• Structural Monitoring and Maintenance
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• v.11 no.2
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• pp.149-171
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• 2024

Drive-by monitoring (also known as indirect monitoring or mobile sensing) of bridges has obvious advantages when compared to other approaches of Structural Health Monitoring. The underlying concept involves leveraging the coupling between the vertical vibrations of the bridge and those generated in the passing vehicle. In this scenario, the vehicle serves as both the initiator and recipient of the vibrations, which can provide information on the structural condition of the bridge. In the literature, a wide range of methods has been proposed, primarily focused on highway bridges. However, limited research has been published to assess the suitability of indirect methods for monitoring railway bridges, bounded to numerical studies based on theoretical simulations and, rarely, on experimental investigations. The aim of this work is to contribute to filling this gap and explore the feasibility of implementing drive-by monitoring for railway bridges using in-service vehicles and discuss its potential applicability, from theoretical and practical point of view, with illustration through real case studies from the Moroccan railway network.

• Smart Structures and Systems
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• v.5 no.4
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• pp.381-395
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• 2009

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.91-114
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• 2016

In this paper, recent research trends and activities on structural health monitoring (SHM) of civil infrastructure in Korea are reviewed. Recently, there has been increasing need for adopting smart sensing technologies to SHM, so this review focuses on smart sensing, monitoring, and assessment for civil infrastructure. Firstly, the research activities on smart sensor technology is reviewed including optical fiber sensors, piezoelectric sensors, wireless smart sensors, and vision-based sensing system. Then, a brief overview is given to the recent advances in smart monitoring and assessment techniques such as vibration-based global monitoring techniques, local monitoring with piezoelectric materials, decentralized monitoring techniques for wireless sensors, wireless power supply and energy harvest. Finally, recent joint SHM activities on several test beds in Korea are discussed to share the up-to-date information and to promote the smart sensors and monitoring technologies for applications to civil infrastructure. It includes a Korea-US joint research on test bridges of the Korea Expressway Corporation (KEC), a Korea-US-Japan joint research on Jindo cable-stayed bridge, and a comparative study for cable tension measurement techniques on Hwamyung cable-stayed bridge, and a campaign test for displacement measurement techniques on Sorok suspension bridge.

• Corrosion Science and Technology
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• v.3 no.2
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• pp.39-46
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• 2004

Development and application of structural health monitoring system in Korea have become active since the early 1990's. In earlier applications, health monitoring systems were installed in several existing bridges in order to collect initial field data by full scale load capacity test for design verification and subsequently monitor long-term performance and durability of the bridge as part of an inspection and maintenance program. Recently, modem and integrated monitoring systems have been introduced in most of the newly constructed long-span bridges since the design stage. This paper outlines the progresses and applications of monitoring systems in Korea for both existing and newly constructed bridges and describes their aims and characteristics.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Smart Structures and Systems
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• v.17 no.1
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.357-382
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• 2015

This paper provides an overview of the use of different Distributed Optical Fiber Sensor systems (DOFSs) to perform Structural Health Monitoring (SHM) in the specific case of civil engineering structures. Nowadays, there are several methods available for extracting distributed measurements from optical fiber, and their use have to be according with the aims of the SHM performance. The continuous-in-space data is the common advantage of the different DOFSs over other conventional health monitoring systems and, depending on the particular characteristics of each DOFS, a global and/or local health structural evaluation is possible with different accuracy. Firstly, the fundamentals of different DOFSs and their principal advantages and disadvantages are presented. Then, laboratory and field tests using different DOFSs systems to measure strain in structural elements and civil structures are presented and discussed. Finally, based on the current applications, conclusions and future trends of DOFSs in SHM in civil structures are proposed.

• 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.

• Smart Structures and Systems
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• v.11 no.5
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• pp.511-531
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• 2013

Recent advances in low-cost remote monitoring systems have made it possible and practical to perform structural health monitoring (SHM) on a large scale. However, it is difficult for a single remote monitoring system to cover a wide range of SHM applications due to the amount of specialization required. For the remote monitoring system to be flexible, sustainable, and robust, this article introduces a new cost-effective, advanced remote monitoring and inspection system named DuraMote that can serve as a next generation supervisory control and data acquisition (SCADA) system for civil infrastructure systems. To evaluate the performance of DuraMote, we conduct experiments at two representative counterpart sites: a bridge and water pipelines. The objectives of this article are to improve upon the existing SCADA by integrating the remote monitoring system (i.e., DuraMote), to describe a prototype SCADA for civil engineering structures, and to validate its effectiveness with long-term field deployment results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.458-464
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• 2008

The structural health monitoring has been gaining more importance in civil engineering areas such as earthquake and wind engineering. The use of health monitoring system can also provide tools for the validation of structural analytical model. However, only few structures such as historical buildings and some important long bridges have been instrumented with structural monitoring system due to high cost of installation, long and complicated installation of system wires. In this paper, the structural monitoring system based on cheap and wireless monitoring system is investigated. The use of advanced technology of micro-electro-mechanical system(MEMS) and wireless communication can reduce system cost and simplify the installation. Further the application of wireless MEMS system can provide enhanced system functionality and due to low noise densities. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS system estimates system parameters accurately.