• Smart Structures and Systems
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• v.2 no.3
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• pp.275-293
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• 2006

For health monitoring purpose usually the structure is instrumented with a large scale and multichannel measurement system. In case of highway bridges, operating vehicle could be utilized to reduce the number of measuring devices. First this paper presents a static damage detection algorithm of using operating vehicle load. The technique has been validated by finite element simulation and simple laboratory test. Next the paper presents an approach of using this technique to field application. Here operating vehicle load data has been used by instrumenting the bridge at single location. This approach gives an upper hand to other sophisticated global damage detection methods since it has the potential of reducing the measuring points and devices. It also avoids the application of artificial loading and interruption of any traffic flow.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.2
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• pp.1-9
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• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.145-155
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• 2002

It is essential for health monitoring of a cable-stayed bridge to provide more accurate and enough information from the sensors. In experimental modal testing, the chosen measurement locations and the number of measurements have a major influence on the quality of the results. The choice is often difficult for complex structures like a cable-stayed bridge. It is extremely important a cable-stayed bridge to minimize the number of sensing operations required to monitor the structural system. In order to obtain the desired accuracy for the structural test, several issues must take into consideration. Two important issues are the number and location of response sensors. There are usually several alternative locations where different sensors can be located. On the other hand, the number of sensors might be limited due to economic constraints. Therefore, techniques such as methodologies, algorithms etc., which address the issue of limited instrumentation and its effects on resolution and accuracy in health monitoring systems are paramount to a damage diagnosis approach. This paper discusses an optimum sensor placement criterion suitable to the identification of structural damage for continuous health monitoring. A Kinetic Energy optimization technique and an Effective Independence Method are analyzed and numerical and theoretical issues are addressed for a cable-stayed bridge. Its application to a cable-stayed bridge is discussed to optimize the sensor placement for identification and control purposes.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.870-874
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• 2004

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by Piezoelectric impedance-based technique associated with longitudinal wave propagation method. The bolt fastening condition is adjusted by torque wrench. In order to estimate the damage condition numerically, three damage indices, impedance peak frequency shift ${\Delta}F$, peak amplitude ratio $\delta$ and quality factor ratio $\gamma$, are proposed in this paper. Furthermore, an assessment method is described for estimation of the damage by using these three damage indices.

• Smart Structures and Systems
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• v.20 no.1
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• pp.43-52
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• 2017

Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types.

• Smart Structures and Systems
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• v.4 no.6
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• pp.795-807
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• 2008

A challenging problem in structural damage detection based on vibration data is the requirement of a large number of sensors and the numerical difficulty in obtaining reasonably accurate results when the system is large. To address this issue, the substructure identification approach may be used. Due to practical limitations, the response data are not available at all degrees of freedom of the structure and the external excitations may not be measured (or available). In this paper, an adaptive damage tracking technique, referred to as the sequential nonlinear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the sub-structure approach are used to identify damages at critical locations (hot spots) of the complex structure. In our approach, only a limited number of response data are needed and the external excitations may not be measured, thus significantly reducing the number of sensors required and the corresponding computational efforts. The accuracy of the proposed approach is illustrated using a long-span truss with finite-element formulation and an 8-story nonlinear base-isolated building. Simulation results demonstrate that the proposed approach is capable of tracking the local structural damages without the global information of the entire structure, and it is suitable for local structural health monitoring.

• Smart Structures and Systems
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• v.17 no.5
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• pp.795-817
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• 2016

A non-contact, in-situ and non-invasive technique for health monitoring of submerged fiber reinforced polymers (FRP) laminates has been developed using ultrasonic guided waves. A pair of mobile transducers at specific angles of incidence to the submerged FRP specimen was used to excite Lamb wave modes. Lamb wave modes were used for comprehensive inspection of various types of manufacturing defects like air gaps and missing epoxy, introduced during manufacturing of FRP using Vacuum Assisted Resin Infusion Molding (VARIM). Further service induced damages like notches and surface defects were also studied and evaluated using guided waves. Quantitative evaluation of transmitted ultrasonic signal in defect ridden FRPs $vis-{\grave{a}}-vis$ healthy signal has been used to relate the extent of damage in FRPs. The developed technique has the potential to develop into a quick, real time health monitoring tool for judging the service worthiness of FRPs.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.92-98
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• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• 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.