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Design of Pavement Subdrainage for Iran
, M.Sc. Thesis Sharif University of Technology ; Tabatabaei, Nader (Supervisor)
Abstract
It is widely recognized today that excess moisture in pavement layers, when combined with heavy truck traffic and moisture-susceptible materials, can reduce the service life. Temperatures below freezing can also contribute to durability problems of saturated materials. A major objective in pavement design is to keep the base, subbase, subgrade, and other susceptible paving materials from becoming saturated or even being exposed to constant high moisture levels over time. The use of subsurface drainage has gained popularity over the past two decades, and many pavement agencies now routinely specify drainable pavement structures to reduce moisture-related problems in avements. In this...
Development of a Pavement Management Software Package for Iran
, M.Sc. Thesis Sharif University of Technology ; Tabatabaee, Nader (Supervisor)
Abstract
A pavement management system (PMS) is a set of tools that aid the decision makers in finding optimum strategies for maintaining pavements in a serviceable condition over a given period of time. A frame work for pavement management system for Iran was proposed by Tabatabaee et al. with the support of Transportation Research Institute of Ministry of Road and Transportation. In the proposed frame work, two horizons of development, short and long term, have been envisaged for more rapid and efficient utilization of the system within the technical and administrative divisions of the Ministry. This thesis describes the software package developed based on the proposed frame work for the...
Development of Pavement Performance Prediction Models Based on the Assumptions of Availablity and Ubavailabilty of Accurate Data
, M.Sc. Thesis Sharif University of Technology ; Tabatabaei, Nader (Supervisor) ; Shafahi, Yusof (Supervisor)
Abstract
Accurate prediction of pavement performance is essential to a pavement infrastructure management system. Selection of the prediction model is based on the extent of available data, assumptions used in performance modeling, ease of use and management purposes. Therefore, two methods were proposed in this thesis based on the assumptions of availability and unavailibility of accurate data. The first method presents a two-stage model to classify and accurately predict the performance of a pavement infrastructure system. Sections with similar characteristics are classified into groups using a support vector classifier (SVC). Then, a recurrent neural network (RNN) is utilized to predict...