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Comprehensive and practical, Pavement Asset Management  provides an essential resource for educators, students and those in public agencies and consultancies who are directly responsible for managing road and airport pavements.

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

Road maintenance operations involve the preservation of the optimal functionality of the pavement. Sometimes the rehabilitation of the pavement layout does not have long lasting effects due to a lack of compliance with the constraints imposed by the technical specifications for the design of materials. The purpose of this paper is to present an efficient BIM tool to help in road maintenance operations through the management of data arising from laboratory testing of road pavement bituminous materials required for the quality control of mixtures. The database associated to the BIM model is a collection of three years of data derived from laboratory investigation on bituminous mixtures’ samples adopted for the maintenance of four main roads located in southern Italy. An algorithm that interacts with the three-dimensional road model has been implemented in order to give road administrations an easy-to-read alert signal for the road pavement structure of the road network that may present the most critical conditions due to poor mechanical and physical features.

Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers for urban roads. In addition, unpaved road condition index (URCI) that has a good index for unpaved road evaluation used for comparison. A 53 km of forest roads were selected containing the most influential factors and variability of conditions. Eventually, 201 road segments were delineated between 150–300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from the forest road pavement condition index (FRPCI), URCI, and PCI. Linear regression was used to derive equations between distress and URCI and PCI scores to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. In addition, regression analysis showed that the FRPCI model with a 0.77 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85–45 and 1.2–53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.

With the advent of the information age, the traditional pavement management technology of operating expressways can no longer meet the higher requirements for the improvement of engineering quality in the information age. This paper proposes a method of integrated analysis based on BIM (building information modeling) and GIS (geographic information system), builds an intelligent platform for highway operation and maintenance, and solves the problem of data islands in highway maintenance and management.

Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may offer significant advantages as a convenient alternative for road maintenance management. Although existing BIM platforms are not fully equipped for this purpose, defining original modules and scripts can extend their capabilities, allowing for the handling of road condition information and maintenance management. In this context, this paper presents an operative framework designed to leverage BIM benefits for road maintenance management, particularly in terms of virtual inspection, asset condition assessment, and maintenance design. To achieve this, specific original and customized smart objects and routines were coded in I-BIM platforms, tailored to different scales, aims, and detail levels. These smart objects incorporate user-defined extended attributes related to pavement condition and maintenance planning (such as roughness, rutting, structural capacity). In particular, the authors have developed original virtual smart objects in different platforms, serving as “containers” for the survey information. These objects are adapted to display quality levels of the pavement segments in a realistic and user-friendly environment. Additionally, original routines were coded to automatically import survey data from external datasets and associate this information with the appropriate objects. This customized and extended approach, not available in commercial platforms, can effectively support maintenance operators.

Modern roadways provide road users with a comfortable and safe ride to their destinations. Due to increasing traffic demands and maximum allowable loads, road authorities should also pay attention to the structural soundness of road pavements while seeking cost-effective and timely maintenance or minor rehabilitation activities. This means that a sustainable pavement preservation strategy is needed that includes an optimal pavement condition assessment to support the appropriate decision-making processes. To address this need, this research study proposes an approach to integrate Non-Destructive Testing (NDT) data and ground truth data to predict the long-term performance of flexible pavements. Appropriate mechanistic models that take into account the nature of Asphalt Concrete (AC) materials are used for the analysis to increase the accuracy of the results when it comes to protecting and extending pavement life. The results indicated that examining viscoelastic behavior for AC appears to be a more conservative approach for the response analysis, as well as the fatigue performance analysis, compared to the most conventional assumptions for linear elastic materials. In accordance with common sense, AC temperature was considered as a critical factor for the related investigation. Overall, it may not be a good and reliable practice to continue the process of pavement management and maintenance decisions based on the approach of only one analysis type.

As jointed plain concrete pavements (JPCP) age in South Korea, the cost of pavement maintenance is increasing annually. To extend the life of jointed concrete pavements through preventive maintenance, this study used 2017 pavement management system data to analyze the effects of traffic volume, alkali–silica reaction (ASR) grade, age, smoothness, and damaged area on the remodeling index (RMI—a measure of expressway pavement condition). In addition, this study evaluates the final RMI as well as the corresponding pavement condition and change in RMI value after conducting preventive maintenance in lieu of resurfacing or overlaying. The results demonstrated that the effect of ASR grade increased as the RMI forecast year increased and that change in surface distress (△SD) increased with age (most intensively when the pavement was 15–20 years of age). Moreover, change in international roughness index (△IRI) increased with age and traffic volume (similarly within 15–20 years of pavement age). Hence, preventive maintenance is a must for sections with high traffic volume and age even if the RMI is low. Finally, performing repairs through preventive maintenance decreases the number of expressway sections requiring resurfacing and overlaying, thus extending the life of the concrete pavement.

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.