• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.539-542
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• 2002

Crack sealing is a maintenance procedure that is commonly used to reduce pavement degradation. If cracks in pavements are not sealed, surface water penetration can reduce the strength of the sub-base layers, which can result in increased deflections of the pavement. Reduced strength of the sub-base also accelerates the deterioration of the surface, due to development of greater cracking and potholes. Crack sealing is performed to reduce water and debris penetration, thereby helping to maintain pavement structural capacity and limiting future degradation. The process of sealing cracks in pavements is however dangerous, costly, and labor-intensive operation. Labor turnover and training are increasing problems related to crack sealing crews, and as traffic volumes increase. Automating crack sealing can reduce labor and road user costs, improve work quality, and decrease worker exposure to roadway hazards. The main objective of this research is to develop an automated system for sealing cracks in pavement. Extension of the algorithms and tools presented in this research is also recommended for future study.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.561-564
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• 2004

Crack sealing automation machines' have been continually developed since the early 1990's because of the effectiveness of crack sealing that would be able to improve safety, quality and productivity. It has been considered challenging problem to detect crack network in pavement which includes noise (oil marks, skid marks, previously sealed cracks and inherent noise). It is required to develop crack network mapping and modeling algorithm in order to accurately inject sealant along to the middle of cut crack network. The primary objective of this study is to propose a crack network mapping and modeling algorithm using neural network for improving the accuracy of the algorithm used in the APCS. It is anticipated that the effective use of the proposed algorithms would be able to reduce error rate in image processing for detecting, mapping and modeling crack network as well as improving quality and productivity compared to existing vision algorithms.