As existing infrastructure systems are aged and deteriorated rapidly, state agencies started searching for more advanced ways to maintain their valuable assets to the acceptable level. One of them is the application of digital image processing. Recently, in the civil engineering domain, digital image processing methods have been developed to the areas of pavement conditions, underground pipeline inspection, and steel bridge coating assessment. The main reasons to count on the advanced technology are due to such advantages as accuracy, objectivity, speed, and consistency. These distinct advantages have brought attention to state agencies to minimize the shortcomings of existing inspection practices. This paper deals with a digital image processing method to apply it to the evaluation of steel bridge coating conditions. Infrastructure condition assessment can be made more accurately and quickly with the aid of computerized processing system. The proposed method in this paper was designed to recognize the existence of bridge coating rust defects. It was developed by making pair-wise comparisons between a defective group and a non-defective group and generating eigenvalues to separate two groups. An automated defect recognition method can make a decision whether a given digitized image contains defects.
As existing infrastructure systems are aged and deteriorated rapidly, state agencies started searching for more advanced ways to maintain their valuable assets to the acceptable level. One of them is the application of digital image processing. Recently, in the civil engineering domain, digital image processing methods have been developed to the areas of pavement conditions, underground pipeline inspection, and steel bridge coating assessment (H. D. Cheng et al. 1999, S. K. Sinha et al. 2003, S. Lee et al. 2005). The main reasons to count on the advanced technology are due to such advantages as accuracy, objectivity, speed, and consistency. These distinct advantages have brought attention to state agencies to minimize the shortcomings of existing inspection practices. The conditions of steel bridge painting surfaces can be evaluated accurately and quickly by applying digital image processing. Also, machine vision-dependent inspections can provide more consistent inspection results than human visual inspections. Because conventional inspection heavily relies on individual abilities, inspection results are errorprone and may have wide variations between inspectors. The results can be different depending on personal preferences, work experiences, and the workload of the inspectors. It is pretty important to develop reliable infrastructure condition assessment for better maintenance of the assets. In case of bridge coating, bridge managers can more realistically develop long-term cost-effective maintenance programs if they have dependable coating condition data. Also, they can make decisions as to whether a bridge shall be painted again immediately or later. Efficient coating condition assessment is also essential for the successful implementation of steel bridge coating warranty contracting. Under the warranty contracting, an owner and a contractor inspect steel bridge coating conditions on a regular basis and decide whether additional maintenance actions are needed. However, it is extremely difficult to determine if a bridge contains more defects than an allowable level. If they are in conflict, they will go through a lengthy process to reach an agreement.
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