Structural health monitoring (SHM) has drawn significant attention in recent decades because of its potential to reduce maintenance costs and increase the reliability of structures. An important class of structures that can potentially benefit from SHM are bridges, many of which are structurally deficient due to lack of adequate maintenance. Through condition assessment of these bridges, an effective plan of maintenance can be determined, offering the possibility to prolong service life, as well as to prevent catastrophic disasters due to sudden collapse. To date, numerous damage detection algorithms have been proposed. Still, challenges remain in applying such algorithms to monitor bridges in the field. In reality, the extent of an SHM system is limited by available budgets, which define the number of sensors that can be deployed. This dissertation first presents a damage detection algorithm using static strain developed for efficient structural condition assessment with a few sensor nodes. A laboratory moving vehicle experiment has been developed for validation of the approach. However, just a few sensor nodes in SHM system cannot provide detailed information on damage location. A solution to include many sensors within a limited budget with increased efficiency is to use a Wireless Smart Sensor Network (WSSN) because of the merits of low cost, easy installation, and effective data management. An acceleration-based SHM algorithm for WSSN has been developed with a decentralized network topology. This approach has been implemented into a modularized damage detection service. The SHM application is designed to leverage the on-board computation capability of the WSSN, reducing the transmitted data size by distributing the computation burden. The SHM application for WSSN has been validated in lab-scale experiments on a truss bridge model. Nonetheless, the real challenge of SHM is in the deployment on full-scale bridges for continuous monitoring. The usability and stability of WSSN has been validated on an architectural staircase in the Siebel Center. Based on the usability investigation, the deployment of the world0́9s largest WSSN on the Jindo Bridge, a cable-stayed bridge has been achieved in South Korea. The main purpose of the deployment was to validate the bridge monitoring system using WSSN and energy harvesting devices in a long-term manner. The ultimate goal of this dissertation is to deploy the developed on-board decentralized damage identification application using WSSN on a historic truss bridge. As a first step, a series of dynamic tests were conducted for modal analysis using both wired and wireless sensor systems. During the tests, the functionality of the wireless sensor system with ISHMP Services Toolsuite was confirmed. For model-based damage identification approach developed herein, a finite element (FE) model was created. The initial FE model was updated based on a visual estimate of the corrosion. The updated model was used to generate baseline information for damage detection. Finally, the WSSN-based autonomous SHM system using the decentralized damage detection application was deployed on the historic bridge. The permanent SHM system was installed on the bridge, and the damage detection application was successfully run on the bridge. The damage detection results using the comprehensive application will be compared with those from the measured data. In summary, this dissertation provides a robust SHM system for bridge structures in use of WSSN. The decentralized damage detection approach is experimentally validated for WSSN. The performance of WSSN and energy harvesting devices will be evaluated.

Smart sensors are technologies designed to facilitate the monitoring operations. For instance, power consumption can be minimized through on-board processing and smart interrogation algorithms, and state detection enhanced through collaboration between sensor nodes. Applied to structural health monitoring, smart sensors are key enablers of sparse and dense sensor networks capable of monitoring full-scale structures and components. They are also critical in empowering operators with decision making capabilities. The objective of this Special Issue is to generate discussions on the latest advances in research on smart sensing technologies for structural health monitoring applications, with a focus on decision-enabling systems. This Special Issue covers a wide range of related topics such as innovative sensors and sensing technologies for crack, displacement, and sudden event monitoring, sensor optimization, and novel sensor data processing algorithms for damage and defect detection, operational modal analysis, and system identification of a wide variety of structures (bridges, transmission line towers, high-speed trains, masonry light houses, etc.).

Presents the research and applications on sensing technologies to monitor and control the structure and health of buildings, bridges, installations, and other constructed facilities.

Structural Health Monitoring (SHM) deals with assessment, evaluation and technical diagnosis of different structural systems of strategic importance. Extensive knowledge of SHM shall lead to a clear understanding of risk and reliability assessment of structures, which is currently mandatory for structures of strategic importance like bridges, offshore structures, etc.This comprehensive compendium features explanations and salient illustrations of SHM with applications to civil engineering structures, in general and offshore structures, in particular. The book is unique with respect to its contents, experimental case studies in lab scale and text presentation style. A detailed subject matter of this nature is currently scarce in the literature market.The must-have volume is a useful reference text for senior undergraduate and postgraduate students, professionals, academics and researchers in civil engineering, ocean engineering, mechanical engineering, and structural engineering.

This 2-volume set of books, comprising over 2,700 total pages, presents 325 fully original presentations on recent advances in structural health monitoring, as applied to commercial and military aircraft (manned and unmanned), high-rise buildings, wind turbines, civil infrastructure, power plants and ships. One general theme of the books is how SHM can be used for condition-based maintenance, with the goal of developing prediction-based systems, designed to save money over the life of vehicles and structures. A second theme centers on technologies for developing systems comprising sensors, diagnostic data and decision-making, with a focus on intelligent materials able to respond to damage and in some cases repair it. Finally the books discuss the relation among data, data interpretation and decision-making in managing a wide variety of complex structures and vehicles. More recent technologies discussed in the books include SHM and environmental effects, energy harvesting, non-contact sensing, and intelligent networks.Material in these books was first presented in September, 2011 at a conference held at Stanford University and sponsored by the Air Force Office of Scientific Research, the Army Research Office, the Office of Naval Research and the National Science Foundation. Some of the highlights of the books include: SHM technologies for condition-based maintenance (CBM) and predictive maintenance Verification, validation, qualification, data mining, prognostics systems for decision-making Structural health, sensing and materials in closed-loop intelligent networks Military and aerospace, bioinspired sensors, wind turbines, monitoring with MEMS, damage sensing, hot spot monitoring, SHM and ships, high-rise structures Includes a fully-searchable CD-ROM displaying many figures and charts in full color