• Smart Structures and Systems
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• v.20 no.2
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• pp.139-150
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• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.21-32
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• 2003

Developments and applications of the structural health monitoring (SHM) systems have become active particularity for long-span bridges in Korea. They are composed of sensors, data acquisition system, data transmission system, information processing, damage assessment, and information management. In this paper, current status of research and application activities on SHM systems for civil infra-structures in Korea are briefly introduced by 4 parts: (1) current status of bridge monitoring systems on existing and newly constructed bridges, (2) research and development activities on smart sensors such as optical fiber sensors and piezo-electric sensors, (3) structural damage detection methods using measured data, and (4) a test road project for pavement design verification and enhancement by the Korea Highway Corporation. Finally the R＆D activities of a new engineering research center entitled Smart Infra-Structure Technology Center at Korea Advanced Institute of Science and Technology are also briefly described.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.423-438
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• 2010

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fulls-cale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

• Smart Structures and Systems
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• v.19 no.5
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• pp.499-512
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• 2017

Traditional structural dynamic analysis and Structural Health Monitoring (SHM) of large scale concrete civil structures rely on manufactured embedding transducers to obtain structural dynamic properties. However, the embedding of manufactured transducers is very expensive and low efficiency for signal acquisition. In dynamic structural analysis and SHM areas, piezoelectric transducers are more and more popular due to the advantages like quick response, low cost and adaptability to different sizes. In this paper, the applicable feasibility assessment of the designed "artificial" piezoelectric transducers called Concrete Piezoelectric Smart Module (CPSM) in dynamic structural analysis is performed via three major experiments. Experimental Modal Analysis (EMA) based on Ibrahim Time Domain (ITD) Method is applied to experimentally extract modal parameters. Numerical modal analysis by finite element method (FEM) modeling is also performed for comparison. First ten order modal parameters are identified by EMA using CPSMs, PCBs and FEM modeling. Comparisons are made between CPSMs and PCBs, between FEM and CPSMs extracted modal parameters. Results show that Power Spectral Density by CPSMs and PCBs are similar, CPSMs acquired signal amplitudes can be used to predict concrete compressive strength. Modal parameter (natural frequencies) identified from CPSMs acquired signal and PCBs acquired signal are different in a very small range (~3%), and extracted natural frequencies from CPSMs acquired signal and FEM results are in an allowable small range (~5%) as well. Therefore, CPSMs are applicable for signal acquisition of dynamic responses and can be used in dynamic modal analysis, structural health monitoring and related areas.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.106-113
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• 2009

Recently, due to many industrial accidents at construction sites, a variety of researches for structural health monitoring (SHM) of buildings are progressing. For real site application of SHM, one of the advanced technologies has blown as wireless sensor networks (WSN). In this paper, we proposed WIVA(WSN Monitoring framework based on 3D Visualization and Augmented Reality in Mobile Devices) system that applies 3D visualization and AR technology to mobile devices with camera based on WSN in order to expand the extent of information can observe. Moreover, we performed experiments to validate effectiveness in 3D and AR mode that utilize WSN data based on IEEE 802.15.4. In real implementation scenario, we demonstrated a fire occurrence test in 3-story building miniature.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.12
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• pp.830-842
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• 2015

This paper aims to develop a SI(structural identification) technique using the kinetic energy optimization technique(KEOT) and the direct matrix updating method(DMUM) to decide on optimal location of sensors and to update FE model respectively, which ultimately contributes to a composition of more effective SHM. Owing to the characteristic structural flexing behavior of cable bridges, which makes them vulnerable to any vibration, systematic and continuous structural health monitoring (SHM) is pivotal for them. Since it is necessary to select optimal measurement locations with the fewest possible measurements and also to accurately assess the structural state of a bridge for the development of an effective SHM, a SI technique is as much important to accurately determine the modal parameters of the current structure based on the data optimally obtained. In this study, the KEOT was utilized to determine the optimal measurement locations, while the DMUM was utilized for FE model updating. As a result of experiment, the required number of measurement locations derived from KEOT based on the target mode was reduced by approximately 80 % compared to the initial number of measurement locations. Moreover, compared to the eigenvalue of the modal experiment, an improved FE model with a margin of error of less than 1 % was derived from DMUM. Finally, the SI technique for long-span bridges proposed in this study, which utilizes both KEOT and DMUM, is proven effective in minimizing the number of sensors while accurately determining the structural dynamic characteristics.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.21-26
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• 2007

This paper presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in a significant impedance variation, particularly both horizontal and vertical shifts in the frequency domain, which may lead to erroneous diagnostic results of real structures. A new damage detection strategy has been proposed based on the correlation coefficient (CC) between the reference impedance data and a concurrent impedance data with an effective frequency shift which is defined as the shift causing the maximum correlation. The proposed technique was applied to a lab-sized steel truss bridge member under the temperature varying environment. From an experimental study, it has been demonstrated that a narrow cut inflicted artificially to the steel structure was successfully detected using the proposed SHM strategy.

• Earthquakes and Structures
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• v.19 no.1
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• pp.29-44
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• 2020

The effectiveness and the sensitivity of a Wireless impedance/Admittance Monitoring System (WiAMS) for the prompt damage diagnosis of two single-storey single-span Reinforced Concrete (RC) frames under cyclic loading is experimentally investigated. The geometrical and the reinforcement characteristics of the RC structural members of the frames represent typical old RC frame structure without consideration of seismic design criteria. The columns of the frames are vulnerable to shear failure under lateral load due to their low height-to-depth ratio and insufficient transverse reinforcement. The proposed Structural Health Monitoring (SHM) system comprises of specially manufactured autonomous portable devices that acquire the in-situ voltage frequency responses of a network of twenty piezoelectric transducers mounted to the RC frames. Measurements of external and internal small-sized piezoelectric patches are utilized for damage localization and assessment at various and increased damage levels as the magnitude of the imposed lateral cycle deformations increases. A bare RC frame and a strengthened one using a pair of steel crossed tension-ties (X-bracing) have been tested in order to check the sensitivity of the developed WiAMS in different structural conditions since crack propagation, damage locations and failure mode of the examined frames vary. Indeed, the imposed loading caused brittle shear failure to the column of the bare frame and the formation of plastic hinges at the beam ends of the X-braced frame. Test results highlighted the ability of the proposed SHM to identify incipient damages due to concrete cracking and steel yielding since promising early indication of the forthcoming critical failures before any visible sign has been obtained.

• Smart Structures and Systems
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• v.5 no.4
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• pp.457-468
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• 2009

A novel approach for integrating active sensing data interrogation algorithms for structural health monitoring (SHM) applications is presented. These algorithms cover Lamb wave propagation, impedance methods, and sensor diagnostics. Contrary to most active-sensing SHM techniques, which utilize only a single signal processing method for damage identification, a suite of signal processing algorithms are employed and grouped into one package to improve the damage detection capability. A MATLAB-based user interface, referred to as HOPS, was created, which allows the analyst to configure the data acquisition system and display the results from each damage identification algorithm for side-by-side comparison. By grouping a suite of algorithms into one package, this study contributes to and enhances the visibility and interpretation of the active-sensing methods related to damage identification. This paper will discuss the detailed descriptions of the damage identification techniques employed in this software and outline future issues to realize the full potential of this software.