Rate Of Pavement Deterioration Research Articles

A machine learning-based approach to assess impacts of autonomous vehicles on pavement roughness.

Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected fromthe camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e. international roughness index) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance and driving speed, plus 20 othercontext variables. A total of 1707 observations is extracted from the long-term pavement performance database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration and that CAV deteriorates pavement faster than HDV by 8.1% on average. According to the sensitivity analysis, CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Assessing Critical Road Sections: A Decision Matrix Approach Considering Safety and Pavement Condition

Identifying critical road sections that require prompt attention is essential for road agencies to prioritize monitoring, maintenance, and rehabilitation efforts and improve overall road conditions and safety. This study suggests a decision matrix with a hierarchical structure that factors in the pavement deterioration rate, infrastructure safety, and crash history to identify these sections. A Markov mixed hazard model was used to assess each section’s deterioration rate. The safety of the road sections was rated with the International Road Assessment Program star rating protocol considering all road users. Early detection of sections with fast deterioration and poor safety conditions allows for preventive measures to be taken and to reduce further deterioration and traffic crashes. Additionally, including crash history data in the decision matrix helps to understand the possible causes of a crash and is useful in developing safety policies. The proposed method is demonstrated using data from 4725 road sections, each 100 m, in Addis Ababa, Ethiopia. The case study results show that the proposed decision matrix can effectively identify critical road sections which need close attention and immediate action. As a result, the proposed method can assist road agencies in prioritizing inspections, maintenance, and rehabilitation decisions and effectively allocate budgets and resources.

Large-scale evaluation of pavement performance models utilizing automated pavement condition survey data

Pavement performance models are an essential component of a pavement management system (PMS) and have a direct impact on future pavement condition prediction, selection of pavement maintenance and rehabilitation (M&R) methods, and budget planning and allocations. Therefore, it is critical to develop and maintain pavement performance models as accurate as possible. The California Department of Transportation (Caltrans) has implemented a modern pavement management system called PaveM. To maintain the intended functions of PaveM, regular updates of its databases and key components are necessary, including pavement performance models. This paper aims to evaluate the network-level pavement performance models in PaveM by utilizing the concept of deterioration rate and the most recent automated pavement condition survey (APCS) data. First, the concept of pavement deterioration rate was defined. The actual deterioration rates were calculated using two latest cycles of APCS data and then compared to the predicted deterioration rates obtained using APCS data and the current configurations of PaveM. Two typical pavement distresses (International Roughness Index and Caltrans’ asphalt pavement alligator B cracking) for one selected pavement treatment (thin overlay) were predicted using PaveM and compared to the actual APCS measurements. The results from this study show that the concept of deterioration rate was effective in evaluating the overall quality of performance models in PaveM as well as identifying the time spans in which the models may over- or under-predict pavement performance.

Analysis of failed flexible road pavement deterioration rates in Benin City urban roads of Nigeria

Analysis of failed flexible road pavement deterioration rates in Benin City urban roads of Nigeria

Computer vision based asphalt pavement segregation detection using image texture analysis integrated with extreme gradient boosting machine and deep convolutional neural networks

Aggregate segregation is a major form of defect that accelerates the pavement deterioration rate. Therefore, asphalt pavement segregation needs to be detected accurately and early during the quality survey process. This study proposes and verifies a computer vision based method for automatic identification of aggregate segregation. The new method includes Extreme Gradient Boosting Machine integrated with Attractive Repulsive Center-Symmetric Local Binary Pattern (ARCSLBP-XGBoost) and Deep Convolutional Neural Network (DCNN). Experimental results obtained from a repetitive random data sampling process with 20 runs show that the ARCSLBP-XGBoost is a capable approach for detecting asphalt pavement segregation with outstanding performance measurement metrics (classification accuracy rate = 0.95, precision = 0.93, recall = 0.98, and F1 score = 0.95).

Environment-friendly recycled asphalt pavement design for road maintenance applications.

Potholes are one of the most common road distresses in Kuwait especially after winter season in 2018. Pavement deterioration rate significantly increases as the pavement exposes to moisture. Paving road requires using high-quality materials. This paper aims to investigate the feasibility of using recycled asphalt pavement in hot mix asphalt (HMA) design. Questionnaires and a series of laboratory tests were conducted to analyze the effects of pores on the society and test the recycled and the regular mixtures performance to choose the best option regarding it. Marshal test portrayed that the recycled mixture has a high stability and flow. According to the tensile strength ratio test requirements which set a minimum ratio of 75%, it is recommended to reduce the proportion of recycled aggregate. The Hamburg wheel tracker (HWTD) test proved that the recycled asphalt mixture has a low rutting depth for wet and dry samples compared to the general one. On the other hand, the wet recycled mixture requires a reduction by of 20% to satisfy the specifications. The physical properties for both mixtures were compared, and the voids filled with asphalt (VFA) and voids in mineral aggregate (VMA) of the recycled mixture are lower than those in the regular mixture. The recycled mixture reveals a higher efficiency in saving costs and improving mixtures used for potholes maintenance activities. All the results proved that the mixtures consisting of recycled asphalt are most preferred since they are affordable and perform reasonably well compared to mixtures made of regular asphalt. As a future work, smaller percentages of recycled aggregate should be tested to check the robustness and sustainability of the designed recycled asphalt mixture using different tests such as Hamburg wheel tracker test (wet samples) and indirect tensile strength test. Furthermore, more experiments can be run to test other mix parameters and properties such as durability.

A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty

ABSTRACT This study approaches a multi-stage stochastic mixed-integer programming model for the high-level complexity of large-scale pavement maintenance scheduling problems. The substance of some parameters in the mentioned problems is uncertain. Ignoring the uncertainty of these parameters in the pavement maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions. In this study, annual budget and pavement deterioration rate are considered uncertain parameters. On the other hand, pavement agencies generally face large-scale pavement networks. The complexity of the proposed stochastic model increases exponentially with the number of network sections and scenarios. The problem is solved using the Progressive Hedging Algorithm (PHA), which is suitable for large-scale stochastic programming problems, by achieving an effective decomposition over scenarios. A modified adaptive strategy for choosing the penalty parameter value is applied that aims to improve the solution process. A pavement network including 251 sections is considered the case study for this investigation, and the current study seeks optimal maintenance scheduling over a finite analysis period. The performance of the stochastic model is compared with that of the deterministic model. The results indicate that the introduced approach is competent to address uncertainty in maintenance and rehabilitation problems.

The performance evaluation of heavy and light duty pavements in Victoria/Australia

ABSTRACT Reported in this paper is an evaluation of structural and functional performance of heavy duty and light duty pavements by addressing the effects of several factors on the rate of pavement deterioration. The sample used herein is a network of freeways and arterials from Victoria/Australia covering four road classes, namely M, A, B and C. The first two road classes represent heavy duty pavement and the last two road classes represent light duty pavements. These road classes differ in qualities, functions, duties, geometric standards, and traffic volumes and loadings. This paper aims to assess the performance of the four road classes by comparing their pavement conditions and environmental factors affecting the rate of pavement deterioration. More than 2300 kilometers of road length was selected from 40 highway sections to reflect Victoria’s network conditions. The parameters considered for assessing road performance were traffic loading, pavement strength, and environmental factors. Hierarchical linear regression analysis was used to explore the significance of different predictor variables for all road classes. The study confirms that the rate of roughness and rutting progression per year for all road classes is very close, and the environmental factors affect roughness and rutting progressions for light duty pavements only.

Pavement Deterioration Rate and Maintenance Cost for Low-Volume Roads

Allocated budgets for maintenance of road networks are normally limited. Therefore, not all roads receive the required attention they deserve in a timely manner. These roads are left to deteriorate until the next maintenance round. The cost associated with delayed maintenance is significantly excessive. A Pavement Maintenance Management System (PMMS) can be a useful tool for evaluation, prioritization of Maintenance and Rehabilitation (M&R) projects, and determination of funding requirements and allocations. The pavement condition is normally indexed using a parameter called Pavement Condition Index (PCI) which represents an overall assessment of surface defects by type, severity and extent. Periodic collections of PCI over time for different sections within the roadway network provide an approach to monitor changes in pavement serviceability over time and can produce useful data to predict and evaluate required maintenance solutions and their associated cost. The researchers intend to use available data collected over the span of a year and a half on sections within the roadway network at the campus of Al-Zaytoonah University of Jordan (ZUJ) to study the relation between the maintenance cost and the pavement deterioration rate. This study may incorporate variables such as pavement age, traffic volumes, maintenance history and pavement condition assessment results. The available records of PCI will be analyzed and the findings will be clearly presented. The practical inclusion of the findings within the current PMMS used at the university will also be detailed.

Short and Long-Term Field Performances and Optimal Timing of Chip Seal in Hot and Humid Climates

Chip seal is a preventive maintenance technique typically applied on relatively low traffic roads to reduce pavement deterioration rate and to defer the need for costly rehabilitation activities. This study aims to address the common challenges with chip seal application in hot and humid climates such as Louisiana to ensure the maximum benefits are achieved. Specifically, this study evaluated the short and long-term field performances and optimal timing of chip seal by analyzing the cracking, roughness, and overall pavement conditions of 47 flexible and composite pavement sections in Louisiana. Furthermore, potential moisture damage in asphalt concrete after chip seal application was assessed. Results indicated that chip seal extended pavement service life by 4–17 years based on the pre-treatment pavement conditions and pavement type (flexible or composite). Based on the cost benefit analysis, it is recommended to use alternating cycles of asphalt overlay and chip seal on low volume roads (less than 5,300 vehicles per day) with chip seal applied when the pavement condition index (PCI) of the pavement drops to a value between 70 and 74. In this case, significant monetary savings could be achieved when compared with adding chip seals at different time periods (outside the recommended range of PCI between 70 and 74). Results also showed that the application of chip seal does not seem to contribute to moisture damage. Instead, shallow groundwater conditions present in the State seem to contribute to moisture damage in asphalt pavements owing to moisture entrapment underneath the asphalt layer.

Maintenance prioritization of interlocking concrete block pavement using fuzzy logic

The combined influence of traffic and environment causes every pavement to deteriorate over time. Maintenance actions help to delay the rate of pavement deterioration. Pavement maintenance demand large amount of fund, resources and time. Prioritization of maintenance work enables to allocate fund and resources in an effective way based on the performance evaluation of the pavement section(s). There are different maintenance prioritization mechanisms exist to evaluate a pavement. From the review of past literature, it is observed that fuzzy logic has not been used to evaluate interlocking concrete block pavements (ICBP). This research is an attempt to implement fuzzy logic for the purpose of maintenance prioritization on ICBP. The distresses of ICBP considered in this study are rutting, depression and damaged paver blocks. The distress density is given as inputs in the fuzzy prioritization model. The outputs are the pavement condition index (PCI) and the maintenance strategy to be adopted. An experts’ survey was conducted to define the boundaries of the membership functions of the input (distresses) and output (PCI and maintenance strategies) parameters. The triangular membership function is used in this study. The maintenance strategies suggested are routine and preventive maintenance, major and minor rehabilitation and reconstruction. The fuzzy model developed in this work has been demonstrated with a case study.

Performance of Chip Seal Treatments in Two Different Climatic Regions

Over time, new pavements deteriorate because of the effect of traffic loads and the environment. Pavement preservation treatments, such as chip seals, are a cost-effective alternative for extending the service life of the pavement without incurring in costly rehabilitation or reconstruction activities. Chip seals are preservation treatments that can help protect the pavement structure, reduce the rate of pavement deterioration, improve skid resistance, and address minor surface problems. As part of the National Center for Asphalt Technology and Minnesota Road Research Facility Pavement Preservation Study, chip seal test sections were placed on low-traffic-volume roads in Alabama and Minnesota. The two locations were selected to represent different climate conditions. Lee County Road 159 in Auburn, Alabama, is subjected to warm, wet, no-freeze climate, while County State Aid Highway (CSAH 8) is located in a cold, wet, freeze area in Pease, Minnesota. Treatments have been in service for approximately 6 years in the southern sections, and 2 years in the northern sections. During this time, cracking, roughness, rutting, and macrotexture data were collected periodically to evaluate pavement performance. The results determined that cracking is a predominant form of distress for these treatments. While the treatments are not expected to address rutting or roughness, the results indicated little variation in the case of the southern treatments, and an increasing trend in international roughness index in the northern sections, likely related to the appearance of thermal cracking. Macrotexture data may be used to assess the functional life of the treatments.

Effectiveness of crack sealing in reducing the rate of pavement deterioration

Cracks in highway and airfield pavements pose safety challenges because they can lead to serious surface defects that impair the operations of aircraft or automobiles that use these pavements. It is therefore important to apply treatments that reduce the rate of deterioration caused by cracking. This paper presents a model for determining the effectiveness of crack sealing in terms of the reduction in the rate of pavement deterioration. The model utilises a functional form that accounts for the pre-sealing level of deterioration and the effort (maintenance dollars) expended on the crack sealing treatment, thus facilitating a more intuitive engineering interpretation of the model formulation. Linear and non-linear regression techniques were used to estimate the model coefficients. Using sensitivity analysis, the model quantified the extent to which the level of pre-sealing pavement condition and maintenance treatment expenditure influence the effectiveness (reduction in the rate of deterioration) of the sealing treatment. The paper also shows that the sealing effectiveness due to the crack sealing treatment does not exceed a certain limit (cap) and that this cap is a function of the pre-sealing pavement condition. Furthermore, the paper’s results suggest that the consequences of deferring or not carrying out sealing of a cracked pavement depend on the pre-treatment pavement condition.

Developing Performance Prediction Models for Pavement Management Systems in Local Governments in Absence of Age Data

Pavement Management Systems (PMS) use a strategic and data-driven approach to optimize budget allocation to various maintenance and rehabilitation (M&R) projects. Performance prediction models are used in PMS to determine the optimum timing for M&R interventions on every pavement section. Many local roadway agencies use empirical regression models which are based on past condition and age data. Often, these agencies are faced with limited resources for data collection and a high staff turnover rate, which all result in inadequate or unreliable construction history and pavement age data. This paper recommends a simple practical approach for local governments to develop performance prediction models in the absence of reliable pavement age data. Also, best practices for data pre-processing and validation of the model prediction capability are synthesized. Instead of using regression models based on condition and age, the pavement deterioration rate at each condition level is estimated. Similar to the Markovian transition probability concept, it is assumed that deterioration rates for every family of pavements are independent of time and only dependent on the current condition level. For every pair of subsequent condition measurements on a section, the difference in condition score is normalized by the difference in measurement time. These deterioration rates are then classified into bins based on the initial condition level for every pair of measurements. The average deterioration rate for all data records in each bin is then used to build a deterioration curve. This approach is demonstrated in this paper using real but anonymous agency data.

Field Performance of Chip Seals for Pavement Preservation

Over time, new pavements deteriorate due to the effect of traffic loads and the environment. If appropriate treatments are applied during the early stages of deterioration, it is possible to extend the service life of the pavement without incurring in costly rehabilitation or reconstruction activities. Chip seals are preservation treatments that can help protect the pavement structure, reduce the rate of pavement deterioration, improve skid resistance, and address minor surface problems. As part of the National Center for Asphalt Technology Pavement Preservation Study, chip seal test sections were placed in a low traffic volume road (Lee County Road 159) in Auburn, Alabama. The location consists of a two-lane county road that provides dead end access to a quarry and an asphalt plant, resulting in a high percentage of heavy loads. At the time of treatment, the existing pavement was 14 years old and consisted of a 5.5 in. hot-mix asphalt layer over a 6.0 in. granular base. Treatments were applied in the summer of 2012 and have been in service for approximately 4.5 years. During this time, cracking, roughness, rutting, and macrotexture data were collected weekly to evaluate pavement performance. The results determined that the performance of the treated sections is highly dependent on the initial condition of the pavement, particularly the percentage of area cracked. Pavements that are treated while still in good condition tend to remain in that category for a longer time. Macrotexture may also be used to evaluate the functional performance of the chip seals.

Effective Systems for Rating Pavement Condition

Existing systems for rating pavement condition are primarily based on current pavement conditions, distresses, or both. These systems do not account for the pavement's deterioration rates. For instance, two pavement sections rated fair this year may or may not have similar ratings next year. A balanced and comprehensive system for rating pavement condition should be based on the pavement's conditions, distresses, and deterioration rates. In a research study sponsored by FHWA, dual systems of rating pavement condition were developed on the basis of the pavement's functional and structural aspects and rates of deterioration. These rating systems account for current and future pavement conditions and distresses. Therefore, the systems provide more accurate information to decision makers about management of the pavement network while being simple enough for communication with legislators and the general public. The functional rating is based on ride quality (international roughness index) and safety (skid resistance and rutting) and is expressed by the remaining functional period (RFP). The structural rating is based on cracking, faulting, and rutting and is expressed by the remaining structural period (RSP). The RFP is defined by the shortest time in years between now and the year when one of the conditions reaches its threshold value. Similarly, the RSP is the shortest period in years between now and when a threshold is reached. The main advantage of the RFP and RSP is that both are proportional to the elapsed time. Their values decrease by 1 year for each calendar year.

Back-calculation of transition probabilities for Markovian-based pavement performance prediction models

This paper presents a new technique to estimate the transition probabilities used in the Markovian-based pavement performance prediction models. The proposed technique is based on the ‘back-calculation’ of the discrete-time Markov model using only two consecutive cycles of pavement distress assessment. The transition probabilities, representing the pavement deterioration rates, are the main elements of the Markov model used in predicting future pavement conditions. The paper also presents a simplified procedure for evaluating the pavement state of distress using the two major pavement defect groups, namely cracking and deformation. These two defect groups are to be identified and evaluated for pavement sections using visual inspection and simple linear measurements. The extent of these two major defect groups is measured using the defected pavement areas (or lengths) and the defect severity is measured based on the average crack width and average deformation depth. A case study is presented to demonstrate the ‘back-calculation’ of transition probabilities. In particular, the impacts of the pavement section length on the distress rating and on the estimation of the transition probabilities have been investigated. The results have indicated that the estimated transition probabilities become highly unstable as the section length gets larger and the sample size becomes smaller.

Network-level optimization of pavement maintenance renewal strategies

Infrastructure systems in the US are in urgent need of maintenance and rehabilitation. According to the most recent factsheet published by the American Society of Civil Engineers, one of the top five infrastructure concerns of today in the US is the transportation system. The major challenge facing maintenance managers in state Departments of Transportation (DOTs) today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives. Absence of simpler decision-making tools exacerbates the matter. This paper presents the development and implementation of a simpler, yet useful, network-level pavement maintenance optimization model, which is a Linear Program (LP) subject to budget constraints and the agencies’ pavement performance goals in terms of total lane-miles in each pavement condition state. A decision-making tool is developed using Frontline Systems’ Risk Solver Platform add-in for Microsoft Office Excel. This decision-making tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Pavement condition data pertaining to one of the Districts within a state DOT is used to test the model presented herein. Within this context, nine treatment types along with their corresponding unit prices ($/Lane-Mile), five pavement condition states, pavement deterioration rates, network-level pavement performance targets, and available annual maintenance budget for a 15-year planning horizon are defined. The results presented show how an annual highway maintenance budget needs to be allocated or determined to achieve the District’s value proposition for various scenarios. Comparing the results of these varying scenarios provides insight on long-term strategies and the impact of target constraints on budget expenditures.

Investigating the accelerated deterioration of flexible pavement using two-stage design analysis approach

The accelerated deterioration of flexible pavement and its relation to design strength requirements is a major problem facing highway engineers. The goal of this research is to investigate a possible relationship between the accelerated pavement deterioration rates and pavement design strength using a two-stage design analysis approach. The approach in this study applies a two-stage solution to the three popular design methods of flexible pavement (i.e. California Department of Transportation (Caltrans) method, the American Association of State Highway and Transportation Officials (AASHTO) method, and the Asphalt Institute (AI) method) to yield pavement designs using stage load applications values specified for each investigated design load applications level. The resulting two-stage pavement designs are then used to obtain an indicator called the stage design strength ratio (percentage) defined as the ratio of the relative strength change to the design relative strength. The sample results indicate that the stage design strength ratio is relatively low compared to the corresponding increase in load applications especially at advanced service times. This could be a major contributing factor to the accelerated deterioration of flexible pavement. Therefore, it is recommended to provide initially stronger pavement structures. This can be done by either designing flexible pavement for a longer design period than the typical 20 years suggested by most design methods or using a higher terminal present serviceability index as in the case of the AASHTO design method.

Decision Support Models for Asset Management of Low-Volume Roads

Pavement maintenance decisions are based on skimpy rules and rules of thumb. Generally, the selection of roads for maintenance is based on a worst-first policy. The timing of maintenance intervention and standards adopted for maintenance govern the service life of the pavement. The rate of pavement deterioration depends on the timing of maintenance intervention, choice of the type of maintenance treatment, and the quality of the construction. In the present investigation, the periodic performance data of typical low-volume rural roads constructed under the rural road development programs in India are considered. Performance prediction models are developed. The models are used to predict the performance during the design life. Different maintenance interventions are considered at different pavement conditions. The effect of the timing of maintenance on agency costs and vehicle operation costs is quantified and presented.