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Showing 3 results for Pavement Condition

Behbahani H., Mohammad Elahi S.,
Volume 1, Issue 1 (9-2003)
Abstract

This Paper is the result of a research project on a pavement management system that was performed by the Transportation Division of Iran University of Science and Technology. Information used in the project was gathered from 20 zones of the Tehran Municipality. Any maintenance and repair system for roads has a number of general and coordinated activities in conjunction with programming, designing, construction, Maintenance, Evaluation, and research on road pavement. Prediction of pavement condition is one of the most important parts of, such system. Prediction models have their application at the network level as well as project level activities. At the network level it is used in predicting the condition for budget programming. While in project level it is used in economical analysis. Many factors have been used in determination of pavement condition. These factors are the design life of the pavement, loading, climatic condition, and the type of road. To be able to plan for future improvements we need to predict the future condition of the pavement. In this paper, factors affecting the prediction of pavement condition are discussed. A model is developed exclusively for Tehran based on the distress data collected.
H. Behbahani, S.m. Elahi,
Volume 4, Issue 1 (3-2006)
Abstract

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.
H. Shahnazari, M. A. Shahin, M. A. Tutunchian,
Volume 12, Issue 1 (1-2014)
Abstract

Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.

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