Yu, J; Guo, J; Zhang, Q; Xing, L and Lv, S (2026) Two-stage algorithm for automatic repair of pavement cracks. Engineering, Construction and Architectural Management, 33(1), pp. 144-170. ISSN 0969-9988
Abstract
Purpose – To develop an automated system for identifying and repairing cracks in asphalt pavements, addressing the urgent need for efficient pavement maintenance solutions amidst increasing workloads and decreasing budgets. Design/methodology/approach – The research was conducted in two main stages: Crack identification: Utilizing the U-Net deep learning model for pixel-level segmentation to identify pavement cracks, followed by morphological operations such as thinning and spur removal to refine the crack trajectories. Automated crack repair path planning: Developing an enhanced hybrid ant colony greedy algorithm (EAC-GA), which integrates the ant colony (AC) algorithm, greedy algorithm (GA) and three local enhancement strategies – PointsExchange, Cracks2OPT and Nearby Cracks 2OPT – to plan the most efficient repair paths with minimal redundant distance. Findings – The EAC-GA demonstrated significant advantages in solution quality compared to the GA, the traditional AC and the AC-GA. Experimental validation on repair areas with varying numbers of cracks (16, 26 and 36) confirmed the effectiveness and scalability of the proposed method. Originality/value – The originality of this research lies in the application of advanced deep learning and optimization algorithms to the specific problem of pavement crack repair. The value is twofold: Technological innovation in the field of pavement maintenance, offering a more efficient and automated approach to a common and costly issue. The potential for significant economic and operational benefits, particularly in the context of reduced maintenance budgets and increasing maintenance demands.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ant colony algorithm; image segmentation; path planning; pavement crack |
| Index terms: | methodology, optimization algorithm, validation, repair, asphalt pavement, strategy, workload, effectiveness, technological innovation, deep learning |
| Subjects: | innovation and technology management, maintenance engineering, research methods, algorithms, artificial intelligence, professional development, performance management, management, infrastructure and transport systems |
| Topics: | Information Management, Research Practice, Business Strategy, Human Resources, Digital Applications, Engineering Principles, Quality Management |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here