Abstract: Typically, available funds are not adequate to satisfy all the required improvement, repair and/or maintenance projects for the highways and roads networks in most countries, including Egypt. Under current policies and funding levels, further deterioration in the highways can be expected, since the budget needed for highway maintenance is greater that the funding levels available. As a result, highway agencies must seek more cost-effective methods for highway network preservation. Pavement performance prediction models are generally used to forecast changes in condition over some future time period. Predicted conditions are used in several pavement management activities. Markov chain theory has been used in this paper to develop future pavement deterioration prediction model for highways in Egypt, and to forecast the future pavement performance. Transition Probability Matrices (TPM) were generated for two highways in Egypt as a case study; namely, the Alexandria-Cairo Agricultural R an

• Publication Date: 11-Oct-2015
• DOI: 10.15224/978-1-63248-065-1-44
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Abstract: Nowadays climate change phenomena is identified as an environmental issue all over the world. In result of human industrial activities, measurements of green house gases are increased which leads to global warming and its sequences. In the last decades, concerns about average temperature rising and its potential destructive results were noted. Also water resources which is one of the most effective in human life, are not in security. So to efficient management, it is worthy to calculate the climate change impacts on important parameters in water resources such as runoff. But rainfallrunoff models are complex and in other hand data mining models had impressive progress in recent years and are helpful to predict runoff. Genetic programming (GP), artificial neural network (ANN) and support vector machine (SVM) are such data mining tools that have many uses in various fields. In the present paper, climate precipitation and temperature are estimated by HadCM3 AOGCM and statistic downscaling

• Publication Date: 11-Oct-2015
• DOI: 10.15224/978-1-63248-065-1-45
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• Downloads: 0