SİLİS DUMANI İKAMELİ ÇİMENTOLARIN YSA VE ANFIS MODELLERİ İLE BASINÇ DAYANIMLARININ TAHMİNİ

Öz

Çevresel farkındalık, çimento üretim aşamalarında gerek yapay gerekse doğal mineral katkılar kullanılarak klinker miktarının azaltılması ve çimentolu kompozitlerin çeşitli özelliklerini iyileştirilmesi amacıyla yoğun çalışmaların yapılmasını sağlamaktadır. Bu bağlamda yapılan çalışmalarda, çevre için zararlı, ancak çimentolu kompozitlerin birçok özelliğini iyileştirmesi açısından faydalı olan silis dumanı üzerinde yoğunlaşıldığı görülmektedir. Bu çalışmada, silis dumanı ikameli çimento harçlarının deneysel sonuçları incelenerek yapay sinir ağları (YSA) ve uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) ile basınç dayanımlarının tahmini için üçer model tasarımı yapılmıştır. Modellerin mimarisinde deneylerden elde edilen ve 2, 7, 28 ve 90. gün basınç dayanım sonuçları olan 120 numune eğitim aşamaları için kullanılmıştır. Test aşamasında ise 20 sonuç kullanılmış ve modellerde hidratasyon yaşı (gün), Portland çimento (PÇ), silis dumanı giriş verisi, betonun basınç dayanımı ise çıkış verisi olarak kullanılmıştır. Test sonuçları ile deneysel sonuçlar karşılaştırıldığında, en iyi sonuçları veren modelin R2, MAPE ve RMSE için sırasıyla 0,9995, %0,5490 ve 0,3572 ile YSA-2, 0,9987, %1,0200 ve 0,5664 ile ANFIS-3 modeli olduğu belirlenmiştir. Bu verilere göre, deneysel sonuçlarla tahmin edilen sonuçlar arasındaki uyumun iyi olduğu ve hem YSA hem de ANFIS ile oluşturulan modellerle yüksek doğruluk derecesinde tahmin yapılabileceği kanaatine ulaşılmıştır.

Anahtar Kelimeler

• Portland çimento
• Silis dumanı
• Basınç dayanımı
• YSA
• ANFIS.

Prediction of Compressive Strengths of Silica Fume Substituted Cements Using ANN and ANFIS Models

Öz

Environmental awareness has led to intensive efforts to reduce the amount of clinker and improve various properties of cementitious composites by using both artificial and natural mineral additives in cement production stages. In this context, studies have focused on silica fume, which is harmful to the environment but beneficial in terms of improving many properties of cementitious composites. In this study, the experimental results of silica fume substituted cement mortars were analyzed and three models were designed for the prediction of compressive strength using artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). In the architecture of the models, 120 specimens obtained from the experiments with compressive strength results at 2, 7, 28 and 90 days were used for the training stages. In the test phase, 20 results were used and hydration age (days), Portland cement (PC), silica fume were used as input parameters and compressive strength of concrete was used as output parameter in the models. When the test results were compared with the experimental results, it was determined gave the best results that ANN-2 model with 0.9995, 0.5490% and 0.3572, and ANFIS-3 model with 0.9987, 1.0200% and 0.5664 for R2, MAPE and RMSE, respectively. According to these data, it is concluded that the agreement between the experimental results and the predicted results is good and that high accuracy can be predicted with the ANN and ANFIS models.

Anahtar Kelimeler

• Portland cement
• Silica fume
• Compressive strength
• ANN
• ANFIS.

Kaynakça

1. Aali, K. A., Parsinejad, M., & Rahmani, B. (2009) Estimation of Saturation Percentage of Soil Using Multiple Regression, YSA, and ANFIS Techniques, Computing and Information Science, 2(3), 127-136. doi:10.5539/cis.v2n3p127
2. Adesanya, E., Aladejare, A., Adediran, A., Lawal, A., & Illikainen, M. (2021) Predicting shrinkage of alkali-activated blast furnace-fly ash mortars using artificial neural network (ANN), Cement and Concrete Composites, 124, 104265. doi: https://doi.org/10.1016/j.cemconcomp.2021.104265
3. Adil, G., Kevern, J. T., & Mann, D. (2020) Influence of silica fume on mechanical and durability of pervious concrete, Construction and Building Materials, 247, 118453. doi: https://doi.org/10.1016/j.conbuildmat.2020.118453
4. Amin, M. N., Javed, M. F., Khan, K., Shalabi, F. I., & Qadir, M. G. (2021) Modeling compressive strength of eco-friendly volcanic ash mortar using artificial neural networking, Symmetry, 13(11), 2009. doi: https://doi.org/10.3390/sym13112009
5. Armaghani, D. J., & Asteris, P. G. (2021) A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength, Neural Computing and Applications, 33(9), 4501-4532. doi: https://doi.org/10.1007/s00521-020-05244-4
6. Buscema, M (2002) A brief overview and introduction to artificial neural networks, Substance Use & Misuse, 37(8-10), 1093-1148. doi: https://doi.org/10.1081/JA-120004171
7. Caldas, P. H. C. H., de Azevedo, A. R. G., & Marvila, M. T. (2023) Silica fume activated by NaOH and KOH in cement mortars: Rheological and mechanical study, Construction and Building Materials, 400, 132623. doi: https://doi.org/10.1016/j.conbuildmat.2023.132623
8. Chang, W., & Zheng, W. (2022) Compressive strength evaluation of concrete confined with spiral stirrups by using adaptive neuro-fuzzy inference system (ANFIS), Soft Computing, 26(21), 11873-11889. doi: https://doi.org/10.1007/s00500-022-07001-2

9. Choi, Y. C., & Park, B. (2019) Enhanced autogenous healing of ground granulated blast furnace slag blended cements and mortars, Journal of Materials Research and Technology, 8(4), 3443–3452. doi: https://doi.org/10.1016/j.jmrt.2019.06.010
10. Dao, D. V., Ly, H. B., Trinh, S. H., Le, T. T., & Pham, B. T. (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete, Materials, 12(6), 983. doi: https://doi.org/10.3390/ma12060983
11. Eskandari-Naddaf, H., & Kazemi, R. (2017) ANN prediction of cement mortar compressive strength, influence of cement strength class, Construction and Building Materials, 138, 1-11. doi: https://doi.org/10.1016/j.conbuildmat.2017.01.132
12. Guerra, M. I., de Araújo, F. M., de Carvalho Neto, J. T., & Vieira, R. G. (2024) Survey on adaptative neural fuzzy inference system (ANFIS) architecture applied to photovoltaic systems, Energy Systems, 15(2), 505-541. doi: https://doi.org/10.1007/s12667-022-00513-8
13. Güvenç, U., & Koçak, B. (2023) Kademeli İleri Geri Yayılım ve Gauss Fonksiyon Modelleri ile Pomza ve Diatomit İçeren Çimento Harçlarının Basınç Dayanımlarının Tahmini, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(2), 682-698. doi: https://doi.org/10.29130/dubited.1106267
14. Hamdia, K. M., Lahmer, T., Nguyen-Thoi, T., & Rabczuk, T. (2015) Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS, Computational Materials Science, 102, 304-313. doi: https://doi.org/10.1016/j.commatsci.2015.02.045
15. Jaf, D. K. I., Abdalla, A., Mohammed, A. S., Abdulrahman, P. I., Kurda, R., & Mohammed, A. A. (2024) Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete, Heliyon, 10(4), e25997. doi: https://doi.org/10.1016/j.heliyon.2024.e25997
16. Jain, B., & Sancheti, G. (2023) Influence of silica fume and iron dust on mechanical properties of concrete, Construction and Building Materials, 409, 133910. doi: https://doi.org/10.1016/j.conbuildmat.2023.133910
17. Jang, J. S. (1993) ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. doi: https://doi.org/10.1109/21.256541
18. Juturu, R., Murty, V. R., & Selvaraj, R. (2024) Efficient adsorption of Cr (VI) onto hematite nanoparticles: ANN, ANFIS modelling, isotherm, kinetic, thermodynamic studies and mechanistic insights, Chemosphere, 349, 140731. doi: https://doi.org/10.1016/j.chemosphere.2023.140731
19. Karaboga, D., & Kaya, E. (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey, Artificial Intelligence Review, 52, 2263-2293. doi: https://doi.org/10.1007/s10462-017-9610-2
20. Khan, M. I., & Siddique, R. (2011) Utilization of silica fume in concrete: Review of durability properties. Resources, Conservation and Recycling, 57, 30-35. doi: https://doi.org/10.1016/j.resconrec.2011.09.016
21. Khashman, A., & Akpinar, P. (2017) Non-destructive prediction of concrete compressive strength using neural networks, Procedia Computer Science, 108, 2358-2362. doi: https://doi.org/10.1016/j.procs.2017.05.039
22. Kocak, B. (2023) Yapay zeka tabanlı uygulamalarla diatomit ve pomza ikameli çimento harçlarının basınç dayanımlarının tahmini, Yüksek Lisans Tezi, Düzce Üniversitesi Lisansüstü Eğitim Enstitüsü, Düzce.
23. Kocak, B., Pınarcı, İ., Güvenç, U., & Kocak, Y. (2023) Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications, Construction and Building Materials, 385, 131516. doi: https://doi.org/10.1016/j.conbuildmat.2023.131516
24. Kumar, B, & Kumar, N (2024) Forecasting Marshall stability of waste plastic reinforced concrete using SVM, ANN, and tree-based techniques, Multiscale and Multidisciplinary Modeling, Experiments and Design, 1-19. doi: https://doi.org/10.1007/s41939-024-00501-8
25. Lee, S. C. (2003) Prediction of concrete strength using artificial neural networks, Engineering structures, 25(7), 849-857. doi: https://doi.org/10.1016/S0141-0296(03)00004-X
26. Mansouri, I., & Kisi, O. (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches, Composites Part B: Engineering, 70, 247-255. doi: https://doi.org/10.1016/j.compositesb.2014.11.023
27. Mosquera, C. H., Acosta, M. P., Rodríguez, W. A., Mejía‐España, D. A., Torres, J. R., Martinez, D. M., & Abellán‐García, J. (2024) ANN‐based analysis of the effect of SCM on recycled aggregate concrete, Structural Concrete. doi: https://doi.org/10.1002/suco.202400024
28. Nguyen, T. T., Pham Duy, H., Pham Thanh, T., & Vu, H. H. (2020) Compressive Strength Evaluation of Fiber‐Reinforced High‐Strength Self‐Compacting Concrete with Artificial Intelligence, Advances in Civil Engineering, 2020(1), 3012139. doi: https://doi.org/10.1155/2020/3012139
29. Okoji, A. I., Anozie, A. N., & Omoleye, J. A. (2022) Evaluating the thermodynamic efficiency of the cement grate clinker cooler process using artificial neural networks and ANFIS, Ain Shams Engineering Journal, 13(5), 101704. doi: https://doi.org/10.1016/j.asej.2022.101704
30. Ozcan, G., Kocak, Y., & Gulbandilar, E. (2018) Compressive strength estimation of concrete containing zeolite and diatomite: an expert system implementation, Computers and Concrete, An International Journal, 21(1), 21-30. doi: https://doi.org/10.12989/cac.2018.21.1.021
31. Öztemel, E. (2012) Yapay sinir ağları, Papatya Yayincilik, 3. Baskı, ISBN: 978-975-6797-39-6, Istanbul.
32. Öztürk, K., & Şahin, M. E. (2018) Yapay sinir ağları ve yapay zekâ’ya genel bir bakış, Takvim-i Vekayi, 6(2), 25-36. Erişim adresi: https://dergipark.org.tr/en/download/article-file/596690
33. Ramachandra, R., & Mandal, S. (2023) Prediction of fly ash concrete type using ANN and SVM models, Innovative Infrastructure Solutions, 8(1), 47. doi: https://doi.org/10.1007/s41062-022-01014-4
34. Sabour, M. R., Dezvareh, G. A., & Niavol, K. P. (2021) Application of artificial intelligence methods in modeling corrosion of cement and sulfur concrete in sewer systems. Environmental Processes, 8, 1601-1618. doi: https://doi.org/10.1007/s40710-021-00542-y
35. Sakthivel, P. B., Ravichandran, A., & Alagumurthi, N. (2016) Modeling and prediction of flexural strength of hybrid mesh and fiber reinforced cement-based composites using artificial neural network (ANN), GEOMATE Journal, 10(19), 1623-1635. Erişim adresi: https://geomatejournal.com/geomate/article/view/1993
36. Sevim, UK, Bilgic, HH, Cansiz, OF, Ozturk, M, & Atis, CD (2021) Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques, Construction and Building Materials, 271, 121584. doi: https://doi.org/10.1016/j.conbuildmat.2020.121584
37. Sharma, N., Thakur, M. S., Kumar, R., Malik, M. A., Alahmadi, A. A., Alwetaishi, M., & Alzaed, A. N. (2022) Assessing waste marble powder impact on concrete flexural strength using Gaussian process, SVM, and ANFIS, Processes, 10(12), 2745. doi: https://doi.org/10.3390/pr10122745
38. Singh, N. B., & Middendorf, B. (2020) Geopolymers as an alternative to Portland cement: An overview, Construction and Building Materials, 237, 117455. doi: https://doi.org/10.1016/j.conbuildmat.2019.117455
39. Song, H. W., Pack, S. W., Nam, S. H., Jang, J. C., & Saraswathy, V. (2010) Estimation of the permeability of silica fume cement concrete, Construction and Building Materials, 24(3), 315-321. doi: https://doi.org/10.1016/j.conbuildmat.2009.08.033
40. Topçu, İ. B., Gülbandılar, E., & Koca, A. B. (2018) Reaktif Pudra Betonlarının Basınç Dayanımının ANFIS ile Tahmini, Politeknik Dergisi, 21(1), 165-171. doi: https://doi.org/10.2339/politeknik.385923
41. TS EN 196-1 (2016) Çimento deney metotları - Bölüm 1: Dayanım tayini, Türk Standartları, Ankara, Türkiye.
42. TS EN 197-1 (2012) Çimento- Bölüm 1: Genel Çimentolar Bileşim, Özellikler ve Uygunluk Kriterleri, Türk Standartları, Ankara, Türkiye.
43. Vakhshouri, B., & Nejadi, S. (2018) Prediction of compressive strength of self-compacting concrete by ANFIS models, Neurocomputing, 280, 13-22. doi: https://doi.org/10.1016/j.neucom.2017.09.099
44. Verma, N. K., Meesala, C. R., & Kumar, S. (2023) Developing an ANN prediction model for compressive strength of fly ash-based geopolymer concrete with experimental investigation, Neural Computing and Applications, 35(14), 10329-10345. doi: https://doi.org/10.1007/s00521-023-08237-1
45. Walia, N., Singh, H., & Sharma, A. (2015) ANFIS: Adaptive neuro-fuzzy inference system-a survey, International Journal of Computer Applications, 123(13), 32-38. doi: https://doi.org/10.5120/ijca2015905635
46. Waris, M. I., Plevris, V., Mir, J., Chairman, N., & Ahmad, A. (2022) An alternative approach for measuring the mechanical properties of hybrid concrete through image processing and machine learning, Construction and Building Materials, 328, 126899. doi: https://doi.org/10.1016/j.conbuildmat.2022.126899
47. Wang, X., Pan, Z., Zhu, C., & Zhu, H. (2014) Reaction degree of silica fume and its effect on compressive strength of cement-silica fume blends, Journal of Wuhan University of Technology-Mater. Sci. Ed., 29, 721-725. doi: https://doi.org/10.1007/s11595-014-0986-4