|Commenced in January 2007||Frequency: Monthly||Edition: International||Paper Count: 5|
Prediction of the ultimate bearing capacity of piles (Qu) is one of the basic issues in geotechnical engineering. So far, several methods have been used to estimate Qu, including the recently developed artificial intelligence methods. In recent years, optimization algorithms have been used to minimize artificial network errors, such as colony algorithms, genetic algorithms, imperialist competitive algorithms, and so on. In the present research, artificial neural networks based on colonial competition algorithm (ANN-ICA) were used, and their results were compared with other methods. The results of laboratory tests of short piles in clayey soils with parameters such as pile diameter, pile buried length, eccentricity of load and undrained shear resistance of soil were used for modeling and evaluation. The results showed that ICA-based artificial neural networks predicted lateral bearing capacity of short piles with a correlation coefficient of 0.9865 for training data and 0.975 for test data. Furthermore, the results of the model indicated the superiority of ICA-based artificial neural networks compared to back-propagation artificial neural networks as well as the Broms and Hansen methods.
This paper deals with use of pond ash and RBI Grade 81 for improvement in CBR values of clayey soil and grade-III materials used for base course of flexible pavement. The pond ash is a thermal power plant waste and RBI Grade 81 is chemical soil stabilizer. The geotechnical properties like Maximum Dry Density (MDD), Optimum Moisture Content (OMC), Unconfined Compressive Strength (UCS), CBR value and Differential Free Swell (DFS) index of soil are tested in the laboratory for different mixes of soil, pond ash and RBI Grade 81 for different proportions. The mixes of grade-III material, pond ash and RBI Grade 81 tested for CBR test. From the study it is found that the geotechnical properties of clayey soil are improved significantly, if pond ash added with RBI Grade 81. The optimum mix recommended for subgrade is soil: pond ash: RBI Grade 81 in proportions of 76:20:4. The CBR value of grade-III base course treated with 20% pond ash and 4% RBI Grade 81 is increased by 125.93% as compared to untreated grade-III base course.