پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 414 |
| تعداد مقالات | 5,478 |
| تعداد مشاهده مقاله | 6,010,530 |
| تعداد دریافت فایل اصل مقاله | 5,299,927 |
Artificial Neural Network and Optimization Algorithm to Improve Soil Resistance by Means of Aggregation Size Variation | ||
| AUT Journal of Civil Engineering | ||
| مقاله 5، دوره 3، شماره 2، اسفند 2019، صفحه 179-186 اصل مقاله (834.38 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2018.14988.5512 | ||
| نویسندگان | ||
| Ahmadreza Mazaheri1؛ Farshid Jafarian* 2 | ||
| 1Department of Civil Engineering, Engineering Faculty, Ayatollah Borujerdi University, Borujerd, Iran | ||
| 2Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran | ||
| چکیده | ||
| Nowadays, extensive researches are conducted on soil improvement and most of them are focused on the enhancement of soil strength properties. In this study, the aggregation of soil particles including Gravel, Sand, and Clay was investigated on soil resistance. To do this, the number of 25 different testing experiments were experimentally performed according to the Design of Experiment (DOE). The soil strength parameter was determined by means of direct shear tests on large samples. Based on the experimental results, the effect of aggregation size on shear strength was extensively investigated. To do this, Artificial Neural Network (ANN) was employed to evaluate the effect of different combinations of soil particles on bearing capacity. To provide more precise predictive model, the ANN was trained using the Genetic Algorithm (GA). After that, the optimal condition of mix design with optimal load-bearing capacity was found by a hybrid strategy of EOA-ANN. It was obtained 1091.94 (KPa) that had not been reported using the experiments. To evaluate the reliability of the proposed method, a new experimental test was conducted based on the optimal parameters found by hybrid strategy and it was verified with corresponding estimated ones. At the end, very good agreement was reported between the new experiment and the optimal results. It shows that the employed intelligent method in this paper can be successfully used for the enhancement of soil properties in other investigations. | ||
| کلیدواژه‌ها | ||
| Soil Strength؛ Neural Networks؛ Direct Shear Tests؛ Aggregation Size | ||
| مراجع | ||
|
[1] K. Ishihara, J. Koseki, Discussion on the cyclic shear strength of fines-containing sands, Earthquake geotechnical engineering, Proceedings of the 11th international conference on soil mechanics and foundation engineering, Rio De Janiero, Brazil, (1989) 101-106.
[2] S. S. Park, Y. S. Kim, Liquefaction Resistance of Sands Containing Plastic Fines with Different Plasticity, Journal of Geotechnical and Geoenvironmental Engineering, 139(5) (2013) 825-830.
[3] F. B. Martins, L. Bressani, M. R. Coop, A.V.D. Bica, Some aspects of the compressibility behavior of a clayey sand, Canadian Geotechnical Journal, 38(6) (2011) 1177-1186.
[4] F. B. Martins, L. Bressani, M. R. Coop, A.V.D. Bica, Some aspects of the compressibility behavior of a clayey sand, Canadian Geotechnical Journal, 38(6) (2011) 1177-1186.
[5] A. Nocilla, M. Coop, F. Colleselli, The mechanics of an Italian silt: An example of ‘transitional’ behaviour, Géotechnique, 56(4) (2006) 261-271.
[6] A. Firat Cabalar, W. Mustafa, Fall Cone Tests On Clay- Sand Mixtures, Engineering Geology, 192 (2015) 154-165.
[7] D. H. Hsiao, V. Phan, Y. T. Hsieh, H. Y. Kuo, Engineering behavior and correlated parameters from obtained results of sand–silt mixtures, Soil Dynamics and Earthquake Engineering, 77 (2015) 137-151
[8] B. Thai Pham, L. H. Son, T. A. Hoang, D. M. Nguyen, D. T. Bui, Prediction of shear strength of soft soil using machine learning methods, Prediction of shear strength of soft soil using machine learning methods, 166(3) (2019) 181-191.
[9] D. Kanungo, S. Sharma, A. Pain, Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters, Frontiers of Earth Science, 8(3) (2014) 439-456.
[10] S. Kiran, B. Lal, S. Tripathy, Shear strength prediction of soil based on probabilistic neural network, Indian Journal of Science and Technology, 9(41) 2016.
[11] H. K. Kim, J.C. Santamarina, Sand–rubber mixtures (large rubber chips), Canadian Geotechnical Journal, 45(10) (2008) 1457-1466.
[12] Z. Feng, K. Sutter, Dynamic Properties of Granulated Rubber/Sand Mixtures, Geotechnical Testing Journal, 23(3) (2000) 338-344.
[13] K. Hyunki, Spatial variability in soils: stiffness and strengh, A Thesis Presented to The Academic Faculty, (2005).
[14] S. Pamukcu, S. Akbulut, Thermoelastic Enhancement of Damping of Sand Using Synthetic Ground Rubber, 132(4) (2006) 501-510.
[15] O. Caraşca, Soil Improvement by Mixing: Techniques and Performances, Energy Procedia, 85 (2016) 85-92.
[16] U. Kim, D. Kim, L. Zhuang, Influence of fines content on the undrained cyclic shear strength of sand–clay mixtures, Soil Dynamics and Earthquake Engineering, 83 (2016) 124-134.
[17] A. Tahmasebi poor, A. Barari, M. Behnia, T. Najafi, Determination of the ultimate limit states of shallow foundations using gene expression programming (GEP) approach, Soils and Foundations, Soils and Foundations, 55(3) (2015) 650-659.
[18] A. Barari, L. Ibsen, Undrained response of bucket foundations to moment loading, Applied Ocean Research, 36 (2012) 12-21.
[19] K. Terzaghi, Theoretical Soil Mechanics, (2007).
[20] H. Güllü, Prediction of peak ground acceleration by genetic expression programming and regression: A comparison using likelihood-based measure, Engineering Geology, 141-142 (2012) 92-113.
[21] S. K. Das, P. K. Basudhar, Undrained lateral load capacity of piles in clay using artificial neural network, Computers and Geotechnics, 33(8) (2006) 454-459.
[22] [12] Y. M. Najjar, C. Huang, Simulating the stress– strain behavior of Georgia kaolin via recurrent neuronet approach, Computers and Geotechnics, 34(5) (2007) 346-361.
[23] S. K. Das, P. Samui, A. K. J. G. G. Sabat, Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil, Geotechnical and Geological Engineering,29(3) (2011) 329-342.
[24] H. Ardalan, A. Eslami, N. Nariman-Zadeh, Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms, Computers and Geotechnics, 36(4) (2009) 616-625.
[25] S. K. Das, P. K. Basudhar, Prediction of residual friction angle of clays using artificial neural network, Engineering Geology, 100(3) (2008) 142-145.
[26] R. A. Mozumder, A. I. Laskar, Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network, Computers and Geotechnics, 69 (2015) 291-300.
[27] F. Jafarian, D. Umbrello, S. Golpayegani, Z. Darake, Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining, Materials and Manufacturing Processes, 31(13) (2016) 1683-1691.
[28] F. Jafarian, H. Amirabadi, J. Sadri, H.R. Banooie, Simultaneous Optimizing Residual Stress and Surface Roughness in Turning of Inconel718 Superalloy, Materials and Manufacturing Processes, 29(3) (2014) 337-343.
[29] F. Jafarian, H. Amirabadi, J. Sadri, Experimental measurement and optimization of tensile residual stress in turning process of Inconel718 superalloy, Measurement, 63 (2015) 1-10.
[30] F. Jafarian, M. Taghipour, H.J.J.o.M.S. Amirabadi, Technology, Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation, 27(5) (2013) 1469-1477.
| ||
|
آمار تعداد مشاهده مقاله: 685 تعداد دریافت فایل اصل مقاله: 356 |
||