Crack is one of the most important pavement condition indicators that are immediately relevant to water ingress and pavement deterioration. In practices of pavement management, crack width has been extensively referenced by highway agencies to determine pavement crack severity. Accurate measurement of pavement crack width is meaningful for highway agencies in understanding the mechanism of crack formation, and in predicting crack propagation. This article presents a new automatic method for measuring crack width using the binary crack map images. The proposed method introduces a new crack width definition and formulates it using the Laplace's Equation so that crack width can be continuously and unambiguously measured. Two algorithms, including the crack blob extraction algorithm and the crack boundary extraction algorithm, are developed to implement the proposed formulation in an automated fashion. Experimental tests using both synthetic data and field data are conducted to demonstrate the accuracy and reliability of the proposed method. A case study on crack width propagation is also performed to demonstrate the practical capacity of the proposed method. The results of the experimental tests and the outcome of the case study have demonstrated that the proposed method, together with the existing crack map extraction algorithms, provides a promising means for consistent and unambiguous crack width measurement supporting automated pavement condition evaluation.

Cracking characterization is one of the most important tasks in automated pavement data analysis. Although cracking detection and segmentation algorithms have become more reliable in recent years, accurate cracking classification remains a constant challenge to pavement engineers. Conventionally, manual recognition uses cracking orientation and topological features to classify cracking into different types such as alligator cracking and transverse cracking. However, the rules to classify cracking are often complicated and subjective, which compromises the reliability of computerized implementation. This study develops a support vector machine (SVM)–based method to intelligently identify cracking types in an automated manner. Pavement cracks are grouped using a minimum rectangular cover (MRC) model. Using the relative location, orientation, and size of the MRC, as well as the cracking characteristics such as cracking density and cracking connectivity, three SVM models are compared in this study. It is found that an 88.07% accuracy is achieved for 10,134 MRCs collected from four highway sections using the two-phase SVM model. The proposed methodological framework would improve overall accuracy in cracking classification.

The nonballasted rail tracks have been extensively applied in the new high‐speed railway system development in China, that is, the China Railway Track System (CRTS). However, many defects have been identified during the operation of the CRTS, among which concrete slab crack is recognized as one of the most common, yet critical defects that require accurate identification and timely maintenance attention. Because of the unique outlook of the cracks in nonballasted rail track slabs captured in the survey imagery, direct adaptations of the existing crack extraction methods show dramatically degraded performance. A new automatic crack identification method is developed in this study by employing a region‐based active contour framework with the intensity cluster energy. The proposed method embodies three major contributions, including (1) a heavy penalization energy component that could effectively avoid both under‐ and overevolutions; (2) a multiphase level‐set function that effectively evolves the contours generated with different intensity clusters; and (3) a two‐step implementation of the framework that significantly improves the efficiency. The experimental test evaluates the performance of the proposed method using the data collected on high‐speed rail tracks in Hebei Province, China. The proposed method accurately identifies more than 93.0% of digitized cracks in different crack patterns and challenging backgrounds using a data set consisting of 1,500 synthetic images and 150 actual images. In addition, it shows promising performance in comparison with other popular state‐of‐the‐art crack detection algorithms in terms of accuracy and computational efficiency. The proposed method has demonstrated the promising capacity to support a reliable and efficient nonballasted rail track crack identification and to facilitate the subsequent maintenance cost‐effectively.

The mixed traffic flow has an increasingly impact on the operation of urban traffic. To study the evolution law of multi-group behaviors in pedestrian crossing, we used the evolutionary game theory to establish a multi-group behavior evolution model for pedestrian crossing. The process of concern started from the risk perception and multi-group behavior choice. The evolutionary stability strategies, evolution trends, and factors affecting the evolutionary path of multi-group behaviors are discussed in this paper. This study found that evolutionary strategy equilibrium of pedestrians, drivers, and traffic managers not only relied on their own earning, but also on those of the other two groups. The factors affecting its behavior included the revenue factor and the limiting factor. Evolutionary game theory was used to analyze the multi-group interaction behavior of pedestrians, vehicle drivers, and traffic managers in the process of pedestrian crossing, as well as to analyze the behavior of traffic subjects in the process of pedestrian crossing. This paper provides a basis for decision-making for the traffic management department to manage road traffic, offering a new idea from the perspective of evolution for solving the conflict of interest at the crosswalk of the road section.

State highway agencies need pavement-condition data to select candidates for pavement maintenance and rehabilitation. However, it is a challenge for pavement engineers to simultaneously assess a number of attributes that represent different aspects of pavement condition. In conventional practice, empirical comprehensive evaluation methodologies, such as fuzzy set theory and analytical hierarchy process, have been used to aggregate multiple distresses into integrated pavement-performance indices. These methodologies, however, are mostly based on experts’ or engineers’ judgment rather than data-driven approaches. In this paper, a framework of applying a data-driven approach to conduct comprehensive pavement evaluation and ranking is presented. The method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced and applied to rank pavement sections with various evaluation attributes. Principal component analysis (PCA) is employed to combine distresses with high correla...

Measurement of pavement rut is critical to highway agencies for both pavement design and rehabilitation. Historically, rut depth is the only pavement permanent deformation indicator collected with various measurement technologies. Recently, the new draft protocol called AASHTO Standard Practice PP69-10 outlines a set of procedures to derive multiple measures of pavement permanent deformation, including percent deformation, water entrapment depth, rut depth, etc. Multiple parameters can be useful for project-level analysis, but may not be realistic indices for overall permanent deformation evaluation at network level. In this paper, a three-step procedure is applied to evaluate the overall permanent deformation. Transverse profile data are collected on over 200 km (125miles) National Highway Systems using the 1-mm PaveVision3D Ultra data collection system. Twelve attributes for characterising permanent deformation are calculated according to the requirement of PP69-10. Scoring functions of these attributes are elicited using utility theory-based methods with statistics of attributes from the collected sample data-sets. Analytical Hierarchy Process is performed for the development of a comprehensive permanent deformation evaluation system. Additive weights are obtained for all attributes based on pairwise comparisons. A case study is conducted to compare current rut depth-based practice with the proposed new comprehensive evaluation system.