The pavement experiences deterioration due to traffic and environment, i.e., unsatisfactory riding quality and structural inadequacy, over time. Thus, predicting pavement performance over time is one of the key elements of any pavement maintenance management system (PMMS). It can be used as an efficient tool to program/schedule the maintenance applications and expenditures, and thus the necessary funds can be allocated. Using a combination of independent variables for any selected pavement section can generate section-wise condition assessment and prediction models. Moreover, these models can be used to select the most cost-effective maintenance alternative to be applied to that pavement section. The present study developed an expert system based on pavement performance models which combines the available maintenance data with the knowledge acquired from the experts of the General Administration of Operation and Maintenance in Riyadh, Saudi Arabia. Eight regression models were first developed for four maintenance and rehabilitation (M&R) strategies, i.e., no maintenance, routine maintenance, overlay, and reconstruction for low and high traffic. Then, a practical expert system was developed to aid pavement maintenance engineers in finding the most effective and efficient M&R strategies and suitable time for the application. The regression models revealed that the effect of routine maintenance and reconstruction is greater in low traffic than in high traffic, while the effect of overlay is greater in high traffic than in low traffic. Based on this initial system, another improved one can be developed using the machine learning technique.

Riyadh City established and implemented a Pavement Maintenance Management System (PMMS) through the General Directorate of Maintenance and operation. The system was created to address the difficulties that come with maintaining and reserving the pavement network. To evaluate pavement conditions, Riyadh (PMMS) uses visual checks, structural capacity roughness, and skid resistance. An Urban Distress Index (UDI) is calculated during the visual assessment process. Distressed pavement types, severity, and quantity are taken into account when calculating UDI values. As a result, the procedure gathers extensive data on the pavement’s condition. However, the procedure is time-consuming and very costly. The Automatic Road Analyzer car provides data on road roughness in accordance with the International Roughness Index (IRI). The IRI data are often generated quite quickly and at a cheap cost as compared to the distress survey. This study’s aim is to examine whether a sample of Riyadh city pavement sections can be connected to the IRI depending on the distress type. The research develops statistical models that correlate IRI values with several distress-types associated with roadway classes. Correlating the International Roughness Index values to distress type will eliminate the necessity to implement the manual inspection at a network- level. This saves money and time for PMMS employees when preparing annual maintenance requirements and setting priorities. The finding of the study, of the relationship between the pavement distresses and the International Roughness Index showed a statistically significant relationship between pavement roughness and some ride-quality distresses, like depression and patching, as well as some non-ride quality distresses like potholes and rutting. In addition, for both main and secondary streets, an analysis of variance shows the existence of a correlation between the two variables.

This study investigates the rheological, thermal, and microstructural performance of three novel high-elasticity polymer modifiers (HEMs) incorporated into asphalt binders. The modifiers were evaluated at their recommended dosages using a multi-scale framework combining rotational viscosity, dynamic shear rheometry (frequency sweeps, Cole-Cole plots, Black diagrams, and master curves), bending beam rheometry, differential scanning calorimetry (DSC), fluorescence microscopy (FM), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). Results show that HEM-B achieved the highest values of the superpave rutting parameter (G*/sinδ = 5.07 kPa unaged, 6.73 kPa aged), reflecting increased high-temperature stiffness but also higher viscosity, which may affect workability. HEM-C exhibited the lowest total enthalpy (1.18 W·g⁻¹) and a glass transition temperature of −7.7 °C, indicating improved thermal stability relative to other binders. HEM-A showed the greatest increase in fluorescent area (+85%) and the largest reduction in fluorescent number (−60%) compared with base asphalt, demonstrating more homogeneous phase dispersion despite higher enthalpy. Comparison with SBS confirmed that the novel HEMs not only meet but exceed conventional performance thresholds while revealing distinct modification mechanisms, dense cross-linking (HEM-B), functionalized thermoplastic compatibility (HEM-C), and epoxy-tackified network formation (HEM-A). These findings establish quantitative correlations between rheology, thermal stability, and microstructure, underscoring the importance of dosage, compatibility, and polymer network architecture. The study provides a mechanistic foundation for optimizing high-elasticity modifiers in asphalt binders and highlights future needs for dosage normalization and long-term aging evaluation.

The Ministry of Transport (MOT) in the Kingdom of Saudi Arabia had collected a massive amount of friction measurements using a Mu-meter covering most of the major highway network in the kingdom. Traffic accident data of 89 high accident rate locations from four main different highway classes were extracted from the MOT accident records. Pavement skid resistance for the selected locations was determined from the pavement skid resistance records. The objective of this paper is to utilize these data to investigate the effects of pavement skid resistance on traffic accidents. The analysis included establishing relationships between skid resistance and accident number, accident significance and accident density. It was determined that a decreasing skid resistance leads to an increase in traffic accidents. Acritical value of skid resistance was also established based on number, significance and density of accidents.

Aluminum-based composites with characteristics such as low density and high strength to weight ratio have been identified to be one of the best-emerging alternatives. The lightweight composite is gaining popularity, particularly in the automotive industry. The composite’s qualities make it a prospective material to replace significant materials that are now used in the automobile industry. For lightweight products, various weight reduction solutions were proposed. In the present work, one such lightweight composite was fabricated by using a stir casting process, which includes reinforcement powders viz. carbon nanotube and fly ash to pure aluminum. The use of fly ash helps in reducing the overall associated cost of the material as well as provides low density. The work aims to identify the amount of fly ash (by weight %) suitable to avail good mechanical properties. In concern with the mechanical properties, density, yield strength, ultimate tensile strength, and wear resistance of the composite specimen were examined. Moreover, the artificial neural network was adopted to identify minimum volumetric wear for a given set of conditions. From the results, it was perceived that with the increase in fly ash content, the volumetric wear of the fabricated composite decreases. However, with the increase in load and speed, the volumetric wear rate increases.

Optimization techniques keep road performance at a good level using a cost-effective maintenance strategy. Thus, the trade-off between cost and road performance is a multi-objective function. This paper offers a new multi-objective stochastic algorithm for discrete variables, which is called the integer search algorithm (ISA). This algorithm is applied to an optimal pavement maintenance management system (PMMS), where the variables are discrete. The PMMS optimization can be achieved by maximizing the condition of pavement with a minimum cost at specified constraints, so the PMMS is a constrained multi-objective problem. The ISA and genetic algorithm (GA) are applied to improve the performance condition rating (PCR) of the pavement in developing countries, where the annual budget is limited, so a minimum cost for three years’ maintenance is scheduled. Study results revealed that the ISA produced an optimal solution for multi-function objectives better than the optimal solution of GA.