• Wind and Structures
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• v.12 no.6
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• pp.479-504
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• 2009

Structural health monitoring (SHM) systems have been recently embraced in long span cable-supported bridges, in which buffeting-induced stress monitoring is one of the tasks to ensure the safety of the bridge under strong winds. In line with this task, this paper presents a SHM-oriented finite element model (FEM) for the Tsing Ma suspension bridge in Hong Kong so that stresses/strains in important bridge components can be directly computed and compared with measured ones. A numerical procedure for buffeting induced stress analysis of the bridge based on the established FEM is then presented. Significant improvements of the present procedure are that the effects of the spatial distribution of both buffeting forces and self-excited forces on the bridge deck structure are taken into account and the local structural behaviour linked to strain/stress, which is prone to cause local damage, are estimated directly. The field measurement data including wind, acceleration and stress recorded by the wind and structural health monitoring system (WASHMS) installed on the bridge during Typhoon York are analyzed and compared with the numerical results. The results show that the proposed procedure has advantages over the typical equivalent beam finite element models.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.641-659
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• 2010

Structural Health Monitoring (SHM) gradually becomes a technique for ensuring the health and safety of civil infrastructures and is also an important approach for the research of the damage accumulation and disaster evolving characteristics of civil infrastructures. It is attracting prodigious research interests and the active development interests of scientists and engineers because a great number of civil infrastructures are planned and built every year in mainland China. In a SHM system the sheer number of accompanying wires, fiber optic cables, and other physical transmission medium is usually prohibitive, particularly for such structures as offshore platforms and long-span structures. Fortunately, with recent advances in technologies in sensing, wireless communication, and micro electro mechanical systems (MEMS), wireless sensor technique has been developing rapidly and is being used gradually in the SHM of civil engineering structures. In this paper, some recent advances in the research, development, and implementation of wireless sensors for the SHM of civil infrastructures in mainland China, especially in Dalian University of Technology (DUT) and Harbin Institute of Technology (HIT), are introduced. Firstly, a kind of wireless digital acceleration sensors for structural global monitoring is designed and validated in an offshore structure model. Secondly, wireless inclination sensor systems based on Frequency-hopping techniques are developed and applied successfully to swing monitoring of large-scale hook structures. Thirdly, wireless acquisition systems integrating with different sensing materials, such as Polyvinylidene Fluoride(PVDF), strain gauge, piezoresistive stress/strain sensors fabricated by using the nickel powder-filled cement-based composite, are proposed for structural local monitoring, and validating the characteristics of the above materials. Finally, solutions to the key problem of finite energy for wireless sensors networks are discussed, with future works also being introduced, for example, the wireless sensor networks powered by corrosion signal for corrosion monitoring and rapid diagnosis for large structures.

• Smart Structures and Systems
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• v.7 no.5
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• pp.393-416
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• 2011

Hybrid acceleration-impedance sensor nodes on Imote2-platform are designed for damage monitoring in steel girder connections. Thus, the feasibility of the sensor nodes is examined about its performance for vibration-based global monitoring and impedance-based local monitoring in the structural systems. To achieve the objective, the following approaches are implemented. First, a damage monitoring scheme is described in parallel with global vibration-based methods and local impedance-based methods. Second, multi-scale sensor nodes that enable combined acceleration-impedance monitoring are described on the design of hardware components and embedded software to operate. Third, the performances of the multi-scale sensor nodes are experimentally evaluated from damage monitoring in a lab-scaled steel girder with bolted connection joints.

• Journal of Ocean Engineering and Technology
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• v.24 no.3
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• pp.1-10
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• 2010

In this study, hybrid smart sensor nodes were developed for the autonomous structural health monitoring of prestressed concrete (PSC) girders. In order to achieve the objective, the following approaches were implemented. First, we show how two types of smart sensor nodes for the hybrid health monitoring were developed. One was an acceleration-based smart sensor node using an MEMS accelerometer to monitor the overall damage in concrete girders. The other was an impedance-based smart sensor node for monitoring the local damage in prestressing tendons. Second, a hybrid monitoring algorithm using these smart sensor nodes is proposed for the autonomous structural health monitoring of PSC girders. Finally, we show how the performance of the developed system was evaluated using a lab-scaled PSC girder model for which dynamic tests were performed on a series of prestress-loss cases and girder damage cases.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.711-730
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• 2010

This study presents the design of autonomous smart sensor nodes for damage monitoring of tendons and girders in prestressed concrete (PSC) bridges. To achieve the objective, the following approaches are implemented. Firstly, acceleration-based and impedance-based smart sensor nodes are designed for global and local structural health monitoring (SHM). Secondly, global and local SHM methods which are suitable for damage monitoring of tendons and girders in PSC bridges are selected to alarm damage occurrence, to locate damage and to estimate severity of damage. Thirdly, an autonomous SHM scheme is designed for PSC bridges by implementing the selected SHM methods. Operation logics of the SHM methods are programmed based on the concept of the decentralized sensor network. Finally, the performance of the proposed system is experimentally evaluated for a lab-scaled PSC girder model for which a set of damage scenarios are experimentally monitored by the developed smart sensor nodes.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.5
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• pp.39-45
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• 2006

Structural health monitoring has been conducted by non-destructive evaluation method when a turbine rotor system of an aircraft engine has cracks. Local stiffness of a turbine rotor system is degraded and critical speed is changed due to the presence of cracks in rotor. Critical speed which is affected by location and depth of crack, is obtained using compliance matrix of cracked rotor. The database of the obtained critical speed is used to evaluate structural health monitoring of a rotor system of a gas turbine engine.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.669-674
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• 2007

To develop a promising hybrid structural health monitoring (SHM) system, a combined use of structural vibration and electro-mechanical (EM) impedance is proposed. The hybrid SHM system is designed to use vibration characteristics as global index and EM impedance as local index. The proposed health monitoring scheme is implemented into prestressed concrete (PSC) girder bridges for which a series of damage scenarios are designed to simulate various prestress-loss situations at which the target bridges car experience during their service life. The measured experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the occurrence of damage and furthermore to indicate its location.

• Smart Structures and Systems
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• v.14 no.2
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• pp.85-104
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• 2014

Damage detection is a challenging, complex, and at the same time very important research topic in civil engineering. Identifying the location and severity of damage in a structure, as well as the global effects of local damage on the performance of the structure are fundamental elements of damage detection algorithms. Local damage detection is essential for structural health monitoring since local damages can propagate and become detrimental to the functionality of the entire structure. Existing studies present several methods which utilize sensor data, and track global changes in the structure. The challenging issue for these methods is to be sensitive enough in identifYing local damage. Autoregressive models with exogenous terms (ARX) are a popular class of modeling approaches which are the basis for a large group of local damage detection algorithms. This study presents an algorithm, called Influence-based Damage Detection Algorithm (IDDA), which is developed for identification of local damage based on regression of the vibration responses. The formulation of the algorithm and the post-processing statistical framework is presented and its performance is validated through implementation on an experimental beam-column connection which is instrumented by dense-clustered wired and wireless sensor networks. While implementing the algorithm, two different sensor networks with different sensing qualities are utilized and the results are compared. Based on the comparison of the results, the effect of sensor noise on the performance of the proposed algorithm is observed and discussed in this paper.