Detection of Anomalous Structural Behaviour Using Wavelet Analysis

Moyo, Pilate; Brownjohn, James

doi:10.1006/mssp.2001.1449

Cited by 70 publications

(41 citation statements)

References 11 publications

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“…Data-driven methods are typically based on statistical or signal processing techniques such as moving principal component analysis [14] and wavelet transforms [15]. Existing data-driven approaches focus on the analysis of response measurements and ignore distributed temperature measurements.…”

Section: Introductionmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

102

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This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures -a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.

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Section: Introductionmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

102

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“…As well as providing information long term creep and short term linear stress-strain relationships, the data have been used [23,24] for developing procedures for detecting performance anomalies. In particular, the recording during the construction process provided valu-able information on early-life strain development and reference characteristics for events such as post-tensioning and concrete pouring.…”

Section: Tuas Second Link: Long Term Performance Monitoringmentioning

confidence: 99%

Lessons from monitoring the performance of highway bridges

Brownjohn

Moyo

Omenzetter

et al. 2005

Struct. Control Health Monit.

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Monitoring programs on four very different highway bridges originating from a range of requirements related to calibration of numerical models, assessment of load capacity and long term tracking of performance are summarized in order to draw out lessons relevant to the future development of structural health monitoring 'systems'. These lessons concern validation of structural models, appropriate methods for instrumentation, communication, data management and system identification. The paper presents experience obtained by collaboration in a form intended to educate, by example, bridge operators about potential and limitations of SHM systems.

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“…Moyo and Brownjohn [11] applied the wavelet analysis and Omenzetter and Brownjohn [12] used Auto-Regressive Integrated Moving Average (ARIMA) models to identify events and changes in the structural state in a bridge. Lanata and Del Grosso [13] applied the Proper Orthogonal Decomposition (POD) to identify in time and locate in space the initiation of damage.…”

Section: Introductionmentioning

confidence: 99%

Damage detection using data-driven methods applied to moving-load responses

Cavadas

Smith

Figueiras

2013

Mechanical Systems and Signal Processing

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Developed economies depend on complex and extensive systems of infrastructure to maintain economic prosperity and quality of life. In recent years, the implementation of Structural Health Monitoring (SHM) systems on full-scale bridges has increased. The goal of these systems is to inform owners of the condition of structures, thereby supporting surveillance, maintenance and other management tasks.Data-driven methods, that involve tracking changes in signals only, are well-suited for analyzing measurements during continuous monitoring of structures. Also, information provided by the response of structures under moving loads is useful for assessment of condition. This paper discusses the application of data-driven methods on moving-load responses in order to detect the occurrence and the location of damage. First, an approach for using moving-load responses as time series data is proposed. The work focuses on two data-driven methodsMoving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) -that have already been successful for damage detection during continuous monitoring. The performance of each method is assessed using data obtained by simulating the crossing of a point-load on a simple frame.

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Detection of Anomalous Structural Behaviour Using Wavelet Analysis

Cited by 70 publications

References 11 publications

Predicting thermal response of bridges using regression models derived from measurement histories

Predicting thermal response of bridges using regression models derived from measurement histories

Lessons from monitoring the performance of highway bridges

Damage detection using data-driven methods applied to moving-load responses

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