- Volume 19 Issue 6
DOI QR Code
An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming
- Received : 2016.01.16
- Accepted : 2017.02.15
- Published : 2017.06.25
High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.
high performance concrete;concrete strength;genetic programming;local search;semantics
- Abrams, D.A. (1927) , "Water-cement ration as a basis of concrete quality", ACI Mater. J., 23(2), 452-457.
- Aitcin, P.C. (2003), "The durability characteristics of high performance concrete: A review", Cement Concrete Compos., 25(4), 409-420. https://doi.org/10.1016/S0958-9465(02)00081-1
- Bhanja, S. and Sengupta, B. (2005), "Influence of silica fume on the tensile strength of concrete", Cement Concrete Res., 35(4), 743-747. https://doi.org/10.1016/j.cemconres.2004.05.024
- Castelli, A., Silva, S. and Vanneschi, L. (2014), "A C++ framework for geometric semantic genetic programming", Gen. Program. Evol. Mach., 16(1), 73-81.
- Castelli, M., Castaldi, D., Giordani, I., Silva, S., Vanneschi, L., Archetti, F. and Maccagnola, D. (2013a), An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics, Progress in Artificial Intelligence, Volume 8154 of the series Lecture Notes in Computer Science, 78-89.
- Castelli, M., Manzoni, L., Vanneschi, L., Silva, S. and Popovic, A. (2015b), "Self-tuning geometric semantic genetic programming", Gen. Program. Evol. Mach., 17(1), 55-74.
- Castelli, M., Trujillo, L. Vanneschi, L., Silva, S., Z-Flores, E. and Legrand, P. (2015a), "Geometric semantic genetic programming with local search", Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, July.
- Castelli, M., Trujillo, L., Vanneschi, L. and Popovic, A. (2015d), "Prediction of energy performance of residential buildings: A genetic programming approach", Energy Build., 102, 67-74. https://doi.org/10.1016/j.enbuild.2015.05.013
- Castelli, M., Trujillo, L., Vanneschi, L. and Popovic, A. (2016a), "Prediction of relative position of CT slices using a computational intelligence system", Appl. Soft Comput., 46, 537-542. https://doi.org/10.1016/j.asoc.2015.09.021
- Castelli, M., Vanneschi, L. and De Felice, M. (2015c), "Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case", Energy Econ., 47, 37-41. https://doi.org/10.1016/j.eneco.2014.10.009
- Castelli, M., Vanneschi, L. and Silva, S. (2013b), "Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators", Exp. Syst. Appl., 40(17), 6856-6862. https://doi.org/10.1016/j.eswa.2013.06.037
- Castelli, M., Vanneschi, L., Manzoni, L. and Popovic, A. (2016b), "Semantic genetic programming for fast and accurate data knowledge discovery", Swarm Evolut. Comput., 26, 1-7. https://doi.org/10.1016/j.swevo.2015.07.001
- Cevik, A. and Sonebi, M. (2008), "Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique", Comput. Concrete, 5(5), 475-490. https://doi.org/10.12989/cac.2008.5.5.475
- Goncalves, I., Silva, S., Fonseca, C.M. (2015), "On the generalization ability of geometric semantic genetic programming", Proceedings of the 18th European Conference on Genetic Programming, March.
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009), "The WEKA data mining software: An update", ACM SIGKDD Expl. Newslett., 11(1), 10-18. https://doi.org/10.1145/1656274.1656278
- Haykin, S. (1999), Neural Networks: A Comprehensive Foundation, Prentice Hall.
- Hoffmann, L. (2009), "Multivariate isotonic regression and its algorithms", Ph.D. Dissertation, Wichita State University, Kansas, U.S.A.
- Khan, A., Do, J. and Kim, D. (2016), "Cost effective optimal mix proportioning of high strength self compacting concrete using response surface methodology", Comput. Concrete, 17(5), 629-638. https://doi.org/10.12989/cac.2016.17.5.629
- Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, U.S.A.
- Koza, J.R. (2010), "Human-competitive results produced by genetic programming", Gen. Program. Evol. Mach., 11, 251-284. https://doi.org/10.1007/s10710-010-9112-3
- Krawiec, K. and Lichocki, P. (2009), "Approximating geometric crossover in semantic space", Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Quebec, Canada, July.
- Kumar, M., Singh, S.K. and Singh, N.P. (2012), "Heat evolution during the hydration of Portland cement in the presence of fly ash, calcium hydroxide and super plasticizer", Thermochim. Acta, 548, 27-32. https://doi.org/10.1016/j.tca.2012.08.028
- Marti-Vargas, J.R., Ferri, F.J. and Yepes, V. (2013), "Prediction of the transfer length of prestressing strands with neural networks", Comput. Concrete, 12(2), 187-209. https://doi.org/10.12989/cac.2013.12.2.187
- Moraglio, ., Krawiec, K. and Johnson, C.G. (2012), "Geometric semantic genetic programming", Proceedings of the 12th International Conference on Parallel Problem Solving from Nature, Volume 7491 of Lecture Notes in Computer Science, 21-31.
- Mosabepranah, M. . and Eren, O. (2016), "Statistical flexural toughness modeling of ultra-high performance concrete using response surface method", Comput. Concrete, 17(4), 477-488. https://doi.org/10.12989/cac.2016.17.4.477
- Nagaraj, T. and Banu, Z. (1996), "Generalization of Abrams' law", Cement Concrete Res., 26(6), 933-942. https://doi.org/10.1016/0008-8846(96)00065-8
- Oluokun, F.A. (1994), "Fly ash concrete mix design and the watercement ratio law", ACI Mater. J., 91(4), 362-371.
- Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253
- Peng, C.H., Yeh, I. and Lien, L.C. (2009), "Modeling strength of high-performance concrete using genetic operation trees with pruning techniques", Comput. Concrete, 6(3), 203-223. https://doi.org/10.12989/cac.2009.6.3.203
- Popovics, S. (1990), "Analysis of concrete strength versus watercement ratio relationship", ACI Mater. J., 87(5), 517-529.
- Ramadoss, P. and Nagamani, K. (2012), "Statistical methods of investigation on the compressive strength of high-performance steel fiber reinforced concrete", Comput. Concrete, 9(2), 153-169. https://doi.org/10.12989/cac.2012.9.2.153
- Ramezanianpour, A.A., Shahhosseini, V. and Moodi, F. (2009), "A fuzzy expert system for diagnosis assessment of reinforced concrete bridge decks", Comput. Concrete, 6(4), 281-303. https://doi.org/10.12989/cac.2009.6.4.281
- Seber, G. and Wild, C. (2003), Nonlinear Regression, Wiley Series in Probability and Statistics, Wiley.
- Vanneschi, L. (2017), "An introduction to geometric semantic genetic programming", Proceedings of the NEO 2015, Tijuana, Mexico, September.
- Vanneschi, L., Castelli, M., Manzoni, L. and Silva, S. (2013), "A new implementation of geometric semantic GP and its application to problems in pharmacokinetics", Proceedings of the EuroGP 2013, European Conference on Genetic Programming, 205-216.
- Vanneschi, L., Silva, S., Castelli, M. and Manzoni, L. (2014), Geometric Semantic Genetic Programming for Real Life Applications, Genetic Programming Theory and Practice XI, 191-209.
- Viet-Thien-An, V., Röbler, C., Bui, D. and Horst-Michael, L. (2014), "Rice husk ash as both pozzolanic admixture and internal curing agent in ultra-high performance concrete", Cement Concrete Compos., 53, 270-278. https://doi.org/10.1016/j.cemconcomp.2014.07.015
- Yeh, I. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
- Yeh, I. (2008), "Modeling slump of concrete with fly ash and superplasticizer", Comput. Concrete, 5(6), 559-572. https://doi.org/10.12989/cac.2008.5.6.559
- Z-Flores, E., Trujillo, L., Schütze, O. and Legrand, P. (2014), Evaluating the Effects of Local Search in Genetic Programming, EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation, Volume 288 of the Series Advances in Intelligent Systems and Computing, 213-228.