Estimating the compressive strength of concrete, using vacuum dewatering technique

• Autorzy: Subhash D. , Gupta S. M. , Setia S. , Pavlykivskyi V.

Identyfikatory

DOI

10.5604/01.3001.0013.5880

• Języki publikacji: EN

Abstrakty

EN

Purpose: Investigate the potential of vacuum dewatering process of on three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. Design/methodology/approach: For this study a data set of 90 experimental observations obtained from laboratory testing with and without application of vacuum dewatering after designing and casting the concrete of said three grades. The standard cubes of size 150 mm × 150 mm × 150 mm were obtained by core cutting and tested for compression after 3, 7, 14, 21 and 28 days of proper curing. Accuracy of prediction of compressive strength of concrete by application of M5P, ANN and SVM as artificial intelligence techniques and their feasibility are assessed to estimate the compressive strength of the concrete enacted with vacuum dewatering technique. A total data set was segregated in two groups. A group of 63 observations was used for model development and smaller group of 27 observations was used for testing the models. Findings: Overall performance of ANN based developed model is better than M5P and SVM based models for predicting the compressive strength of concrete for this data set. Research limitations/implications: Investigated three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. The experimental research involved only testing of cubes only. Practical implications: Using ANN based developed model makes it possible to quickly and accurately predict the compressive strength of concrete. Originality/value: The results of comparing three models for predicting the compressive strength of concrete and the optimal values of ANN based developed models are presented. Earlier no one has applied M5P, ANN and SVM modelling to predict the compressive strength of vacuum dewatered concrete.

Słowa kluczowe

EN

vacuum dewatering; concrete compressive strength; artificial neural network; support vector machine

PL

odwadnianie próżniowe; wytrzymałość betonu na ściskanie; sztuczna sieć neuronowa; maszyna wektorów wspierających

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2019
• Tom: Vol. 99, nr 1/2
• Strony: 30--41
• Opis fizyczny: Bibliogr. 34 poz.

Twórcy

autor

Subhash D.

• National Institute of Technology Kurukshetra, Kurukshetra, India

autor

Gupta S. M.

• National Institute of Technology Kurukshetra, Kurukshetra, India

autor

Setia S.

• National Institute of Technology Kurukshetra, Kurukshetra, India

autor

Pavlykivskyi V.

• Kharkov University, pr. Gagarina, 187, 61080, Kharkov, Ukraine

Bibliografia

• [1] K.P. Billner, Applications of vacuum concrete, ACI Journal Proceedings 48/3 (1952) 581-591.
• [2] S.S Pickard, Vacuum-Dewatered Concrete, Concrete International 3/11 (1981) 49-55.
• [3] L. Dongxu, W. Xuequan, A study on the application of vacuum microwave composite dewatering technique in concrete engineering, Cement and Concrete Research 24/1 (1994) 159-164, DOI: https://doi.org/10.1016/0008-8846(94)90097-3.
• [4] S. Hatanaka, H. Hattori, E. Sakamoto, N. Mishima, Study on mechanism of strength distribution development in vacuum-dewatered concrete based on the consolidation theory, Materials and Structures 43/9 (2010) 1283-1301, DOI: https://doi.org/10.1617/s11527-010-9580-1.
• [5] Evaluating Strength of Design Concrete Mix with Different Aggregate Using Vacuum Dewatered Process, The Masterbuilder, October 2015, Available at: https://www.masterbuilder.co.in/strength-of-design-concrete-mix-with-different-aggregates-using-vacuumdewatered-process/.
• [6] O. Şimşek, Effects of vacuum processing on strength and surface hardness properties of concrete, Journal of ASTM International 2/2 (2005) 1-8, DOI: https://doi.org/10.1520/JAI11870.
• [7] R.S. Dubey, Construction of a rigid pavement by vacuum dewatering method, Indian Concrete Journal 71/1 (1997) 13-19.
• [8] E. Tophan Tripathy, Vacuum dewatered flooring in durable construction, International Journal of Civil Engineering Research 4/1 (2013) 49-54.
• [9] A.M. Neville, Properties of Concrete, Fifth Edition, Longman Scientific and Technical Publishers, 2011.
• [10] R. Malinowski, Durability aspects, Proceedings of the 3rd International Symposium on Concrete Technology, Monterrey, 1977.
• [11] P.G. Malone, Use of permeable formwork in placing and curing concrete, Technical Report No. WES/ERDC-TR-SL-99-12, 1999, Available at: https://erdc-library.erdc.dren.mil/xmlui/bitstream/handle/11681/11261/13429.pdf?sequence=1&isAllowed=y.
• [12] I.A. Rubaratuka, Influence of formwork materials on the surface quality of reinforced concrete structures, International Journal of Engineering and Applied Sciences 4/5 (2013) 31-34.
• [13] S. Singla, et al, An Experimental investigation of Fiber Reinforced Vacuum Dewatered Concrete, Civil Engineering and Construction Review Dec (2006) 44-48.
• [14] E. Sancak, Effect of Vacuum Dewatering Application on the Chemical Corrosion and Mechanical Properties of Concrete. Asian Journal of Applied Sciences 1/1 (2008) 79-86, DOI: https://doi.org/10.3923/ajaps.2008.79.86.
• [15] S. Singh Nain, R. Sai, P. Sihag, S. Vambol, V. Vambol, Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy, Archives of Materials Science and Engineering 95/1 (2019) 12-19, DOI: https://doi.org/10.5604/01.3001.0013.1422.
• [16] S. Vambol, V. Vambol, O. Kondratenko, V. Koloskov, Y. Suchikova, Substantiation of expedience of application of high-temperature utilization of used tires for liquefied methane production, Journal of Achievements in Materials and Manufacturing Engineering 87/2 (2018) 77-84, DOI: https://doi.org/10.5604/01.3001.0012.2830.
• [17] S. Ragimov, V. Sobyna, S. Vambol, V. Vambol, A. Feshchenko, A. Zakora, E. Strejekurov, V. Shalomov, Physical modelling of changes in the energy impact on a worker taking into account high-temperature radiation, Journal of Achievements in Materials and Manufacturing Engineering 91/1 (2018) 27-33, DOI: https://doi.org/10.5604/01.3001.0012.9654.
• [18] M. Kumar, P. Sihag, Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning-Based Models, Irrigation and Drainage 68/3 (2019) 588-601, DOI: https://doi.org/10.1002/ird.2332.
• [19] S. Vambol, V. Vambol, V. Sobyna, V. Koloskov, L. Poberezhna, Investigation of the energy efficiency of waste utilization technology, with considering the use of low-temperature separation of the resulting gas mixtures, Energetika 64/4 (2018) 186-195, DOI: https://doi.org/10.6001/energetika.v64i4.3893.
• [20] H. Chen, C. Qian, C. Liang, W. Kang, An approach for predicting the compressive strength of cementbased materials exposed to sulfate attack, PloS ONE 13/1 (2018) e0191370, DOI: https://doi.org/10.1371/journal.pone.0191370.
• [21] P. Sihag, N.K. Tiwari, S. Ranjan, Prediction of cumulative infiltration of sandy soil using random forest approach, Journal of Applied Water Engineering and Research 7/2 (2019) 118-142, DOI: https://doi.org/10.1080/23249676.2018.1497557.
• [22] A. Parsaie, A.H. Haghiabi, M. Saneie, H. Torabi, Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS), Frontiers of Structural and Civil Engineering 11/1 (2017) 111-122, DOI: https://doi.org/10.1007/s11709-016-0354-x.
• [23] J.R. Quinlan, Learning with continuous classes, Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Vol. 92, 1992, 343-348).
• [24] S. Deswal, M. Pal, Artificial neural network based modeling of evaporation losses in reservoirs, International Journal of Mathematical, Physical and Engineering Sciences 2/4 (2008) 177-181.
• [25] S. Haykin, Neural networks: a comprehensive foundation, Prentice Hall PTR, 1994.
• [26] P. Chopra, R.K. Sharma, M. Kumar, Prediction of compressive strength of concrete using artificial neural network and genetic programming, Advances in Materials Science and Engineering 2016 (2016) Article ID: 7648467, DOI: http://dx.doi.org/10.1155/2016/7648467.
• [27] H.Y. Yang, Y.F. Dong, Modelling concrete strength using support vector machines, Applied Mechanics and Materials 438-439 (2013) 170-173, DOI: https://doi.org/10.4028/www.scientific.net/AMM.438-439.170.
• [28] IS 8112:1989, Indian Standard, Specifications for 43 Grades of Ordinary Portland Cement, Bureau of Indian Standards, New Delhi, Reaffirmed, 2005, Available at: https://law.resource.org/pub/in/bis/S03/is.8112.1989.pdf.
• [29] IS 4031:1988, Methods of physical tests for hydraulic cement, Bureau of Indian Standards, BIS, New Delhi, Reaffirmed, 2005, Available at: http://www.iitk.ac.in/ce/test/IS-codes/is.4031.6.1988.pdf.
• [30] IS 383:1970, Specification for Coarse and Fine Aggregate From Natural Sources for Concrete, Bureau of Indian Standards, New Delhi, Second Revision, 1997, Available at: https://archive.org/details/gov.in.is.383.1970.
• [31] IS 10262:2009, Indian Standard, Concrete Mix Proportioning – Guidelines First Revision Bureau of Indian Standards, New Delhi, Available at: http://civiconcepts.com/wp-content/uploads/2019/01/IS-10262-2.pdf.
• [32] IS 456:2000, Indian Standard Code of Practice for Plain and Reinforced Concrete, Fourth Revision, Bureau of Indian Standards, New Delhi, Available at: http://www.iitk.ac.in/ce/test/IS-codes/is.456.2000.pdf.
• [33] IS 9103:1999, Indian Standard, Concrete Admixture Specification, Bureau of Indian Standards, New Delhi, Reaffirmed, 2008, Available at: http://www.iitk.ac.in/ce/test/IS-codes/is.9103.1999.pdf.
• [34] IS 516:2004, Indian Standard, Method of Test for Strength of Concrete, Bureau of Indian Standards, New Delhi, Reaffirmed, 2008, Available at: http://www.iitk.ac.in/ce/test/IS-codes/is.516.1959.pdf.