Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks

The paper presents the prediction of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using the artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand, while the output was the ultimate bearing capacity. The artificial neural network algorithm uses a back propagation model. The training of artificial neural network model has been conducted and the weights were obtained which described the relationship between the input parameters and output ultimate bearing capacity. Further, the sensitivity analysis has been performed and the parameters affecting the ultimate bearing capacity of different regular shaped skirted footing resting on the sand were identified. The study shows that the prediction accuracy of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.

[1]  Thorsten Behrens,et al.  Digital soil mapping using artificial neural networks , 2005 .

[2]  Fereydoon Sarmadian,et al.  Modeling of Some Soil Properties Using Artificial Neural Network and Multivariate Regression in Gorgan Province, North of Iran , 2008 .

[3]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[4]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[5]  M. Y. Al-Aghbari,et al.  Bearing capacity of strip foundations with structural skirts , 2004 .

[6]  Ramli Nazir,et al.  Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study , 2016 .

[7]  Mustafa Laman,et al.  Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil , 2012 .

[8]  Andrei V. Lyamin,et al.  ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil , 2009 .

[9]  Sung Eun Cho,et al.  Probabilistic stability analyses of slopes using the ANN-based response surface , 2009 .

[10]  Sungyani Tripathy.  Load carrying capacity of skirted foundation on sand , 2013 .

[11]  Hisham T. Eid,et al.  Bearing Capacity and Settlement of Skirted Shallow Foundations on Sand , 2013 .

[12]  S. Golmoghani-Ebrahimi,et al.  Experimental Evaluation of Bearing Capacity of Skirted Footings , 2013 .

[13]  Amr Z. EL Wakil,et al.  Bearing capacity of Skirt circular footing on sand , 2013 .

[14]  C. Kayadelen,et al.  Estimation of effective stress parameter of unsaturated soils by using artificial neural networks , 2008 .

[15]  Ali Firat Cabalar,et al.  Triaxial compression behavior of sand and tire wastes using neural networks , 2010, Neural Computing and Applications.

[16]  H. Guterman,et al.  Knowledge extraction from artificial neural network models , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[17]  Rakesh Kumar Dutta,et al.  Performance of square footing with structural skirt resting on sand , 2008 .

[18]  Yahia E.-A. Mohamedzein,et al.  Improving the performance of circular foundations using structural skirts , 2006 .

[19]  M. Al-Aghbari,et al.  The use of skirts to improve the performance of a footing in sand , 2020 .

[20]  H. I. Park,et al.  Development of Neural Network Model to Estimate the Permeability Coefficient of Soils , 2011 .

[21]  Ramli Nazir,et al.  Prediction of bearing capacity for thin-wall spread foundations using ICA-ANN predictive model , 2015 .

[22]  Khademi Faezehossadat,et al.  PREDICTING THE 28 DAYS COMPRESSIVE STRENGTH OF CONCRETE USING ARTIFICIAL NEURAL NETWORK , 2016 .

[23]  Akbar A. Javadi,et al.  Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression , 2011, Comput. Geosci..

[24]  Ramli Nazir,et al.  An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand , 2015 .

[25]  Peter Keith Woodward,et al.  Artificial neural network for stress-strain behavior of sandy soils: Knowledge based verification , 2005 .

[27]  Danial Jahed Armaghani,et al.  Prediction of bearing capacity of thin-walled foundation: a simulation approach , 2018, Engineering with Computers.

[28]  Prabir Kumar Basudhar,et al.  Undrained lateral load capacity of piles in clay using artificial neural network , 2006 .

[29]  Kamlesh Dutta,et al.  Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network , 2015 .

[30]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[31]  Hisham T. Eid,et al.  Comparative study on the behavior of square foundations resting on confined sand , 2009 .

[32]  Ersin Kolay,et al.  Estimating of the Dry Unit Weight of Compacted Soils Using General Linear Model and Multi-layer Perceptron Neural Networks , 2014, Appl. Soft Comput..

[33]  Orabi Al-Rawi,et al.  Modeling Soil Specific Surface Area with Artificial Neural Networks , 2014 .

[34]  Felipe Villalobos.  BEARING CAPACITY OF SKIRTED FOUNDATIONS IN SAND , 2007 .

[35]  Rakesh Kumar,et al.  Research Paper EFFECT OF SKIRT ON PRESSURE SETTLEMENT BEHAVIOUR OF MODEL CIRCULAR FOOTING IN MEDIUM DENSE SAND , 2014 .

[36]  Rafał F. Obrzud,et al.  Numerical modeling and neural networks to identify constitutive parameters from in situ tests , 2009 .

[37]  Murat Ornek,et al.  Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils , 2013, Neural Computing and Applications.