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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.
Introduction
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.
This paper concerns with rust defects on highway steel bridges. Rust defects are one of the most commonly
observed defects on coating surfaces and are to be taken care of appropriately since they can severely affect the
structural integrity of bridges and generate unpleasant appearance to passing drivers. A rust defect assessment method needs to be developed to maintain good quality steel bridge painting. For more objective rust defect
recognition, digital image recognition methods have been developed for the past few years and they are expected to
replace or complement conventional painting inspection methods. This paper proposes a digital image processing
method to assess a steel bridge coating surface. The image processing method was developed based on eigenvalues
and can be used to recognizing the existence of bridge coating rust defects. An automated defect recognition method
can make a decision whether a given digitized image contains defects. The next part shows how much deteriorated
infrastructure systems are currently while focusing on bridges, followed by a step-by-step procedure for a system
development.
Deteriorated Bridge Infrastructure Conditions
The report cards published by American Society of Civil Engineers (ASCE) are important indicators to understand
current conditions of major civil infrastructure systems in this country. A wide range of civil facilities are included
for evaluation such as bridges, roads, dams, schools, transit, energy, and so on, and graded on an A to F grading
scale (ASCE report card 2009). Lots of leading civil engineers are involved in preparing the report card and the
analysis of reports, studies, and other sources are performed. Unfortunately, the overall American infrastructure
received a failing grade since the beginning of the studies. The overall grades in 1998, 2001, 2005, and 2009 are D,
D+, D, and D, respectively. Also, the report cards indicate the estimated investment needs for the next 5 years to
recover infrastructure systems to the acceptable level. The dollar amount needed has been increased. The 2001
ASCE study indicated that the estimated cost for infrastructure renewal was $1.3 trillion dollars, $260 billion
annually. But, the 2005 study addressed that the renewal cost was $1.6 trillion, and the 2009 study recorded the
highest point, $2.2 trillion dollars. As existing infrastructure systems are aged rapidly, more and more investment
funding becomes necessary to eliminate deficiencies. However, the available funding amount is much less than
required and is typically limited. Thus, it is very important to set up an efficient management plan on how to
consume limited resources each year. In case of bridges, the GPAs in 1998, 2001, 2005, and 2009 are C-, C, C, and
C, respectively (see Figure 1). The points are a little bit higher than the other infrastructure facilities, but still are not
satisfactory.
NACE International (2009) proved that the corrosion of metallic structures has made significant impact to every
industrial sector in this country. The study to estimate the total economic impact of metallic corrosion in the United
States was performed from 1999 to 2001 by CC Technologies Laboratories, Inc. with support from the Federal
Highway Administration (FHWA) and National Association of Corrosion Engineers (NACE). The results of the
study showed that the total annual direct cost of corrosion in this country is estimated to be $276 billion that is
equivalent to 3.1% of the nation’s Gross Domestic Product (GDP). This number did not consider indirect or user
costs which are incurred by owners and operators of structures, manufacturers of products, and suppliers of services.
Indirect costs include such factor as lost productivity due to outages, delays, failures, and litigation. The study
roughly estimated the indirect cost to be equal to the direct cost. Then, the total amount caused from corrosion
becomes $552 billion, representing 6% of the GDP. The study divides the U.S. economy into five major sector
categories to analyze corrosion direct cost: infrastructure, utilities, transportation, production and manufacturing,
and government. The biggest portion comes from utilities that accounted for 34.7% of the total direct cost, and
transportation is the second largest category, 21.5%. Infrastructure takes the third place, 16.4%. Under the category
of infrastructure, there are four subcategories: highway bridges, hazardous materials storage, gas and liquid
transmission pipelines, and waterways and ports. Among these four, highway bridges take the first place and annual
direct cost is estimated as $8.3 billion to replace deficient bridges, repair concrete bridge decks and substructures,
and maintain bridge painting. The study concluded that corrosion is naturally occurring phenomenon commonly
found in the metal-based structures and is continuously developed by the reaction with environment. But, it is
controllable and preventable by inventing corrosion-resistive materials and improving corrosion maintenance
practices. The study suggested that the U.S. must find ways to implement better corrosion practices and effectively
manage existing corroded structures.
Development of Automated Defect Recognition Method
The methodology for the development of a defect recognition method can be classified into three stages: (1) image
acquisition, (2) image processing, and (3) data analysis. The detailed description of each stage is given as follows.
Image Acquisition
In the image acquisition stage, steel bridge coating images have to be taken first. Every digital image was acquired
by visiting highway steel bridges on the Interstate Highway 65 in Indiana. The color of coating was blue that is one
of the most commonly used painting colors. During the data acquisition with a digital camera, bridge coating images
were taken at a distance of around 3 feet (0.92 m) from the steel beam surfaces to acquire clear coating images.
From the acquired digital images, image data set were prepared for further analysis. Two kinds of testing sets were
created: a defective group and a non-defective group. Digital images in the non-defective group contained no rust
defects observed.
Conclusions and Limitations
This paper presented a novel approach to recognize the existence of bridge coating rust defects by utilizing a digital
image processing to better assess a bridge coating surface. The image defect recognition method was developed by
making pair-wise comparisons and calculating eigenvalues which were chosen as a key feature to distinguish
defective images from non-defective images.
The rust defect recognition method was realized by taking the following three stages: image acquisition, image
processing, and data analysis. In the image acquisition stage, bridge painting digital images were acquired and
prepared to generate two types of data sets: defective and non-defective. In the image processing stage, a pair-wise
comparison was performed to generate eigenvalues. The first comparison was performed between two different nondefective
images where total 105 data points were generated. And, the next comparison was carried out between a
defect image and a non-defective image where total 225 data points were generated. Large and small eigenvalues
were generated and distributed on a two-dimensional distribution map. Also, five statistical values were calculated
and presented in tables. The results from this experimental study were summarized in details in the above discussion
section. Experimental results demonstrated that an eigenvalue-based defect recognition method is effective to
distinguish defective images from non-defective images.
Some limitations this research work identified need to be addressed. Digital image processing is an effective tool to
assess external conditions of a facility. However, there is a limitation to examine internal conditions. In case internal
conditions of a structure are in question, additional technology should be considered. Also, this research work was
performed to propose a generic methodology to detect bridge coating defects and present testing results. In order to
put this technique into practice, more comprehensive field testing is required. It would be better to work with DOT
personnel to obtain a right to access more steel bridges and take more field images. By doing this, the validity of this
methodology can be enhanced.