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To properly plan for construction, repair, maintenance, and reconstruction of highways the minimum acceptable roadway condition is needed information. This, along with other pavement management tools, will help select the most desirable roadway alternatives. In this research the minimum acceptable conditions are developed based on an opinion survey of non-technical but high-level decision makers. Roadway roughness, expressed as international roughness index (IRI), is used as the measurement criteria. Because IRI is a widely known, acceptable, and a uniformly measurable index, it is used for the purpose of this research. The minimum IRI values developed here will help managers, planners, and engineers in prioritizing their plans and projects. Iran has a central planning system, hence having a minimum acceptable IRI will help in producing homogeneity in decision making. A questionnaire is sent to top level and influential managementlevel officials who have a decisive input in highway matters. The officials are asked to choose the minimum acceptable service level of different types of roadways and classifications. Naturally, roadways with higher levels of importance would require higher service levels. The answers to the survey questionnaires are investigated to determine a preferred minimum acceptable roadway condition. The IRI is computed using a mechanical device enabling a more uniform data collection. The IRI was first proposed by The World Bank as a standard roughness statistic. Extensive research has proven that the IRI can be related to pavement condition. The result of the opinion survey is investigated to determine the minimum levels acceptable for each category. The responses show distinct preference patterns for most of the roadway types. Survey results are investigated by plotting and analyzing them. Based on road user’s perception of roadway condition using guidelines from AASHTO, the Corp of Engineers, and related research work. The appropriate IRI limits and ranges are determined for Iran’s highways. These values are adjusted to obtain final values for Iran. The result, shown in a table, gives upper and lower IRI values accepted and recommended for Iran’s highways. The result of this research work is specifically useful in developing specifications for new pavement design, accepting new pavement from contractors, pavement management, highway planning, and in roadway life cycle cost analysis decision making. The results are subject to refinement over time.

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When geogrid reinforcement is used as a treatment method for improving soft subgrade as a roadway foundation, a top layer of subgrade is usually excavated and backfilled with geogrid-reinforced aggregates. This treatment method produces an adequate platform for the planned roadway construction site, where heavy traffic loading is constantly moving. This paper presents a quantitative assessment of subgrade improvement by geogrid reinforcement based on numerical modelling and parametric studies. First of all, the preliminary numerical models were verified by comparing the analysis results with previous studies. Secondly, the major numerical models in this study were assumed to be a simplified simulation of a geogrid-reinforced two-layer system with an aggregate layer above a subgrade layer. The numerical models were applied a quasi-static loading and unloading cycle, in order to monitor the permanent deformation at the surface of the models. Afterwards, thickness of aggregate layer, and subgrade CBR values were varied in order to summarize the outcomes of each case. This approach makes it possible to quantify the effects of geogrid reinforcement and aggregate material in terms of an enhanced California Bearing Ratio (CBR) of a single subgrade clay layer. Results have shown that when the aggregate thickness is up to 450mm, the contribution of enhanced CBR is mostly from aggregate material. However, when the aggregate thickness is about 150mm with a relatively weak subgrade material, the inclusion of geogrid material can contribute about 50% of the enhanced value.

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Roadway striping is a major operation and maintenance (O&M) task that must be performed to maintain safe vehicular traffic on roadways, which require re-striping or new striping every few seasons depending on the degree of deterioration due to weathering, abrasion, and damage during snow removal. Performance is normally assessed using manually collected data such as working hours, material consumption, and approximate striping distance achieved during a striping season, but the performance analysis methods currently used do not provide a clear holistic picture and are insufficient to permit an in-depth analysis. In order to address this deficiency, this study utilized telematics technology to automatically collect data that could then be used to improve striping performance without the need for additional staff or equipment. This paper presents the telematics data collection and implementation in two areas: 1) providing performance analyses using telematics data and 2) developing performance metrics for future performance measurement. As a result of the study, performance analyses revealed there was sufficient room for improvement and several recommendations were made. Performance metrics were provided using Monte Carlo and triangular distribution.