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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.

Pavement rehabilitation and reconstruction methods with CIR (cold in-place recycling) are alternatives that can effectively reduce the high stresses and waste produced by conventional pavement strategies. An attempt was made to predict the performance, particularly low-temperature cracking resistance characteristics of CIR mixtures. These were prepared with the mix design procedure developed at the URI (University of Rhode Island) for the FHWA (Federal Highway Administration) to reduce wide variations in the application of CIR mixtures production. This standard was applied to RAP (reclaimed asphalt pavement) to produce CIR mixtures with CSS-1h asphalt emulsion as the additive. By adjusting the number of gyrations of the SGC (Superpave gyratory compactor) for compaction, the field density of 130 pcf was represented accurately. To secure a base line, HMA (hot mix asphalt) samples were produced according to the Superpave volumetric mix design procedure. The specimens were tested using the IDT (indirect tensile) tester according to the procedure of AASHTO T 322 procedure at temperatures of -20, -10 and 0 o C (-4, 14, and 32 o F, respectively). The obtained results for the creep compliance and tensile strength were used as input data for the MEPDG (mechanistic empirical pavement design guide). The analysis results indicated that no thermal or low-temperature cracking is expected over the entire analysis period of 20 years for both HMA and CIR mixtures. Thus, it appears that CIR is a sustainable rehabilitation technique which is also suitable for colder climates, and it is recommended to conduct further investigation of load-related distresses such as rutting and fatigue cracking.

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The majority of roads in most countries are two-lane highways. These lanes quickly reach their capacity and must be upgraded on a regular basis. To do so, we must first determine the capacity of the street. The primary objective of this study was to determine the effect of carriageway width, the radius of the horizontal curve, and gradients on Passenger Car Unit (PCU) values as well as on capacity of two-lane undivided Highways, and more importantly, to develop a multiple linear regression model to determine the capacity of the highway when all of these factors are present, which has not been previously reported. Green shield’s model was used to estimate the capacity of each element for all thirty-six sections using flow and speed data. Different models were built using regression analysis to estimate capacity independently, and the combined model was developed as a result. It has been noted that with proportionate increases in carriageway width and radius of the curve, there is an equivalent rise in PCU values and highway capacity, providing improved comfort and safety to road users. It was also discovered that when the value of the gradient increases cause increase in PCU values but the highway capacity decreases, thereby increasing the vehicle operating cost. Where all of these characteristics are present simultaneously in a section, the resulting multiple linear regression model was proven to be appropriate. It is believed to be valuable to practitioners as well as in the development or revision of Indian highway capacity manuals.