Computers and Concrete

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The utility of proper orthogonal decomposition for dimensionality reduction in understanding behavior of concrete

  • Manoj, A. (Department of Civil Engineering, National Institute of Technology Karnataka) ;
  • Narayan, K.S. Babu (Department of Civil Engineering, National Institute of Technology Karnataka)
  • Received : 2019.07.04
  • Accepted : 2021.06.29
  • Published : 2021.08.25
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Abstract

Properties of wet and set concrete are influenced by a wide range of variables. With new formulations being tried and adopted, understanding workability, strength and durability characteristics of these formulations is of utmost importance. From among the wide range of variables that affect properties of concrete, identification of the most vital, interplay between variables, quantification of influence, for judicious manipulation of mix proportioning, placement, compaction and curing, to get the desired and targeted end results can vastly be improved by employing the state of the art data handling tools. Group method of data handling (GMDH), a set of mathematical algorithms, is of great usage potential in multi-variable data modeling, optimization and pattern recognition. Proper Orthogonal Decomposition (POD) a subset of GMDH, a technique for systematic dimensionality reduction and pattern recognition, is of great importance in studying complex datasets. This paper presents the need for adoption of GMDH techniques in concrete technology with an account of trends in this direction and also provides an illustration of POD's utility as a valid decision-making tool in dimensionality reduction and projection of behavior of concrete subjected to elevated temperature.

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References

  1. Aggarwal, R., Kumar, M., Sharma, R.K. and Sharma, M.K. (2015), "Predicting compressive strength of concrete", Int. J. Appl. Sci. Eng., 13(2), 171-185. https://doi.org/10.6703/IJASE.2015.13(2).171.
  2. Ahmed, M.A., Mallick, J. and Hasan, M.A. (2016), "A study of factors affecting the flexural tensile strength of concrete", J. King Saud Univ.-Eng. Sci., 28(2), 147-156. https://doi.org/10.1016/j.jksues.2014.04.001.
  3. Al-Ghalib, A.A. and Mohammad, F.A. (2016), "Damage and repair classification in reinforced concrete beams using frequency domain data", Mater. Struct., 49(5), 1893-1903. https://doi.org/10.1617/s11527-015-0621-7.
  4. Aldahdooh, M.A.A., Bunnori, N.M. and Johari, M.A.M. (2013), "Evaluation of ultra-high-performance-fiber reinforced concrete binder content using the response surface method", Mater. Des., 52, 957-965. https://doi.org/10.1016/j.matdes.2013.06.034.
  5. Arizio, E., Piazza, R., Cairns, W.R.L., Appolonia, L. and Botteon, A. (2013), "Statistical analysis on ancient mortars : A case study of the Balivi Tower in Aosta (Italy)", Constr. Build. Mater., 47, 1309-1316. https://doi.org/10.1016/j.conbuildmat.2013.06.026.
  6. Bal, L. and Buyle-Bodin, F. (2013), "Artificial neural network for predicting drying shrinkage of concrete", Constr. Build. Mater., 38, 248-254. https://doi.org/10.1016/j.conbuildmat.2012.08.043.
  7. Bartlett, M.S. (1950), "Tests of significance in factor analysis", Br. J. Stat. Psychol., 3(2), 77-85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x
  8. Boukhatem, B., Kenai, S., Hamou, A.T., Ziou, D. and Ghrici, M. (2012, "Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique", Comput. Concrete, 10(6), 557-573. https://doi.org/10.12989/cac.2012.10.6.557.
  9. Boukhatem, B., Kenai, S., Tagnit-Hamou, A. and Ghrici, M. (2011), "Application of new information technology on concrete: an overview", J. Civil Eng. Manage., 17(2), 248-258. https://doi.org/10.3846/13923730.2011.574343.
  10. Calabrese, L., Campanella, G. and Proverbio, E. (2012), "Noise removal by cluster analysis after long time AE corrosion monitoring of steel reinforcement in concrete", Constr. Build. Mater., 34, 362-371. https://doi.org/10.1016/j.conbuildmat.2012.02.046.
  11. Chou, J., Ngo, N. and Pham, A. (2015), "Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression", J. Comput. Civil Eng., 30(1), 1-9. https://doi.org/10.1061/(asce)cp.1943-5487.0000466.
  12. Degala, S., Rizzo, P., Ramanathan, K. and Harries, K.A. (2009), "Acoustic emission monitoring of CFRP reinforced concrete slabs", Constr. Build. Mater., 23(5), 2016-2026. https://doi.org/10.1016/j.conbuildmat.2008.08.026.
  13. Falchi, L., Varin, C., Toscano, G. and Zendri, E. (2015), "Statistical analysis of the physical properties and durability of water-repellent mortars made with limestone cement, natural hydraulic lime and pozzolana-lime", Constr. Build. Mater., 78, 260-270. https://doi.org/10.1016/j.conbuildmat.2014.12.109.
  14. Filho, F.M.A., Barragan, B.E., Casas, J.R. and El Debs, A.L.H.C. (2010), "Hardened properties of self-compacting concrete - A statistical approach", Constr. Build. Mater., 24, 1608-1615. https://doi.org/10.1016/j.conbuildmat.2010.02.032.
  15. Folliard, K.J., Juenger, M., Schindler, A., Riding, K., Poole, Jonathan, Kallivokas, L.F., Slatnick, S., Whigham, J. and Meadows, J.L. (2008), "Prediction model for concrete behavior-Final report", FHWA/TX-08/0-4563-1, Center for Transportation Research, The University of Texas.
  16. Ghizdavet, Z., stefan, B.M., Nastac, D., Vasile, O. and Bratu, M. (2016), "Sound absorbing materials made by embedding crumb rubber waste in a concrete matrix", Constr. Build. Mater., 124, 755-763. https://doi.org/10.1016/j.conbuildmat.2016.07.145.
  17. Gonzalez-Taboada, I., Gonzalez-Fonteboa, B., Martinez-Abella, F. and Perez-Ordonez, J. L. (2016), "Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming", Constr. Build. Mater., 106, 480-499. https://doi.org/10.1016/j.conbuildmat.2015.12.136.
  18. Gryllias, K., Koukoulis, I., Yiakopoulos, C., Antoniadis, I. and Provatidis, C. (2009), "Morphological processing of proper orthogonal modes for crack detection in beam structures", J. Mech. Mater. Struct., 4(6), 1063-1088. https://doi.org/10.2140/jomms.2009.4.1063.
  19. Gulotta, D., Goidanich, S., Tedeschi, C. and Toniolo, L. (2015), "Commercial NHL-containing mortars for the preservation of historical architecture. Part 2: Dsurability to salt decay", Constr. Build. Mater., 96, 198-208. https://doi.org/10.1016/j.conbuildmat.2015.08.006.
  20. Hellebois, A., Launoy, A., Pierre, C., De Laneve, M. and Espion, B. (2013), "100-year-old Hennebique concrete, from composition to performance", Constr. Build. Mater., 44, 149-160. https://doi.org/10.1016/j.conbuildmat.2013.03.017.
  21. Jeffers, J.N.R. (1967), "Two case studies in the application of principal component analysis", J. R. Stat. Soc. Ser. C (Appl. Stat.), 16(3), 225-236.
  22. Jolliffe, I.T. (2002), Principal Component Analysis, 2nd Edition, International Encyclopedia of Education, Springer.
  23. Kellouche, Y., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2017), "Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network", Neur. Comput. Appl., 1-20. https://doi.org/10.1007/s00521-017-3052-2.
  24. Kheder, G.F., Al Gabban, A.M. and Abid, S.M. (2003), "Mathematical model for the prediction of cement compressive strength at the ages of 7 and 28 days within 24 hours", Mater. Struct., 36(10), 693-701. https://doi.org/10.1007/BF02479504.
  25. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X.
  26. Liang, Y.C., Lee, H.P., Lim, S.P., Lin, W.Z., Lee, K.H. and Wu, C.G. (2002), "Proper orthogonal decomposition and its applications-Part I: Theory", J. Sound Vib., 252(3), 527-544. https://doi.org/10.1006/jsvi.2001.4041.
  27. Lu, Y., Li, J., Ye, L. and Wang, D. (2013), "Guided waves for damage detection in rebar-reinforced concrete beams", Constr. Build. Mater., 47, 370-378. https://doi.org/10.1016/j.conbuildmat.2013.05.016.
  28. Madandoust, R., Ghavidel, R. and Nariman-Zadeh, N. (2010), "Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV", Comput. Mater. Sci., 49(3), 556-567. https://doi.org/10.1016/j.commatsci.2010.05.050.
  29. Manoj, A. and Babu Narayan, K.S. (2019), "Proper orthogonal decomposition for generation of organized data in concrete technology", Ukieri Concr. Congr. Concr. Glob. Build., Jalandhar, India, March.
  30. Manoj, A. and Narayan, K.S.B. (2021), "Application of proper orthogonal decomposition in concrete performance appraisal", Rec. Trend. Civil Eng., 13-21. https://doi.org/10.1007/978-981-15-8293-6_2.
  31. Mehta, P.K. and Monteiro, P.J.M. (2014), Concrete: Microstructure, Properties, and Materials, 4th Edition, McGraw-Hill Education.
  32. Moropoulou, A., Polikreti, K., Bakolas, A. and Michailidis, P. (2003). "Correlation of physicochemical and mechanical properties of historical mortars and classification by multivariate statistics", Cement Concrete Res., 33, 891-898. https://doi.org/10.1016/S0008-8846(02)01088-8.
  33. Newlands, M.D. (2019), "A concrete education: futureproofing for the digital world", Ukieri Concr. Congr. Concr. Glob. Build., Jalandhar, India, March.
  34. Ni, H.G. and Wang, J.Z. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30(8), 1245-1250. https://doi.org/10.1016/S0008-8846(00)00345-8.
  35. Pan, Y., Prado, A., Porras, R., Hafez, O.M. and Bolander, J.E. (2017), "Lattice modeling of early-age behavior of structural concrete", Mater. (Basel), 10(3), 1-34. https://doi.org/10.3390/ma10030231.
  36. Rampazzi, L., Pozzi, A., Sansonetti, A., Toniolo, L. and Giussani, B. (2006), "A chemometric approach to the characterisation of historical mortars", Cement Concrete Res., 36(6), 1108-1114. https://doi.org/10.1016/j.cemconres.2006.02.002.
  37. Riding, K.A., Poole, J.L., Folliard, K.J., Juenger, M.C.G. and Schindler, A.K. (2012), "Modeling hydration of cementitious systems", ACI Mater. J., 109(2), 225-234.
  38. Santos, J.P., Cremona, C., Orcesi, A.D. and Silveira, P. (2016), "Early damage detection based on pattern recognition and data fusion", J. Struct. Eng., 143(2), 04016162. https://doi.org/10.1061/(asce)st.1943-541x.0001643.
  39. Ta, V.L., Bonnet, S., Senga Kiesse, T. and Ventura, A. (2016), "A new meta-model to calculate carbonation front depth within concrete structures", Constr. Build. Mater., 129, 172-181. https://doi.org/10.1016/j.conbuildmat.2016.10.103.
  40. Taffese, W.Z. and Sistonen, E. (2017), "Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions", Autom. Constr., 77, 1-14. https://doi.org/10.1016/j.autcon.2017.01.016.
  41. Thirumalaiselvi, A. and Sasmal, S. (2019), "Acoustic emission monitoring and classification of signals in cement composites during early-age hydration", Constr. Build. Mater., 196, 411-427. https://doi.org/10.1016/j.conbuildmat.2018.11.067.
  42. Tobias, S. and Carlson, J.E. (1969), "Brief report: Bartlett's test of sphericity and chance findings in factor analysis", Multiv. Behav. Res., 4(3), 375-377. https://doi.org/10.1207/s15327906mbr0403_8
  43. Yaragal, S.C., Narayan, K.S.B., Venkataramana, K., Kishor, S., Kulkarni, K.S., Gowda, H.C.C., Reddy, G.R. and Sharma, A. (2010), "Studies on normal strength concrete cubes subjected to elevated temperatures", J. Struct. Fire Eng., 1(4), 249-262. https://doi.org/10.1260/2040-2317.1.4.249.
  44. Yi, S.T., Moon, Y.H. and Kim, J.K. (2005), "Long-term strength prediction of concrete with curing temperature", Cement Concrete Res., 35(10), 1961-1969. https://doi.org/10.1016/j.cemconres.2005.06.010.