Principal component analysis for concrete mix by ranking method

  • Authors

    • A Mani Deepika
    • T Vijaya Gowri
    • P Sravana
    https://doi.org/10.14419/ijet.v7i3.29.19288
  • Cement Concrete, Water-Cement Ratio, Compressive Strength and Principal Component Analysis

  • Abstract

    Cement concrete mixture design for pavement was studied through a series of laboratory experiments based on an actual project program. The main purpose of this research is to investigate the minimum cement content required with an appropriate water-cement ratio (w/c) to meet given workability, strength, and durability requirements in a concrete pavement; and to reduce costs. An experimental program was conducted to test [9] concrete mixtures, designed according to Taguchi’s orthogonal selection with w/c of 0.36, 0.45 and 0.55 and cured by different methods such as Air Curing (AC), Water Curing (WC) and Plastic Bag Curing (PBC) separately. Compressive strength (CS) of 150mm cubes are found for [3-7] and14 days. Cube compressive strength, water to cement ratio (w/c), Cement, Coarse Aggregate, Fine Aggregate were the parameters to be used for optimization by varying water-cement ratios, cement quantities, Coarse Aggregate (CA) quantities and Fine Aggregates (FA) quantities. The data sets of mix designs were analyzed by Principal Component Analysis (PCA) to draw most influencing variables on the performance of concrete. From the analysis, it is found that w/c is the most influencing ingredient in case of AC and PBC and CA for WC on Compressive Strength of concrete.

     

     

  • References

    1. [1] A Ning, Xie Jun, Zheng Xiaohua, and GAO Xiaoni (2015). Application of PCA in Concrete Infrared Thermography Detection. Second International Workshop on Materials Engineering and Computer Sciences (IWMECS 2015).

      [2] BS 1881: Part 116 (1983). Method for determination of compressive strength of concrete cubes. British Standards Institution, London.

      [3] Calabrese L, Campanella G and Proverbio E (2010). Use of Cluster Analysis of Acoustic Emission Signals in Evaluating Damage Severity in Concrete Structures. J. Acoustic Emission, 28.

      [4] Emilio Garcia-Taengua and Jose R. Marti-Vargas (2016). Multivariate Analysis of the Fresh State Parameters of Self-Consolidating Concrete. Proceedings of the eighth International RILEM Symposium on Self-Compacting Concrete, 8th International RILEM Symposium on Self-Compacting Concrete (SCC2016), 15-18 May 2016, Washington DC, United States. RILEM Publications S.A.R.L. (France), pp. 221-231. ISBN 978-2-35158-156-8, White Rose Research Online URL for this paper, http://eprints.whiterose.ac.uk/95562/.

      [5] Filiz Kard_Yen, H.Hasan ÖRKCÜ (2006). The Comparison of Principal Component Analysis and Data Envelopment Analysis in Ranking of Decision Making Units. G.U. Journal of Science 19(2): 127-133.

      [6] Herve´ Abdi and Lynne J. Williams (2010). Principal component analysis. WIREs Computational Statistics, Volume 2, July/August 2010.

      [7] Lianjie Niu, Junhong LI and Ning Cui (2013). The Principal Component Analysis about Optimal Ratio for High Performance Pavement Concrete. Applied Mechanics and Materials Online: 2013-11-08 ISSN: 1662-7482, Vols. 446-447, pp 1417-1420, doi:10.4028/www.scientific.net/AMM.446-447.1417 © 2014 Trans Tech Publications, Switzerland.

      [8] Markus Ringnér (2008). What is principal component analysis? Nature Biotechnology Volume 26 Number 3 March 2008.

      [9] Mehdi Nikoo, Farshid Torabian Moghadam and Aukasz Sadowski (2015). Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks, Hindawi Publishing Corporation Advances in Materials Science and Engineering, Volume 2015, Article ID 849126, 8 pages. http://dx.doi.org/10.1155/2015/849126.

      [10] MINITAB Statistical Package Release 13 for Windows 95 and 98. Minitab, 2000.

      [11] Mohammad S. Islam and Shahria Alam (2013). Principal Component and Multiple Regression Analysis for Steel Fiber Reinforced Concrete (SFRC) Beams. International Journal of Concrete Structures and Materials, Vol.7, No.4, pp.303–317, December 2013.

      [12] Neville, A.M., (1996). Properties of Concrete, Fourth and Final Edition. John Wiley and Sons, Inc., New York, USA.

      [13] Nicolas Stoffels, Vincent Sircoulomb, Guillaume Hermand and Ghaleb Hoblos (2014). Principal Component Analysis for Fault Detection and Structure Health Monitoring. Seventh European Workshop on Structural Health Monitoring, July 8-11, 2014. La Cité, Nantes, France.

      [14] Pritam Sahaa, Nabanita Roya, Deotima Mukherjeea and Ashoke Kumar Sarkarb (2016). Application of Principal Component Analysis for Outlier Detection in Heterogeneous Traffic Data. The seventh International Conference on Ambient Systems, Networks and Technologies (ANT 2016), Procedia Computer Science 83 107 – 114.

      [15] Potdar, Akshay, Longstaff, Andrew P., Fletcher, Simon and Mian, Naeem S. (2015). Application of multi sensor data fusion based on Principal Component Analysis and Artificial Neural Network for machine tool thermal monitoring. In: Laser Metrology and Machine Performance XI, LAMDAMAP 2015. EUSPEN, Huddersfield, UK, pp. 228-237, ISBN: 9780956679055.

      [16] Zhaoran Wang, Fang Han and Han Liu (2013). Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. Proceedings of the 16th International Conference on Arti_cial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, USA. Volume 31 of JMLR: W&CP.

  • Downloads

  • How to Cite

    Mani Deepika, A., Vijaya Gowri, T., & Sravana, P. (2018). Principal component analysis for concrete mix by ranking method. International Journal of Engineering & Technology, 7(3.29), 453-457. https://doi.org/10.14419/ijet.v7i3.29.19288

    Received date: 2018-09-09

    Accepted date: 2018-09-09