Damage Detection in Civil Engineering Structure Considering Temperature Effect

Abstract

This paper concerns damage identification of a bridge located in Luxembourg. Vibration responses were captured from measurable and adjustable harmonic swept sine excitation and hammer impact. Different analysis methods were applied to the data measured from the structure showing interesting results. However, some difficulties arise, especially due to environmental influences (temperature and soil-behaviour variations) which overlay the structural changes caused by damage. These environmental effects are investigated in detail in this work. First, the modal parameters are identified from the response data. In the next step, they are statistically collected and processed through Principal Component Analysis (PCA) and Kernel PCA. Damage indexes are based on outlier analysis.

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

“…The kernel function describes the distribution of the data using Gaussian or extreme value distribution parameters. The successful application of KPCA was carried out by Nguyen et al [113], who used a Gaussian kernel on modal parameters obtained from a progressive damage test conducted on the Champangshiehl Bridge in Luxembourg under varying environmental and soil effects. Oh et al [114] implemented KPCA on recorded hanger tensions from the in-service Yeongjong Grand Bridge in Seoul, using a Gaussian kernel to characterise the nonlinear relationship between the hanger tensions and the unmeasured environmental and operational conditions, prior to using extreme value statistics for successful abnormality identification.…”

“…The utilisation of dimensionality reducing methodologies such as PCA and FA produce reliable results in linear investigations (Yan et al [103] and Deraemaeker et al [109], respectively) and provide a versatile platform for nonlinear applications (Lämsä & Raiko's [110] non-linear FA application on the Z24 bridge, Nguyen et al [113]'s KPCA application on the Champangshiehl Bridge). However, the careful selection of data distribution parameters and number of extracted principle components/common factors is required for successful applications.…”

Structural Health Monitoring (SHM) of Civil Structures

“…Research in the use of pattern recognition algorithms for the mitigation of environmental and operational effects has produced positive results, without the need to measure such variables. The utilisation of dimensionality reducing methodologies such as PCA and FA produce reliable results in linear investigations (Yan et al [103] and Deraemaeker et al [109], respectively) and provide a versatile platform for nonlinear applications (Lämsä & Raiko's [110] non-linear FA application on the Z24 bridge, Nguyen et al [113]'s KPCA application on the Champangshiehl Bridge). However, the careful selection of data distribution parameters and number of extracted principle components/common factors is required for successful applications.…”

Comparison of different excitation‐ and data sampling‐methods in structural health monitoring

“…Modal features like eigenfrequencies are rather dependent on the temperature distribution T ( x,y,z,t ) of a bridge (also variations up to 15%), which has to be measured in the healthy reference state, thus enabling later temperature compensation . This correction of measured data is mandatory and can be done in a physical or statistical manner, for example, based on principal component analysis (PCA) . While many works removed temperature effects from eigenfrequencies, modeshapes were studied for the localization in Reference only from numerical examples.…”