Peter Runcie

On Damage Identification in Civil Structures Using Tensor Analysis

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. In structural health monitoring, the data are usually highly redundant and correlated. The measured variables are not only correlated with each other at a certain time but also are autocorrelated themselves over time. Matrix-based two-way analysis, which is usually used in structural health monitoring, can not capture all these relationships and correlations together. Tensor analysis allows us to analyse the vibration data in temporal, spatial and feature modes at the same time. In our approach, we use tensor analysis and one-class support vector machine for damage detection, localization and estimation in an unsupervised manner. The method shows promising results using data from lab-based structures and also data collected from the Sydney Harbour Bridge, one of iconic structures in Australia. We can obtain a damage detection accuracy of 0.98 and higher for all the data. Locations of damage were captured correctly and different levels of damage severity were well estimated.

On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data

Structural health monitoring is a condition-based technology to monitor infrastructure using sensing systems. Since we usually only have data associated with the healthy state of a structure, one-class approaches are more practical. However, tuning the parameters for one-class techniques (like one-class Support Vector Machines) still remains a relatively open and difficult problem. Moreover, in structural health monitoring, data are usually multi-way, highly redundant and correlated, which a matrix-based two-way approach cannot capture all these relationships and correlations together. Tensor analysis allows us to analyse the multi-way vibration data at the same time. In our approach, we propose the use of tensor learning and support vector machines with artificial negative data generated by density estimation techniques for damage detection, localization and estimation in a one-class manner. The artificial negative data can help tuning SVM parameters and calibrating probabilistic outputs, which is not possible to do with one-class SVM. The proposed method shows promising results using data from laboratory-based structures and also with data collected from the Sydney Harbour Bridge, one of the most iconic structures in Australia. The method works better than the one-class approach and the approach without using tensor analysis.

An Update on a Large Scale SHM Deployment on Sydney’s Harbour Bridge and Associated Research Activities

Sydney’s iconic Harbour Bridge was opened to the public in 1932. It is one of the largest steel span bridges in the world. In recent years the bridge owner required a way to remotely monitor structural supports under the road deck. The initial objective was to provide early warning of problems so that preventative maintenance can be carried out without disrupting road users. The business goal was to extend the life of the asset without a significant increase in maintenance expenditure. NICTA1 developed and implemented a large scale structural health monitoring system that uses over 2,400 sensors to monitor approximately 800 structural components. There is ongoing research into condition assessment and predictive analysis. In this paper we discuss the monitoring system, analytical techniques used, the research, development and implementation process and also future directions for the work. This paper is intended to be an update and overview only –other papers from us describe the work in more technical detail. doi: 10.12783/SHM2015/199

Damage Characterization in Concrete Jack Arch Bridges Using Symbolic Time Series Analysis

We present a statistical time series based algorithm to characterize a progressively deteriorating crack in a steel reinforced concrete arch bridge section. The method relies only on the measured time responses. The captured time responses are transformed from the state space into “symbol space”, ultimately to reduce the dimension of the system. Symbol space is constructed according to the measured time responses in the nominal state of the structure; it consists of a finite number of partitions which are mutually exclusive and exhaustive and each partition is assigned with a distinct symbol. By mapping time data from state space into the constructed symbol space, each time series is described by a sequence of symbols according to the placement of each data point of time series in the symbol space. Symbol sequence is statically characterized by probability of occurrence of each symbol in that sequence; therefore, a vector containing the probability of occurrence of all the symbols is formed which defines damage sensitive feature vector. Any change in the structural conditions leads to a deviation in the damage sensitive vector from its nominal state. The feasibility of the method is demonstrated through the detection and localization of a gradually evolving deterioration. A test bed was constructed to replicate a concrete jack arch which is a main structural component on the Sydney Harbor Bridge– one of Australia’s iconic structures. The structure is a concrete cantilever beam with an arch section which is located on the eastern side of the bridge underneath the bus lane. It is assumed that the structure is subjected to Gaussian white noise excitation. A crack is introduced in the structure using a cutting saw and its length is progressively increased in four stages while the depth was constant; these four damage cases correspond to less than 0.5% reduction in the first three modes of the structure. The presented method not only can detect the change in the condition of the structure but also can localize the location of deterioration. The damage identification algorithm developed demonstrated the feasibility of applying symbolic time series analysis to dimensionality reduction and damage characterization in structural health monitoring. doi: 10.12783/SHM2015/284