i-manager Publications

Models to Predict the Mechanical Properties of Ternary Blended Fiber Reinforced SCC by using Mars and RVM

B. Seshaiah*, P. Srinivasa Rao**, P. Subbarao***

*,*** Department of Civil Engineering Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

** Department of Civil Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India.

Periodicity:December - February'2022
DOI : https://doi.org/10.26634/jste.10.4.18623

Abstract

This paper explores the applicability of Multivariate Adaptive Regression Splines (MARS) and Relevance Vector Machine (RVM) to predict the mechanical properties of fiber reinforced Self-Compacting Concrete (SCC) made up of quartz sand, micro silica and ground granulated blast-furnace slag (GGBS). Mechanical properties were evaluated for three grades of SCC mixes (M40, M50, and M60 with varying percentage of fibers (0.5%, 1.0%, and 1.5%) in each mix. Influencing variables were identified from the results of experimental investigations on various SCC mixes with and without fibers. Based on the output and input parameters, MARS establishes appropriate relations by using the concept of divide and conquers strategy. RVM is based on probabilistic classification utilizing the Bayesian formulation. About 70% of the mixed data of various SCC mixes was used for training after normalization of the data. The developed MARS and RVM models were verified with the rest of the data. It was found that MARS and RVM models could predict the mechanical properties of several SCC mixes very efficiently. The maximum variation between the predicted mechanical properties and the experimental observation is about ±12% for MARS model and ±15% for RVM model. Further, the efficacy of models has been tested through several statistical parameters. The developed models are useful to design the optimum experiments and for preliminary analysis of structures and components made up of similar SCC mixes.

Keywords

Self-Compacting Concrete, Supplementary Cementitious Materials, Mechanical Properties, Multivariate Adaptive Regression Splines (MARS), Relevance Vector Machine (RVM), Statistical Parameters.

How to Cite this Article?

Seshaiah, B., Rao, P. S., and Subbarao, P. (2022). Models to Predict the Mechanical Properties of Ternary Blended Fiber Reinforced SCC by using Mars and RVM. i-manager’s Journal on Structural Engineering, 10(4), 8-20. https://doi.org/10.26634/jste.10.4.18623

References

[1]. AL-Ameeri, A. (2013). The effect of steel fiber on some mechanical properties of self-compacting concrete, American Journal of Civil Engineering, 1(3), 102-110. https://doi.org/10.11648/j.ajce.20130103.14

[2]. Avci-Karatas, C. (2019). Prediction of ultimate load capacity of concrete-filled steel tube columns using multivariate adaptive regression splines (MARS), Steel and Composite Structures,33(4),583-594. https://doi.org/10.12989/scs.2019.33.4.583

[3]. Caesarendra, W., Widodo, A., & Yang, B. S. (2010). Application of relevance vector machine and logistic regression for machine degradation assessment. Mechanical Systems and Signal Processing, 24(4), 1161-1171. https://doi.org/10.1016/j.ymssp.2009.10.011

[4]. Cheng, M. Y., & Cao, M. T. (2016). Estimating strength of rubberized concrete using evolutionary multivariate adaptive regression splines. Journal of Civil Engineering and Management, 22(5), 711-720. https://doi.org/10.3846/13923730.2014.897989

[5]. da Silva, M. A., Pepe, M., de Andrade, R. G. M., Pfeil, M. S., & Filho, R. D. T. (2017). Rheological and mechanical behavior of high strength steel fiber-river gravel self compacting concrete. Construction and Building Materials, 150, 606-618. https://doi.org/10.1016/j.conbuildmat.2017.06.030

[6]. Das, S. K., & Samui, P. (2008). Prediction of liquefaction potential based on CPT data: A relevance vector machine approach. In Proceedings of the 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG) (pp. 2856- 2861).

[7]. Dawei, H., Ian, C., & Weiping, K. (2012). Flow modelling using relevance vector machine (RVM). In Proceedings of the 5th International Conference on Hydroinformatics, Cardiff, UK.

[8]. Dinakar, P., Babu, K. G., & Santhanam, M. (2008). Mechanical properties of high-volume fly ash selfcompacting concrete mixtures, Structural Concrete, 9(2), 109-116.

[9]. El-Dieb, A. S. (2009). Mechanical, durability and microstructural characteristics of ultra-high-strength selfcompacting concrete incorporating steel fibers. Materials & Design, 30(10), 4286-4292. https://doi.org/10.1016/j.matdes.2009.04.024

[10]. El-Dieb, A. S., & Taha, M. R. (2012). Flow characteristics and acceptance criteria of fiberreinforced self-compacted concrete (FR-SCC). Construction and Building materials, 27(1), 585-596. https://doi.org/10.1016/j.conbuildmat.2011.07.004

[11]. European Project Group. (2005). The European Guidelines for Self-Compacting Concrete: Specification, Production and Use. The European Project Group. Retrieved from https://www.theconcreteinitiative.eu/images/ECP_Documents/EuropeanGuidelinesSelfComp actingConcrete.pdf

[12]. Felekoğlu, B., Türkel, S., & Baradan, B. (2007). Effect of water/cement ratio on the fresh and hardened properties of self-compacting concrete. Building and Environment, 42(4), 1795-1802. https://doi.org/10.1016/j.buildenv.2006.01.012

[13]. Friedman, J.H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1-67. https://doi.org/10.1214/aos/1176347963

[14]. Kamal, M. M., Safan, M. A., Etman, Z. A., & Kasem, B. M. (2014). Mechanical properties of self-compacted fiber concrete mixes. HBRC Journal, 10(1), 25-34. https://doi.org/10.1016/j.hbrcj.2013.05.012

[15]. Kaur, J., & Kaur, K. (2017). A fuzzy approach for an IoTbased automated employee performance appraisal, CMC: Computers, Materials & Continua, 53(1), 23-36. https://doi.org/10.3970/cmc.2017.053.024

[16]. Khaloo, A., Raisi, E. M., Hosseini, P., & Tahsiri, H. (2014). Mechanical performance of self-compacting concrete reinforced with steel fibers. Construction and Building Materials, 51, 179-186. https://doi.org/10.1016/j.conbuildmat.2013.10.054

[17]. Li, J., & Yang, E. H. (2018). Probabilistic-based assessment for tensile strain-hardening potential of fiberreinforced cementitious composites. Cement and Concrete Composites, 91, 108-117. https://doi.org/10.1016/j.cemconcomp.2018.05.003

[18]. Liu, K., & Xu, Z. (2011). Traffic flow prediction of highway based on wavelet relevance vector machine. Journal of Information & Computational Science. 8(9), 1641–1647.

[19]. Lokuge, W., Wilson, A., Gunasekara, C., Law, D. W., & Setunge, S. (2018). Design of fly ash geopolymer concrete mix proportions using multivariate adaptive regression spline model. Construction and Building Materials, 166, 472-481. https://doi.org/10.1016/j.conbuildmat.2018.01.175

[20]. Mansouri, I., Safa, M., Ibrahim, Z., Kisi, O., Tahir, M. M., Baharom, S., & Azimi, M. (2016). Strength prediction of rotary brace damper using MLR and MARS. Structural Engineering and Mechanics, 60(3), 471-488.

[21]. Okamura, H. M, & Ouchi, M. (2003). Selfcompacting concrete. Journal of Advances Concrete Technology, 1, 5-15. https://doi.org/10.3151/jact.1.5

[22]. Okamura, H., & Ozawa, K. (1995). Mix design for selfcompacting concrete. Concrete Library of Japanese Society of Civil Engineers, 25, 107-120.

[23]. Parra, C., Valcuende, M., & Gómez, F. (2011). Splitting tensile strength and modulus of elasticity of selfcompacting concrete. Construction and Building Materials, 25(1), 201-207. https://doi.org/10.1016/j.conbuildmat.2010.06.037

[24]. Seshaiah, B., Rao, P. S., & Rao, P. S. (2021). Effect of mineral admixtures on the properties of steel fibre reinforced SCC proportioned using plastic viscosity and development of regression & ANN model. Computers and Concrete, 27(6), 523-535. https://doi.org/10.12989/cac.2021.27.6.523

[25]. Shah, V. S., Shah, H. R., Samui, P., Murthy, A. R., Merono, P., Gomez, F., & Liu, B. (2014). Prediction of fracture parameters of high strength and ultra-high strength concrete beams using minimax probability machine regression and extreme learning machine. Computers, Materials and Continua, 44(2), 73-84. https://doi.org/10.3970/cmc.2014.044.073

[26]. Soutsos, M. N., Le, T. T., & Lampropoulos, A. P. (2012). Flexural performance of fibre reinforced concrete made with steel and synthetic fibres. Construction and Building Materials, 36, 704-710. https://doi.org/10.1016/j.conbuildmat.2012.06.042

[27]. Tipping, M.E. (1999). The relevance vector machine. Advances in Neural Information Processing Systems, 12, 652-658.

[28]. Tipping, M. E. (2001). Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211-244.

[29]. Wang, X., Ye, M., & Duanmu, C. J. (2009). Classification of data from electronic nose using relevance vector machines. Sensors and Actuators B: Chemical, 140(1), 143-148. https://doi.org/10.1016/j.snb.2009.04.030

[30]. Wei, L., Yang, Y., Nishikawa, R. M., Wernick, M. N., & Edwards, A. (2005). Relevance vector machine for automatic detection of clustered microcalcifications. IEEE Transactions on Medical Imaging, 24(10), 1278-1285. https://doi.org/10.1109/TMI.2005.855435

[31]. Widodo, A., Kim, E. Y., Son, J. D., Yang, B. S., Tan, A. C., Gu, D. S., Choi, B. K., & Mathew, J. (2009). Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 36(3), 7252-7261. https://doi.org/10.1016/j.eswa.2008.09.033

[32]. Yehia, S., Douba, A., Abdullahi, O., & Farrag, S. (2016). Mechanical and durability evaluation of fiberreinforced self-compacting concrete. Construction and Building Materials, 121, 120-133. https://doi.org/10.1016/j.conbuildmat.2016.05.127

[33]. Yuvaraj, P., Murthy, A. R., Iyer, N. R., Samui, P., & Sekar, S. K. (2013a). Multivariate adaptive regression splines model to predict fracture characteristics of high strength and ultra high strength concrete beams. CMC: Computers, Materials & Continua, 36(1), 73-97.

[34]. Yuvaraj, P., Murthy, A. R., Iyer, N. R., Samui, P., & Sekar, S. K. (2014a). Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine. International Journal of Damage Mechanics, 23(7), 979-1004. https://doi.org/10.1177/1056789514520796

[35]. Yuvaraj, P., Murthy, A. R., Iyer, N. R., Sekar, S. K., & Samui, P. (2013b). Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams. Engineering Fracture Mechanics, 98, 29-43. https://doi.org/10.1016/j.engfracmech.2012.11.014

[36]. Yuvaraj, P., Murthy, A. R., Iyer, N. R., Sekar, S. K., & Samui, P. (2014b). ANN model to predict fracture characteristics of high strength and ultra high strength concrete beams. CMC-Computers Materials & Continua, 41(3), 193-213.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.