Steel Bridge Painting Research Articles

Lifecycle Decision Framework for Steel Bridge Painting

Lifecycle Decision Framework for Steel Bridge Painting

Impact on the environment from steel bridge paint deterioration using lead isotopic tracing, paint compositions and soil deconstruction

Deterioration and repair of lead paint on steel structures can result in contamination of the ambient environment but other sources of lead such as from past use of leaded paint and gasoline and industrial activities can also contribute to the contamination. Using a combination of high precision lead isotopic tracing, detailed paint examination, including with scanning electron microscopy, and soil deconstruction we have compared paint on a steel bridge and bulk soil and lead-rich particles separated from soil. The majority of Pb found in the paint derives from Australian sources but some also has a probable US origin. The isotopic data for the bulk soils and selected particles lie on a mixing line with end members the geologically ancient Broken Hill lead and possible European lead which is suggested to be derived from old lead paint and industrial activities. Data for gasoline-derived particulates lie on this array and probably contribute to soil Pb. Although paint from the bridge can be a source of lead in the soils, isotopic tracing, paint morphology and mineralogical identification indicate that other sources, including from paint, gasoline and industrial activities, are contributing factors to the lead burden. Even though physical characteristics and elemental composition are the same in some particles, the isotopic signatures demonstrate that the sources are different. Plots using 206Pb/208Pb vs 206Pb/207Pb ratios, the common representation these days, do not allow for source discrimination in this investigation.

Lifecycle analysis concerning partial deterioration measures of steel bridge painting

Lifecycle analysis concerning partial deterioration measures of steel bridge painting

Effectiveness of neuro-fuzzy recognition approach in evaluating steel bridge paint conditions

The development of digital image recognition techniques has contributed to increased precision in pattern recognition and led to numerous applications in industries. In September 1999, the Indiana Department of Transportation (INDOT) first tried out digital image recognition techniques to steel bridge coating assessment. The purpose of this tryout was to obtain a rust percentage, which was required in the INDOT bridge painting warranty contract, when conducting steel bridge coating investigation. Despite the advantages of digital image recognition, some problems that may cause inaccurate recognition results still exist. Nonuniform illumination (i.e., brightness or darkness or shadow) is one of them. The neuro-fuzzy recognition approach (NFRA) was developed to minimize the effect of nonuniform illumination. In this technical note, the framework of NFRA, its application to steel bridge coating assessment, and its performance comparison to three other image recognition methods will be presented.Key words: neuro-fuzzy recognition approach (NFRA), artificial neural network (ANN), double sampling plan, multiresolution pattern classification (MPC), iterated conditional modes (ICM).

Performance Comparison of Bridge Coating Defect Recognition Methods

Abstract Digital image recognition methods are now being utilized for steel bridge painting surface assessment. Through the use of digital image processing methods, the rust percentage on steel bridge surfaces can be objectively computed. The rust percentage may be crucial for the successful implementation of steel bridge painting warranty contracting since it can be used to decide whether painting contractors should repair painting defects at the end of the warranty period or at any regular inspection. To implement painting warranty contracting, however, appropriately designed recognition methods must be developed first. In this paper, several recognition methods are proposed and evaluated based on their rust recognition performance, and these methods fall into two categories. One uses artificial intelligence methods such as neural networks and fuzzy logic as the backbone for recognition, while the others apply statistical approaches to detect rust areas. Illumination is considered in some of the proposed methods to determine how it affects the recognition results. To test the performance of the proposed methods, a series of comparisons were made based on different environmental conditions.

Budget Allocation for Steel Bridge Paint Maintenance

Government authorities are responsible for managing their available budget so that they gain maximum benefit. Therefore, they must make intelligent decisions as to which projects will be funded and the degree of funding. Dynamic programming (DP), integer programming (IP), and greedy heuristic (GH) approaches have been applied to optimize a two-year budget allocation for repainting steel bridges in Indiana. The optimal solution and the objective function values resulting from application of DP, IP, and GH are similar. The Indiana Department of Transportation (INDOT) divides Indiana into six districts. The results show that District 1 has the highest budget allocation percentage (28%). It has the lowest percent in the first year and the highest percent in the second year. District 2 has the lowest percentage (11%). A sensitivity analysis has been completed to show the potential alternative solutions that assist INDOT decision makers in solving their budget allocation problems.

Artificial intelligence application to bridge painting assessment

Digital image recognition has been experimented for steel bridge painting assessment by the Indiana Department of Transportation (INDOT) in September 1999. Although the application was successfully carried out as a whole, there are still some minor problems left to be improved. Nonuniform illumination is one of the problems that affect the accuracy of recognition results. To address this problem, the neuro-fuzzy recognition approach (NFRA) is proposed, which segments an image into three areas based on illumination and conducts area-based thresholding with the help of an artificial neural network (ANN) and a fuzzy adjustment system. In this paper, the framework of NFRA will be presented, followed by the application of NFRA to steel bridge painting assessment and its performance comparison with the multiresolution pattern classification (MPC) method and the iterated conditional modes (ICM) algorithm. The conclusions will be presented last.

Life-Cycle Cost Based Maintenance Plan for Steel Bridge Protection Systems

Life-cycle cost analysis was used to compare different alternative strategies for steel bridge paint systems. It was also used as a tool for steel bridge paint rehabilitation planning. The existing paint systems are lead-based and zinc-vinyl, while the new system is an inorganic/organic zinc, epoxy, and urethane paint system (three-coat). Economic analysis using present value (PV) and equivalent uniform annual cost (EUAC) was applied to compare several steel bridge paint system alternatives. The PV and EUAC were also used to compare different rehabilitation scenarios within the same alternative. Life-cycle cost analysis computations indicate that the three-coat paint system was better than others. Researchers concluded that the best scenario for three-cost system rehabilitation was doing spot repairs every 15 years of paint life. A maintenance plan based on life-cycle cost analysis also favored the “spot repairs every 15 years” scenario. A sensitivity analysis was also conducted to account for uncertainty in the cost, conditions, and subjective data.

Statewide Performance Function for Steel Bridge Protection Systems

A performance function is the relationship between the bridge paint condition rating and time. This function reflects the level of service of the bridge paint. The performance functions of steel bridge paint for state structures maintained by the Indiana Department of Transportation (INDOT) were developed using both regression analysis and Markov chains. The regression and Markov models were used to predict the condition rating of a bridge’s paint at a given age. The probabilistic model that was developed with Markov chains was used to reflect the stochastic nature of bridge paint conditions. These deterministic and stochastic methods have been applied to the existing and the new bridges’ paint systems in INDOT structures. The existing paint systems are lead-based and zinc-vinyl. On the other hand, the new paint system is three-coat. The results indicate that both models are very efficient in representing the deterioration model for bridge paint systems based on historical data.

Life-Cycle Cost Analysis using Deterministic and Stochastic Methods: Conflicting Results

Life-cycle cost analysis is an essential approach to differentiating alternative rehabilitation strategies for steel bridge paint systems. An economic analysis ~EA!, which is a deterministic method, and the Markov decision process ~MDP!, which is a stochastic method, were used to carry out the life-cycle cost analysis. These analyses were applied to data from two state Departments of Trans- portation. The deterioration curves for steel bridge paint condition rating against age were constructed. Different rehabilitation scenarios were proposed for steel bridge paint. The EA and the MDP were used to analyze and differentiate among the proposed rehabilitation scenarios. The results of the EA were different from those of MDP for the two data sets. MDP favored the ''do nothing'' scenario until the end of paint life and then a complete repainting. EA indicated that the scenario ''do spot repairs at state 3 of the paint life'' and repeat that until the end of the bridge life was superior. The results were analyzed to determine the reason for the conflict.

Attribute–Double-Sampling Method for Infrastructure Quality Assurance

Infrastructure-construction quality could be better assured by imposing more responsibility for quality process control on contractors, and by having owners place more effort on specifying clear inspection procedures and acceptance criteria. This type of specification, with better allocation of the responsibility between owners and contractors, is generally called quality-assurance (QA) specification. The purpose of this paper is to present results of research in developing a quality-acceptance method called Attribute–Double-Sampling Method (ADSM) for QA specifications. Application of ADSM for quality assurance on steel-bridge–painting construction is used to demonstrate the theory and application of the methodology, but the ADSM is generic and can be applied to a wide range of infrastructure-quality assessments. It enables the users to select sampling size and to control risk while accepting or rejecting the quality of a finished infrastructure product.