Assessment of pavement deflection under vehicle loads using a 3D-DIC system in the field

This study aims to introduce the use of 3D-digital image correlation (DIC) to the in situ testing of pavements and to support the development of techniques for a rapid evaluation of the conservation status of existing roads. Little research was found on this topic. The passage of a car wheel on an asphalt pavement was adopted as a case study. The DIC measurements were compared to those gathered by contact sensors. From a qualitative point of view, the DIC measurements captured the realistic shape of a deflection basin. From a quantitative point of view, the deflection values provided by the DIC system had a mean error of 0.015 mm and a standard deviation of 0.011 mm. At the moment of highest load, these errors had a mean value and standard deviation of − 0.016 mm and 0.021 mm, respectively. Thus, to improve the accuracy of the system, we propose modifying the camera support, speckle pattern, and control of natural lightWe thank Extraco S.A. for allowing us to perform the test on their company facilities. This work was supported by the Strategic Researcher Cluster BioReDes funded by the Xunta de Galicia (Regional Government of Galicia) under project ref. ED431E 2018/09 and by the Xunta de Galicia under the grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2020-22)” under project ref. ED341B 2016/30. This work was also made possible due to the Spanish Agencia Estatal de Investigación (AEI), since Dr. Bastos was contracted in the frame of the postdoctoral grant Juan de la Cierva – Formación (FJC2019-039743-I/AEI/10.13039/501100011033)S

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

SMARTI - Sustainable Multi-functional Automated Resilient Transport Infrastructure

The world’s transport network has developed over thousands of years; emerging from the need of allowing more comfortable trips to roman soldiers to the modern smooth roads enabling modern vehicles to travel at high speed and to allow heavy airplanes to take off and land safely. However, in the last two decades the world is changing very fast in terms of population growth, mobility and business trades creating greater traffic volumes and demand for minimal disruption to users, but also challenges, such as climate change and more extreme weather events. At the same time, technology development to allow a more sustainable transport sector continue apace. It is within this environment and in close consultation with key stakeholders, that this consortium developed the vision to achieve the paradigm shift to Sustainable Multifunctional Automated and Resilient Transport Infrastructures. SMARTI ETN is a training-through-research programme that empowered Europe by forming a new generation of multi-disciplinary professionals able to conceive the future of transport infrastructures and this Special Issue is a collection of some of the scientific work carried out within this context. Enjoy the read

THE DEVELOPMENT AND ASSESSMENT OF AN ANALYTICAL STRUCTURAL MAINTENANCE DESIGN SYSTEM FOR ROADS

Previous work at Plymouth has shown the deflected shape as measured by the Deflectograph can be used to estimate the thickness of surfacing. This thesis extends the earlier work to develop relations between deflected shape and the stiffness and thickness of pavement layers. A literature study has been carried out to identify the factors causing the deterioration of flexible pavements. The literature study also assesses the various pavement evaluation equipment that is available and describes various methods of analysis and the interpretation of the pavement surface deflection shape that have been proposed. The properties of the materials of the various layers of the flexible pavement have been reviewed. Various structural models of a pavement are considered and the study indicates that the finite element method provides a most accurate prediction of actual pavement response to a moving wheel load. A 3D finite element model of a flexible pavement has been produced and partially validated with data obtained from the TRRL. A Fortran Programe has been developed to convert absolute deflections predicted by the 3D model into equivalent Deflectograph deflections. The model has been used to carry out parametric study to establish appropriate relationships between the deflected shape and material properties of the pavement layers. Relationships have been established to determine the thickness, modular ratio and modulus of the pavement layers and its support subgrade from measurement of the deflected shape. An Analytical Pavement Evaluation and Design System has been set up based on the relationships. The system has been validated by comparing the material properties obtained from the laboratory testing with those predicted by the design model using the Deflectograph measurements obtained from local roads with measurement of layer thickness and subgrade strength measured in-situ

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 2 comprises the second part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

Experience Gained Investigating, Acquiring and Operating the First Traffic Speed Deflectometer in Australia

AbstractTo manage a pavement one must know something about its condition. The more you know, the better informed your decisions are. In Australia, road agencies have been utilising automated data collection systems to assess the functional condition of their pavements for the best part of the last two decades. However, during this time, the assessment of structural condition has been limited to manual, slow moving or point-based testing. This has made collection of this data across entire networks unrealistic, even though this information is desirable. Therefore, it is no surprise that Australian road agencies have shown significant interest in the Traffic Speed Deflectometer (TSD), a device they hope can provide the network-level structural information they desire.This paper provides the background leading to the initial Australian TSD trials and how ARRB Group was able to procure a TSD for Australia and New Zealand. It also details the integration of additional data collection modules and how they allow the functional and structural condition of the pavement to be measured at the same time. Finally, the paper documents some of the lessons learnt throughout the TSD's acceptance testing and first 18 months of use on the Australian and New Zealand road networks. During this time, over 60,000 km were surveyed with the TSD

Machine learning for homogeneous grouping of pavements

Abstract Machine learning for homogeneous grouping of pavements. Kanan Mukhtarli Rapid pavement deterioration is a major problem in areas with harsh weather conditions or high traffic loading. Despite many studies focused on the pavement management systems, there is not, to the date, a robust method explaining how to process large amounts of pavement data to create homogeneous groups for rehabilitation-related decision making. This thesis employs machine learning to develop an approach capable of partitioning pavement data with a close response to casual factors like traffic and weather conditions and considering its performance through international roughness index and deflections. Two different methods: K-means and Self Organizing Maps (SOM) clustering techniques were tested to understand the correlation between daily factors and pavements deterioration. The goodness of clustering was tested using extrinsic and intrinsic evaluation methods. It was concluded from the results that SOM clustering provided better results as it relies on a soft clustering method where one point can represent two clusters at the same time. Moreover, it became obvious from the methodology that including the previous year’s data has very little to no effect on homogeneous groups. Techniques discussed and developed in this study can help road asset managers with decision making for the maintenance and rehabilitation of pavement. Moreover, future researchers can use the results of this study to further develop the idea of building decision support systems for pavement rehabilitation

Final Report

AASHO Road TestHighway Research BoardNational Academy of Sciences - National Research Counci