• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korea institute for structural maintenance and inspection
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• v.1 no.2
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• pp.87-94
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• 1997

This research aims to develop an algorithm of optimal transducer placement using Kinetic Energy of the structural system. The structural vibration response-based health monitoring is considered one of the best for the system which requires a long-term, continuous monitoring. In its experimental modal testing, however, it is difficult to decide on the measurement locations and their number, especially for complex structures, which have a major influence on the quality of the results. In order to minimize the number of sensing operations and optimize the transducer location while maximizing the accuracy of results, this paper discusses about an optimum transducer placement criterion suitable for the identification of structural damage. As a criterion algorithm, it proposes the Kinetic Energy Optimization Technique (EOT), and then addresses the numerical issues which are subsequently applicable to actual experiment where a bridge model is used. By using the experimental data, it compares the EOT with the EIM (Effective Independence Method) which is generally used to optimize the transducer placement for the damage identification and control purposes. The comparison conclusively shows that the EOT algorithm proposed in this paper is preferable when a structure is to be instrumented with fewer sensors.

• Computational Structural Engineering
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• v.10 no.3
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• pp.157-165
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• 1997

This research aims to develop an algorithm of optimal transducer placement for health monitoring of large structural system. The structural vibration response-based health monitoring is considered one of the best for the system which requires a long-term, continuous monitoring. In its experimental modal testing, however, it is difficult to decide on the measurement locations and their number, especially for complex structures, which have a major influence on the quality of the results. In order to minimize the number of sensing operations and optimize the transducer location while maximizing the accuracy of results, this paper discusses about an optimum transducer placement criterion suitable for the identification of structural damage for continuous health monitoring. As a criterion algorithm, it proposes the Kinetic Energy Optimization Technique (EOT), and then addresses the numerical issues which are subsequently applicable to actual experiment where a bridge model is used. By using the experimental data, it compares the EOT with the EIM(Effective Indefence Method) which is generally used to optimize the transducer placement for the damage identification and control purposes. The comparison conclusively shows that the EOT algorithm proposed in this paper is preferable when a structure is to be instrumented with fewer sensors for monitoring purpose.

• Smart Structures and Systems
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• v.15 no.2
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• pp.447-468
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• 2015

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

• Smart Structures and Systems
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• v.17 no.3
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• pp.363-389
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• 2016

Deficient modes that cannot be always identified from different sets of measurement data may exist in the application of operational modal analysis such as the stochastic subspace identification techniques in large-scale civil structures. Based on a recent work using the long-term ambient vibration measurements from an instrumented cable-stayed bridge under different wind excitation conditions, a benchmark problem is launched by taking the same bridge as a test bed to further intensify the exploration of mode identifiability. For systematically assessing this benchmark problem, a recently developed SSI algorithm based on an alternative stabilization diagram and a hierarchical sifting process is extended and applied in this research to investigate several sets of known and blind monitoring data. The evaluation of delicately selected cases clearly distinguishes the effect of traffic excitation on the identifiability of the targeted deficient mode from the effect of wind excitation. An additional upper limit for the vertical acceleration amplitude at deck, mainly induced by the passing traffic, is subsequently suggested to supplement the previously determined lower limit for the wind speed. Careful inspection on the shape vector of the deficient mode under different excitation conditions leads to the postulation that this mode is actually induced by the motion of the central tower. The analysis incorporating the tower measurements solidly verifies this postulation by yielding the prevailing components at the tower locations in the extended mode shape vector. Moreover, it is also confirmed that this mode can be stably identified under all the circumstances with the addition of tower measurements. An important lesson learned from this discovery is that the problem of mode identifiability usually comes from the lack of proper measurements at the right locations.

• Geomechanics and Engineering
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• v.32 no.1
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• pp.111-123
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• 2023

The slope of an open cut tunnel is located above the exit of the Leijia tunnel on the Changgan high-speed railway. During the excavation of the open cut tunnel foundation pit, the slope slipped twice, a large landslide of 92500 m³ formed. The landslide body and unstable slope body not only caused the foundation pit of the open cut tunnel to be buried and the anchor piles to be damaged but also directly threatened the operational safety of the later high-speed railway. Therefore, to study the stability change in the slope of the open cut tunnel under heavy rain and excavation conditions, a 3D numerical calculation model of the slope is carried out by Midas GTS software, the deformation mechanism is analyzed, anti-sliding measures are proposed, and the effectiveness of the anti-sliding measures is analyzed according to the field monitoring results. The results show that when rainfall occurs, rainwater collects in the open cut tunnel area, resulting in a transient saturation zone on the slope on the right side of the open cut tunnel, which reduces the shear strength of the slope soil; the excavation at the slope toe reduces the anti-sliding capacity of the slope toe. Under the combined action of excavation and rainfall, when the soil above the top of the anchor pile is excavated, two potential sliding surfaces are bounded by the top of the excavation area, and the shear outlet is located at the top of the anchor pile. After the excavation of the open cut tunnel, the potential sliding surface is mainly concentrated at the lower part of the downhill area, and the shear outlet moves down to the bottom of the open cut tunnel. Based on the deformation characteristics and the failure mechanism of the landslides, comprehensive control measures, including interim emergency mitigation measures and long-term mitigation measures, are proposed. The field monitoring results further verify the accuracy of the anti-sliding mechanism analysis and the effectiveness of anti-sliding measures.

• International Journal of Contents
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• v.11 no.2
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• pp.37-49
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• 2015

Sensors, equipment, ICT facilities and their corresponding software have a relatively short lifetime relative to that of constructional structure, so these devices have to be continuously fixed or exchanged during maintenance and management. Furthermore, software or analysis tools should be periodically upgraded according to advances in ICT and analysis technology. Conventional monitoring systems have serious problems in that it is difficult for site engineers to modify or upgrade hardware and analysis algorithms. Moreover, we depend on the original system developer when we want to modify or upgrade inner program structures. In this paper, we propose a novel design for integrated maintenance and management of a monitoring system by applying the mobile cloud concept. The system is intended for use in disaster prevention of constructional structures, including bridges, tunnels, and in traditional buildings in a local heritage village, we analyze the status of these structures over a long term or a short-term period as well as in disaster situations. Data are collected over a mobile cloud and future expectations are analyzed according to probabilistic and statistical techniques. We implement our integrated monitoring system to solve the existing problems mentioned above. The final goal of this study is to design and implement a monitoring system for more than 10,000 structures spread within Korea. Furthermore, we can specifically apply the monitoring system presented here to a bridge made from timber in Asan Oeam Village and a traditional house in Andong Hahoe Village to monitor for possible disasters. The entire system design and implementation can be developed on the LinkSaaS platform and the monitoring services can also be implemented on the platform. We prove that the proposed system has good performance by performing a TTA authentication test, web accommodation test, and operation test using emulated data.

• Smart Structures and Systems
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• v.17 no.3
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• pp.471-489
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• 2016

Modal identification based on ambient vibration data has attracted extensive attention in the past few decades. Since the excitation for ambient vibration tests is mainly from the environmental effects such as wind and traffic loading and no artificial excitation is applied, the signal to noise (s/n) ratio of the data acquired plays an important role in mode identifiability. Under ambient vibration conditions, certain modes may not be identifiable due to a low s/n ratio. This paper presents a study on the mode identifiability of an instrumented cable-stayed bridge with the use of acceleration response data measured by a long-term structural health monitoring system. A recently developed fast Bayesian FFT method is utilized to perform output-only modal identification. In addition to identifying the most probable values (MPVs) of modal parameters, the associated posterior uncertainties can be obtained by this method. Likewise, the power spectral density of modal force can be identified, and thus it is possible to obtain the modal s/n ratio. This provides an efficient way to investigate the mode identifiability. Three groups of data are utilized in this study: the first one is 10 data sets including six collected under normal wind conditions and four collected during typhoons; the second one is three data sets with wind speeds of about 7.5 m/s; and the third one is some blind data. The first two groups of data are used to perform ambient modal identification and help to estimate a critical value of the s/n ratio above which the deficient mode is identifiable, while the third group of data is used to perform verification. A couple of fundamental modes are identified, including the ones in the vertical and transverse directions respectively and coupled in both directions. The uncertainty and s/n ratio of the deficient mode are investigated and discussed. A critical value of the modal s/n ratio is suggested to evaluate the mode identifiability of the deficient mode. The work presented in this paper could provide a base for the vibration-based condition assessment in future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.2
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• pp.1-7
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• 2018

Suspension bridge cables made of high strength steel wires require periodical maintenance in accordance with the axial force of cable-band bolts, since the bolts in suspension bridges can undergo tension decrease due to creep of cable wires, bolt relaxation, load fluctuation, and cable re-arrangement, etc. Consequently, this study is aimed at investigating and subsequently evaluating the critical factors with respect to the bolt tension-decrease phenomenon in SR suspension bridge in Korea, based on field monitoring, theoretical studies, and field record management works. From the observation, it is interesting to note that the decrease in the bolt tension force is typically accompanied by plastic deformation of the zinc plating layers in the cable wires. In addition, a framework corresponding to generic methodologies to characterize the deformation in terms of the bolt tension-decrease and long-term history management has been developed in this exploratory study.