Prediction of flexural strength of FRC pavements by soft computing techniques

• Autorzy: Kimteta A. , Thakur M.S. , Sihag P. , Upadhya A. , Sharma N.

Identyfikatory

DOI

10.5604/01.3001.0016.1393

• Języki publikacji: EN

Abstrakty

EN

Purpose: The mechanical characteristics of concrete used in rigid pavements can be improved by using fibre-reinforced concrete. The purpose of the study was to predict the flexural strength of the fibre-reinforced concrete for ten input variables i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer/high range water reducer, glass fibre, polypropylene fibre, steel fibres, length and diameter of fibre and further to perform the sensitivity analysis to determine the most sensitive input variable which affects the flexural strength of the said fibre-reinforced concrete. Design/methodology/approach: The data used in the study was acquired from the published literature to create the soft computing modes. Four soft computing techniques i.e., Artificial neural networks (ANN), Random forests (RF), Random trees RT), and M5P, were applied to predict the flexural strength of fibre-reinforced concrete for rigid pavement using ten significant input variables as stated in the ‘purpose’. The most performing algorithm was determined after evaluating the applied models on the threshold of five statistical indices, i.e., the coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error. The sensitivity analysis for most sensitive input variable was performed with out-performing model, i.e., ANN. Findings: The testing stage findings show that the Artificial neural networks model outperformed other applicable models, having the highest coefficient of correlation (0.9408), the lowest mean absolute error (0.8292), and the lowest root mean squared error (1.1285). Furthermore, the sensitivity analysis was performed using the artificial neural networks model. The results demonstrate that polypropylene fibre-reinforced concrete significantly influences the prediction of the flexural strength of fibre-reinforced concrete. Research limitations/implications: Large datasets may enhance machine learning technique performance. Originality/value: The article's novelty is that the most suitable model amongst the four applied techniques has been identified, which gives far better accuracy in predicting flexural strength.

Słowa kluczowe

EN

flexural strength; fibre reinforced concrete; artificial neural network; random forest; random tree; M5P based model

PL

wytrzymałość na zginanie; beton zbrojony włóknami; sztuczna sieć neuronowa; las losowy; drzewo losowe; model oparty na M5P

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2022
• Tom: Vol. 117, nr 1
• Strony: 13--24
• Opis fizyczny: Bibliogr. 38 poz.

Twórcy

autor

Kimteta A.

• Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

autor

Thakur M.S.

• Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

autor

Sihag P.

• Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

• https://orcid.org/0000-0002-7761-0603

autor

Upadhya A.

• Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

autor

Sharma N.

• Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, Zip Code 173229, India

Bibliografia

• [1] N. Sharma, M.S. Thakur, P.L. Goel, P. Sihag, A review: Sustainable compressive strength properties of concrete mix with replacement by marble powder, Journal of Achievements in Materials and Manufacturing Engineering 98/1 (2020) 11-23. DOI: https://doi.org/10.5604/01.3001.0014.0813
• [2] B. Ali, L.A. Qureshi, R. Kurda, Environmental and economic benefits of steel, glass, and polypropylene fiber reinforced cement composite application in jointed plain concrete pavement, Composites Communications 22 (2020) 100437. DOI: https://doi.org/10.1016/j.coco.2020.100437
• [3] M.E. Arslan, Effects of basalt and glass chopped fibers addition on fracture energy and mechanical properties of ordinary concrete: CMOD measurement, Construction and Building Materials 114 (2016) 383-391. DOI: https://doi.org/10.1016/j.conbuildmat.2016.03.176
• [4] M.M. Hilles, M.M. Ziara, Mechanical behavior of high strength concrete reinforced with glass fiber, Engineering Science and Technology, an International Journal 22/3 (2019) 920-928. DOI: https://doi.org/10.1016/j.jestch.2019.01.003
• [5] J.M. Yang, H.O. Shin, D.Y. Yoo, Benefits of using amorphous metallic fibers in concrete pavement for long-term performance, Archives of Civil and Mechanical Engineering 17/4 (2017) 750-760. DOI: https://doi.org/10.1016/j.acme.2017.02.010
• [6] A.S. Altoubat, J.R. Roesler, D.A. Lange, K.A. Rieder, Simplified method for concrete pavement design with discrete structural fibers, Construction and Building Materials 22/3 (2008) 384-393. DOI: https://doi.org/10.1016/j.conbuildmat.2006.08.008
• [7] I. Hussain, B. Ali, T. Akhtar, M.S. Jameel, S.S. Raza, Comparison of mechanical properties of concrete and design thickness of pavement with different types of fiber-reinforcements (steel, glass, and polypropylene), Case Studies in Construction Materials 13 (2020) e00429. DOI: https://doi.org/10.1016/j.cscm.2020.e00429
• [8] J.H. Lee, B. Cho, E. Choi, Flexural capacity of fiber reinforced concrete with a consideration of concrete strength and fiber content, Construction and Building Materials 138 (2017) 222-231. DOI: https://doi.org/10.1016/j.conbuildmat.2017.01.096
• [9] M.N. Soutsos, T.T. Le, A.P. Lampropoulos, Flexural performance of fibre reinforced concrete made with steel and synthetic fibres, Construction and Building Materials 36 (2012) 704-710. DOI: https://doi.org/10.1016/j.conbuildmat.2012.06.042
• [10] P.S. Song, S. Hwang, Mechanical properties of high-strength steel fiber-reinforced concrete, Construction and Building Materials 18/9 (2004) 669-673. DOI: https://doi.org/10.1016/j.conbuildmat.2004.04.027
• [11] A. Wasim, M.I. Khan, S. Mourad, Evaluation of mechanical properties of steel fiber reinforced concrete with different strengths of concrete, Construction and Building Materials 168 (2018) 556-569. DOI: https://doi.org/10.1016/j.conbuildmat.2018.02.164
• [12] M. Hasani, F.M. Nejad, J. Sobhani, M. Chini, Mechanical and durability properties of fiber reinforced concrete overlay: experimental results and numerical simulation, Construction and Building Materials 268 (2021) 121083. DOI: https://doi.org/10.1016/j.conbuildmat.2020.121083
• [13] J. Akbari, A. Abed, Experimental Evaluation of Effects of Steel and Glass Fibers on Engineering Properties of Concrete: Engineering Properties of Concrete, Frattura ed Integrità Strutturale 14/54 (2020) 116-127. DOI: https://doi.org/10.3221/IGF-ESIS.54.08
• [14] O. Kelestemur, S. Yildiz, B. Gokcer, E. Arici, Statistical analysis for freeze–thaw resistance of cement mortars containing marble dust and glass fiber, Materials and Design 60 (2014) 548-555. DOI: https://doi.org/10.1016/j.matdes.2014.04.013
• [15] J. Liu, Y. Jia, J. Wang, Experimental study on mechanical and durability properties of glass and polypropylene fiber reinforced concrete, Fibers and Polymers 20/9 (2019) 1900-1908. DOI: https://doi.org/10.1007/s12221-019-1028-9
• [16] N. Nalanth, P.V. Venkatesan, M.S. Ravikumar, Evaluation of the fresh and hardened properties of steel fibre reinforced self-compacting concrete using recycled aggregates as a replacement material, Advances in Civil Engineering 2014 (2014) 671547. DOI: https://doi.org/10.1155/2014/671547
• [17] N. Sharma, M.S. Thakur, P. Sihag, M.A. Malik, R. Kumar, M. Abbas, C.A. Saleel, Machine learning techniques for evaluating concrete strength with waste marble powder, Materials 15/17 (2022) 5811. DOI: https://doi.org/10.3390/ma15175811
• [18] B. Ali, V. Behnood, M.M. Gharehveran, K.E. Alyamac, Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm, Construction and Building Materials 142 (2017) 199-207. DOI: https://doi.org/10.1016/j.conbuildmat.2017.03.061
• [19] M. Sarddemir, Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks, Advances in Engineering Software 40/5 (2009) 350-355. DOI: https://doi.org/10.1016/j.advengsoft.2008.05.002
• [20] Y. Ayaz, A.F. Kocamaz, M.B. Karakoc, Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers, Construction and Building Materials 94 (2015) 235-240. DOI: https://doi.org/10.1016/j.conbuildmat.2015.06.029
• [21] M.C. Kang, D.Y. Yoo, R. Gupta, Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete, Construction and Building Materials 266/B (2021) 121117. DOI: https://doi.org/10.1016/j.conbuildmat.2020.121117
• [22] A. Marani, M.L. Nehdi, Machine learning prediction of compressive strength for phase change materials integrated cementitious composites, Construction and Building Materials 265 (2020) 120286. DOI: https://doi.org/10.1016/j.conbuildmat.2020.120286
• [23] M. Małek, M. Jackowski, W. Łasica, M. Kadela, M. Wachowski, Mechanical and material properties of mortar reinforced with glass fiber: An experimental study, Materials 14/3 (2021) 698. DOI: https://doi.org/10.3390/ma14030698
• [24] İ.B. Topçu, M. Sarddemir, Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic, Computational Materials Science 41/3 (2008) 305-311. DOI: https://doi.org/10.1016/j.commatsci.2007.04.009
• [25] A. Hammoudi, K. Moussaceb, C. Belebchouche, F. Dahmoune, Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates, Construction and Building Materials 209 (2019) 425-436. DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.119
• [26] A. Upadhya, M.S. Thakur, N. Sharma, P. Sihag, Assessment of Soft Computing-Based Techniques for the Prediction of Marshall Stability of Asphalt Concrete Reinforced with Glass Fiber, International Journal of Pavement Research and Technology 15 (2022) 1366-1385. DOI: https://doi.org/10.1007/s42947-021-00094-2
• [27] Z.H. Duan, S.C. Kou, C.S. Poon, Prediction of compressive strength of recycled aggregate concrete using artificial neural networks, Construction and Building Materials 40 (2013) 1200-1206. DOI: https://doi.org/10.1016/j.conbuildmat.2012.04.063
• [28] W. Zhang, C. Wu, H. Zhong, Y. Li, L. Wang, Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization, Geoscience Frontiers 12/1 (2021) 469- 477. DOI: https://doi.org/10.1016/j.gsf.2020.03.007
• [29] M.S. Thakur, S.M. Pandhiani, V. Kashyap, A. Upadhya, P. Sihag, Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques, Arabian Journal for Science and Engineering 46/5 (2021) 4951-4969. DOI: https://doi.org/10.1007/s13369-020-05314-8
• [30] L. Breiman, Random forests, Machine Learning 45/1 (2001) 5-32. DOI: https://doi.org/10.1023/A:1010933404324
• [31] S. Saad, M. Ishtiyaque, H. Malik, Selection of most relevant input parameters using WEKA for artificial neural network based concrete compressive strength prediction model, Proceedings of the IEEE 7th Power India International Conference “PIICON”, Bikaner, India, 2016, 1-6. DOI: https://doi.org/10.1109/POWERI.2016.8077368
• [32] Y. Wang, Y. Zhang, Y. Chen, Prediction of concrete slump model based on BP neural network, International Core Journal of Engineering 7/10 (2021) 252-259. DOI: https://doi.org/10.6919/ICJE.202110_7(10).0038
• [33] M. Kaya Keles, A.E. Keles, U. Kilic, Prediction of concrete strength with data mining methods using artificial bee colony as feature selector, Proceedings of the International Conference on Artificial Intelligence and Data Processing “IDAP”, Malatya, Turkey, 2018, 2-4. DOI: https://doi.org/10.1109/IDAP.2018.8620905
• [34] S. Hesami, I.S. Hikouei, S.A.A. Emadi, Mechanical behavior of self-compacting concrete pavements incorporating recycled tire rubber crumb and reinforced with polypropylene fiber, Journal of Cleaner Production 133 (2016) 228-234. DOI: https://doi.org/10.1016/j.jclepro.2016.04.079
• [35] M.O. Kim, C.A. Bordelon, Age-dependent properties of fiber-reinforced concrete for thin concrete overlays, Construction and Building Materials 137 (2017) 288- 299. DOI: https://doi.org/10.1016/j.conbuildmat.2017.01.097
• [36] D.V. Soulioti, N.M. Barkoula, A. Paipetis, T.E. Matikas, Effects of fibre geometry and volume fraction on the flexural behaviour of steel‐fibre reinforced concrete, Strain 47/s1 (2011) e535-e541. DOI: https://doi.org/10.1111/j.1475-1305.2009.00652.x
• [37] A.N. Al-Gemeel, Y. Zhuge, O. Youssf, Use of hollow glass microspheres and hybrid fibres to improve the mechanical properties of engineered cementitious composite, Construction and Building Materials 171 (2018) 858-870. DOI: https://doi.org/10.1016/j.conbuildmat.2018.03.172
• [38] B. Ramesh, V. Gokulnath, M. Ranjith Kumar, Detailed study on flexural strength of polypropylene fiber reinforced self-compacting concrete, Materials Today: Proceedings 22/3 (2020) 1054-1058. DOI: https://doi.org/10.1016/j.matpr.2019.11.292