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Using typical structure of asphalt pavement in Harbin area of China, and the formula of generalized friction coefficient between base and surface layers of asphalt pavement in cold area is established.

Through structural characteristics analysis of asphalt pavement in cold area, the generalized formula of friction coefficient between base and surface layers of asphalt pavement in cold area is derived. The formula can quickly calculate the friction coefficient between layers of asphalt pavement.

Based on quantitative analysis to the contacting state between layers of asphalt pavement in cold area, the relationships between generalized friction coefficient and resilient modulus of asphalt mixtures, temperature shrinkage coefficient and temperature have been established.

The findings can enrich the description methods about the contacting state between layers of asphalt pavement, and have a certain theoretical and practical value. Through the application of the formula of generalized friction coefficient between layers, it can provide a technical basis for the asphalt pavement design, construction and maintenance in cold area.

A large number of roads have been constructed in the rural areas of India to connect habitations with the nearest major roads. With time, the pavements of these roads have deteriorated and they need some kind of maintenance, although they all do not need maintenance at the same time, as they have all not deteriorated to the same level. Hence, they have to be prioritized for maintenance.

In order to present a scientific methodology for prioritizing pavement maintenance, the factors affecting prioritization and the relative importance of each were identified through an expert survey. Analytic Hierarchy Process (AHP) was used to scientifically establish weight (importance) of each factor based on its relative importance over other factors. The proposed methodology was validated through a case study of 203 low volume rural roads in the state of Himachal Pradesh in India. Ranking of these roads in order of their priority for maintenance was presented as the final result.

The results show that pavement distresses, traffic volume, type of connectivity and the socioeconomic facilities located along a road are the four major factors to be considered in determining the priority of a road for maintenance.

The methodology provides a comprehensive, scientific and socially responsible pavement maintenance prioritization method which will automatically select roads for maintenance without any bias.

Timely maintenance of roads will also save budgetary expenditure of restoration/reconstruction, leading to enhancement of road service life. The government will not only save money but also provide timely benefit to the needy population.

Road transportation is the primary mode of inland transportation in rural areas. Timely maintenance of the pavements will be of great help to the socioeconomic development of rural areas.

The proposed methodology lays special emphasis on rural roads which are small in length, but large in number. Instead of random, a scientific method for selection of roads for maintenance will be of great help to the public works department for better management of rural road network.

Pavement is one of the main elements of the roads network. It is extremely essential to study and understand the factors affecting its performance and highlight the most important ones for decision-makers and pavement experts to consider during the design, construction and maintenance stages. The purpose of this paper was to identify the factors affecting pavement performance and rank them according to their importance using Analytic Hierarchy Process (AHP) for decision-makers and pavement experts to consider during the design, construction and maintenance stages.

A survey was developed considering 29 factors found in the literature that affect pavement performance. The survey was sent to pavement professionals in Qatar to rate their perception of factors affecting pavement performance to enhance roads' sustainability. 205 responses were collected and analyzed using AHP.

The findings indicate that the factor “unconsidered heavy vehicles volume” is the most critical factor that affects pavement performance. The second most critical factor affecting the pavement performance is the “low asphalt content” due to escalating binder aging, reducing fatigue life of the pavement and decreasing the durability of roads. The third and fourth factors are “poor mechanical and thermal properties” and “unexpected high traffic volume,” respectively. These two factors are strongly attached to the first and second factors since the traffic volume affects the pavement performance less but similar to the heavy vehicles and a mix with poor mechanical and thermal properties is related indirectly to the asphalt content in the mix.

The research provides help for decision-makers in the construction industry to improve the performance of pavements using a multi-criteria decision-making tool. This paper's outcome would help the pavement management professionals in the construction industry to improve pavement performance and management, increase the pavement's life cycle and reduce maintenance costs.

The present study aims to demonstrate the performance assessment of flexible pavement structure in probabilistic framework with due consideration of spatial variability modeling of input parameter.

The analysis incorporates mechanistic–empirical approach in which numerical analysis with spatial variability modeling of input parameters, Monte Carlo simulations (MCS) and First Order Reliability Method (FORM) are combined together for the reliability analysis of the flexible pavement. Random field concept along with Cholesky decomposition technique is used for the spatial variability modeling of the input parameter and implemented in commercially available finite difference code FLAC for the numerical analysis of pavement structure.

Results of the reliability analysis, with spatial variability modeling of input parameter, are compared with the corresponding results obtained without considering spatial variability of parameters. Analyzing a particular three-layered flexible pavement structure, it is demonstrated that spatial variability modeling of input parameter provides more realistic treatment to property variations in space and influences the response of the pavement structure, as well as its performance assessment.

Research is based on reliability analysis approach, which can also be used in decision-making for quality control and flexible pavement design in a given environment of uncertainty and extent of spatially varying input parameters in a space.

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.

Porous asphalt has been used for than 50 years, but it was originally developed in 1970 at Franklin institute in Philadelphia, Pennsylvania. By 1974 the first formalized procedure was created by the federal highway administration to design mixtures. Many researches on porous asphalt mixture have been conducted for the past two decades. However, there remains some concern about the potential adverse impacts of infiltrated surface water on the underlying groundwater. The purpose of this paper is to presents a short review on the application of porous asphalt pavement stormwater treatment.

In this paper, a critical review on history and benefits is presented followed by review of general studies of using porous asphalt pavement, and some recent scientific studies that examine potential contamination of soil and groundwater because of infiltration systems.

This paper indicates that porous asphalt pavement is more efficient than conventional pavements in terms of retaining pollutants, improving the quality of water and runoff while maintaining infiltration.

This paper may also help reduce land consumption by reducing the need for traditional storm-water management structures. However, on the other hand, the priority objectives which is minimizing increased flooding and pollution risks while increasing performance efficiency and enhancing local environmental quality-of-life is achieved.

With the majority of highway projects in China having entered their operational phases, the maintenance and repair of the pavement is receiving increasing attention. One problem that needs to be addressed urgently is that of how to raise the proper funds for highway maintenance to ensure the sustainable operation of the project. To this end, the aim of this study is to investigate the capital demand for operation and maintenance of a project by means of a refinancing scheme, in order to reduce the possibility of project bankruptcy and to enhance the economic value of the project.

Based on an analysis of the dynamic complexity of the highway pavement maintenance system, a Markov model is used to predict pavement performance, and an optimal capital structure decision model is proposed for highway public–private partnership (PPP) project refinancing, using the method of system dynamics (SD). The proposed model is then applied to a real case study.

Results show that the proposed model can be used to predict accurately the dynamic changes in the demand for road maintenance funds and refinancing during the period of operation, before making the optimal decision for the refinancing capital structure.

Although many scholars have studied the optimal refinancing capital structure of PPP projects, the dynamic changes inherent in the demand for maintenance funds for highway PPP projects are seldom considered. Therefore, in the approach used here the influence of the dynamic change of road maintenance capital demand on refinancing is investigated, and SD is used for the optimal capital structure decision-making model of highway PPP project refinancing, to make the decision-making process more reasonable and scientific.

Pavement deterioration prediction models play a crucial role in determining maintenance strategies and future funding needs. While deterioration prediction models have been studied extensively in the past, applications of these models to local street networks have been limited. This study aims to address this gap by sharing the results of network level deterioration prediction models developed at a local level.

Network level pavement deterioration prediction models are developed using Markov chains for the local street network in Syracuse, New York, based on pavement condition rating data collected over a 15-year time period. Transition probability matrices are generated by calculating the percentage of street sections that transition from one state to another within one duty cycle. Bootstrap sampling with replacement is used to numerically generate 95% confidence intervals around the transition probability values.

The overall local street network is divided into three cohorts based on street type (i.e. avenues, streets and roads) and two cohorts based on pavement type. All cohorts demonstrated very similar deterioration trends, indicating the existence of a fast-paced deterioration mechanism for the local street network of Syracuse.

This study contributes to the body of knowledge in deterioration modeling of local street networks, especially in the absence of key predictor variables. Furthermore, this study introduces the use of bootstrap sampling with replacement method in generating confidence intervals for transition probability values.

This paper describes the use of an analytical hierarchy process (AHP) in determining the rational weights of importance of pavement maintenance priority ranking factors. These weights were obtained by capturing the local people’s perception towards this vital part of the pavement management system (PMS). In this regard, different groups of individuals were asked to estimate the weight of importance in pavement maintenance of different factors for ranking pavement sections. These factors were road class, pavement condition, operating traffic, riding quality, safety condition, maintenance cost, and the overall importance of the road section to the community. The AHP method of pair‐wise comparison was employed to get the factor weights, which were compared with the weights obtained from the direct assignment method. It was concluded that the two methods were statistically similar which confirms that the results of the direct assignment method can be used safely with a sound reliability and consistency. This conclusion comes from the fact that the AHP method has a high reputation and applications, and it uses a high‐precision technique for obtaining the weights (priorities) of alternatives or items. Priority factor weights were used in developing a pavement maintenance priority ranking procedure for a road network. This procedure was validated by real case studies, and found to be logically and efficiently able to handle the ranking of a huge number of pavement sections for maintenance and repair.

This paper aims to apply a pavement design by LEDFAA for a sample airport, and design results involving layer thickness, modulus and cumulative damage factor (CDF) achieved are shown in figures.

Finite element (FE) simulation is applied for sample airport pavement and based on results involving stress and strain, CDF amount is shown by using related equations. To analyze the accuracy of modeling, a comparison has been made between the values of ABAQUS and case study results at Denver International Airport (DIA).

The present study includes a comparison between the two conventional methods for runway pavement design. There is linear relation between layered elastic design (LED) and FE method results, so CDF rate achieved by the FE method is always smaller than the LED method. To assess the accuracy of the applied modeling with ABAQUS software, the validation was done using the deformations under the concrete slabs of DIA. The results are compatible with the results acquired from the case study, and the high accuracy of modeling was approved. This research shows that B-777 on rigid pavements and A-340-500/600 on flexible pavements have the most CDF contribution, among other aircrafts. Also, CDF rate for any aircraft in the LED method is higher than the FE method.

To assess the accuracy of the applied modeling with ABAQUS software, the validation was done using the deformations under the concrete slabs of DIA. The results are compatible with the results acquired from the case study, and the high accuracy of modeling was approved.