Full Text:   <1700>

Summary:  <450>

CLC number: TU43

On-line Access: 2016-04-05

Received: 2015-02-10

Revision Accepted: 2015-07-10

Crosschecked: 2016-03-16

Cited: 0

Clicked: 1961

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hossein Rezaei

http://orcid.org/0000-0003-0766-5833

Ehsan Momeni

http://orcid.org/0000-0003-4084-485X

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.4 P.273-285

http://doi.org/10.1631/jzus.A1500033


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


Author(s):  Hossein Rezaei, Ramli Nazir, Ehsan Momeni

Affiliation(s):  Faculty of Engineering, Lorestan University, Khorram Abad 68151-44316, Iran; more

Corresponding email(s):   mehsan23@live.utm.my

Key Words:  Thin-walled foundation, Sand, Bearing capacity, Artificial neural network (ANN), Particle swarm optimization (PSO)


Hossein Rezaei, Ramli Nazir, Ehsan Momeni. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study[J]. Journal of Zhejiang University Science A, 2016, 17(4): 273-285.

@article{title="Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study",
author="Hossein Rezaei, Ramli Nazir, Ehsan Momeni",
journal="Journal of Zhejiang University Science A",
volume="17",
number="4",
pages="273-285",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1500033"
}

%0 Journal Article
%T Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
%A Hossein Rezaei
%A Ramli Nazir
%A Ehsan Momeni
%J Journal of Zhejiang University SCIENCE A
%V 17
%N 4
%P 273-285
%@ 1673-565X
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1500033

TY - JOUR
T1 - Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
A1 - Hossein Rezaei
A1 - Ramli Nazir
A1 - Ehsan Momeni
J0 - Journal of Zhejiang University Science A
VL - 17
IS - 4
SP - 273
EP - 285
%@ 1673-565X
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1500033


Abstract: 
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.

The authors proposed an improved ANN-based predictive model of bearing capacity for thin-wall shallow foundations based on comprehensive testing data. This paper is well written and the model footing tests were carefully done

基于人工智能的薄壁浅地基的承重能力研究

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。

关键词:薄壁地基;沙;承重能力;人工神经网络;粒子群优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Adams, M.T., Collin, J.G., 1997. Large model spread footing load tests on geosynthetic reinforced soil foundations. Journal of Geotechnical and Geoenvironmental Engineering, 123(1):66-72.

[2]Akbas, S.O., Kulhawy, F.H., 2009. Axial compression of footings in cohesionless soils. I: Load-settlement behavior. Journal of Geotechnical and Geoenvironmental Engineering, 135(11):1562-1574.

[3]Al-Aghbari, M.Y., Mohamedzein, Y.A., 2004. Model testing of strip footings with structural skirts. Proceedings of the ICE-Ground Improvement, 8(4):171-177.

[4]Al-Aghbari, M.Y., Dutta, R.K., 2008. Performance of square footing with structural skirt resting on sand. Geomechanics and Geoengineering, 3(4):271-277.

[5]Alvarez Grima, M., Babuška, R., 1999. Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanics and Mining Sciences, 36(3):339-349.

[6]Benali, A., Nechnech, A., 2011. Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks. International Seminar, Innovation and Valorization in Civil Engineering and Construction Materials, Rabat, Morocco, p.23-25.

[7]Briaud, J.L., Gibbens, R., 1999. Behavior of five large spread footings in sand. Journal of Geotechnical and Geoenvironmental Engineering, 125(9):787-796.

[8]Chen, Q., Abu-Farsakh, M.Y., Sharma, R., et al., 2007. Laboratory investigation of behavior of foundations on geosynthetic-reinforced clayey soil. Transportation Research Record: Journal of the Transportation Research Board, 2004:28-38.

[9]Dreyfus, G., 2005. Neural Networks: Methodology and Application. Springer Berlin Heidelberg, Germany.

[10]Eberhart, R.C., Shi, Y., 2001. Tracking and optimizing dynamic systems with particle swarms. Proceedings of the Congress on Evolutionary Computation, Seoul, Korea, p.94-100.

[11]Eid, H.T., 2013. Bearing capacity and settlement of skirted shallow foundations on sand. International Journal of Geomechanics, 13(5):645-652.

[12]Eid, H.T., Alansari, O.A., Odeh, A.M., et al., 2009. Comparative study on the behavior of square foundations resting on confined sand. Canadian Geotechnical Journal, 46(4):438-453.

[13]Fausett, L.V., 1994. Fundamentals of Neural Networks: Architecture, Algorithms and Applications. Prentice-Hall, Englewood Cliffs, NJ, USA.

[14]Garrett, J.H., 1994. Where and why artificial neural networks are applicable in civil engineering. Journal of Computing in Civil Engineering, 8(2):129-130.

[15]Gibbens, R.M., Briaud, J.L., 1995. Load Tests on Five Large Spread Footings on Sand and Evaluation of Prediction Methods. MS Thesis, Texas A&M University, College Station, TX, USA.

[16]Goh, A.T., 1996. Pile driving records reanalyzed using neural networks. Journal of Geotechnical Engineering, 122(6):492-495.

[17]Habib, P.A., 1974. Scale effect for shallow footings on dense sand. Journal of Geotechnical and Geoenvironmental Engineering, 100(GT1):95-99.

[18]Hagan, M.T., Demuth, H.B., Beale, M.H., et al., 1996. Neural Network Design. PWS Publishing Company, Boston, USA.

[19]Holland, J., 1975. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, USA.

[20]Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359-366.

[21]Jadav, K., Panchal, M., 2012. Optimizing weights of artificial neural networks using genetic algorithms. International Journal of Advanced Research in Computer Science and Electronics Engineering, 1:47-51.

[22]Jahed Armaghani, D., Tonnizam Mohamad, E., Momeni, E., et al., 2014. An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range Granite. Bulletin of Engineering Geology and the Environment, 74(4):1301-1319.

[23]Kalinli, A., Acar, M.C., Gunduz, Z., 2011. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology, 117(1-2):29-38.

[24]Kennedy, J., Eberhart, R., 1995. Particle swarm optimization. IEEE International Conference on Neural Networks, Perth, Australia, p.1942-1948.

[25]Khari, M., Kassim, K.A., Adnan, A., 2014. Sand samples’ preparation using mobile pluviator. Arabian Journal for Science and Engineering, 39(10):6825-6834.

[26]Kiefa, M.A., 1998. General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering, 124(12):1177-1185.

[27]Lee, Y., Oh, S.H., Kim, M.W., 1991. The effect of initial weights on premature saturation in back-propagation learning. International Joint Conference on Neural Networks, Seattle, USA, 1:765-770.

[28]Liu, T.X., Zhang, S.W., Wu, Q.Y., et al., 2012. Research of agricultural land classification and evaluation based on genetic algorithm optimized neural network model. In: Wu, Y.W. (Ed.), Software Engineering and Knowledge Engineering: Theory and Practice. Springer Berlin Heidelberg, Germany, p.465-471.

[29]Lok, T.M.H., Che, W.F., 2004. Axial capacity prediction for driven piles using ANN: model comparison. Geotechnical Engineering for Transportation Projects, Los Angeles, USA, p.697-704.

[30]Madabhushi, S.P.G., Houghton, N.E., Haigh, S.K., 2006. A new automatic sand pourer for model preparation at University of Cambridge. Proceedings of the 6th International Conference on Physical Modelling in Geotechnics, London, UK, p.217-222.

[31]Majdi, A., Beiki, M., 2010. Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. International Journal of Rock Mechanics and Mining Sciences, 47(2):246-253.

[32]Marto, A., Hajihasaani, M., Momeni, E., 2014. Prediction of bearing capacity of shallow foundation through hybrid artificial neural networks. Applied Mechanics and Materials, 567:681-686.

[33]Mendes, R., Cortes, P., Rocha, M., et al., 2002. Particle swarms for feed forward neural net training. Proceedings of the IEEE International Conference on Neural Networks, Honolulu, HI, USA, p.1895-1899.

[34]Meulenkamp, F., Alvarez Grima, M., 1999. Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. International Journal of Rock Mechanics and Mining Sciences, 36(1):29-39.

[35]Meyerhof, G.G., 1963. Some recent research on the bearing capacity of foundations. Canadian Geotechnical Journal, 1(1):16-26.

[36]Momeni, E., Maizir, H., Gofar, N., et al., 2013. Comparative study on prediction of axial bearing capacity of driven piles in granular materials. Jurnal Teknologi, 61(3):15-20.

[37]Momeni, E., Nazir, R., Jahed Armaghani, D., et al., 2014. Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57:122-131.

[38]Momeni, E., Nazir, R., Jahed Armaghani, D., et al., 2015a. Application of artificial neural network for predicting shaft and tip resistance of concrete piles. Earth Sciences Research Journal, 19(1):85-93.

[39]Momeni, E., Nazir, R., Jahed Armaghani, D., et al., 2015b. Bearing capacity of precast thin-walled foundation in sand. Geotechnical Engineering, 168(6):539-550.

[40]Momeni, E., Jahed Armaghani, D., Hajihassani, M., et al., 2015c. Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement, 60:50-63.

[41]Nazir, R., Momeni, E., Marsono, K., et al., 2013. Precast spread foundation in industrialized building system. Proceedings of the 3rd International Conference on Geotechnique, Construction Materials and Environment, Nagoya, Japan, p.13-15.

[42]Nazir, R., Momeni, E., Hajihassani, M., 2014. Prediction of spread foundation’s settlement in cohesionless soils using a hybrid particle swarm optimization-based ANN approach. International Conference on Advances in Civil, Structural and Mechanical Engineering, London, UK, p.20-24.

[43]Ornek, M., Laman, M., Demir, A., et al., 2012. Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils and Foundations, 52(1):69-80.

[44]Padmini, D., Ilamparuthi, K., Sudheer, K., 2008. Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics, 35(1):33-46.

[45]Pal, M., Deswal, S., 2008. Modeling pile capacity using support vector machines and generalized regression neural network. Journal of Geotechnical and Geoenvironmental Engineering, 134(7):1021-1024.

[46]Rabbani, E., Sharif, F., Koolivand Salooki, M., et al., 2012. Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties. Journal of Rock Mechanics and Mining Sciences, 56:100-111.

[47]Rashidian, V., Hassanlourad, M., 2013. Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotechnical and Geological Engineering, 31(4):1231-1248.

[48]Shahin, M.A., 2015. A review of artificial intelligence applications in shallow foundations. International Journal of Geotechnical Engineering, 9(1):49-60.

[49]Shahin, M.A., Jaksa, M.B., Maier, H.R., 2001. Artificial neural network application in geotechnical engineering. Australian Geomechanics, 36(1):49-62.

[50]Shi, Y., Eberhart, R., 1998. Parameter selection in particle swarm optimization. Evolutionary Programming VII: 7th International Conference, San Diego, California, USA, p.591-600.

[51]Shi, Y., Eberhart, R., 1999. Empirical study of particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation, New York, p.1945-1950.

[52]Singh, V.K., Singh, D., Singh, T.N., 2001. Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences, 38(2):269-284.

[53]Soleimanbeigi, A., Hataf, N., 2006. Prediction of settlement of shallow foundations on reinforced soils using neural networks. Geosynthetics International, 13(4):161-170.

[54]Taylor, R.N., 1995. Geotechnical Centrifuge Technology, 1st Edition. Chapman & Hall, London, UK.

[55]Terzaghi, K., 1943. Theoretical Soil Mechanics. John Wiley and Sons, Inc., New York.

[56]Tonnizam Mohamad, E., Jahed Armaghani, D., Momeni, E., 2014. Prediction on unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bulletin of Engineering Geology and the Environment, 74(3):745-757.

[57]Tripathy, S., 2013. Load Carrying Capacity of Skirted Foundation on Sand. MS Thesis, National Institute of Technology, Rourkela, India.

[58]Vesic, A.S., 1973. Analysis of ultimate loads of shallow foundations. Journal of the Soil Mechanics and Foundations Division, 99(1):45-73.

[59]Villalobos, F., 2007. Bearing capacity of skirted foundations in sand. VI Congreso Chileno de Geotecnia, Valparaiso, Chile.

[60]Wakil, A.Z.E.L., 2013. Bearing capacity of skirt circular footing on sand. Alexandria Engineering Journal, 52(3):359-364.

[61]Zhao, J.B., Tu, J.W., Shi, Y.Q., 2010. An ANN model for predicting level ultimate bearing capacity of PHC pipe pile. Earth and Space, p.3168-3176.

[62]Zorlu, K., Gokceoglu, C., Ocakoglu, F., et al., 2008. Prediction of uniaxial compressive strength of sandstones using petrography-based models. Engineering Geology, 96(3-4):141-158.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE