Characterization and economization of cementitious tile bond adhesives using machine learning technique
Source abstract
Abstract
Cementitious Tile Adhesives (CTAs) play a key role in ensuring the elegant outlook of buildings by improving the serviceability of various tile applications, hence making them an integral part of the dry-mix mortar industry with a significant market share globally. However, CTAs require special raw material for their production, making them a less economical option for sustainable construction applications. Hence, the current research work undertook an effort to explore the potential of different type of fine materials to produce CTAs to obtain a cheap value-added product. A total of 36 mixture proportions were prepared with six different sources of fine aggregates (i.e., silica sand, quartz sand, dune sand, river sand 1, river sand 2, and river sand 3), with six varying binder-to-fine aggregates ratios, and their mechanical strength parameters were evaluated. Further microstructure analysis was performed for fine aggregates, and best and worst performing mixture proportions to gain a deep understanding about their mechanical performance. The results revealed that the mixture proportions utilizing silica sand outperformed all other formulations in terms of compressive strength, and tensile strength with a value of 25.28 MPa, and 1.35 MPa, respectively, while dune sand performed the best in terms of shear strength 1.92 MPa with cement content of 40% at 28 days. Moreover, microstructural investigation also showed the better microstructural performance of silica sand as compared to that of all other sources of sand. To increase the applicability of the undertaken research project, an Artificial Intelligence (AI) based neural network model, along with predictive equations, was developed for the prediction of compressive strength (R2=0.9831), shear strength (R2=0.9808), and tensile strength (R2=0.9862). Hence, it can be concluded that the current research work provides a foundation for an effective product development process to produce tile bond adhesives using different type of raw materials available leading to economical and sustainable manufacturing of cementitious adhesives.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Innovations in Concrete and Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (76)
Show source-provided references
1. The Victorian Emporium (2011, August 04). Publications – History of Tiles. Retrieved October 24,2023 from 〈https://www.thevictorianemporium.com/publications/history/article/history_of_tiles〉.
2. Hayes, W.C. (1965). Most Ancient Egypt: William C. Hayes. Edited by Keith C. Seele.
3. Glazes, colourants and decorations in early Islamic glazed ceramics from the Vega of Granada (9th to 12th centuries CE)
4. Review: architecture from prehistory to post-modernism: the western tradition by Marvin Trachtenberg, Isabelle Hyman10.2307/990260 ↗
5. Harry. (2023, September 29). The History Of Ceramic Tiles You Should Know. Unique Tiles. 〈https://www.uniquetiles.com/the-history-of-ceramic-tiles-you-should-know〉.
6. Carter, C.B., & Norton, M.G. (2013). Ceramic materials. In Springer eBooks. https://doi.org/10.1007/978–1-4614–3523-5.10.1007/978-1-4614-3523-5 ↗
7. Baraldi, L., & by ACIMAC, M.M.E.S. (2016). World production and consumption of ceramic tiles. Oceania, 56, 0–4.
8. Menafn. (2023). Ceramic Tiles Market To Worth USD 77.82 Billion By 2028 | With CAGR 5.2%. Middle East North Africa Financial Network. 〈https://menafn.com/1105548644/Ceramic-Tiles-Market-To-Worth-USD-7782-Billion-By-2028-With-CAGR-52〉.
9. BS EN 12004–1:2017 Adhesives for ceramic tiles Requirements, assessment and verification of constancy of performance, classification and marking. (n.d.). Retrieved October 11, 2023, from 〈https://www.en-standard.eu/bs-en-12004–1-2017-adhesives-for-ceramic-tiles-requirements-assessment-and-verification-of-constancy-of-performance-classification-and-marking/〉.
10. ISO 13007–2:2010 - Ceramic tiles — Grouts and adhesives — Part 2: Test methods for adhesives. (n.d.). Retrieved October 11, 2023, from 〈https://www.iso.org/standard/54599.html〉.
11. Fixing of stoneware “Porcelanic” tiles by means of ceramic tile adhesives modified with redispersible polymer powder
12. Bond between 19th Century lime mortars and glazed ceramic tiles10.1016/j.conbuildmat.2014.02.043 ↗
13. Redispersible polymer powder modified cementitious tile adhesive as an alternative to ordinary cement-sand grout10.1016/j.heliyon.2021.e08411 ↗
14. Felixberger, J.K. (2008). Polymer-modified thin-bed tile adhesive. Institut De Promocio Ceramica: Castelló, Spain.
15. Polymer-based admixtures10.1016/S0958-9465(97)00065-6 ↗
16. ISO 13007–1:2014 - Ceramic tiles — Grouts and adhesives — Part 1: Terms, definitions and specifications for adhesives. (n.d.). Retrieved October 11, 2023, from 〈https://www.iso.org/standard/60880.html〉.
17. Historical Evolution Of Ceramic Tile Adhesives And Their Contribution To Expanding The Market For Ceramic Tiles
18. Mordor Intelligence. (n.d.). DRY MIX MORTAR MARKET SIZE & SHARE ANALYSIS - GROWTH TRENDS & FORECASTS (2023 - 2028).
19. Industry ARC. (n.d.). Dry Mix Mortar market - forecast(2023 - 2028). In IndustryARC. 〈https://www.industryarc.com/Research/Dry-Mix-Mortar-Market-Research-503183〉.
20. ACI Committee 548, Polymers in Concrete, & Fowler, D. W. (1992). Guide for the Use of Polymers in Concrete. American Concrete Institute.
21. Physico-chemical characterization of EVA-modified mortar and porcelain tiles interfaces10.1016/j.cemconres.2009.07.020 ↗
22. Adherence evaluation in Tile-Mortar interface10.4028/www.scientific.net/MSF.730-732.403 ↗
23. Interactions of polymers and organic admixtures on portland cement hydration10.1016/0008-8846(87)90076-7 ↗
24. Evaluation of various cellulose ethers performance in ceramic tile adhesive mortars10.1016/j.ijadhadh.2012.09.007 ↗
25. Influence of cement with different calcium sulfate phases on cementitious tile adhesive mortars: microstructure and performance aspects10.1016/j.ijadhadh.2020.102744 ↗
26. Use of the rice and corn husk ashes as an innovative pozzolanic material in ceramic tile adhesive production10.1016/j.procs.2021.12.288 ↗
27. Effect of fly ash addition on the mechanical properties of tile adhesive10.1016/j.conbuildmat.2004.08.007 ↗
28. The effect of fly ash and granulated blast furnace slag on slip and tensile adhesion strength of tile adhesives mortars10.1051/matecconf/202032201017 ↗
29. Machine learning prediction of concrete compressive strength using rebound hammer test
30. Prediction of concrete compressive strength employing machine learning techniques
31. Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate10.1016/j.jclepro.2023.138673 ↗
32. Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete10.1016/j.conbuildmat.2019.117021 ↗
33. Singh, P., Bhardwaj, S., Dixit, S., Shaw, R.N., & Ghosh, A. (2021). Development of Prediction Models to Determine Compressive Strength and Workability of Sustainable Concrete with ANN (pp. 753–769). https://doi.org/10.1007/978–981-16–0749-3_59.10.1007/978-981-16-0749-3_59 ↗
34. Applications of gene expression programming and regression techniques for estimating compressive strength of bagasse ash based concrete10.3390/cryst10090737 ↗
35. Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network
36. Xu, Y., Li, J., Yu, X., Xiao, L., Luo, T., Wei, C., & Li, L. (2023). Composition prediction of pore solution in hardened concrete materials based on machine learning. Developments in the Built Environment, 16, 100285. https://doi.org/10.1016/j.dibe.2023.100285.10.1016/j.dibe.2023.100285 ↗
37. X-ray diffraction techniques for mineral characterization: a review for engineers of the fundamentals, applications, and research directions10.3390/min12020205 ↗
38. Manufacturing of sustainable untreated coal ash masonry units for structural applications10.3390/ma15114003 ↗
39. X-ray diffraction and scattering by nanomaterials
40. Recycling untreated coal bottom ash with added value for mitigating alkali–silica reaction in concrete: a sustainable approach10.3390/su122410631 ↗
41. Atul Verma. (2015). Evaluation of sea sand and river sand properties and their comparison.
42. Cellulose ethers influence on water retention and consistency in cement-based mortars10.1016/j.cemconres.2010.09.004 ↗
43. Influence of water-cement ratio and cement content on the properties of polymer-modified mortars10.1016/S0008-8846(99)00060-5 ↗
44. Influence of water-cement ratio and cement content on the properties of polymer-modified mortars10.1016/S0008-8846(99)00060-5 ↗
45. Influence of polymers on microstructure and adhesive strength of cementitious tile adhesive mortars10.1016/j.cemconres.2004.06.039 ↗
46. High-strength concrete with different fine aggregate10.1016/S0008-8846(02)00860-8 ↗
47. Effect of sand content on strength and pore structure of cement mortar10.1007/s11595-017-1607-9 ↗
48. Evaluation of sand quality and its effect on mortar and cement concrete10.22232/stj.2020.08.02.10 ↗
49. Effect of fine aggregate properties on cement mortar strength
50. Effects of sand content, superplasticizer dosage, and mixing time on compressive strength of mortar
51. In-plane shear capacity of tuff masonry walls with traditional and innovative Composite Reinforced Mortars (CRM)10.1016/j.conbuildmat.2019.03.133 ↗
52. Effect of water to binder ratio and sand to binder ratio on shrinkage and mechanical properties of high-strength engineered cementitious composite10.1016/j.conbuildmat.2019.07.303 ↗
53. Characterizing the interfacial zones of fly ash based alkali activated adhesives to various substrates10.3389/fmats.2022.1049383 ↗
54. Khatib, J., & Ellis, D.J. (2001). Mechanical Properties of Concrete Containing Foundry Sand. American Concrete Institute, 200, 733–748. https://doi.org/10.14359/10612.10.14359/10612 ↗
55. Experimental studies and drying shrinkage prediction model for concrete containing waste foundry sand
56. Mechanical properties and size effects in lightweight mortars containing expanded perlite aggregate10.1617/s11527-010-9662-0 ↗
57. Effect of porosity and water absorption on compressive strength of fly ash based geopolymer and OPC paste10.1088/1757-899X/957/1/012035 ↗
58. Cevik, S., Mutuk, T., Oktay, B.M., Demirbas, A.K., 2017. Mechanical and microstructural characterization of cement mortars prepared by waste foundry sand (WFS). J. Aust. Ceram. Soc. 53 (2), 829e837.10.1007/s41779-017-0096-9 ↗
59. Gupta, L.K., Vyas, A.K., 2018. Impact on mechanical properties of cement sand mortar containing waste granite powder. Constr. Build. Mater. 191, 155e164.10.1016/j.conbuildmat.2018.09.203 ↗
60. Investigation on electromagnetic and microwave absorption properties of copper slag-filled cement mortar10.1016/j.cemconcomp.2016.10.003 ↗
61. Buyuksagis, I.S., Uygunoglu, T., Tatar, E., 2017. Investigation on the usage of waste marble powder in cement-based adhesive mortar. Constr. Build. Mater. 154, 734e742.10.1016/j.conbuildmat.2017.08.014 ↗
62. Progress in the immobilization of radioactive wastes in cement10.1016/0008-8846(92)90058-4 ↗
63. Brick-Mortar bond and masonry compressive strength10.1061/(ASCE)0899-1561(2005)17:2(229) ↗
64. Influence of shear bond strength on compressive strength and stress–strain characteristics of masonry10.1617/s11527-008-9358-x ↗
65. Experimentation and predictive models for properties of concrete added with active and inactive SiO2 fillers10.3390/ma12020299 ↗
66. Bond strength and compressive stress-strain characteristics of brick masonry10.1016/j.jobe.2016.11.006 ↗
67. Predictive modeling for US commercial building energy use: a comparison of existing statistical and machine learning algorithms using CBECS microdata10.1016/j.enbuild.2017.12.031 ↗
68. Frost, J. (2022, February 27). How to choose between linear and nonlinear regression. Statistics by Jim. https://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/.
69. Simulation of flows around cylinders by a Lagrangian vortex scheme10.1016/0141-1187(83)90072-X ↗
70. State-of-the-art in artificial neural network applications: a survey10.1016/j.heliyon.2018.e00938 ↗
71. The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network10.1016/j.tws.2021.107499 ↗
72. Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector10.1016/j.measurement.2014.01.030 ↗
73. Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach10.1016/j.conbuildmat.2019.117000 ↗
74. Training feedforward networks with the Marquardt algorithm10.1109/72.329697 ↗
75. Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques10.1016/j.rineng.2022.100489 ↗
76. An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria