• Smart Structures and Systems
• /
• 제13권5호
• /
• pp.849-868
• /
• 2014

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

• Smart Structures and Systems
• /
• 제20권2호
• /
• pp.139-150
• /
• 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.

• Smart Structures and Systems
• /
• 제24권5호
• /
• pp.617-630
• /
• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Smart Structures and Systems
• /
• 제25권3호
• /
• pp.265-277
• /
• 2020

Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile.

• 한국구조물진단유지관리공학회 논문집
• /
• 제11권2호
• /
• pp.134-144
• /
• 2007

본 연구에서는 구조물의 모니터링 시스템을 위하여 최근에 급속하게 발전하는 스마트 센서 기술을 이용하여 스마트 센서 장치를 개발하였고 다양한 실험을 통하여 개발한 스마트 센서의 기본적 성능 평가와 모형 구조물을 이용한 손상 검출 실험을 실시하였다. 본 논문은 Part 1로써 스마트 센서의 개발과 성능 평가에 관한 것이고 Part 2에서는 스마트 센서를 이용한 손상 검출 결과를 유선 계측 시스템을 이용한 실험결과와 비교하였다. 스마트 센서는 고 출력의 무선 모뎀과 고 성능 MEMS 센서, AVR 마이크로컨트롤러를 이용하여 개발하였으며 센서의 제어와 운영을 위한 임베디드 프로그램을 개발하였다. 스마트 센서의 성능을 검증하기 위하여 민감도와 분해능 분석 실험과 캔틸레버 보와 가진기를 이용한 데이터 획득 실험, 실 구조물을 이용한 현장 적용 실험을 실시하였다. 실험 결과, 개발한 스마트 센서의 성능에 대한 만족스런 결과를 얻었다.

• Smart Structures and Systems
• /
• 제19권5호
• /
• pp.523-538
• /
• 2017

A series of field vibration tests are conducted on the Runyang Suspension Bridge during both the construction and operational stages. The purpose of this study is devoted to the analysis of the dynamic characteristics of the suspension tower. After the tower was erected, an array of accelerometers was deployed to study the evolution of its modal parameters during the construction process. Dynamic tests were first performed under the freestanding tower condition and then under the tower-cable condition after the superstructure was installed. Based on the identified modal parameters, the effect of the pile-soil-structure interaction on dynamic characteristics of the suspension tower is investigated. Moreover, the stiffness of the pile foundation is successfully identified using a probabilistic finite model updating method. Furthermore, challenges of identifying the dynamic properties of the tower from the coupled responses of the tower-cable system are discussed in detail. It's found that compared with the identified results from the freestanding tower, the longitudinal and torsional natural frequencies of the tower in the tower-cable system have changed significantly, while the lateral mode frequencies change slightly. The identified modal results from measurements by the structural health monitoring system further confirmed that the vibrations of the bridge subsystems (i.e., the tower, the suspended deck and the main cable) are strongly coupled with one another.

• Journal of Construction Engineering and Project Management
• /
• 제1권2호
• /
• pp.1-10
• /
• 2011

Bridges are critical and valuable components in any road and rail transportation network. Therefore bridge remediation has always been a top priority for asset managers and engineers, but identifying the nature of true defect deterioration and associated remediation treatments remains a complex task. Nowadays Decision Support Systems (DSS) are widely used to assist decision makers across an extensive spectrum of unstructured decision environments. The main objective of this research is to develop a requirements-driven methodology for bridge monitoring and maintenance which has the ability to assess the bridge condition and find the best remediation treatments using Simple Multi Attribute Rating Technique (SMART); with the aim of maintaining a bridge within acceptable limits of safety, serviceability and sustainability.

• Smart Structures and Systems
• /
• 제6권5_6호
• /
• pp.711-730
• /
• 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.

• Smart Structures and Systems
• /
• 제24권6호
• /
• pp.723-732
• /
• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Smart Structures and Systems
• /
• 제11권4호
• /
• pp.365-386
• /
• 2013

Monitoring systems currently applied to concrete bridges include strain gauges, inclinometers, accelerometers and displacement transducers. In general, vertical displacements are one of the parameters that more often need to be assessed because their information reflects the overall response of the bridge span. However, the implementation of systems to continuously and directly observe vertical displacements is known to be difficult. On the other hand, strain gauges and inclinometers are easier to install, but their measurements provide no more than indirect information regarding the bridge deflection. In this context, taking advantage of the information collected through strain gauges and inclinometers, and the processing capabilities of current computers, a procedure to evaluate bridge girder deflections based on polynomial functions is presented. The procedure has been implemented in an existing software system - MENSUSMONITOR -, improving the flexibility in the data handling and enabling faster data processing by means of real time visualization capabilities. Benefiting from these features, a comprehensive analysis aiming at assessing the suitability of polynomial functions as an approximate solution for deflection curves, is presented. The effect of boundary conditions and the influence of the order of the polynomial functions on the accuracy of results are discussed. Some recommendations for further instrumentation plans are provided based on the results of the present analysis. This work is supported throughout by monitoring data collected from a laboratory beam model and two full-scale bridges.