@article { author = {Marandi, S.M. and H. Bagheripour, M. and قاسمی, مجتبی and Safapour, P. and قاسمی, مجتبی}, title = {Fuzzy - Neural Network and Cost Optimization using Genetic Algorithm in Modeling Marshal Stability of Stabilized Soils with Emulsion Bitumen and Cement}, journal = {Journal of Transportation Research}, volume = {4}, number = {1}, pages = {-}, year = {2007}, publisher = {}, issn = {1735-3459}, eissn = {2008-3351}, doi = {}, abstract = {Intelligent methods for predicting some quantities are powerful tools for optimization of predicting models these days. The bitumen emulsion is able to easily mix with wet soil so that the bitumen may be easily distributed and scattered in the soil. The aggregates with PI<6 are quite suitable for stabilization of soils with bitumen emulsion. In this research, two aims are followed, one is Marshal Stability modeling for samples stabilized with emulsion bitumen and cement for roads sub-layers, and the other is cost optimization of using these binding materials in roads construction. 170 stabilized samples were made and tested for training the fuzzy neural network. The Marshal Stability of these samples were modeled by fuzzy neural network and also with using genetic algorithm, the roads execution costs were optimized. For modeling, the MATLAB software programming was used. The results showed that, with presented model, the construction costs decrease considerably.The overall conclusions of this research are summarized as follows: The fuzzy neural network presented, is able to determine the Marshall Stability of the samples stabilized by bitumen emulsion and cement with an accepted error %, and establishes a logical relation between the portions of used cement and emulsion. The fuzzy neural network model is a suitable tool for modeling and estimating the natural problems. The genetic algorithm optimization model presented in this research is able to optimize the performance costs of soil stabilization by cement and bitumen emulsion, and consequently reduces total costs of road projects, considerably. The presented diagram for Marshall Stability – cement and emulsion content is able to specify the required cement and emulsion portions as compared to Marshall Stability.}, keywords = {Fuzzy Neural Network,Genetic algorithm,emulsion and cement,Marshal Stability}, title_fa = {شبکه عصبی- فازی و بهینه‌یابی هزینه‌ها توسط الگوریتم ژنتیک در مدل‌سازی مقاومت مارشال1 در تثبیت خاک با امولسیون و سیمان}, abstract_fa = {امروزه روش‌های هوشمند در پیش‌بینی پاره‌ای از کمیت‌ها می‌توانند به عنوان ابزاری قدرتمند برای بهینه سازی مدل‌های پیش‌بینی بکار روند. در این تحقیق دو هدف، یکی مدل‌سازی مقاومت مارشال نمونه‌های تثبیت شده با امولسیون قیر و سیمان جهت زیرسازی جاده ها و دیگری بهینه یابی هزینه های اجرایی کاربرد امولسیون قیر و سیمان در تثبیت این نوع زیرسازی دنبال شده است. به این منظور 170 آزمایش بر روی نمونه‌های تثبیت شده برای آموزش شبکه عصبی- فازی انجام شده و مورد استفاده قرار گرفته اند. سپس مقاومت مارشال نمونه‌ها، توسط شبکه هوشمند عصبی - فازی مدل شده و با استفاده از الگوریتم ژنتیک هزینه های اجرایی بهینه یابی شده است. در مدل سازی انجام شده از نرم افزار MATLAB استفاده شده است. نتایج این تحقیق نشان می‌دهد که با مدل پیشنهادی می‌توان تا حدود زیادی در هزینه‌های ساخت جاده‌ها صرفه‌جویی کرد.}, keywords_fa = {مدل سازی مقاومت مارشال,تثبیت خاک,امولسیون,سیمان}, url = {https://www.trijournal.ir/article_11371.html}, eprint = {https://www.trijournal.ir/article_11371_4b18c1bb9b1746980909682176d889ad.pdf} }