Keywords: evolutional image processing, pattern recognition, concrete structure, crack, integrity assessment, bridge management.

At present, public infrastructure such as bridges and roads can be seen all around. The continuous construction of such infrastructure is posing an important problem of how to maintain and efficiently manage an existing structure. It does not make much sense economically to demolish and rebuild all such structures for the mere reason that their damage is progressing. Therefore, it is necessary to develop a system that enables us to make correct judgements on which structures should be rebuilt and which ones should be repaired. While such judgements would not be difficult if structures present visually observable, apparent damage, it would be quite difficult if they contained damage inside, showing no trace of it on the outside.

To make proper judgements on complex damage levels, it is necessary to correlate visually observable damage with various test-results for assessing damage levels as well as expert knowledge. However, because available data for the assessment of integrity is not sufficient both in quality and in quantity, we have to utilize the insufficient data from various aspects that we have and estimate the degree and the advancement of damage based on intuition and experience. Thus, integrity assessment has been dependant upon intuition and the judgements of experts. On the other hand, with the increasing need for maintenance and repairs of these structures, the shortage of experts is growing. So, it is necessary to build immediately a system that supports an efficient maintenance plan.

In this paper, a system which extracts the image of cracks from the brightness intensity information on an input image was developed by performing the evolutional image processing for the input image shown up on the concrete structures. Evolutional image processing is proposed in order to bring an input digital image close to the target image, and the process order of some existing filters are dealt with as an optimization problem. In this study, we could find the semi-optimal parameter for the evolutional image processing, and recognize the correct image of the cracks in almost same position with the input image. Moreover, in the image of cracks after some filtering processing, the parts recognized as the crack (black pixels) for the parts that seem not cracked were fewer than the binary image after the binary-coding processing. But, if the suitable value for the parameter of some filters is not set, the image of cracks after some filtering processing may not be obtained correctly.

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