Effects of Significant Variables on Compressive Strength of Soil-Fly Ash Geopolymer: Variable Analytical Approach Based on Neural Networks and Genetic Programming
Publication: Journal of Materials in Civil Engineering
Volume 30, Issue 7
Abstract
The identification of significant input variables to the output provides very useful information for mix design for soil-fly ash geopolymer in order to obtain the optimum compressive strength. The importance of input variables to the output of soil-fly ash geopolymer is quantified by Garson’s algorithm and connection weights approach in an artificial neural networks (ANN) model, whereas model analysis and fitness method are used in a genetic programming (GP) model. The former approaches in the ANN model use the connection weights among the input, hidden, and output layers to evaluate the importance of the input variables. The latter methods in the GP model assess the frequency of variables used in the model and the value of fitness for the evaluation. The assessment results identify the percentages of fly ash, water, and soil as important input variables to the output. The percentage of hydroxide and the ratios of silicate to hydroxide and alkali activator to ash are ranked as less important input variables. The positive or negative relationships between these input variables and the output demonstrate a very significant influence on the strength development of soil-fly ash geopolymer, showing a positive or negative effect on the compressive strength.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The first author expresses gratitude to Swinburne Sarawak Research Centre for Sustainable Technologies for the financial support throughout her study.
References
Affenzeller, M., S. Winkler, S. Wagner, and A. Beham. 2009. Genetic algorithms and genetic programming. Boca Raton, FL: Taylor & Francis Group.
Aliabdo, A. A., A. E. M. A. Elmoaty, H. A. Salem. 2016. “Effect of water addition, plasticizer and alkaline solution constitution on fly ash based geopolymer concrete performance.” Constr Build. Mater. 121: 694–703.
ASTM. 2005a. Standard specification for coal fly ash and raw or calcined natural pozzolan for use in concrete. ASTM-C618. West Conshohocken, PA: ASTM.
ASTM. 2005b. Standard test method for compressive strength of hydraulic cement mortars (using 2-in. or [50-mm] cube specimens). ASTM-C109/C109M. West Conshohocken, PA: ASTM.
Atici, U. 2011. “Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network.” Expert Syst. Appl. 38 (8): 9609–9618.
Bakri, A. M. M. A., O. A. K. A. A. Kareem, and S. Myint. 2009. Study on the effect of alkaline activators ratio in preparation of fly ash-based geopolymer. Perlis, Malaysia: Universiti Malaysia Perlis.
Castelli, M., L. Vanneschi, and S. Silva. 2013. “Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators.” Expert Syst. Appl. 40 (17): 6856–6862.
Chopra, P., R. K. Sharma, and M. Kumar. 2016. “Prediction of compressive strength of concrete using artificial neural network and genetic programming.” Adv. Mater. Sci. Eng. 2016: 1–10.
Cladera, A., J. L. Pérez-Ordóñez, and F. Martínez-Abella. 2014. “Shear strength of RC beams. Precision, accuracy, safety and simplicity using genetic programming.” Comput. Concr. 14 (4): 479–501.
Cristelo, N., S. Glendinning, L. Fernandes, and A. N. T. Pinto. 2013. “Effects of alkaline-activated fly ash and portland cement on soft soil stabilisation.” Acta Geotech. 8 (4): 395–405.
Cristelo, N., S. Glendinning, T. Miranda, D. Oliveira, and R. Silva. 2012. “Soil stabilisation using alkaline activation of fly ash for self compacting rammed earth construction.” Constr. Build. Mater. 36: 727–735.
Davidovits, J. 2008. Geopolymer chemistry and application. Saint-Quentin, France: Institute Geopolymer.
Duxson, P., J. L. Provis, G. C. Lukey, and J. S. J. V. Deventer. 2007. “The role of inorganic polymer technology in the development of ‘green concrete’.” Cem. Concr. Res. 37 (12): 1590–1597.
Flood, A., and N. Kartam. 1994. “Neural networks in civil engineering. I: Principles and understanding.” J. Comput. Civ. Eng. 8 (2): 131–148.
Garg, A., A. Garg, and K. Tai. 2014a. “A multi-gene genetic programming model for estimating stress-dependent soil water retention curves.” Comput. Geosci. 18 (1): 45–56.
Garg, A., A. Garg, K. Tai, and S. Sreedeep. 2014b. “An integrated SRM multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes.” Eng. Appl. Artif. Intell. 30: 30–40.
Garson, G. D. 1991. “Interpreting neural-network connection weights.” Artif. Intell. Expert 6 (4): 47–51.
Gevrey, M., I. Dimopoulos, and S. Lek. 2003. “Review and comparison of methods to study the contribution of variables in artificial neural network models.” Ecol. Modell. 160 (3): 249–264.
Hardjito, D., and B. V. Rangan 2005. Development and properties of low-calcium fly ash-based geopolymer concrete, 48. Perth, Australia: Curtin Univ. of Technology.
Heah, C. Y., H. Kamarudin, A. M. M. A. Bakri, M. Bnhussain, M. Luqman, I. K. Nizar, C. M. Ruzaidi, and Y. M. Liew. 2012. “Study on solids-to-liquid and alkaline activator ratios on kaolin-based geopolymers.” Constr. Build. Mater. 35: 912–922.
Hong-Guang, N., and W. Ji-Zong. 2000. “Prediction of compressive strength of concrete by neural networks.” Cem. Concr. Res. 30 (8): 1245–1250.
Kewalramani, M. A., and R. Gupta. 2006. “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks.” Autom. Constr. 15 (3): 374–379.
Komljenovi, M., Z. Bascarevi, and V. Bradic. 2010. “Mechanical and microstructural properties of alkali-activated fly ash geopolymers.” J. Hazard. Mater. 181 (1–3): 35–42.
Kong, D. L. Y., and J. G. Sanjayan. 2008. “Damage behavior of geopolymer composites exposed to elevated temperatures.” Cem. Concr. Compos. 30 (10): 986–991.
Kostic, S., and D. Vasovic. 2015. “Prediction model for compressive strength of basic concrete mixture using artificial neural networks.” Neural Comput. Appl. 26 (5): 1005–1024.
Koza, J. R. 1994. “Genetic programming as a means for programming computers by natural selection.” Stat. Comput. 4 (2): 87–112.
Leong, H. Y., D. E. L. Ong, J. G. Sanjayan, and A. Nazari. 2015. “A genetic programming predictive model for parametric study of factors affecting strength of geopolymers.” R. Soc. Chem. Adv. 5 (104): 85630–85639.
Leong, H. Y., D. E. L. Ong, J. G. Sanjayan, and A. Nazari. 2016. “The effect of different and ratios of alkali activator on compressive strength of fly ash based-geopolymer.” Constr. Build. Mater. 106: 500–511.
Leong, H. Y., D. E. L. Ong, J. G. Sanjayan, and A. Nazari. 2017. “Soil-fly ash geopolymer: Strength and microstructure assessment of soil, fly ash, alkali activators and water.” ASCE J. Mater. Civ. Eng., in press.
Liew, Y. M., H. Kamarudin, A. M. M. A. Bakri, M. Binhussain, M. Luqman, I. K. Nizar, C. M. Ruzaidi, and C. Y. Heah. 2011. “Influence of solids-to-liquid and activator ratios on calcined kaolin cement powder.” Phys. Procedia 22: 312–317.
Mitchell, J. K., and K. Soga. 2005. Fundamentals of soil behavior. New York: Wiley.
Mozumder, R. A., and A. I. Laskar. 2015. “Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network.” Comput. Geotech. 69: 291–300.
Nazari, A. 2013. “Compressive strength of geopolymers produced by ordinary portland cement: Application of genetic programming for design.” Mater. Des. 43: 356–366.
Nazari, A., and F. P. Torgal. 2013a. “Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP.” Expert Syst. Appl. 40 (14): 5427–5438.
Nazari, A., and F. P. Torgal. 2013b. “Predicting compressive strength of different geopolymers by artificial neural networks.” Ceram. Int. 39 (3): 2247–2257.
Nikoo, M., F. T. Moghadam, and A. Sadowski. 2015. “Prediction of concrete compressive strength by evolutionary artificial neural networks.” Adv. Mater. Sci. Eng. 2015: 1–8.
Olanrewaju, O. A., A. A. Jimoh, and P. A. Kholopane. 2012. “Evaluating GHG components using artificial intelligence: Connection weight approach.” In Proc., 2012 IEEE, 1214–1217. New York: IEEE.
Olden, J. D., and D. A. Jackson. 2002. “Illuminating the ‘black box’: A randomization approach for understanding variable contributions in artificial neural networks.” Ecol. Model. 154 (1–2): 135–150.
Olden, J. D., M. K. Joy, and R. G. Death. 2004. “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data.” Ecol. Model. 178 (3–4): 389–397.
Patankar, S. V., S. S. Jamkar, and Y. M. Ghugal. 2013. “Effect of water-to-geopolymer binder ratio on the production of fly ash based geopolymer concrete.” Int. J. Adv. Technol. Civ. Eng. 2 (1): 79–83.
Pile, P. 2013. “What is expanding PowerPile geopolymer pillar?” PowerPile Pillars, Accessed November 26, 2016. http://powerpile.com/what-is-powerpile-expanding-polymer-pillar.
Sarıdemir, M. 2010. “Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash.” Constr. Build. Mater. 24 (10): 1911–1919.
Sarıdemir, M. 2014. “Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design.” Mater. Des. 56: 297–304.
Sarker, P. K. 2009. “Analysis of geopolymer concrete columns.” Mater. Struct. 42 (6): 715–724.
Shrest, P. 2013. Development of geopolymer concrete for precast structures. Arlington, TX: Univ. of Texas.
Sivanandam, S. N., and S. N. Deepa. 2008. Introduction to genetic algorithms. Berlin: Springer.
Sobhani, J., M. Najimi, A. R. Pourkhorshidi, and T. Parhizkar. 2010. “Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models.” Constr. Build. Mater. 24 (5): 709–718.
Sreekanth, J., and B. Datta. 2012. “Genetic programming: Efficient modeling tool in hydrology and groundwater management.” In Genetic programming—New approaches and successful, edited by S. V. Soto, 225–238. Rijeka, Croatia: InTech.
Sukmak, P., S. Horpibulsuk, and S.-L. Shen. 2013a. “Strength development in clay-fly ash geopolymer.” Constr. Build. Mater. 40: 566–574.
Sukmak, P., S. Horpibulsuk, S.-L. Shen, P. Chindaprasirt, and C. Suksiripattanapong. 2013b. “Factors influencing strength development in clay-fly ash geopolymer.” Constr. Build. Mater. 47: 1125–1136.
Tennakoon, C., A. Nazari, J. G. Sanjayan, and K. Sagoe-Crentsil. 2014. “Distribution of oxides in fly ash controls strength evolution of geopolymers.” Constr. Build. Mater. 71: 72–82.
Topcu, I. B., and M. Sarıdemir. 2008. “Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic.” Comput. Mater. Sci. 41 (3): 305–311.
Uretek. 2014. “Geopolymer injection.” Methods, Accessed October 22, 2016. http://www.uretekworldwide.com/solutions/methods/geopolymer-injection.
Xu, H., and J. S. J. V. Deventer. 2000. “The geopolymerisation of alumino-silicate minerals.” Int. J. Miner. Process. 59 (3): 247–266.
Yadollahi, M. M., A. Benli, and R. Demirboğa. 2015. “Prediction of compressive strength of geopolymer composites using an artificial neural network.” Mater. Res. Innovations 19 (6): 453–458.
Yegnanarayana, B. 2006. Artificial neural networks. New Delhi, India: Prentice-Hall of India.
Zhang, M., H. Guo, T. El-Korchi, G. Zhang, and M. Tao. 2013. “Experimental feasibility study of geopolymer as the next-generation soil stabilizer.” Constr. Build. Mater. 47: 1468–1478.
Zuhua, Z., Y. Xiao, Z. Huajun, and C. Yue. 2009. “Role of water in the synthesis of calcined kaolin-based geopolymer.” Appl. Clay Sci. 43 (2): 218–223.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 30 • Issue 7 • July 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 28, 2016
Accepted: Oct 19, 2017
Published online: Apr 26, 2018
Published in print: Jul 1, 2018
Discussion open until: Sep 26, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.