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Roads play a pivotal role in the overall economic growth of any country. Developed countries allocated sufficient budget to make new roads and to maintain the existing roads. They also have a proper pavement management system (PMS) in practice to manage roads, whereas developing countries suffer from budgetary issues to make new roads and maintain the existing road network. Therefore, this paper explores the awareness of PMS via direct and indirect methods in Pakistan with a proposed framework of the low-cost model and pavement maintenance indicators for developing countries. This paper also performs a scientometric assessment of PMS. A detailed literature review has been carried out for this study, followed by a quantitative study from experienced professionals. The scientometric data is collected from the Scopus database from 1975 to 2020, whereas the data for PMS awareness assessment has been collected using questionnaires from different experts working directly and indirectly in the road management sector. The data has been analyzed using the arithmetic mean because of the nature of the questions and scope of the study. The direct method results show that experts are aware of PMS for a new road, but they have no PMS to rehabilitate roads. The indirect method results show that the authorities are applying various components of PMS, but there is no proper PMS in practice. This paper helps decision-makers to make better decisions and policies for improved road maintenance and rehabilitation. The proposed framework in the study can significantly assist the UN-SDG 9 (Facilitate Sustainable Infrastructure in Developing Countries) and 11 (Affordable and Sustainable Transport System).

Pavement crack detection has always been an important problem in pavement maintenance. With rapid development in computer vision technology, crack detection using deep learning has become one of the major tasks in automatic pavement condition detection and assessment. This paper aims to develop a method of crack grid detection based on convolutional neural network. First, an image denoising operation is conducted to improve image quality. Next, the processed images are divided into grids of different scales (10 × 10, 20 × 20, 30 × 30), and each grid is fed into a convolutional neural network for detection. The pieces of the grids with cracks are marked and then returned to the original images. Finally, on the basis of the detection results, threshold segmentation is performed only on the marked grids. Information about the crack parameters is obtained via pixel scanning and calculation, which realizes complete crack detection. The experimental results show that 30 × 30 grids perform the best with the accuracy value of 97.33%. The advantage of automatic crack grid detection is that it can avoid fracture phenomenon in crack identification and ensure the integrity of cracks. Thresholding smaller grids allows better combinations of the differences between objects and backgrounds, which makes segmentation results more accurate.

Pavement Management System is designed to provide objective information and useful data for analysis so that road managers can make more consistent, cost-effective, and defensible decisions related to the preservation of a pavement network. During the process of road network maintenance and rehabilitation, road authorities strive to select an optimum maintenance strategy from a number of alternatives. Mathematical optimization models, supported by suitable data, can assist decision making about allocating funds between alternative maintenance tasks and about the size of the maintenance budget. It can be done through the analysis of costs and benefits by comparing the various maintenance alternatives with the help of an optimization method known as solver. The road segment mainly included in study was road from Hosanna Menhariya to Wachemo University and other important access roads. These roads are divided into different sections in not more than 100m length. The Study involves data collection, data analysis and the selection of optimal maintenance strategy by using a method known as Solver (Add-ins in Microsoft excel). In this study, patching was selected as possible maintenance among the other alternatives. The result of solver analysis for patching indicates that as 74,574 birr allocated for the maintenance of pavement per kilometer in different three segments under the municipality having the constraint budget of 152,018.45 birr/km. The optimized solution shows that about 20962.5 birr would be saved in one year per km with in municipality.

Transportation agencies should measure pavement performance to appropriately strategize road preservation, maintenance, and rehabilitation activities. The international roughness index (IRI), which is a means to quantify pavement roughness, is a primary performance indicator. Many attempts have been made to correlate pavement roughness to other pavement performance parameters. Most existing correlations, however, are based on traditional statistical regression, which requires a hypothesis for the data. In this study, a novel approach was developed to predict asphalt concrete (AC) pavement IRI, utilizing datasets extracted from the Long-Term Pavement Performance (LTPP) database. IRI prediction is categorized by two models: (i) IRI progression over the pavement’s service life without maintenance/rehabilitation and (ii) the drop in IRI after maintenance. The first model utilizes the recurrent neural network algorithm, which deals with time-series data. Therefore, historical traffic data, environmental information, and distress (rutting, fatigue cracking, and transverse cracking) measurements were extracted from the LTPP database. A long short-term memory network was used to solve the vanishing gradient problem. Finally, an optimal model was achieved by setting the sequence length to 2 years. The second model utilizes an artificial neural network algorithm to correlate the impacting factors to the IRI value after maintenance. The impacting factors include maintenance activities; initial (new construction), milled, and overlaid AC thicknesses; as well as IRI value before maintenance activities. Combining the two models allows for the prediction of IRI values over AC pavement’s service life.

The U.S. Air Force (USAF) estimates it has a $33 billion deferred maintenance backlog within its $263 billion infrastructure portfolio. While the USAF has had recent success advocating for addition...

Pavement Management Systems (PMS) can benefit the highway agencies in several ways both at network and project levels, especially in selecting cost-effective alternatives. Whether it is new construction, maintenance, or rehabilitation, PMS helps the authorities attain the best possible utilisation of available resources and public money. This investigative paper proposes a network level PMS for the roads of Tiruchirappalli city, India. Data pertaining to inventory, traffic, maintenance history, and serviceability of the study roads are gathered at regular intervals for a period of 7 years. The pavement sections are grouped into homogeneous clusters by k-means clustering, and Multiple Linear Regression Models are developed for the clusters to predict pavement performance. Based on the pavement condition, different maintenance treatments are identified through expert opinion surveys. An optimisation programme UPMMS-GAMS is developed using Generic Algebraic Modelling System (GAMS) software, to consolidate the city's PMS. Pavement condition, age, maintenance options, and maintenance cost are given as model inputs. Minimum performance levels and the maximum number of maintenance activities are constraints that must be guaranteed. The optimisation programme defines the maintenance and rehabilitation strategies to be employed each year to maintain the pavement sections above a minimum desired level.

In this paper, a performance curve is used to optimize flexible pavement maintenance. The main aim of this study is to develop a performance model based on Benkelman Beam (BB) results by using prediction models derived from the data for Pavement Condition Index (PCI) that was obtained from three districts belonging to the General Authority for Roads, Bridges and Land Transport (GARBLT) which consist of about Central district (26sections), Middle-Delta (22 sections), and East-Delta (14 sections) with a total length of 124 Kilometers. The proposed model was validated by comparing the predicted values with actual (PCI) with a coefficient of determination R^2 () equals 0.87. Structural evaluation of in-service pavements is a key activity for both the project and network-level pavement management systems. Benkelman Beam was used for measuring the deflection. Test points were taken at a distance of 1.5 m from the edge of the pavement. Since the deflections measured by the Benkelman Beam are influenced by the pavement temperature and seasonal variations in climate, therefore, pavement was recorded temperature for making subsequent corrections to the deflection values. Since the Structure Number (SN) evaluation of in-service pavements is a key component for both the Structural Condition Index (SCI), the resulting deflections from (BB) were converted to structural number (SN) using a model and the validity has been checked by taking samples from the pavement layers, which revealed a strong correlation between them with a coefficient of determination (R^2) of 0.62. The structure number in 2018 is predicted from the proposed model and then compared with actual field measurements for the same year. A conclusion is made regarding the validity of the proposed prediction model with a coefficient of determination (R^2) equals 0.91. Since (BB) reading is important to determine Pavement Condition Index (PCI) value.

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

Although pavement maintenance and rehabilitation (M&R) techniques are usually examined in economic terms, there is a growing need to address their environmental footprints. The objective of this study is to assess the environmental impacts of M&R techniques. Life cycle assessment (LCA) can help in the decision-making process of selecting suitable maintenance techniques based on their environmental impacts. This study investigates: patching, rout & sealing, hot in-place recycling, and cold in-place recycling. Global warming potential (GWP), acidification potential, human health particulate, eutrophication potential, ozone depletion potential, and smog potential are estimated as environmental impacts for each maintenance activity. Materials, equipment use (for construction and M&R), and transportation were the main elements considered. A sensitivity test is performed to identify the significant factors for the LCA. The study concluded that GWP was the most important impact category. Rout & sealing and cold in-place recycling produced the lowest GWP emissions. Notably, pavement patching and hot in-place recycling showed significant detrimental environmental impacts.

Airport Pavement Management System (APMS) is a useful tool, including a set of procedures for collecting, analyzing, maintaining, and reporting pavement data, thus assisting airports in finding optimum cost-effective treatments to preserve their pavement assets. The paper provides an in-depth overview of the APMS from an extensive literature review with the aim to identify numerous issues within APMS, such as the components, Pavement Condition Indices, software utilization, and the comprehensive implementation process. The methodology adopted for this research is a descriptive-based study approach on the various airport pavement manuals, guidelines and advisory circulars, journal articles, and book publications for the APMS applications. The airport pavement management systems and the case studies in various airports internationally will be included in the review. The study includes various subjects such as major components, benefit and cost approach, management in different levels, software utilization, maintenance, and rehabilitation (M&R) policies in the implementation of the APMS. Additionally, the research examines the pavement performance indicators that are the key elements for evaluating pavement conditions. Besides, the APMS software programs can store historical information, analyze data, develop models, and generate reports for M&R in association with the budget, including estimating future pavement life. The study summarizes the condition data required for the implementation and operation of an APMS, as well as the information generated by the APMS. The review highlights the benefits of an APMS in providing the airport operators and engineers far more informed position for decision-making to forecast future pavement maintenance requirements for an adequate and timely M&R.

Presently, most of the road agencies use Non-Destructive (NDT) tools to help them prioritise pavement maintenance and rehabilitation (M&R) activities at the network level, thus optimising the limited budgetary resources. One of the most widely used NDT techniques for pavement structural evaluations, at the network level assessment, is the Falling Weight Deflectometer (FWD). Using a database comprising of a wide array of typical layer moduli and thicknesses of traditional flexible pavements, that were generated based on multiple Monte Carlo numerical simulations, as a reference datum, this study successfully developed probabilistic models that allow for analysing the condition of a flexible pavement, at the network level, from FWD surface deflection data, namely the Deflection Bowl Parameters (DBPs), to identify which layers of the pavement structure present a probability of structural failure or damage.

Road and pavement maintenance affect directly traffic safety and comfort. At the same time, maintenance treatments cause traffic discomfort and absorb valuable resources, thus, the choice of maintenance and rehabilitation technique is a complex, yet crucial issue. Road operators need to seek for the financial prerequisites for the success of maintenance strategy. Subsequently, the expected economic benefits will be inextricably linked to road performance features. The study herewith presented enlightens the economic aspect of mainly used pavement maintenance techniques, so as road operators identify the preferred one as regards budget needs. A comprehensive method to evaluate the current maintenance and strengthening techniques for flexible pavements using economic assessment tools is the main task of this paper. The results are meant to be a useful tool for road operators to conclude to the preferable, in terms of financial needs, alternative for maintenance and strengthening works.

The detection and classification of pavement distress (PD) play a critical role in pavement maintenance and rehabilitation. Research on PD automation detection and measurement has been actively con...

The choice between continuously reinforced concrete pavement(CRCP) and jointed concrete pavement(JCP) for different concrete pavement applications has long been controversial. Research on these two...

Axle load spectra constitute a crucial part of the data for pavement design and pavement distress analysis. Typically, axle load spectra represent static load from vehicles and do not include dynam...

With ever-increasing road mileages worldwide, more pavement deterioration and ageing present great challenges to the maintenance and rehabilitation (M&R) of road pavement. In this study, an intelli...

Worldwide, more than 1.25 million people die annually in road traffic accidents and between 20 and 50 million more are injured. By 2030, highway-related crashes are projected to be the 5th leading cause of death in the world. Road accidents have several contributing factors, including roadway conditions, vehicle conditions, and factors related to the road users. While some of these factors have been studied extensively by researchers very few focused on quantifying the relationship between accidents frequency and pavement quality. Before 1990s, due to the lack of pavement data collection technology, it was very difficult to carry out state-wide scale studies relating pavement quality and road safety. However, in the past decades, there has been a huge growth and awareness in the importance of road safety, leading to a significant increase of research in the topic. Researchers started to study other contributing factors to accidents occurrence and due to the development of high-speed friction measurement tools, agencies can now include friction into network level Pavement Management Systems (PMSs). The objective of this article is to contribute to the incorporation of safety concerns into Pavement Management by performing an exploratory analysis to assess the impact in the agency, user and total costs of performing two different maintenance policies. The methodology consisted in evaluating the evolution of the condition of the road pavements using pavement performance prediction models, followed by the prediction of the expected number of accidents and, finally the calculation of the agency and user costs. The results obtained provided a strong starting point to understand the requirements to solve this type of problems that not only affect road agencies but also the society in general.

[EN] Pavement condition assessment is a critical step in road pavement management. In contrast to the automatic and objective methods used for rural roads, the most commonly used method in urban areas is the development of visual surveys usually filled out by technicians that leads to a subjective pavement assessment. While most previous studies on automatic identification of distresses focused on crack detection, this research aims not only to cover the identification and classification of multiple urban flexible pavement distresses (longitudinal and transverse cracking, alligator cracking, raveling, potholes, and patching), but also to quantify them through the application of Convolutional Neural Networks. Additionally, this study also proposes a methodology for an automatic pavement assessment considering the different stages developed in this research. This methodology allows for a more efficient and reliable pavement assessment, minimizing the cost and time required by the current visual surveys., The study presented in this paper is part of the research project titled SIMEPU Sistema Integral de Mantenimiento Eficiente de Pavimentos Urbanos, funded by the Spanish Ministries of Science and Innovation and Universities, as well as the European Regional Development Fund under Grant No. RTC-2017-6148-7. The authors also acknowledge the support of partner companies Pavasal Empresa Constructora, S.A. and CPS Infraestructuras, Movilidad y Medio Ambiente, S.L. and the Valencia City Council.

Roadway stack plays an indispensable role in the whole cycle of road exploration design, construction and maintenance management. However, due to human factors, the settlement error of road 100-meter piles between the actual station and the designed station is unavoidable, which has significant influences on the subsequent road maintenance management. In this paper, in order to calibrate this type of error, a calibration strategy and the Geographic Information System (GIS) secondary development is realized, which combines ArcGIS model builder with Python script. Finally, an automatic calibration tool is developed. The automatic batching processing method for the roadway stack calibration improves the accuracy and efficiency of the vehicle-based pavement maintenance.

This paper evaluates the amount of noise reduction when using different pavement materials in two adjacent lanes, where the close-proximity (CPX) method is introduced to analyze the tire/road noise before and after pavement maintenance. We consider four types of pavement materials, including ECA-10, PUC-10, PAC-13, and double-layer porous asphalt pavement (PAC-13+PUC-10), where these materials and their combinations are paved on two adjacent lanes. We measure the tire/road noise with the CPX method using a two-wheel trailer that can install two types of tires in different tests. This study provides some guidelines on controlling traffic noise pollution by using the combination of low noise pavement materials. From the experimental and theoretical results, one can see that the highest amount of noise reduction can be obtained when both the inner and outer lanes use the double-layer porous asphalt pavement. To make a balance between the noise reduction performance and the road maintenance cost, one can have the suboptimal choice, where the inner lane uses PUC-10 and the outer lane uses the double-layer porous asphalt pavement.

Performance evaluation of asphalt concrete (AC) pavements requires measurement of the temperature at a specified depth. Most of the guidelines recommend a fixed depth from the top of the pa...

The evaluation of roadways utilizing complex contemporary datasets is currently conducted periodically because of the collection methods’ high cost. The study presents a data-driven framework on the use of a vehicle, a smartphone, an on-board diagnostic (OBD) device and machine learning for the rating of pavement surfaces. The proposed system architecture has been field-tested for the detection of pavement anomalies and the classification of five rating categories. Further, the proposed system may provide daily information on roadway pavement surface conditions, which can be used by engineers for automating the planning of pavement maintenance operations and improving public safety.

Although many research projects have been completed to investigate asphalt crack treatment/repair effectiveness, all of them reported local investigation results with small sample sizes, in...

This report presents a case study for constructing aggregate subgrade improvement (ASI) layers using quarry by-product aggregates (QBA), a quarry mix of large primary crushed rocks (PCR) and sand-sized quarry fines. The construction took place at Larry Power Road in Bourbonnais Township in Kankakee County, Illinois, where the Illinois Department of Transportation placed two QBA mixes. The first mix (QBA_M1) consisted of 45% quarry by-products and 55% railroad ballast–sized 3×1 PCR. The second mix (QBA_M2) consisted of 31% and 69% quarry by-products and PCR, respectively. Two conventional ASI sections were also constructed conforming to Illinois Department of Transportation’s CS02 gradation. All sections consisted of a 9 in. (229 mm) QBA/PCR layer topped with a 3 in. (76 mm) dense-graded capping layer. Laboratory studies preceded the construction to recommend optimum quarry by-product content in the QBA materials and construction practice. The Illinois Center for Transportation research team monitored the quality and uniformity of the construction using nondestructive testing techniques such as dynamic cone penetrometer, lightweight deflectometer, and falling weight deflectometer. The segregation potential was monitored by visual inspection and imaging-based techniques. Short-term field evaluation of the constructed QBA layers, particularly QBA_M2 with a 31% quarry by-product content, showed no evidence of abnormal segregation and did not jeopardize the structural integrity of the QBA ASI layers, which had slightly lower but comparable strength and stiffness profiles to the conventional ASI sections. The use of QBA materials in ASI was field validated as a sustainable construction practice to provide stable pavement foundation layers.

Crack sealing treatment includes the use of a router to create a reservoir on the intended cracks, whereas crack filling is given without any modification to the crack wall. With sealing treatment the pavement can have longer service period because of its good adhesion and cohesion performance. However, the initial cost of sealing is higher due to the use of extra material, equipment and labor. As a result, the aim of this study is to investigate the long-term cost-effectiveness in between these two treatment techniques in Texas and to observe if the longer service period can compensate the higher initial cost of sealing treatment. For this purpose, the initial cost inputs have been identified and a highway design and maintenance standard model (HDM-III) used to evaluate the most cost-effective pavement maintenance between crack sealing and filling treatment over a 25-year analysis period. Agency, capital and net present value have been calculated by using HDM-III based upon average daily traffic (ADT), altitude and road length. The results of this study indicated that (1) crack sealing is more cost-effective pavement maintenance compared to crack filling over a long period of time; (2) agency cost can be reduced by 15% and 14% based on ADT and road length analysis, respectively, by implementing crack sealing treatment; (3) capital cost in terms of ADT and road length with crack sealing maintenance can be saved by 24% and 19%, respectively; (4) the net present value with crack sealing treatment after a 25-year analysis period is higher for ADT and road length compared to crack filling by 18% and 19%, respectively; (5) the cost-effective resealing interval years of 5 years is proposed when the unit cost of routing increased by 25%.

Light pavement rehabilitations and low-cost treatments are extensively employed among transportation agencies on roads with relatively low traffic volumes to optimize available resources. One concern with this approach entails the difficulties of determining the optimal timing for treatment application. Making the best use of limited resources requires improvements in maintenance decision-making for selecting treatments considering all affecting factors and previous experience. This paper presents a machine learning approach in the decision-making process for determining the most appropriate pavement maintenance and rehabilitation alternatives for low-volume paved roads at the network level. Based on regional experts’ recommendations and engineering judgments in Colorado, a wide range of 884 cases of pavement-treatment patterns were generated. Then an artificial neural network (ANN) was trained with pattern-recognition algorithms. Two ANN prediction models were developed on the basis of pavement condition data, represented by six condition indices, and road lengths. The objective of training the models is to evaluate the variability of maintenance practices among five engineering regions within the Colorado Department of Transportation (CDOT). The outcome of this study describes the implementation gaps of pavement-preservation activities among CDOT regions resulting from limited maintenance funding. The regional maintenance selection can be processed by the developed ANN decision-making tool to recommend alternatives from regional recommendations as well as similar applications statewide to fit pavement management needs and expected performance.

Evaluation of pavement condition, which determines pavement maintenance and rehabilitation necessities, is inevitable using structural or non-structural methods. Since factors such as cost and time required for testing limit the use of structural assessment devices, the development of cost-effective methods should be investigated. In this paper, a practical solution has been presented for pavement structural evaluation which is considered as a useful method for assessing pavement layers condition and identifying rehabilitation needs. With this in mind, we developed a relationship between deflection bowl parameters derived from Falling Weight Deflectometer (FWD) and two pavement performance indices, International Roughness Index (IRI) and Pavement Surface Evaluation and Rating index (PASER), by the use of Artificial Neural Network (ANN) and regression models. To obtain the required data, project field surveys were conducted from 318 sections of the main roads of Kermanshah and Ilam provinces in Iran. The results show that our model provides a satisfactory correlation between IRI, PASER, and structural indices which are based on deflection measurements. By comparing the results of ANN and regression models, the superiority of ANN performance over non-intelligent models is appreciable. The findings of this study indicate that using both IRI and PASER indices leads to accurate structural pavement evaluation.

A large number of car accidents are caused by failures in the pavement. Their automatic detection is important for pavement maintenance, however, the current public datasets of images to train and test these systems contain a few hundred samples. In this paper, we introduce a new large dataset of images with more than 2000 samples that contains the five most common risks on pavement manually annotated. We analyze and describe statistically the properties of this dataset and we establish the performance of some baseline methods in order to be useful as a benchmark. We achieve up to 89.35% accuracy in the segmentation of the different types of risk on the pavement

One of the key steps in pavement maintenance is the fast and accurate identification of the distresses, defects, and pavement markings and ability to conduct the maintenance before the irre...

In recent years, the growing survey capability for continuous and frequent inspections of road pavement conditions has raised questions regarding the optimal solutions for data management. Consider...

Three-dimensional (3D) laser scanners have become a mainstream technology for the automatic assessment of pavement condition. The objective of this study is to leverage highly accurate 3D p...

This study aims at establishing guidelines for the design of roadway (e.g. pavement maintenance, horizontal alignment design) for the new autonomous public transport (APT) concerning passenger ride comfort. Bottom-up and top-down approaches are used, in which the former method uses existing bus vehicles and network as a base-line reference while the latter method is based on APT’s vehicle specification and operation.

Preventive maintenance is a new concept of maintenance technology. When the road maintenance department has a good pavement structure or a pavement disease at the initial stage, it will maintain it to prevent the road disease from developing to a deeper level, thereby extending the service life of the pavement. Operating methods and practical methods to keep the intactness and smoothness of the road, improve the quality of the road, reduce the lifetime cost of the road, and extend the duration of the repair or overhaul. This chapter introduces the purpose and significance of preventive maintenance, the background of the implementation of preventive maintenance technology in China, the status of preventive maintenance technology at home and abroad, the overview of preventive maintenance technology commonly used in China and its development trends.

This Special Issue “Sustainable Designed Pavement Materials” has been proposed and organized as a means to present recent developments in the field of environmentally-friendly designed pavement materials. For this reason, articles included in this special issue relate to different aspects of pavement materials, from industry solid waste recycling to pavement materials recycling, from pavement materials modification to asphalt performance characterization, from pavement defect detection to pavement maintenance, and from asphalt pavement to cement concrete pavement, as highlighted in this editorial.

Machine learning (ML) is one of the intelligent methodologies that has shown promising results in the domains of classification and prediction. In this paper, Azure Machine Learning (AML) systems were applied for flexible pavement maintenance classification problems and solutions. For prediction, four parameters were used as the inputs namely severity, density, road functional, and average daily traffic (ADT) while the output parameters were treatment techniques. This paper provides a critical analysis of classification algorithms: two-class support vector machine, multi-class decision forest, and multi-class neural network. Characteristic significance analysis was carried out to investigate how each classifier utilized the information available in the dataset, focusing on the application of azure machine learning classification (AMLC) to the pavement maintenances results prediction. Overall, the findings showed that predictions using Multi-Class Neural Network (MCNN) were obtaining an accuracy of 0.99. The results indicated that prediction obtained from AML training seemed to be more powerful, with a smaller standard error. Azure ML technique showed high accuracy with satisfactory results and capability of predicting pavement maintenance rigorously.

Asphalt Concrete (AC) overlay is one of the typical pavement maintenance strategies on Portland cement concrete (PCC) pavements that enhances its service life. However, reflection cracking is one of the common distresses due to the cyclic expansion and contraction of the joints or cracks in the PCC layer that attributed to the variations in environmental conditions. The current overlay tester (OT) protocol in Texas suggested a single maximum opening displacement (MOD) in PCC pavement types. However, the displacement magnitude varies accordingly. As such, this study focuses on the field quantification of Jointed Concrete Pavement (JCP) and Continuously Reinforced Concrete Pavement’s (CRCP) discontinuity movements with respect to the application of asphalt overlay and seasonal changes. Crackmeters were installed at the JCP and CRCP sections and measurements were recorded during winter and summer. Preliminary results have shown that the PCC pavement joint/crack movements significantly reduced after overlay placement. Furthermore, variations of the movement relative to PCC pavement types and seasonal variations are distinct and significant. Hence, the magnitudes of MOD according to pavement types are suggested to be modified when performing reflection cracking resistance evaluation using the asphalt overlay tester.

To improve microwave heating efficiency of asphalt pavement, a new technique was proposed by coating a very thin layer of magnetic Fe3O4 films on coarse and fine aggregates, using chemical co-preci...

Pavement resurfacing is a maintenance activity that is undertaken to enhance the service life of pavement. This pavement resurfacing activity involves laying a new layer of asphalt concrete over existing pavement after certain time. Due to engineering factors, economic variables and uncertainty in forecasting, the pavement resurfacing decision process is a complicated activity. Various optimization approaches currently used are simplified models for finding optimal frequency and resurfacing intensity within pavement maintenance framework. In this research, artificial bee colony algorithm is proposed to solve this pavement resurfacing optimization problem. This algorithm mimics the collective behaviour of bees while searching for nectar. In this approach, various scenarios are generated, optimality of each case is evaluated, and the information thus generated is used in subsequent evaluation until global optimality is reached. The effectiveness of proposed method is demonstrated through a numerical example. The solution obtained is similar to the exact solution reported in literature. The results indicate optimal resurfacing values can be obtained with little computational effort with proposed approach. The main advantage of proposed algorithm is removal of specification of trigger values for maintenance decision. Keywords: Pavement, Resurfacing, Artificial bee colony, Life cycle cost, Optimization

This paper proposes an Analytic Hierarchical Process (AHP) theory based method to determine the weight of the decision-making influence factors, considering their relative significance and generating an overall ranking for each road section. A case study on the highway network maintenance priority was conducted to illustrate the proposed procedure. A total of five pavement maintenance decision-making related factors were considered in the study, including pavement performance, pavement structure strength, traffic loads, pavement age and road grade. The weightings of the five factors were quantified through AHP method. Then, the comprehensive ranking index value Ui was determined, which indicated the maintenance priority of a road section in network level decision-making. From the aspect of maintenance cost, the sensitivity analysis results were in accordance with the weightings of different maintenance decision-making factors. The pavement maintenance cost was significantly sensitive to the change of pavement performance. The case study clearly demonstrated the applicability and rationality of the AHP theory based decision-making method and it can be used as a guideline for pavement maintenance agencies. Keywords: Asphalt pavement, Network level, Maintenance decision-making, Analytic Hierarchical Process, Factor weight, Priority

Road agencies are facing the challenges of aging pavements, deteriorating networks, and insufficient maintenance budgets. This study addresses two limitations in the current state of practice in pavement management. First, because the evaluation of maintenance strategies has traditionally focused on economic and technical aspects, it neglects the environmental impact of maintenance decisions. Second, current management systems often provide a unique, optimised pavement maintenance strategy based on a specific objective(s) and constraint(s). The main objective of the study is to analyse the effect of including environmental aspects and funding availability in the design of maintenance strategies. To achieve this objective, the study followed a three-step methodology. First, this study reviews existing practices on pavement maintenance and the criteria considered to trigger the application of maintenance treatments and their effects on pavement condition. Then, maintenance strategies are optimised considering three levels of budgetary capacity and a sustainable evaluation which incorporates technical, economic, and environmental aspects over the pavement lifecycle. Finally, a case study dealing with an urban pavement network in Chile is analysed. Results obtained from this case study show that an increment of 2% in maintenance budget allows to account for more sustainable maintenance decisions, such as cold in-place recycling and full-depth slab repair, whose greenhouse gas emissions are lower than other alternatives. Results also show that functional asphalt overlay and microsurfacing are more recommended in flexible pavements when budgetary restrictions are low, whereas recommended treatments for rigid pavements exhibit small variability with budgetary restrictions.

In recent years, due to the destructive and unproductive character of pavement specimen extraction, pavement maintenance technology intensified the use of non-destructive techniques for pavement evaluation which resulted in the development of several devices and evaluation methods. This paper describes the use of technology based on low frequency ultrasonic tomography for evaluation of concrete pavement parameters. The equipment was applied in three experimental sections with different concrete pavements built at the University of Sao Paulo campus. The ultrasonic signal processing is given. The results analysis enables the efficient and reliable identification of thickness and reinforcement position within the concrete slab. Construction problems were evidenced in one of experimental sections with thickness deficiencies and reinforcement in a position below projected. Furthermore, the use of a novel concrete quality indicator was correlated with the presence of transverse cracks and alkali-silica reaction within the sections.

Several information technology approaches have been applied to life cycle analyses to improve decision-making performance and prolong infrastructure life spans. Belief function theory (BFT), also known as the Dempster-Shafer theory of evidence, is a method of representing uncertain, incomplete, and imprecise evidence of a system’s knowledge base. BFT has rarely been applied to pavement management until now. For pavement management decision-making, pavement engineers must formulate relationships between various objectives, incorporate subjective judgment, and pool evidence from various sources. These issues are not adequately addressed in the existing analytical tools used in pavement management. This paper conducts a study of pavement maintenance and rehabilitation (M&R) activities using BFT. Questionnaires are carried out to establish an evidential tree, which is used to quantify the level of uncertainty associated with M&R activities. With respect to the given evidence in the case study, the belief interval of (0.222, 0.778) with uncertainty for the M&R activities can be obtained by using Dempster’s rule of combination. BFT provides a more rigorous but straightforward approach to assisting pavement engineers with M&R decision-making.

The automatic pavement management system has the advantage of providing reliable pavement maintenance and rehabilitation strategies aiming at prolonging existing pavement service life. Therefore, t...