Integrating Non-Destructive Surveys into a Preliminary BIM-Oriented Digital Model for Possible Future Application in Road Pavements Management

The implementation of the digitalization of the linear infrastructure is growing rapidly and new methods for developing BIM-oriented digital models are increasing. The integration of the results obtained from non-destructive surveys carried out along a road infrastructure in a pavement digital model can be a useful method for developing an efficient process from a pavement management systems (PMS) point of view. In fact, several applications to optimize PMS have been thoroughly investigated over the years and several researchers and scientists have investigated significant elements for improving the PMS applied to a transport network, including road infrastructures. This study presents a new, tentative process for implementing into a BIM environment the dataset processed from two surveys carried out in a case study. Moreover, the main reason for this investigation is related to the need for an effective system able to evaluate continuously the pavement conditions and programming maintenance interventions. To date, both the instruments and the methods to detect the pavement configuration have evolved, along with the development of non-destructive technology (NDT) tools such as laser-scanners and ground-penetrating radar. Finally, the main results of the research demonstrate the possibility to provide a digital twin model from the synergistic use of geometric and design information with the results from monitoring conducted on a road infrastructure. The model can be potentially used in future BIM-based PMS applications.

Field Performance Evaluation of High Polymer-Modified Asphalt Concrete Overlays

The objective of this paper was to assess the viability of using high polymer (HP) modified asphalt concrete (AC) mixtures in Virginia as a reflective crack mitigation technique or when deemed appropriate as a tool for increased crack resistance on higher volume facilities. This was achieved by compiling and evaluating routine distress survey data against pre-paving distress survey data for relevant in-service HP pavements constructed between 2015 and 2018 and comparing them with several control in-service conventional polymer-modified asphalt (PMA) pavements. This is the first effort in North America to provide a detailed field performance of HP AC mixtures. In general, none of the evaluated mixtures (HP or PMA) was able to prevent reflective cracking completely. The HP sections showed the most promising performance 5 years after construction regardless of traffic level and the pre-existing pavement conditions. The pavement management system data for the reviewed sections indicated a potential controlling effect of the joint condition of the underlying jointed concrete pavement layer regardless of the asphalt mixture type employed (PMA or HP). Moreover, performance evaluations using the network-level pavement management data were conducted to estimate the life expectancy of HP AC overlays. Two different approaches and three levels of analysis were undertaken. Overall, PMA and HP AC overlays had an average predicted service life of 6.2 and 8.3 years, respectively, indicating a 34% extension of performance life of the AC overlays with high polymer modification.

Enriching Existing 3D Pavement Condition Survey Datasets to Support Paving Project Cost Estimates, Project Planning, Designs, and Automated Machine Guidance

State departments of transportation (DOTs) typically perform annual pavement condition inspections, which serve as an important input into pavement management systems (PMS) software. Road surface defects (cracking, rutting, smoothness, etc.) are analyzed by PMS software to model the deterioration of pavements and to make budget and performance-based recommendations about which roads to maintain and how and when to maintain them. Increasingly at the state DOT level, these data are captured using high-speed 3D lasers (laser triangulation systems) that acquire the 3D shape of the road surface to evaluate its condition. Traditionally the capture of road elevation data relied entirely on the use of survey crews. Although accuracy can be quite high, the process of capturing elevations can require a lot of manpower, is time-consuming, requires lane closures, and results in a relatively small number of points per kilometer of road with which to perform all of the tasks from early project planning through construction. This paper explores an alternate approach that leverages existing 3D laser technology utilized by DOTs to measure the condition of in-service pavements. Typically, these laser systems capture “relatively referenced” 3D profiles of the roadway to evaluate pavement condition based on surface distortion. However, there is often no connection between these “relative” 3D profiles and real-world locations. This new approach involves the addition of high-accuracy blended global navigation satellite system + inertial navigation system positioning systems, as well as specialized software, to map the absolute position of 3D profiles in real-world coordinates.

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

Economic Impact Analysis of the Application of Different Pavement Performance Models on First-Class Roads with Selected Repair Technology

Mathematical expression of the deterioration of individual pavement parameters is, from the point of optimal repair and maintenance strategy decision-making process, an important part of the application of any pavement management system (PMS). The reliability of individual PMS depends on the quality of the inputs and the reliability of its internal sub-systems; thus, deterioration equations derived from high-quality input data play pivotal roles in a system for the prediction of the pavement life cycle. This paper describes the application of pavement performance models within pavement life cycle analysis (LCA) with the use of the integrated system of economic evaluation (ISEH), which is a calculation tool used for first-class roads with a standardized pavement composition of asphalt binders, where changes in operational capability parameters are modeled using individual model simulations. The simulations presented in this paper demonstrate changes in main economic indicators (net present value and internal rate of return) on two different pavement performance models. Both simulations share the same input parameters (traffic intensity, construction intervention, maintenance costs, discount rate) but differ in deterioration evaluation, all of which were applied to each model (a total of five models).

On the Applicability of Laser Scanning for Evaluation of the Pavement Serviceability Parameters

Pavement performance is influenced by man factors such as climate and environmental conditions, traffic and operational conditions and type of pavement. These factors cause a pavement deterioration what leads to the restriction of the pavement serviceability or pavement efficiency. The pavement serviceability is the ability of the pavement to fulfil the service function represented by the actual values of variable parameters such as pavement surface roughness, surface evenness, pavement surface condition. The state of the pavement is assessed using different performance indicators when International Roughness Index (IRI) is most used. This approach allows to classify the state of the pavement in the pavement management system as a most used indexing, generalizes the pavement surface to the response of the testing car tire and the pavement. Laser scanning presented in this paper is able to bring the knowledge about the real pavement surface considering the accuracy of the method and equipment. Realized laser scanning proved the applicability of this method for the measurement of the pavement surface. Because of the complex knowledge of the pavement surface morphology, we can evaluate the pavement serviceability in terms of roughness, surface evenness or even pavement surface condition (rutting or cracks).

Using of Laser Scanners in Pavement Management

The presented article introduces the utilization of laser scanning technology in the field of pavement management (PM) systems. Thanks its many advantages that allow objectification of road surface characteristics in a more holistic and sophisticated way, right decision-making within the PM, both at the network and project level. Specifically, outputs from the installed road scanners, such as SRS, Trimble CX and Lynx SG1 Mobile Mapper, have been combined to improve the accuracy. There has been found data correlation dependence of the development of rut depth for non-rigid pavements by evaluation of data from measurements at the public transport stop Hurbanova in Žilina during the years 1994 to 2018.