New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization |
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| Authors: | Adem Kalinli M Cemal Acar Zeki Gündüz |
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| Institution: | 1. Kayseri Vocational College, University of Erciyes, Kayseri, Turkey;2. Department of Civil Engineering, University of Sakarya, Sakarya, Turkey;1. Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China;2. Key Laboratory of Geotechnical and Underground Engineering, Ministry of Education, Tongji University, Shanghai 200092, China;3. Department of Civil Engineering, The University of Tokyo, Tokyo 113-8656, Japan;1. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. Institute of Geotechnical Engineering, Southeast University, Nanjing 210096, China;3. Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA;1. Department of Civil Engineering, National Institute of Technology (NIT) Patna, Bihar, India;2. Department of Civil Engineering, BITS-Pilani Hyderabad Campus, Hyderabad, Telangana, India;3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;4. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007 (PO Box 123), Australia;5. Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea;6. Center of Excellence for Climate Change Research, King Abdulaziz University, P. O. Box 80234, Jeddah 21589, Saudi Arabia;7. Department Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia;8. Hacettepe University, Department of Geological Engineering, 06800 Beytepe, Ankara, Turkey;1. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India;2. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore;3. School of Civil Engineering, Chongqing University, Chongqing, 400044, China;1. Department of Civil Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;2. College of Civil Engineering and Architecture, Nanchang Institute Technology, Nanchang 330099, China |
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| Abstract: | In this study, two different approaches are proposed to determine the ultimate bearing capacity of shallow foundations on granular soil. Firstly, an artificial neural network (ANN) model is proposed to predict the ultimate bearing capacity. The performance of the proposed neural model is compared with results of the Adaptive Neuro Fuzzy Inference System, Fuzzy Inference System and ANN, which are taken in literature. It is clearly seen that the performance of the ANN model in our study is better than that of the other prediction methods. Secondly, an improved Meyerhof formula is proposed for the computation of the ultimate bearing capacity by using a parallel ant colony optimization algorithm. The results achieved from the proposed formula are compared with those obtained from the Meyerhof, Hansen and Vesic computation formulas. Simulation results showed that the improved Meyerhof formula gave more accurate results than the other theoretical computation formulas. In conclusion, the improved Meyerhof formula could be successfully used for calculating the ultimate bearing capacity of shallow foundations. |
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