Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Qu), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (Lw/B). Apart from Qu, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when Lw/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.中文概要目的薄壁扩展式地基已被广泛应用于土壤强度相对较低的沿海工程。目前,已有很多学者对其进行了人工神经网络的适用性研究,希望用此对地基的承重能力进行预测。但是这些研究多数是基于传统的人工神经网络,学习速度慢且受困于局部极小值。本文拟提出一种改进的基于人工神经网络的预测薄壁浅地基承重能力的模型。方法1. 整合145 组关于地基承重测试的文献数据和实验数据(包括承重能力、摩擦角、沙的单位重量、基脚宽度和长宽比等);除了承重能力,其他参数都是模型输入;2. 研究各参数对地基承重能力的影响,确定最优的人工神经网络模型参数,并对不同的人工神经网络模型进行比较。结论1. 当基脚长宽比从0.5 变为1.12 时,地基的承重能力增加了大约一半;2. 基于粒子群优化算法的人工神经网络模型表现最好;在测试数据中,承重能力的预测值和测量值之间高达0.98的相关系数也表明,在无粘性土中,基于人工神经网络的预测模型适用于薄壁浅地基的承重能力预测。

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Jean-Louis Briaud,et al.  Behavior of Five Large Spread Footings in Sand , 1999 .

[3]  Myung Won Kim,et al.  The effect of initial weights on premature saturation in back-propagation learning , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[4]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

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

[6]  Gérard Dreyfus,et al.  Neural networks - methodology and applications , 2005 .

[7]  Abdul Kadir,et al.  Precast spread foundation in industrialized building system , 2013 .

[8]  Fred H. Kulhawy,et al.  Axial Compression of Footings in Cohesionless Soils. I: Load-Settlement Behavior , 2009 .

[9]  V. Rashidian,et al.  Predicting the Shear Behavior of Cemented and Uncemented Carbonate Sands Using a Genetic Algorithm-Based Artificial Neural Network , 2013, Geotechnical and Geological Engineering.

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

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

[12]  Zhao Jianbin,et al.  An ANN Model for Predicting Level Ultimate Bearing Capacity of PHC Pipe Pile , 2010 .

[13]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[14]  M. Grima,et al.  Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness , 1999 .

[15]  T. N. Singh,et al.  Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks , 2001 .

[16]  Murad Y. Abu-Farsakh,et al.  Laboratory Investigation of Behavior of Foundations on Geosynthetic-Reinforced Clayey Soil , 2007 .

[17]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[18]  Robert Melvin Gibbens.  Load tests on five large spread footings on sand and evaluation of prediction methods , 1995 .

[19]  T. M. H. Lok,et al.  AXIAL CAPACITY PREDICTION FOR DRIVEN PILES USING ANN: MODEL COMPARISON , 2004 .

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

[21]  Danial Jahed Armaghani,et al.  Application of artificial neural network for predicting shaft and tip resistances of concrete piles , 2015 .

[22]  Martin T. Hagan,et al.  Neural network design , 1995 .

[23]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[24]  Mahesh Pal,et al.  Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network , 2008 .

[25]  Ramli Nazir,et al.  Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN , 2014 .

[26]  Robert Babuška,et al.  Fuzzy model for the prediction of unconfined compressive strength of rock samples , 1999 .

[27]  A. Moradzadeh,et al.  Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties , 2012 .

[28]  Pierre A. Habib.  Scale Effect for Shallow Footings on Dense Sand , 1974 .

[29]  Aleksandar S. Vesic,et al.  Analysis of Ultimate Loads of Shallow Foundations , 1973 .

[30]  George Geoffrey Meyerhof,et al.  Some recent research on the bearing capacity of foundations , 1963 .

[31]  Yahia E.-A. Mohamedzein,et al.  Model testing of strip footings with structural skirts , 2004 .

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

[33]  Ramli Nazir,et al.  Comparative Study on Prediction of Axial Bearing Capacity of Driven Piles in Granular Materials , 2013 .

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

[35]  Ramli Nazir,et al.  Prediction of Spread Foundations' Settlement in Cohesionless Soils Using a Hybrid Particle Swarm Optimization-Based ANN Approach , 2014 .

[36]  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 .

[37]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[38]  Nader Hataf,et al.  Prediction of settlement of shallow foundations on reinforced soils using neural networks , 2006 .

[39]  J H Garrett,et al.  WHERE AND WHY ARTIFICIAL NEURAL NETWORKS ARE APPLICABLE IN CIVIL ENGINEERING , 1994 .

[40]  Danial Jahed Armaghani,et al.  An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite , 2015, Bulletin of Engineering Geology and the Environment.

[41]  Michael T. Adams,et al.  LARGE MODEL SPREAD FOOTING LOAD TESTS ON GEOSYNTHETIC REINFORCED SOIL FOUNDATIONS , 1997 .

[42]  M. A. A. Kiefa.  GENERAL REGRESSION NEURAL NETWORKS FOR DRIVEN PILES IN COHESIONLESS SOILS , 1998 .

[43]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[44]  Liu Tingxiang,et al.  Research of Agricultural Land Classification and Evaluation Based on Genetic Algorithm Optimized Neural Network Model , 2012 .

[45]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[46]  Ammar Nechnech,et al.  Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks , 2011 .

[47]  K. A. Kassim,et al.  Sand Samples’ Preparation Using Mobile Pluviator , 2014 .

[48]  Mahesh Panchal,et al.  Optimizing Weights of Artificial Neural Networks using Genetic Algorithms , 2012 .

[49]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[50]  Anthony T. C. Goh,et al.  Pile Driving Records Reanalyzed Using Neural Networks , 1996 .

[51]  K. Terzaghi.  Theoretical Soil Mechanics , 1943 .

[52]  Mohamed A. Shahin,et al.  A review of artificial intelligence applications in shallow foundations , 2015 .

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

[54]  Holger R. Maier,et al.  ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING , 2001 .

[55]  Abbas Majdi,et al.  Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses , 2010 .

[56]  Stuart K. Haigh,et al.  A new automatic sand pourer for model preparation at University of Cambridge , 2006 .

[57]  Candan Gokceoglu,et al.  Prediction of uniaxial compressive strength of sandstones using petrography-based models , 2008 .

[58]  Aminaton Marto,et al.  Bearing Capacity of Shallow Foundation's Prediction through Hybrid Artificial Neural Networks , 2014 .

[59]  Danial Jahed Armaghani,et al.  Bearing capacity of precast thin-walled foundation in sand , 2015 .