Asphalt Binder Modification with Plastomeric Compounds Containing Recycled Plastics and Graphene

Polymer-modified bitumens are usually employed for enhancing the mixture performance against typical pavement distresses. This paper presents an experimental investigation of bitumens added with two plastomeric compounds, containing recycled plastics and graphene, typically used for asphalt concrete dry modification. The goal was to study the effects of the compounds on the rheological response of the binder phase, as well the adhesion properties, in comparison with a reference plain bitumen. The blends (combination of bitumen and compounds) were evaluated through dynamic viscosity tests, frequency sweep tests, and multiple stress creep recovery (MSCR) tests. Moreover, the bitumen bond strength (BBS) test was performed to investigate the behavior of the systems consisting of blends and aggregate substrates (virgin and pre-coated). The rheological tests indicated that both blends performed better than the plain bitumen, especially at high temperature, showing an enhanced rutting resistance. In terms of bond strength, comparable results were found between the blends and reference bitumen. Moreover, no performance differences were detected between the two types of blends.

Improving Rutting Resistance of Flexible Pavement Structure By Using Waste Plastic

On these days traffic is increasing faster rate on roads then various type of defects are produced on road that is rutting, raveling etc A pavement structure have different layers purpose to transfered traffic loads to the sub grade. Rutting is one of the pavement distresses that effects the performance of road pavements. Waste plastic is the type of materials to use for improving the performance of flexible pavements against rutting. In this study utilization of waste plastic water bottles, cold drink bottles, polythene bags, parcel package polythene and films. This waste material clean and shredded small particles (1-3cm) sizes. Aggregate heated 170-200° and mix particles with different percentage (3%, 5%, 7%) properly coated on hot aggregate. This plastic waste coated aggregate is also mixed with hot bitumen. And perform some laboratory test (impact value test, moisture absorption test, marshal value test) on the sample and check the property of rutting resistance.

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

Evaluation and Comparison of Mechanical Properties of Polymer-Modified Asphalt Mixtures

Polymer modification is extensively used in the Kingdom of Saudi Arabia (KSA) because the available asphalt cement does not satisfy the high-temperature requirements. It was widely used in KSA for more than two decades, and there is little information regarding the differences in the performance of different polymers approved for binder modification. Pavement engineers require performance comparisons among various polymers to select the best polymer for modification rather than make their selection based on satisfying binder specifications. Furthermore, the mechanical properties can help select polymer type, producing mixes of better resistance to specific pavement distresses. The study objective was to compare the mechanical properties of the various polymer-modified asphalt (PMA) mixtures that are widely used in the Riyadh region. Control mix and five other mixes with different polymers (Lucolast 7010, Anglomak 2144, Pavflex140, SBS KTR 401, and EE-2) were prepared. PMA mixtures were evaluated through different mechanical tests, including dynamic modulus, flow number, Hamburg wheel tracking, and indirect tensile strength. The results show an improvement in mechanical properties for all PMA mixtures relative to the control mixture. Based on the overall comparison, the asphalt mixture with polymer Anglomk2144 was ranked the best performing mixture, followed by Paveflex140 and EE-2.

Review Paper on Defects in Rigid Pavement

In this project Pavement failure is defined in terms of decreasing serviceability caused by the development of cracks and ruts. Before going into the maintenance strategies, we must look into the causes of failure of rigid pavements. Failures of rigid pavements are caused due to many reasons or combination of reasons. Application of correction in the existing surface will enhance the life of maintenance works as well as that of strengthening layers. It has been seen that only three parameters i.e., unevenness index, pavement cracking and rutting are considered while other distresses have been omitted while going for maintenance operations. Along with the maintenance techniques there are various methods for pavement preservation which will help in enhancing the life of pavement and delaying its failure. The purpose of this study was to evaluate the possible causes of pavement distresses, and to recommend remedies to minimize distress of the pavement. The project describes lessons learnt from pavement failures and problems experienced during the last few years on a number of projects in India. Based on the past experiences’ various pavement preservation techniques and measures are also discussed which will be helpful in increasing the serviceable life of pavement

Automated Detection and Classification of Pavement Distresses using 3D Pavement Surface Images and Deep Learning

Pavement distresses lead to pavement deterioration and failure. Accurate identification and classification of distresses helps agencies evaluate the condition of their pavement infrastructure and assists in decision-making processes on pavement maintenance and rehabilitation. The state of the art is automated pavement distress detection using vision-based methods. This study implements two deep learning techniques, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLO) v3, for automated distress detection and classification of high resolution (1,800 × 1,200) three-dimensional (3D) asphalt and concrete pavement images. The training and validation dataset contained 625 images that included distresses manually annotated with bounding boxes representing the location and types of distresses and 798 no-distress images. Data augmentation was performed to enable more balanced representation of class labels and prevent overfitting. YOLO and Faster R-CNN achieved 89.8% and 89.6% accuracy respectively. Precision-recall curves were used to determine the average precision (AP), which is the area under the precision-recall curve. The AP values for YOLO and Faster R-CNN were 90.2% and 89.2% respectively, indicating strong performance for both models. Receiver operating characteristic (ROC) curves were also developed to determine the area under the curve, and the resulting area under the curve values of 0.96 for YOLO and 0.95 for Faster R-CNN also indicate robust performance. Finally, the models were evaluated by developing confusion matrices comparing our proposed model with manual quality assurance and quality control (QA/QC) results performed on automated pavement data. A very high level of match to manual QA/QC, namely 97.6% for YOLO and 96.9% for Faster R-CNN, suggest the proposed methodology has potential as a replacement for manual QA/QC.

Use of the Pavement Surface Cracking Metric to Quantify Distresses from Digital Images

The development of new laser technologies in recent years has changed pavement data collection, opening the door to a fully automated approach. In this paper the application of the Pavement Surface Cracking Metric (PSCM), inspired by the Universal Cracking Indicator proposed by William Paterson in 1994, and developed by the ASTM E17 group is presented. The method uses quantitative definitions to ensure consistency of the results and eliminate the subjectivity associated with human ratings of pavement distresses. Multiple runs of pavement data have been collected on three asphalt sections to assess the repeatability and reproducibility of the method. The application of the Pavement Surface Cracking Index to convert the PSCM value, which is a physical property of the pavement, into a 100-0 score of the pavement section is also presented. Finally, the use of the PSCM to classify pavement distress and the inclusion of potholes and patching in the metrics are discussed.