Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Design and laboratory evaluation of fog-sealed chip seal on epoxy asphalt pavement for steel bridge deck

In order to improve the surface performace of epoxy asphalt pavement （EAP） for steel bridge deck, an epoxy asphalt chip seal （ ECS） covered by a cationic emulsified asphalt fog seal （i. e., fog-sealed chip seal） isproposed and a laboratory study is conducted to design and evaluate te fog-sealed chip seal. First, the evaluation indices and methods of te chip seal on steel bridge deck pavement were proposed. Secondly, the worst pavement conditions during te maintenance time were simulated by te small traffic load simulation system MMLS3 and the short-term aging test for minimizing the failure probability of chip seal. Finally, the design parameters of fog-sealed chip seal were determined by the experimental analysis and the performance of the designed fog-sealed chip seal was evaluated in thelaboratory. Results indicate that the proposed simulation method of pavement conditions is effective and the maximal load repetitions on the EAPslab specimen are approximately 925 300 times. Moreover, the designed fog-sealedchip sealcan provide a dense surface with sufficient skid resistance,aggregate-asphalt aahesive performance and interlayer shearing resistance.

Network level pavement evaluation with 1 mm 3D survey system

The latest iteration of PaveVision3D Ultra can obtain true 1 mm resolution 3D data at full- lane coverage in all 3 directions at highway speed up to 60 mph. This paper introduces the PaveVision3D Ultra technology for rapid network level pavement survey on approximately 1280 center miles of Oklahoma interstate highways. With sophisticated automated distress analyzer （ADA） software interface, the collected 1 mm 3D data provide Oklahoma Department of Transportation （ODOT） with comprehensive solutions for automated eval- uation of pavement surface including longitudinal profile for roughness, transverse profile for rutting, predicted hydroplaning speed for safety analysis, and cracking and various surface defects for distresses. The pruned exact linear time （PELT） method, an optimal partitioning algorithm, is implemented to identify change points and dynamically deter- mine homogeneous segments so as to assist ODOT effectively using the available 1 mm 3D pavement surface condition data for decision-making. The application of 1 mm 3D laser imaging technology for network survey is unprecedented. This innovative technology allows highway agencies to access its options in using the 1 mm 3D system for its design and management purposes, particularly to meet the data needs for pavement management system （PMS）, pavement ME design and highway performance monitoring system （HPMS）.

Evaluation and Selection of Sealants and Fillers Using Principal Component Analysis for Cracks in Asphalt Concrete Pavements

The objective of this paper was to develop a comprehensive evaluation method and index to evaluate the performance of sealants and fillers for cracks in asphalt concrete pavements using the method of principal component analysis. The performance experiments including cone penetration, softening point, flow, resilience and tension at low temperature respectively were conducted by reference of ASTM D5329 for eight sealants and fillers often used in China. There by a principal component model was developed and weight of every index was calculated. The experimental results show that there are significantly different performances for sealants and fillers often used in China. Principal component analysis is an objective method that evaluates and selects the performance of sealants and fillers for cracks in asphalt concrete pavements.

Application of matter-element analysis based on entropy right to evaluate the pavement condition in permafrost region

Evaluation of pavement performance is one of the most important issues in a pavement-management system.By employing the concept of entropy,the matter-element model for evaluating pavement is established,and the weights of the evaluation indices are obtained from sur-veying data.By calculating the degree of dependence of the matter-element model,the pavement performance evaluation can be obtained by this method.The results show that the matter-element model based on entropy right has good performance for evaluating the pavement condition in permafrost region.

On the Use of FWD Impact-Stiffness Moduli for Determining PCN in Various Pavement Structures

Various technical studies have shown that impact-stiffness modulus values, defined as the ratio of the FWD （falling-weight deflectometer） impact load to its consequent central deflection, can be used to evaluate the PCN （pavement classification number） of a particular flexible or rigid airport pavement, fn a previous study, use was made of the old dynamic stiffness modulus procedure developed by the USCOE （US Army Corps of Engineers）, this procedure was correlated with various FWD measurements conducted on several runways and taxiways in Israel, together with in-situ borings and the use of the new COMFAA-3.0 software. The results, obtained only for flexible pavements, were checked against the relevant results of full-scale trafficking tests conducted by the FAA （Federal Administration Aviation） at its National Airport Pavement Test Facility. The present study analyzes new FWD measurements and in-situ borings conducted on additional rigid and all-asphaltic runways and taxiways in Israel in order to formulate an updated correlative equation for these types of pavements. The paper concludes with an updated recommendation for the use of impact-stiffness modulus outputs from FWD measurements in order to determine the PCN of any type of pavement directly on the basis of local experience.

Effect of Sample Unit Size on Visually Examining Pavement Condition for Asphalt-Surfaced Roads

Road surface condition evaluation involves the collection of data over pavement surface for different types of distresses. The exercise consumes a lot of resources if the whole road section length is surveyed and may be prone to errors as a result of surveyors＇ fatigue. It is therefore important to develop a representative sample to be used when evaluating road condition manually. This study aimed at determining an adequate sample size for section level as well as a way forward for network level condition evaluation of highways in Nepal. Again the study was conducted to quantify the effects of altering the sample unit size for performing a distress survey according to the PCI （pavement condition index） and SDI （surface distress index） method separately for asphalt surfaced roads. The effect of reducing/increasing sample unit size was investigated adopting visual examination through field survey by eight teams in July, 2015, along the section of Banepa-Bardibas highway. The PCI was then calculated for each sample unit using standard deduct curves and PCI calculation methodology as per SHRP （Strategic Highway Research Program） recommendations and the computation of SDI was done as per DoR （Department of Roads） guidelines. The results show that 13% sample unit are needed for SDI and 21% for PCI computation, however, the results are out of the significant level. This is higher than DoR and SHRP guidelines. Again no strong relationship is observed between SDI and PCI values.