S. Harichandran, Ph.D., P.E., F.ASCE
2006 - December 2009
major form of bridge deck deterioration is the surficial
scaling damage and corrosion-induced delamination
of the concrete cover above the top layer of steel
reinforcement. Several techniques are currently available
to detect delaminations such as sounding, impact-echo,
ultrasonic pulse velocity, infrared thermography and
ground-penetrating radar. The sounding test has the
advantage of being fast, simple and inexpensive and
therefore has been widely used by field engineers.
The traditional methods for delamination detection
involve: (1) bar/hammer tapping of the deck and listening
to the acoustic response and (2) dragging a chain
over the deck and listening to the change in the sound.
The delamination is characterized by a dull, hollow
sound. However there are two problems associated with
the current approaches. First, the traffic noise from
adjacent lanes may contaminate the sound signals and
make it hard to detect the delamination. Second, the
detection is performed by listening to the sound,
which is highly subjective and requires extensive
training. The goal of this research is to develop
a refined delamination detection system with improved
major issues need to be addressed to accomplish the
goal. First, an efficient algorithm is needed to eliminate
the traffic noise from the recordings. Second, an
objective delamination detection algorithm is needed
to differentiate between the solid concrete and the
delaminated concrete. To solve the first problem,
a modified independent component analysis (ICA) is
used to separate the traffic noise and the acoustic
signals. For the second problem, different features
of the acoustic signals were compared and mel-frequency
cepstral coffeicients (MFCC) were found to be effective
features. Detection based on MFCC has good performance
and can approximate human hearing. A mutual information
based method was used to select the optimal MFCC for
delamination detection. The selected features were
used to train a classifier which was then used to
classify new recordings.
performance of the modified ICA is shown in Figure
1. The original signal in Fig. 1(a) was mixed with
the noise signal in Fig. 1(b) to create the two simulated
recordings shown in Fig. 1 (c) and (d). The modified
ICA was then used to obtain the recovered signal shown
in Fig. 1(e). As can be seen, the original signal
was successfully recovered from the noisy recordings.
Table 1 compares the performance under different conditions.
The error rate is the proportion of times signals
were misclassified. Using the raw recordings, the
detection using MFCC is very good under quiet conditions,
but becomes poor with increasing noise levels. However,
if the recordings are first filtered using the modified
ICA, then the detection algorithm performs very well
even at high noise levels.
1 Performance of the modified ICA
1. Performance of the Detection Algorithm
Error Rate (%)
Low Noise Level
Moderate Noise Level
High Noise Level
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