A NEW HYBRID FRAMEWORK OF MACHINE LEARNING TECHNIQUE IS USED TOMODEL THE COMPRESSIVE STRENGTH OF ULTRA-HIGH-PERFORMANCE CONCRETE
DOI:
https://doi.org/10.14311/CEJ.2023.03.0025Klíčová slova:
Compressive strength, Support vector regression, Ultra-High-Performance Concrete, Particle swarm optimization, Henry’s Gas Solubility OptimizationAbstrakt
The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs to be investigated in terms of ingredients and their magnitudes to compute the compressive strength of concrete. Empirical determination of relationships between constituents can demand more energy and cost. At the same time, the intelligent systems have enabled us to appraise the compressive strength based on ingredients' composition. Also, choosing eco-friendly materials in concrete as one of the widely-used items worldwide should be encouraged. This study has attempted to model the compressive strength of UHPC. Support Vector Regression (SVR), as a machine learning technique aligned with the Particle Swarm Optimization (PSO) and Henry's Gas Solubility Optimization (HGSO), have been used to simulate the compressive strength of concrete calculated based on different materials used in the present article. Eight constituents were tested to generate the compressive strength values. Various metrics were used to evaluate the modeling process. In this regard, the R2 of test phase modeling for SVR-HGSO was obtained 0.960 while for SVR-PSO, 0.925. In the training stage, the correlation rate was computed 0.902 for SVR-HGSO, which is 1.7 percent higher than SVR-PSO with R2 0.887.
Stažení
Reference
B. A. Graybeal, (2007) "Compressive behavior of ultra-high-performance fiber-reinforced concrete," ACI Mater. J., vol. 104, no. 2, 146.
P. Richard and M. Cheyrezy, (1995) "Composition of reactive powder concrete," Cem. Concr. Res., vol. 25, no. 7, 1501–1511.
Y. L. Voo and S. J. Foster, (2010) "Characteristics of ultra-high performance' ductile’concrete and its impact on sustainable construction,” IES J. Part A Civ. Struct. Eng., vol. 3, no. 3, 168–187.
P. C. AITCIN, (1998) “Arts e scienza del calcestruzzo ad alte prestazioni,” L’Industria Ital. del Cem., vol. 68, no. 731, 350–365.
F. P. Torgal and S. Jalali, (2011) “Cement composites reinforced with vegetable fibres,” in Eco-efficient Construction and Building Materials, Springer, 143–156.
C. Shi, Z. Wu, J. Xiao, D. Wang, Z. Huang, and Z. Fang, (2015) “A review on ultra high performance concrete: Part I. Raw materials and mixture design,” Constr. Build. Mater., vol. 101, 741–751.
Z. Yunsheng, S. Wei, L. Sifeng, J. Chujie, and L. Jianzhong, )2008( “Preparation of C200 green reactive powder concrete and its static–dynamic behaviors,” Cem. Concr. Compos., vol. 30, no. 9, 831–838.
W. Zheng, B. Luo, and Y. Wang, )2013( “Compressive and tensile properties of reactive powder concrete with steel fibres at elevated temperatures,” Constr. Build. Mater., vol. 41, 844–851.
A. A. Pishro and X. Feng, )2018( “Experimental Study on Bond Stress between Ultra High Performance Concrete and Steel Reinforcement,” Civ. Eng. J., vol. 3, no. 12, 1235–1246.
Y. K. Cho, S. H. Jung, and Y. C. Choi, )2019( “Effects of chemical composition of fly ash on compressive strength of fly ash cement mortar,” Constr. Build. Mater., vol. 204, 255–264, Apr. doi: 10.1016/j.conbuildmat.2019.01.208.
M. Lezgy-Nazargah, S. A. Emamian, E. Aghasizadeh, and M. Khani, “Predicting the mechanical properties of ordinary concrete and nano-silica concrete using micromechanical methods,” Sādhanā, vol. 43, no. 12, 196, Dec. 2018, doi: 10.1007/s12046-018-0965-0.
J.-S. Chou and A.-D. Pham, “Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering,” Comput. Civ. Infrastruct. Eng., vol. 30, no. 9, 715–732, Sep. 2015, doi: 10.1111/mice.12121.
M. H. Rafiei, W. H. Khushefati, R. Demirboga, and H. Adeli, (2017) “Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete.,” ACI Mater. J., vol. 114, no. 2.
M. Castelli, L. Trujillo, I. Goncalves, and A. Popovic, (2017) “An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming,” Comput. Concr., vol. 19, no. 6, 651–658.
L. Lam, Y. . Wong, and C. . Poon, (1998) “Effect of Fly Ash and Silica Fume on Compressive and Fracture Behaviors of Concrete,” Cem. Concr. Res., vol. 28, no. 2, 271–283, doi: 10.1016/S0008-8846(97)00269-X.
K. Ganesh Babu and G. Siva Nageswara Rao, (1994) “Early strength behaviour of fly ash concretes,” Cem. Concr. Res., vol. 24, no. 2, 277–284, doi: 10.1016/0008-8846(94)90053-1.
L. G. Li, J. Zhu, Z. H. Huang, A. K. H. Kwan, and L. J. Li, “Combined effects of micro-silica and nano-silica on durability of mortar,” Constr. Build. Mater., vol. 157, 337–347, Dec. 2017, doi: 10.1016/j.conbuildmat.2017.09.105.
H. Eskandari, A. M. Nic, and A. Ghanei, (2016) “Effect of Air Entraining Admixture on Corrosion of Reinforced Concrete,” Procedia Eng., vol. 150, 2178–2184, doi: 10.1016/j.proeng.2016.07.261.
Z. Zhang, B. Zhang, and P. Yan, (2016) “Comparative study of effect of raw and densified silica fume in the paste, mortar and concrete,” Constr. Build. Mater., vol. 105, pp. 82–93, doi: 10.1016/j.conbuildmat.2015.12.045.
A. Madadi, H. Eskandari-Naddaf, and M. Gharouni-Nik, “Lightweight Ferrocement Matrix Compressive Behavior: Experiments Versus Finite Element Analysis,” Arab. J. Sci. Eng., vol. 42, no. 9, pp. 4001–4013, Sep. 2017, doi: 10.1007/s13369-017-2557-4.
B. B. Sabir, “Mechanical properties and frost resistance of silica fume concrete,” Cem. Concr. Compos., vol. 19, no. 4, 285–294, Jan. 1997, doi: 10.1016/S0958-9465(97)00020-6.
M. Mazloom, A. A. Ramezanianpour, and J. J. Brooks, (2004)“Effect of silica fume on mechanical properties of high-strength concrete,” Cem. Concr. Compos., vol. 26, no. 4, 347–357 , doi: 10.1016/S0958-9465(03)00017-9.
S. Bhanja and B. Sengupta, “Influence of silica fume on the tensile strength of concrete,” Cem. Concr. Res., vol. 35, no. 4, pp. 743–747, Apr. 2005, doi: 10.1016/j.cemconres.2004.05.024.
M. Y. Mansour, M. Dicleli, J. Y. Lee, and J. Zhang, (2004) “Predicting the shear strength of reinforced concrete beams using artificial neural networks,” Eng. Struct., vol. 26, no. 6, 781–799, doi: 10.1016/j.engstruct.2004.01.011.
Z. Bajja, W. Dridi, A. Darquennes, R. Bennacer, P. Le Bescop, and M. Rahim, “Influence of slurried silica fume on microstructure and tritiated water diffusivity of cement pastes,” Constr. Build. Mater., vol. 132, 85–93, Feb. 2017, doi: 10.1016/j.conbuildmat.2016.11.097.
M. Rostami and K. Behfarnia, “The effect of silica fume on durability of alkali activated slag concrete,” Constr. Build. Mater., vol. 134, 262–268, Mar. 2017, doi: 10.1016/j.conbuildmat.2016.12.072.
H. Li, H. Xiao, J. Yuan, and J. Ou, “Microstructure of cement mortar with nano-particles,” Compos. Part B Eng., vol. 35, no. 2, 185–189, Mar. 2004, doi: 10.1016/S1359-8368(03)00052-0.
L. P. Singh, S. R. Karade, S. K. Bhattacharyya, M. M. Yousuf, and S. Ahalawat, “Beneficial role of nanosilica in cement based materials – A review,” Constr. Build. Mater., vol. 47, 1069–1077, Oct. 2013, doi: 10.1016/j.conbuildmat.2013.05.052.
A. K. Mukhopadhyay, (2011) “Next-generation nano-based concrete construction products: a review,” Nanotechnol. Civ. Infrastruct., 207–223.
L. G. Li, J. Y. Zheng, J. Zhu, and A. K. H. Kwan, “Combined usage of micro-silica and nano-silica in concrete: SP demand, cementing efficiencies and synergistic effect,” Constr. Build. Mater., vol. 168, 622–632, Apr. 2018, doi: 10.1016/j.conbuildmat.2018.02.181.
M. Jalal, A. Pouladkhan, O. F. Harandi, and D. Jafari, (2015) “Comparative study on effects of Class F fly ash, nano silica and silica fume on properties of high performance self compacting concrete,” Constr. Build. Mater., vol. 94, no. 90, 104.
D. De Domenico and G. Ricciardi, “Shear strength of RC beams with stirrups using an improved Eurocode 2 truss model with two variable-inclination compression struts,” Eng. Struct., vol. 198, p. 109359, Nov. 2019, doi: 10.1016/j.engstruct.2019.109359.
L. Sadowski, M. Nikoo, and M. Nikoo, (2018) “Concrete compressive strength prediction using the imperialist competitive algorithm,” Comput. Concr., vol. 22, no. 4, 355–363, 2018.
S. Czarnecki, M. Shariq, M. Nikoo, and Ł. Sadowski, “An intelligent model for the prediction of the compressive strength of cementitious composites with ground granulated blast furnace slag based on ultrasonic pulse velocity measurements,” Measurement, vol. 172, 108951, Feb. 2021, doi: 10.1016/j.measurement.2020.108951.
Ł. Sadowski, M. Piechówka-Mielnik, T. Widziszowski, A. Gardynik, and S. Mackiewicz, “Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust,” J. Clean. Prod., vol. 212, 727–740, Mar. 2019, doi: 10.1016/j.jclepro.2018.12.059.
C. T. G. Awodiji, D. O. Onwuka, C. Okere, and O. Ibearugbulem, “Anticipating the Compressive Strength of Hydrated Lime Cement Concrete Using Artificial Neural Network Model,” Civ. Eng. J., vol. 4, no. 12, 3005, Dec. 2018, doi: 10.28991/cej-03091216.
J. Kasperkiewicz, J. Racz, and A. Dubrawski, “HPC Strength Prediction Using Artificial Neural Network,” J. Comput. Civ. Eng., vol. 9, no. 4, 279–284, Oct. 1995, doi: 10.1061/(ASCE)0887-3801(1995)9:4(279).
E. Ghafari, M. Bandarabadi, H. Costa, and E. Júlio, (2012) “Design of UHPC using artificial neural networks,” in Brittle Matrix Composites 10, Elsevier, 61–69.
M. F. Javed et al., “Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete,” Crystals, vol. 10, no. 9, p. 737, Aug. 2020, doi: 10.3390/cryst10090737.
S. A. Emamian and H. Eskandari-Naddaf, “Genetic programming based formulation for compressive and flexural strength of cement mortar containing nano and micro silica after freeze and thaw cycles,” Constr. Build. Mater., vol. 241, 118027, Apr. 2020, doi: 10.1016/j.conbuildmat.2020.118027.
A. Behnood and E. M. Golafshani, “Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves,” J. Clean. Prod., vol. 202, 54–64, Nov. 2018, doi: 10.1016/j.jclepro.2018.08.065.
M. Hassan and K. Wille, “Experimental impact analysis on ultra-high performance concrete (UHPC) for achieving stress equilibrium (SE) and constant strain rate (CSR) in Split Hopkinson pressure bar (SHPB) using pulse shaping technique,” Constr. Build. Mater., vol. 144, 747–757, Jul. 2017, doi: 10.1016/j.conbuildmat.2017.03.185.
H.-O. Jang, H.-S. Lee, K. Cho, and J. Kim, “Experimental study on shear performance of plain construction joints integrated with ultra-high performance concrete (UHPC),” Constr. Build. Mater., vol. 152, 16–23, Oct. 2017, doi: 10.1016/j.conbuildmat.2017.06.156.
K. Wille and C. Boisvert-Cotulio, “Material efficiency in the design of ultra-high performance concrete,” Constr. Build. Mater., vol. 86, 33–43, Jul. 2015, doi: 10.1016/j.conbuildmat.2015.03.087.
K.-Q. Yu, J.-T. Yu, J.-G. Dai, Z.-D. Lu, and S. P. Shah, “Development of ultra-high performance engineered cementitious composites using polyethylene (PE) fibers,” Constr. Build. Mater., vol. 158, 217–227, 2018.
F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, and S. Mirjalili, “Henry gas solubility optimization: A novel physics-based algorithm,” Futur. Gener. Comput. Syst., vol. 101, 646–667, Dec. 2019, doi: 10.1016/j.future.2019.07.015.
V. Mohebbi, A. Naderifar, R. M. Behbahani, and M. Moshfeghian, “Determination of Henry’s law constant of light hydrocarbon gases at low temperatures,” J. Chem. Thermodyn., vol. 51, 8–11, Aug. 2012, doi: 10.1016/j.jct.2012.02.014.
T. L. Brown, Chemistry: the central science. Pearson Education, 2009.
R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39–43, doi: 10.1109/MHS.1995.494215.
A. Maleki, “Optimal operation of a grid-connected fuel cell based combined heat and power systems using particle swarm optimisation for residential sector,” Int. J. Ambient Energy, vol. 42, no. 5, 550–557, Apr. 2021, doi: 10.1080/01430750.2018.1562968.
G. Perampalam, K. Poologanathan, S. Gunalan, J. Ye, and B. Nagaratnam, “Optimum Design of Cold‐formed Steel Beams: Particle Swarm Optimisation and Numerical Analysis,” ce/papers, vol. 3, no. 3–4, 205–210, Sep. 2019, doi: 10.1002/cepa.1159.
F. Masoumi, S. Najjar-Ghabel, A. Safarzadeh, and B. Sadaghat, “Automatic calibration of the groundwater simulation model with high parameter dimensionality using sequential uncertainty fitting approach,” Water Supply, vol. 20, no. 8, 3487–3501, Dec. 2020, doi: 10.2166/ws.2020.241.
M. B. Patil, M. N. Naidu, A. Vasan, and M. R. R. Varma, “Water distribution system design using multi-objective particle swarm optimisation,” Sādhanā, vol. 45, no. 1, 21, Dec. 2020, doi: 10.1007/s12046-019-1258-y.
L. Wang, Support vector machines: theory and applications, vol. 177. Springer Science & Business Media, 2005.
V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
A. Al-Fugara, M. Ahmadlou, A. R. Al-Shabeeb, S. AlAyyash, H. Al-Amoush, and R. Al-Adamat, “Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression,” Geocarto Int., 1–20, 2020.
G. Pazouki, E. M. Golafshani, and A. Behnood, “Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network,” Struct. Concr., Feb. 2021, doi: 10.1002/suco.202000047.
Stahování
Publikováno
Jak citovat
Číslo
Sekce
Licence
Copyright (c) 2023 Stavební obzor - Civil Engineering Journal
Tato práce je licencována pod Mezinárodní licencí Creative Commons Attribution-NonCommercial 4.0.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
¨
