Prediction of unconfined compressive strength of clay subgrade soil stabilized with Portland cement and lime using Group Method of Data Handling (GMDH)

Document Type : Research Paper

Authors

1 Sirjan University of Technology

2 Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

3 Young Researchers Club, Rasht Branch, Islamic Azad University, Rasht, Iran

4 Department of Civil Engineering, University of Yazd, Yazd, Iran

Abstract

Unconfined Compressive Strength (UCS) test is commonly employed to determine the strength and quality control of the stabilized layers. This method is time consuming due to the needed time for curing of samples. Also, this method can be costly if the number of samples is increased. In this paper, the group method of data handling (GMDH) was used to develop simple models with sufficient accuracy to predict the UCS of clayey subgrade stabilized with Portland cement and lime. To this end, after stabilization of samples with different percentages of Portland cement and lime at three different moisture contents (dry, wet and optimum moisture content) and curing times of 7, 14, 21, 28 and 60 days, the UCS tests were conducted to establish a comprehensive database including 150 records. In the next step, two different UCS prediction models for clayey subgrade soil stabilized with Portland cement and lime were developed by using the GMDH method. The R2 value for training and testing sets for samples stabilized with Portland cement was 0.9294 and 0.94522, respectively, while the R2 value for training and testing sets for samples stabilized with lime was 0.8897 and 0.880, respectively. In addition, the sensitivity analysis of each model showed that the cement percentage and moisture content have the most impact on the predicted UCS, respectively.

Keywords

References

 
ACI Committee. 1997. “State-of-the-art report on soil cement”. ACI Mater. J., 87(4): 395-417.
Alavi, A. and Gandomi, A. 2011. “A robust data mining approach for formulation of geotechnical engineering systems”. Eng. Comput., 28(3): 242-274.
Alavi, A. H., Gandomi, A. H. and Mollahasani, A. 2012. “A genetic programming-based approach for the performance characteristics assessment of stabilized soil”. PP. 343-376. In: Variants of Evolutionary Algorithms for Real-World Applications, Springer, Berlin.
Al-Dabbas, M. A., Schanz, T. and Yassen, M. J. 2012. “Proposed engineering of gypsiferous soil classification”. Arab. J. Geosci., 5(1): 111-119.
Ardakani, A. and Kordnaeij, A. 2017. “Soil compaction parameters prediction using GMDH-type neural network and Genetic Algorithm”. Eur. J. Environ. Civ. Eng., doi.org/10.1080/19648189.2017.1304269.
Das, B. M. 1990. “Principle of foundation engineering”. PWS-KENT, Boston.
Das, S. K., Samui, P. and Sabat, A. K. 2011. “Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil”. Geotech. Geol. Eng., 29(3): 329-342.
Ferreira, C. 2001. “Gene expression programming: A new adaptive algorithm for solving problems”. Complex Syst., 13(2): 87-129.
GeneXpro Tools 4.0 [Computer software]. 2006. Gepsoft Ltd., Bristol, UK.
Goharriz, M. and Marandi, S. M. 2016. “An optimized neuro-fuzzy group method of data handling system based on gravitational search algorithm for evaluation of lateral ground displacement”. Int. J. Optim. Civ. Eng., 6(3): 385-403.
Güllü, H. 2014. “Function finding via genetic expression programming for strength and elastic properties of clay treated with bottom ash”. Eng. Appl. Artif. Intel., 35: 143-157.
Javadi, A. A. and Rezania, M. 2009. “Applications of artificial intelligence and data mining techniques in soil modeling”. Geomech. Eng., 1(1): 53-74.
Javdanian, H., Haddad, A. and Jafarian, A. 2015. “Evaluation of dynamic behavior of fine-grained soils using group method of data handling”. J. Transport. Infrastruct. Eng., 1(3): 77-92.
Khandelwal, M. and Singh, T. N. 2011. “Predicting elastic properties of schistose rocks from unconfined strength using intelligent approach”. Arab. J. Geosci., 4(3-4): 435-442.
Kogbara, R. B. and Al-Tabbaa, A. 2011. “Mechanical and leaching behaviour of slag-cement and lime-activated slag stabilised/solidified contaminated soil”. Sci. Total Environ., 409(11): 2325-2335.
Kordnaeij, A., Kalantary, F., Kordtabar, B. and Mola-Abasi, H. 2015. “Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties”. Soils Found., 55(6): 1335-1345.
Madandoust, R., Ghavidel, R. and Nariman Zadeh, N. 2010. “Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV”. Comput. Mater. Sci., 49(3): 556-567.
Mallela, J., Quintus, H. V. and Smith, K. 2004. “Consideration of lime-stabilized layers in mechanistic-empirical pavement design”. The National Lime Association, pp. 200-208.
Motamedi, S., Shamshirband, S., Petković, D. and Hashim, R. 2015a. “Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture”. Powder Technol., 278: 278-285.
Motamedi, S., Shamshirband, S., Hashim, R., Petković, D. and Roy, C. 2015b. “RETRACTED-Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies”. Engineering Structures, 98, 49-58.
Mozumder, R. A., Laskar, A. I. and Hussain, M. 2017. “Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines”. Constr. Build. Mater., 132: 412-424.
Najafzadeh, M. 2015. “Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures”. Eng. Sci. Technol., 18(1): 42-51.
Najafzadeh, M., Barani, G. A. and Azamathulla, H. M. 2013. “GMDH to predict scour depth around a pier in cohesive soils”. Appl. Ocean Res., 40: 35-41.
Nelson, J. and Miller, D. J. 1997. “Expansive soils: Problems and practice in foundation and pavement engineering”. John Wiley and Sons.
Sathyapriya, S., Arumairaj, P. D. and Ranjini, D. 2017. “Prediction of unconfined compressive strength of a stabilised expansive clay soil using ANN and regression analysis (SPSS)”. Asian J. Res. Social Sci. Human., 7(2): 109-123.
Suman, S., Mahamaya, M. and Das, S. K. 2016. “Prediction of maximum dry density and unconfined compressive strength of cement stabilised soil using artificial intelligence techniques”. Int. J. Geosynth. Ground Eng., 2(2): 1-11.
Tabatabaei, A. M. 1997. “Road and airport pavement”. University Publication Center, Tehran. (In Persian).
Yang, Y., and Zhang, Q. (1997). A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering, 30(4): 207-222.
Zhang, H., Liu, X., Cai, E., Huang, G. and Ding, C. 2013. “Integration of dynamic rainfall data with environmental factors to forecast debris flow using an improved GMDH model”. Comp. Geosci., 56: 23-31.
Ziari, H., Sobhani, J., Ayoubinejad, J. and Hartmann, T. 2016. “Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods”. Int. J. Pavement Eng., 17(9): 776-788.

Files

History

  • Receive Date: 22 June 2018
  • Revise Date: 17 August 2018
  • Accept Date: 02 September 2018

Share

How to cite

Statistics