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Long-term monitoring and data analysis of the Tamar Bridge

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TLDR
In this paper, an extensive monitoring campaign of the Tamar Suspension Bridge as well as analysis carried out in an attempt to understand the bridge's normal condition are investigated. And the initial steps towards the development of a structural health monitoring system for the TAMAR Bridge are addressed.
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This article is published in Mechanical Systems and Signal Processing.The article was published on 2013-02-01 and is currently open access. It has received 303 citations till now. The article focuses on the topics: Structural health monitoring.

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Citations
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A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges

TL;DR: A new multi-level3D data registration and spatial change classification approach that automate the analysis of both element-level deformations and interactions between the motions of multiple elements based on deviations calculated between two 3D data sets is presented.
Journal ArticleDOI

A Feature Extraction & Selection Benchmark for Structural Health Monitoring

TL;DR: Results obtained show that reduced sets of univariate features, extracted from a single accelerometer sensor, are capable of accurately distinguishing between multiple classes of healthy and damaged states.
Journal ArticleDOI

Application of machine learning methods on real bridge monitoring data

TL;DR: In this paper, the non-linear or rather transient relationship between the air temperature and the bridge temperature is simulated by machine learning (ML) models and different use cases for the application of machine learning regression methods to monitoring data are presented.
Journal ArticleDOI

Application of machine learning methods on real bridge monitoring data

TL;DR: In this paper , the non-linear or rather transient relationship between the air temperature and the bridge temperature is simulated by machine learning (ML) models and different use cases for the application of machine learning regression methods to monitoring data are presented.
Journal ArticleDOI

A dynamic harmonic regression approach for bridge structural health monitoring

TL;DR: The proposed statistical damage-detection methodology can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.
References
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Book

Applied Regression Analysis and Other Multivariable Methods

TL;DR: In this article, the authors compare two straight line regression models and conclude that the Straight Line Regression Equation does not measure the strength of the Straight-line Relationship, but instead is a measure of the relationship between two straight lines.
Book

Empirical Model-Building and Response Surfaces

TL;DR: In this article, the authors present a Second-Order Response Surface Methodology (SRSM) for response surface design, which is based on Maxima and Ridge systems with second-order response surfaces.
Journal ArticleDOI

Empirical Model-Building and Response Surfaces

Eddie Shoesmith
- 01 Mar 1988 - 
TL;DR: This work discusses the use of Graduating Functions, design Aspects of Variance, Bias, and Lack of Fit, and Practical Choice of a Response Surface Design in relation to Second--Order Response Surfaces.
Book ChapterDOI

On the Experimental Attainment of Optimum Conditions

TL;DR: The work described in this article is the result of a study extending over the past few years by a chemist and a statistician, which has come about mainly in answer to problems of determining optimum conditions in chemical investigations, but they believe that the methods will be of value in other fields where experimentation is sequential and the error fairly small.
Book

Applied multivariate techniques

TL;DR: In this article, applied multivariate techniques were applied to the problem of applied multiivariate techniques, and the results showed that the proposed approach was more effective than the traditional multivariate technique.
Related Papers (5)
Frequently Asked Questions (14)
Q1. What contributions have the authors mentioned in the paper "Long-term monitoring and data analysis of the tamar bridge" ?

The current paper outlines the extensive monitoring campaign of the Tamar suspension bridge as well as analysis carried out in the attempt to understand the bridge ’ s normal condition. 

One of the main advantages of polynomial response surface models is their simplicity; they are easily fitted using least-squares methods, and they are very easy to interpret, as coefficient values can indicate the significance of a parameter (as long as input variables are normalised prior to use). 

The first is a Structural Monitoring System (SMS) installed by Fugro Structural Monitoring, which is used to monitor cable loads, structural and environmental temperatures and wind speed and profile. 

It was found that a simple response surface model with input parameters based on the estimated traffic loading, temperature and deck acceleration (in turn dependent on the wind speed and direction) can predict the change in the first modal frequency to a good degree of accuracy. 

The response of a long span bridge to high and low wind speeds was investigated in [6], where it was concluded that the modal frequencies decreased with increased response amplitude levels directly caused by increased wind speed. 

For the frequency that appears most sensitive to temperature (the second, which corresponds to the first lateral symmetric mode), the frequency decreases by approximately 4.5% over a 20°C change in temperature. 

If the simple models used above in an attempt to better understand the bridge’s normal condition are capable of predicting the modal frequency change to a good and most importantly consistent degree, their prediction errors would be a good candidate for a damage indicator that is not affected by environmental and operational conditions. 

Two feature parameters for each of the vertical and horizontal deck acceleration measurements are included; one where only acceleration values occurring when high wind speeds hitting the deck side on are recorded (zero at all other times), the other for acceleration values occurring in all other wind conditions. 

Alternative approaches such as neural networks and support-vector machines have previously been explored in the literature for similar problems [12,13]. 

Eighteen new locked-coil cables were installed and stressed to supplement the original suspension system, primarily to help carry the additional dead load of the new cantilever lanes and associated temporary works (Figure 2). 

Over short time periods (as illustrated in the inset figures) the addition of a temperature variable has no visible effect, however, the general fit to all recorded data (main figures) appears to be improved, which suggests that the temperature has more of a seasonal influence than daily, for this mode at least. 

This was due, as explained previously, to the fact that large drops occur in the time history of the second modal frequency that the model cannot recreate, which are thought to be caused by traffic patterns. 

As the bridge is orientated east-west, the increasing response with increased wind speed occurs, not surprisingly, when the wind hits the bridge side on. 

From toll counts and web cam images, the instantaneous traffic loading on the bridge is estimated to increase by between 100 to 200 tonnes during very busy periods, which occur around 8am on weekdays.