. The proposed methodology achieves this objective by combining probabilistic finite element analysis, structural damage detection algorithms, and reliability-based optimization concepts.

Development and Application of a Structural Health Monitoring System Based on Wireless Smart Aggregates.

Abstract: Structural health monitoring (SHM) systems can improve the safety and reliability of structures, reduce maintenance costs, and extend service life. Research on concrete SHMs using piezoelectric-based smart aggregates have reached great achievements. However, the newly developed techniques have not been widely applied in practical engineering, largely due to the wiring problems associated with large-scale structural health monitoring. The cumbersome wiring requires much material and labor work, and more importantly, the associated maintenance work is also very heavy. Targeting a practical large scale concrete crack detection (CCD) application, a smart aggregates-based wireless sensor network system is proposed for the CCD application. The developed CCD system uses Zigbee 802.15.4 protocols, and is able to perform dynamic stress monitoring, structural impact capturing, and internal crack detection. The system has been experimentally validated, and the experimental results demonstrated the effectiveness of the proposed system. This work provides important support for practical CCD applications using wireless smart aggregates.