چکیده مقاله
Classification of soil is a necessary aspect in geotechnical engineering purposes Compared with traditional methods, smart and soft computing technology can classify different types of soil rapidly and effectively with high precise A database of soil properties is collected and prepared based on the earlier researches and used for training and testing the machine learning classifier algorithms including Naïve Bayes and artificial neural network The input detectable variables consist 104 samples of soil mechanics features including cohesion, internal friction angle, and physical parameters such as water and dry density and used to design the Naïve Bayes and ANN models The results of classification were considered for different soil typed such as clayey fine and coarse sandy components GC, SC, GPGM, CL ML, SC SM, SM, CL The developed network indicated that it can be considered as classifier network for soil classification The results showed that only 6 samples were not correctly identified among the total testing data 34 samples in Naïve Bayes model and 7 samples were not correctly identifiedamong the total testing data 34 samples in artificial neural network model Therefore, these networks can be used to enhance the accuracy and reduce the cost of projects
کلیدواژهها
نویسندگان
Ladan Samadi
Department of Computer Engineering Islamic Azad University Tehran, Iran
Hanan Samadi
College of Science, School of geology University of Tehran Tehran, Iran
شیوه ارجاع
Samadi, Ladan and Samadi, Hanan,1400,Soil Classification Modelling Using Machine Learning Methods,5th National Conference on Computer, Information Technology and Applications of Artificial Intelligence,Ahvaz
ارائهشده در
مجموعه مقالات پنجمین کنفرانس ملی کامپیوتر، فناوری اطلاعات و کاربردهای هوش مصنوعی15 اسفند 1400 · اهواز