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A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

Pavement roads and transportation systems are crucial assets for promoting political stability, as well as economic and sustainable growth in developing countries. However, pavement maintenance backlogs and the high capital costs of road rehabilitation require the use of pavement evaluation tools to assure the best value of the investment. This research presents a methodology for analyzing the collected pavement data for the implementation of a network level pavement management program in Kazakhstan. This methodology, which could also be suitable in other developing countries’ road networks, focuses on the survey data processing to determine cost-effective maintenance treatments for each road section. The proposed methodology aims to support a decision-making process for the application of a strategic level business planning analysis, by extracting information from the survey data.

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).

To study the influence range of the soil foundation depth under the cement-concrete pavement of an airport under an aircraft load, a finite element model of the cement-concrete pavement—cement stabilized gravel base—soil foundation was established using FLAC3D finite element software, and the subprogram of the aircraft moving load was presented. The effects of the static and dynamic load of the aircraft on the pavement depth under different aircraft types, pavement thicknesses, and soil properties were compared and analyzed; the rationality of the numerical calculation was verified based on numerical examples. The results indicate that a heavier aircraft load requires more complex main landing gear, resulting in a greater influence range of the additional stress at the depth of the soil foundation caused by the aircraft dynamic load. A greater thickness of the cement-concrete pavement results in a lower additional stress in the soil foundation caused by the aircraft dynamic load, and the corresponding influence depth of the soil foundation gradually decreases. As the rebound modulus of the soil foundation increases, the additional stress in the soil foundation caused by the aircraft dynamic load increases, resulting in a greater influence of the additional stress on the soil foundation. The analysis also demonstrated that the additional stress depth in the soil foundation caused by the aircraft static load is greater than that caused by the aircraft dynamic load. In particular, the depth of the additional stress in the soil foundation caused by the static load of A380-800 aircraft σ c σ c σ 0 σ 0 = 0.1 exceeded 10 m. This study provided a quantitative reference for the optimization design of pavement structure parameters of different airport grades and the determination of the thickness of soil compaction treatment in the affected area of pavement foundation, which is conducive to the long-term safe operation of airports.

The cost of road construction consists of design expenses, material extraction, construction equipment, maintenance and rehabilitation strategies, and operations over the entire service life. An economic analysis process known as Life-Cycle Cost Analysis (LCCA) is used to evaluate the cost-efficiency of alternatives based on the Net Present Value (NPV) concept. It is essential to evaluate the above-mentioned cost aspects in order to obtain optimum pavement life-cycle costs. However, pavement managers are often unable to consider each important element that may be required for performing future maintenance tasks. Over the last few decades, several approaches have been developed by agencies and institutions for pavement Life-Cycle Cost Analysis (LCCA). While the transportation community has increasingly been utilising LCCA as an essential practice, several organisations have even designed computer programs for their LCCA approaches in order to assist with the analysis. Current LCCA methods are analysed and LCCA software is introduced in this article. Subsequently, a list of economic indicators is provided along with their substantial components. Collecting previous literature will help highlight and study the weakest aspects so as to mitigate the shortcomings of existing LCCA methods and processes. LCCA research will become more robust if improvements are made, facilitating private industries and government agencies to accomplish their economic aims.

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R2) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management.

The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.

This paper presents a recent study on using AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) design software (Pavement ME(TM)) to evaluate the performance of typical Louisiana rigid pavement structures as compared to the existing pavement performance data available in the pavement management system (PMS). In total, 19 projects with two pavement structure types, Portland cement concrete (PCC) over unbound base and PCC over asphalt mixture blanket, were analyzed. Results show that the national model over-predicts transverse cracking and under-predicts joint faulting. Therefore, a preliminary calibration was conducted to adjust Pavement ME for Louisiana's condition. In addition to comparing the measured and predicted performance, the recommended thickness from the current and the new design methods was also compared. It was found that the two design methods are comparable with an average difference of 2 cm or 7 percent (Pavement ME requires a thinner pavement). At the end of this paper, problems, challenges and possible solutions for fully implementing the new design method are discussed.

This paper describes a method for using location data to optimize the routing of pavement data collection vehicles. In much of the developed world, pavement testing is performed on a regular basis; the pavement testing data, in turn, serves as input to Pavement Management Systems. Currently, in the United States of America, state departments of transportation plan this data collection work by providing the list of roads that must be tested and then leave the routing of the vehicles to the equipment operators who typically execute the work in an ad hoc manner. This study presents the processes required to code the list of roads for testing, select appropriate hotels in the region of testing, and apply a Traveling Salesman Problem with Hotel Stops model to derive a route. Applying the processes to a case study shows significant cost savings associated with this method of roadway testing, as opposed to the current ad hoc methods.

This research was conducted to investigate the effect of matric suction on resilient modulus of unbound aggregate base courses. The study characterized the water characteristic curves and resilient modulus versus matric suction relationships of aggregate base courses that were compacted at different water contents and between 98 and 103 % of the modified Proctor density. The soil–water characteristic curve (SWCC) and the relationship between resilient modulus ( M r ) and matric suction (ψ) were established for different unbound granular and recycled asphalt pavement materials. This relationship is important for predicting changes in modulus due to changes in moisture of unbound pavement materials. Resilient modulus tests were conducted according to the National Cooperative Highway Research Program (NCHRP) 1-28A procedure at varying water contents, and the measured SWCC was used to determine the corresponding matric suction. Three reference summary resilient moduli (SRM) were considered: at optimum water content, optimum water content +2 % and optimum water content −2 %. The Bandia and Bargny limestones are characterized by a higher water-holding capacity which explains why the modulus of limestone was more sensitive to water content than for basalt or quartzite. Limestones tend to be more sensitive to changes in water content and thus to matric suction. The shape of the SWCC depends on the particle size distribution and the cementation properties from dehydration of the aggregates. Material properties required as input to the Mechanistic-Empirical Pavement Design Guide (M-EPDG) to predict changes in resilient modulus in response to changes in moisture contents in the field were determined for implementation in the M-EPDG process. Results show that the SRM was more correlated with matric suction than with compaction water content (for resilient modulus testing). The empirical models commonly used to predict the SWCC such as the Perera et al. (Prediction of the SWCC based on grain-size-distribution and index properties. GSP 130 Advances in Pavement Engineering, ASCE, 2005 ) and the M-EPDG (NCHRP in Guide for mechanistic-empirical design of pavement structures. National cooperative highway research program. ARA, Inc., ERES Consultants Division, Champaign, IL, 2004 ) models tend to underestimate the SWCC and cannot provide reasonable estimation. SRM normalized with respect to the SRM at the optimum water content varied linearly with the logarithm of matric suction. Empirical relationships between SRM and matric suction on semi-logarithmic scale were established and are reported.