Characterization and economization of cementitious tile bond adhesives using machine learning technique
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-07-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509524000676 |
| _version_ | 1797326328256528384 |
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| author | Wasim Abbass Akmal Shahzad Fahid Aslam Shaban Shahzad Ali Ahmed Abdullah Mohamed |
| author_facet | Wasim Abbass Akmal Shahzad Fahid Aslam Shaban Shahzad Ali Ahmed Abdullah Mohamed |
| author_sort | Wasim Abbass |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T06:21:46Z |
| format | Article |
| id | doaj.art-d39d1d22290c4a56a457bb66ab690457 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-08T06:21:46Z |
| publishDate | 2024-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-d39d1d22290c4a56a457bb66ab6904572024-02-04T04:44:42ZengElsevierCase Studies in Construction Materials2214-50952024-07-0120e02916Characterization and economization of cementitious tile bond adhesives using machine learning techniqueWasim Abbass0Akmal Shahzad1Fahid Aslam2Shaban Shahzad3Ali Ahmed4Abdullah Mohamed5Civil Engineering Department, University of Engineering and Technology, Lahore, 54890, PakistanCivil Engineering Department, University of Engineering and Technology, Lahore, 54890, PakistanDepartment of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; Corresponding author.Laboratoire Matériaux et Durabilité des Constructions (LMDC), Université de Toulouse, INSA, UPS Génie Civil, 135 Avenue de Rangueil, Toulouse, CEDEX 04 31077, France; Renovia Technologies Pvt limited, Lahore, PakistanCivil Engineering Department, University of Engineering and Technology, Lahore, 54890, PakistanResearch Centre, Future University in Egypt, New Cairo 11835, EgyptCementitious 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.http://www.sciencedirect.com/science/article/pii/S2214509524000676Cementitious tile adhesivesShear strengthTensile strengthDune sandSilica sand |
| spellingShingle | Wasim Abbass Akmal Shahzad Fahid Aslam Shaban Shahzad Ali Ahmed Abdullah Mohamed Characterization and economization of cementitious tile bond adhesives using machine learning technique Case Studies in Construction Materials Cementitious tile adhesives Shear strength Tensile strength Dune sand Silica sand |
| title | Characterization and economization of cementitious tile bond adhesives using machine learning technique |
| title_full | Characterization and economization of cementitious tile bond adhesives using machine learning technique |
| title_fullStr | Characterization and economization of cementitious tile bond adhesives using machine learning technique |
| title_full_unstemmed | Characterization and economization of cementitious tile bond adhesives using machine learning technique |
| title_short | Characterization and economization of cementitious tile bond adhesives using machine learning technique |
| title_sort | characterization and economization of cementitious tile bond adhesives using machine learning technique |
| topic | Cementitious tile adhesives Shear strength Tensile strength Dune sand Silica sand |
| url | http://www.sciencedirect.com/science/article/pii/S2214509524000676 |
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