Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

A major concern in the design of foundations is to achieve a precise estimation of bearing capacity of the underlying soil or rock mass. The present study proposes a new design equation for the prediction of the bearing capacity of shallow foundations on rock masses utilizing artificial neural network (ANN). The bearing capacity is formulated in terms of rock mass rating, unconfined compressive strength of rock, ratio of joint spacing to foundation width, and angle of internal friction for the rock mass. Further, a conventional calculation procedure is proposed based on the fixed connection weights and bias factors of the best ANN structure. A comprehensive database of rock socket, centrifuge rock socket, plate load, and large-scaled footing load test results is used for the model development. Sensitivity and parametric analyses are conducted and discussed. The results clearly demonstrate the acceptable performance of the derived model for estimating the bearing capacity of shallow foundations. The proposed prediction equation has a notably better performance than the traditional equations.

[1]  Adrian F. Williams,et al.  The design and performance of piles socketed into weak rock , 1980 .

[2]  C. G. Chua,et al.  Bayesian Neural Network Analysis of Undrained Side Resistance of Drilled Shafts , 2005 .

[3]  Evert Hoek,et al.  The Hoek-Brown failure criterion-a 1988 update , 1988 .

[4]  James L. McClelland.  Explorations In Parallel Distributed Processing , 1988 .

[5]  Pijush Samui,et al.  Prediction of swelling pressure of soil using artificial intelligence techniques , 2010 .

[6]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[7]  Z. Bieniawski.  Determining rock mass deformability: experience from case histories , 1978 .

[8]  M. Taghi Sattari,et al.  Flow estimations for the Sohu Stream using artificial neural networks , 2012, Environmental Earth Sciences.

[9]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

[10]  C. Kayadelen,et al.  Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils , 2009 .

[11]  Mostafa Sharifzadeh,et al.  Development of a model for analysis of slope stability for circular mode failure using genetic algorithm , 2014, Environmental Earth Sciences.

[12]  K. P. Sudheer,et al.  Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models , 2008 .

[13]  L. C. Wilson.  Tests of bored and driven piles in Cretaceous mudstone at Port Elizabeth, South Africa , 1976 .

[14]  Michael McVay,et al.  Distribution of End Bearing and Tip Shear on Drilled Shafts Founded in Florida Limestone , 2008 .

[15]  C. Thorne.  The capacity of piers drilled into rock , 1980 .

[16]  A. Gandomi,et al.  Nonlinear modeling of shear strength of SFRC beams using linear genetic programming , 2011 .

[17]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[18]  Z. T. Bieniawski,et al.  Engineering Rock Mass Classifications: A Complete Manual for Engineers and Geologists in Mining, Civil, and Petroleum Engineering , 1989 .

[19]  Paolo Carrubba,et al.  Skin friction on large-diameter piles socketed into rock , 1997 .

[20]  Doug Schmucker,et al.  Not As Bad As It Seems: Teaching Probability And Statistics In Civil Engineering , 2004 .

[21]  G. Ozden,et al.  Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks , 2012, Environmental Earth Sciences.

[22]  R. Corotis.  Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp. , 1988 .

[23]  K. Terzaghi,et al.  Rock tunneling with steel supports , 1946 .

[24]  A. Gandomi,et al.  Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks , 2010 .

[25]  F. H. Kulhawy,et al.  Analysis and design of drilled shaft foundations socketed into rock: Final report , 1988 .

[26]  Nader Hataf,et al.  Predicting ultimate bearing capacity of shallow foundations on reinforced cohesionless soils using artificial neural networks , 2005 .

[27]  Adem Kalinli,et al.  New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization , 2011 .

[28]  Amir Hossein Alavi,et al.  Formulation of flow number of asphalt mixes using a hybrid computational method , 2011 .

[29]  Amir Hossein Alavi,et al.  Nonlinear neural-based modeling of soil cohesion intercept , 2011 .

[30]  F. Ren,et al.  Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China , 2013, Environmental Earth Sciences.

[31]  Soyoung Park,et al.  Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea , 2013, Environmental Earth Sciences.

[32]  Serhat Yilmaz,et al.  An assessment of total RMR classification system using unified simulation model based on artificial neural networks , 2011, Neural Computing and Applications.

[33]  Sadi Evren Seker,et al.  Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network , 2012, Rock Mechanics and Rock Engineering.

[34]  Chun Fai Leung,et al.  Load Transfer Behavior of Rock-Socketed Piles , 1989 .

[35]  Saumen Maiti,et al.  A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction , 2014, Environmental Earth Sciences.

[36]  Clyde N. Baker.  Comparison of Caisson Load Tests on Chicago Hardpan , 1985 .

[37]  Naser Abu-Hejleh,et al.  Colorado's Axial Load Tests on Drilled Shafts Socketed in Weak Rocks: Synthesis and Future Needs , 2005 .

[38]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[39]  O. Gunaydin.  Estimation of soil compaction parameters by using statistical analyses and artificial neural networks , 2009 .

[40]  M Spanovich,et al.  Field Evaluation of Caisson-Shale Interaction , 1979 .

[41]  U. Okkan,et al.  Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks , 2013, Environmental Earth Sciences.

[42]  Yuksel Tasdemir,et al.  Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods , 2010 .

[43]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[44]  David M. Jubenville,et al.  Drilled Pier Foundations in Shale, Denver Colorado Area , 1981 .

[45]  Joseph E. Bowles,et al.  Foundation analysis and design , 1968 .

[46]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[47]  Işık Yilmaz,et al.  The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks , 2010 .

[48]  H-Y Ko,et al.  Centrifuge Model Study of Piles Socketed in Soft Rock , 1993 .

[49]  Safiuddin Adil Mohammed.  Impact of AASHTO LRFD bridge design specifications on the design of Type C and AASHTO Type IV girder bridges , 2007 .

[50]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[51]  S. Yamamoto,et al.  Bearing capacity of soft rock foundation on in-situ bearing capacity tests under inclined load , 1995 .

[52]  Yuksel Tasdemir,et al.  Comparison of three artificial neural network approaches for estimating of slake durability index , 2012, Environmental Earth Sciences.

[53]  Pijush Samui,et al.  Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil , 2011 .

[54]  F. H. Kulhawy,et al.  Conduct and interpretation of load tests on drilled shaft foundations: Volume 1, Detailed guidelines: Final report , 1988 .

[55]  R. M. Turner,et al.  Elastic solutions for the design and analysis of rock-socketed piles , 1979 .

[56]  Karlis Kaugars,et al.  Neural network modeling applications in active slope stability problems , 2010 .

[57]  R M Turner,et al.  Endbearing on rock with particular reference to sandstone , 1980 .

[58]  Mohamed Rouainia,et al.  The geotechnics of hard soils-soft rocks , 1998 .

[59]  Amir Hossein Alavi,et al.  Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing , 2011 .

[60]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[61]  T. Charles.  Introduction to tunnel geology; rock defects and loads on tunnel supports , 1946 .

[62]  J B Burland,et al.  ASSESSMENT OF THE DEFORMATION PROPERTIES OF JOINTED ROCK IN THE MASS , 1968 .

[63]  D. L. Webb.  The behaviour of bored piles in weathered diabase , 1976 .

[64]  D. Rumelhart.  Parallel Distributed Processing Volume 1: Foundations , 1987 .

[65]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[66]  Umi Kalthum Ngah,et al.  Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network , 2014, Environmental Earth Sciences.

[67]  Sarath Das,et al.  Classification of slopes and prediction of factor of safety using differential evolution neural networks , 2011 .

[68]  Mark B. Jaksa,et al.  Neural network prediction of pullout capacity of marquee ground anchors , 2005 .

[69]  P. Roy,et al.  On Some Aspects of Variable Selection for Partial Least Squares Regression Models , 2008 .

[70]  Prabir Kumar Basudhar,et al.  Prediction of residual friction angle of clays using artificial neural network , 2008 .

[71]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[72]  R. Goodman.  Introduction to Rock Mechanics , 1980 .

[73]  Samuel G. Paikowsky,et al.  LRFD Design and Construction of Shallow Foundations for Highway Bridge Structures , 2010 .

[74]  A. Goh.  Seismic liquefaction potential assessed by neural networks , 1994 .

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

[76]  G. Sowers,et al.  Introductory Soil Mechanics and Foundations: Geotechnical Engineering , 1982 .

[77]  Oliver H. Briggs,et al.  Rock Sockets in Soft Rock , 1983 .

[78]  Marian Marschalko,et al.  Neural computing models for prediction of permeability coefficient of coarse-grained soils , 2012, Neural Computing and Applications.

[79]  Pijush Samui,et al.  Machine learning techniques applied to prediction of residual strength of clay , 2011 .

[80]  Michael McVay,et al.  LOAD AND RESISTANCE FACTOR DESIGN (LRFD) FOR DEEP FOUNDATIONS , 2004 .

[81]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[82]  Patrick M. Goeke,et al.  Instrumented Drilled Shafts in Clay-Shale , 1980 .

[83]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[84]  Sadi Evren Seker,et al.  Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes , 2013, Environmental Earth Sciences.

[85]  A. Akgun,et al.  Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis , 2010 .