TxDOT Research Library - Publication Details

Show:

Title:

ECR Bridge Decks. Part 1, Damage Detection and Assessment of Remaining Service Life for Various Overlay Repair Options

Report No.:

RC-1549 Pt. 1

Authors:

by Ronald S. Harichandran and Gang Zhang

Published:

2011

Michigan State University. Department of Civil and Environmental Engineering

Type:

Online document

1 PDF (175 pages)

Online Access:

https://mdotjboss.state.mi.us/SpecProv/getDocument...

Access Note:

3.4 MB

Supplement:

Research Spotlight: https://mdotjboss.state.mi.us/SpecProv/getDocumentById.htm?docGuid=efc4a34e-6453-4c8b-b8d6-7ebfa16e0f3a

Summary

Delamination of the concrete cover above upper reinforcing bars is a common problem in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a bar or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The acoustic signals are often contaminated by traffic and ambient noise at the site and the detection is highly subjective. The operator also needs to be well trained. The performance of acoustic NDE methods can be improved by employing a suitable noise-cancelling algorithm and a reliable detection algorithm that eliminates subjectivity. Since the noise is non-stationary and unpredictable, the algorithms should be adaptive. After evaluating different noise cancelling algorithms based on a numerical performance criterion and through visual inspection, a noise cancelling algorithm using a modified independent component analysis (ICA) is used to separate the sounding signals from recordings in a noisy environment. Different feature extraction algorithms were used to extract features of the filtered signals and their performance was evaluated using repeatability, separability and mutual information measures. Mel-frequency cepstral coefficients (MFCC) were identified as the best features for detection. The extracted features were further reduced based on the mutual information value to reduce the negative effect of features with poor separability. The selected features were used to train classifiers and the trained classifiers were used to classify new signals. The error rate was used to evaluate the performance of different classifiers. Radial basis function neural network had the lowest error rate and was selected as the classifier for field applications.

The proposed noise-cancelling and delamination detection algorithms were implemented into a seamless software containing MATLAB, LabVIEW and C/C++ modules. The performance of the system was verified using both experimental and field data. The proposed system showed good noise robustness. The performance of the system was satisfactory if there is sufficient available data for training and the selection of the training data is representative.

Study Sponsor

Print Copy

Collection:

States (MI) Online Only

Call Number:

RC-1502 pt.1

More Like This

Topics

Acoustic detectors
Acoustic signal processing
Algorithms
Bridge decks
Corrosion
Delamination
Epoxy coatings
Noise (Communications)
Nondestructive tests
Reinforcing bars
Service life
Software

Contributed Tags