DEVELOPMENT OF PREDICTION MODELS FOR COMPRESSIVE STRENGTH IN CEMENT MORTAR WITH BENTONITE USING MACHINE LEARNING TECHNIQUES

Yıl 2024, Cilt: 8 Sayı: 2, 214 - 224, 30.08.2024

Yusuf Tahir Altuncı , Kemal Saplıoğlu

https://doi.org/10.46519/ij3dptdi.1469238

Öz

In this study, the effects of bentonite-substituted cement mortar, cement compressive strength, cement quantity, spread values, water absorption percentages by weight, and porosity values on the 28-day compressive strength were investigated using Multiple Regression, Adaptive Neuro-Fuzzy Inference System and the intuitive optimization method known as Particle Swarm Optimization. Based on the results obtained from 18 data points, with 4 of them used for testing and 14 for training, effective and ineffective input parameters were identified in comparison to Multiple Regression. Subsequently, Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System main models were designed according to the obtained results. As a result of the study, it was determined that cement compressive strength, cement quantity and water absorption parameters have a higher impact on compressive strength compared to other parameters. It was found that the best accuracy model was achieved with the Particle Swarm Optimization model, and the results of the Multiple Regression model can also be used in predicting outcomes.

Anahtar Kelimeler

ANFIS, Bentonite, Blended cement mortar, Compressive strength, PSO

Kaynakça

• 1. Yang, H., Long, D., Zhenyu, L., et al. 'Effects of bentonite on pore structure and permeability of cement mortar', Constr Build Mater., Vol. 224, Pages 276-283, 2019.
• 2. Muhammad, N. and Siddiqua, S., 'Calcium bentonite vs sodium bentonite: The potential of calcium bentonite for soil foundation', Mater Today Proc., Vol. 48, Pages 822-827, 2022.
• 3. Dimirkou, A., Ioannou, A. and Doula, M., 'Preparation, characterization and sorption properties for phosphates of hematite, bentonite and bentonite–hematite systems', Adv Colloid Interface Sci., Vol. 97, Issue 1-3, Pages 37-61, 2002.
• 4. Wei, J., Gencturk, B., Jain, A. ans Hanifehzadeh, M., 'Mitigating alkali-silica reaction induced concrete degradation through cement substitution by metakaolin and bentonite', Appl Clay Sci., Vol. 182, Pages 105257, 2019.
• 5. Memon, S. A., Arsalan, R., Khan, S. and Lo, T. Y., 'Utilization of Pakistani bentonite as partial replacement of cement in concrete', Constr Build Mater., Vol. 30, Pages 237-242, 2012.
• 6. Tam, V. W. Y., Butera, A., Le, K. N., Silva, L. C. F. D. and Evangelista, A. C. J., 'A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks', Constr Build Mater., Vol. 324, Pages 126689, 2022.
• 7. Nazari, A. and Sanjayan, J. G., 'Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine', Ceram Int., Vol. 41, Issue 9, Pages 12164-12177, 2015.
• 8. Akkurt, S., Ozdemir, S., Tayfur, G. and Akyol, B., 'The use of GA–ANNs in the modelling of compressive strength of cement mortar', Cem Concr Res., Vol. 33, Issue 7, Pages 973-979, 2003.
• 9. Zhou, X. Q. and Hao, H., 'Modelling of compressive behaviour of concrete-like materials at high strain rate', Int J Solids Struct., Vol. 45, Issue 17, Pages 4648-4661, 2008.
• 10. Zhang, J., Ma, G., Huang, Y,, Sun, J., Aslani, F. and Nener, B., 'Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression', Constr Build Mater., Vol. 210, Pages 713-719, 2019.
• 11. Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H. and Bolandi, H., 'A new predictive model for compressive strength of HPC using gene expression programming', Adv Eng Softw., Vol. 45, Issue 1, Pages 105-114, 2012.
• 12. Shishegaran, A., Khalili, M. R., Karami, B., Rabczuk, T. and Shishegaran, A., 'Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load', Int J Impact Eng., Vol. 139, Pages 103527, 2020.
• 13. Alexander, M. and Beushausen, H., 'Durability, service life prediction, and modelling for reinforced concrete structures – review and critique', Cem Concr Res., Vol. 122, Pages 17-29, 2019.
• 14. Bai, J., Wild, S., Ware, J. A. and Sabir, B. B., 'Using neural networks to predict workability of concrete incorporating metakaolin and fly ash', Adv Eng Softw, Vol. 34, Issue 11-12, Pages 663-669, 2003.
• 15. Liang, M., Chang, Z., Wan, Z., Gan, Y., Schlangen, E. and Šavija, B., 'Interpretable Ensemble-Machine-Learning models for predicting creep behavior of concrete', Cem Concr Compos. Vol. 125, Pages 104295, 2022.
• 16. Ray, S., Haque, M., Ahmed, T. and Nahin, T. T., 'Comparison of artificial neural network (ANN) and response surface methodology (RSM) in predicting the compressive and splitting tensile strength of concrete prepared with glass waste and tin (Sn) can fiber', J King Saud Univ - Eng Sci., Vol. 35, Issue 3, Pages 185-199, 2023.
• 17. Moradi, M. J., Daneshvar, K., Ghazi-nader, D. and Hajiloo, H., 'The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network', Thin-Walled Struct., Vol. 161, Pages 107499, 2021.
• 18. Hammoudi, A., Moussaceb, K., Belebchouche, C. and Dahmoune, F., 'Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates', Constr Build Mater., Vol. 209, Pages 425-436, 2019.
• 19. Congro, M,, Monteiro, V. M., De, A., Brandão, A. L. T., Santos, B. F., Roehl, D. and Silva, F. A., 'Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks', Constr Build Mater., Vol. 303, Pages 124502, 2021.
• 20. Ramkumar, K. B., Rajkumar, P. R, Noor, A. S. and Jegan, M., 'A Review on Performance of Self-Compacting Concrete – Use of Mineral Admixtures and Steel Fibres with Artificial Neural Network Application', Constr Build Mater., Vol. 261, Pages 120215, 2020.
• 21. Felix, E. F, Carrazedo, R. and Possan, E., 'Carbonation model for fly ash concrete based on artificial neural network: Development and parametric analysis', Constr Build Mater., Vol. 266, Pages 121050, 2021.
• 22. Xu, J., Chen, Y., Xie, T., Zhao, X., Xiong, B. and Chen, Z., 'Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques' Constr Build Mater., Vol. 226, Pages 534-554, 2019.
• 23. Xi, X., Yin, Z., Yang, S. and Li, C.Q., 'Using artificial neural network to predict the fracture properties of the interfacial transition zone of concrete at the meso-scale', Eng Fract Mech., Vol. 242, Pages 104788, 2021.
• 24. Xu, J., Zhao, X., Yu, Y., Xie, T., Yang, G. and Xue, J.i 'Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks', Constr Build Mater., Vol. 211, Pages 479-491, 2019.
• 25. Zhao, Y., Hu, H., Song, C. and Wang, Z., 'Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network', Measurement. Vol. 194, Pages 110993, 2022.
• 26. Liu, Q., Iqbal, M. F., Yang, J., Lu, X., Zhang, P. and Rauf, M., 'Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation', Constr Build Mater., Vol. 268, Pages 121082, 2021.
• 27. Shahmansouri, A. A., Yazdani, M., Ghanbari, S., Akbarzadeh, H., Jafari, A. and Farrokh, H., 'Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite', J Clean Prod., Vol. 279, Pages 123697, 2021.
• 28. Mukherjee, A. and Biswas, S. N., 'Artificial neural networks in prediction of mechanical behavior of concrete at high temperature', Nucl Eng Des., Vol. 178, Issue 1, Pages 1-11, 1997.
• 29. Yeh, I. C., 'Modeling of strength of high-performance concrete using artificial neural networks', Cem Concr Res., Vol. 28, Issue 12, Pages 1797-1808, 1998.
• 30. Lee, S. C., 'Prediction of concrete strength using artificial neural networks', Eng Struct., Vol. 25, Issue 7, Pages 849-857, 2003.
• 31. Wen, L., Li, Y., Zhao, W., Cao, W. and Zhang, H., 'Predicting the deformation behaviour of concrete face rockfill dams by combining support vector machine and AdaBoost ensemble algorithm', Comput Geotech. Vol. 161, Pages 105611, 2023.
• 32. Jiang, W., Xie, Y., Li, W., Wu, J. and Long, G., 'Prediction of the splitting tensile strength of the bonding interface by combining the support vector machine with the particle swarm optimization algorithm', Eng Struct., Vol. 230, Pages 111696, 2021.
• 33. Fan, Z., Chiong, R., Hu, Z. and Lin, Y., 'A fuzzy weighted relative error support vector machine for reverse prediction of concrete components', Comput Struct., Vol. 2020, Pages 106171, 2020.
• 34. Luo, H. and Paal, S. G., 'Metaheuristic least squares support vector machine-based lateral strength modelling of reinforced concrete columns subjected to earthquake loads', Structures. Vol. 33, Pages 748-758, 2021.
• 35. Zhou, Y., Zhang, Y., Pang, R. and Xu, B., 'Seismic fragility analysis of high concrete faced rockfill dams based on plastic failure with support vector machine', Soil Dyn Earthq Eng., Vol. 144, Pages 106587, 2021.
• 36. Jueyendah, S., Lezgy-Nazargah, M., Eskandari-Naddaf, H. and Emamian, S. A., 'Predicting the mechanical properties of cement mortar using the support vector machine approach', Constr Build Mater., Vol. 291, Pages 123396, 2021.
• 37. Ling, H., Qian, C., Kang, W., Liang, C. and Chen, H., 'Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment', Constr Build Mater., Vol. 206, Pages 355-363, 2019.
• 38. Basaran, B., Kalkan, I., Bergil, E. and Erdal, E., 'Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms', Compos Struct., Vol. 268, Pages 113972, 2021.
• 39. Saleh, E., Tarawneh, A., Naser, M. Z., Abedi, M. and Almasabha, G., 'You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete', Constr Build Mater., Vol. 330, Pages 127270, 2022.
• 40. Ziyad, B. H., Ziyad, B.F., Kumar, P, et al., 'Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms' Case Stud Constr Mater., Vol. 18, Pages e01893, 2023.
• 41. Fu, W., Sun, B., Wan, H. P., Luo, Y. and Zhao, W., 'A Gaussian processes-based approach for damage detection of concrete structure using temperature-induced strain', Eng Struct., Vol. 268, Pages 114740, 2022.
• 42. Pereira, D. P., Bhagya, J. L. and Waldmann, D., 'Machine learning in mix design of Miscanthus lightweight concrete', Constr Build Mater., Vol. 302, Pages 124191, 2021.
• 43. Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P. and Pilakoutas, K., 'Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models', Cem Concr Res., Vol. 145, Pages 106449, 2021.
• 44. Kaloop, M. R., Kumar, D., Samui, P., Hu, J. W. and Kim, D., 'Compressive strength prediction of high-performance concrete using gradient tree boosting machine', Constr Build Mater., Vol. 264, Pages 120198, 2020.
• 45. Altunci, Y. T. and Özkan, Ş., 'Design Optimization and Statistical Modeling of Compressive Strtength of Cement Mortars Containing Recycled Waste Brick Dust Using Response Surface Methodology', Int J Sustain Eng Technol., Vol. 2, Issue 7, Pages 88-97, 2023.
• 46. Yuan, Z., Wang, L. N. and Ji, X., 'Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS', Adv Eng Softw., Vol. 67, Pages 156-163, 2014.
• 47. Sobhani, J., Najimi, M., Pourkhorshidi, A. R. and Parhizkar, T., 'Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models', Constr Build Mater., Vol. 24, Issue 5, Pages 709-718, 2010.
• 48. Sadrmomtazi, A., Sobhani, J. and Mirgozar, M. A., 'Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS', Constr Build Mater., Vol. 42, Pages 205-216, 2013.
• 49. Ahmadi-Nedushan, B., 'Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models', Constr Build Mater., Vol. 36, Pages 665-673, 2012.
• 50. Madandoust, R., Bungey, J. H. and Ghavidel, R., 'Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models', Comput Mater Sci., Vol. 51, Issue 1, Pages 261-272, 2012.
• 51. Kumar, A., Arora, H. C., Kumar, K. and Garg, H., 'Performance prognosis of FRCM-to-concrete bond strength using ANFIS-based fuzzy algorithm', Expert Syst Appl., Vol. 216, Pages 119497, 2023.
• 52. Pei, Z. and Wei, Y., 'Prediction of the bond strength of FRP-to-concrete under direct tension by ACO-based ANFIS approach', Compos Struct., Vol. 282, Pages 115070, 2022.
• 53. Li, J., Yan, G., Abbud, L. H., et al., 'Predicting the shear strength of concrete beam through ANFIS-GA–PSO hybrid modeling', Adv Eng Softw., Vol. 181, Pages 103475, 2023.
• 54. Vakhshouri, B. and Nejadi, S., 'Prediction of compressive strength of self-compacting concrete by ANFIS models', Neurocomputing. Vol. 280, Pages 13-22, 2018.
• 55. Emiroǧlu, M., Beycioǧlu, A. and Yildiz, S., 'ANFIS and statistical based approach to prediction the peak pressure load of concrete pipes including glass fiber', Expert Syst Appl., Vol. 39, Issue 3, Pages 2877-2883, 2012.
• 56. Akkurt, I., Başyigit, C., Kilincarslan, S. and Beycioglu, A., 'Prediction of photon attenuation coefficients of heavy concrete by fuzzy logic', J Franklin Inst., Vol. 347, Issue 9, Pages 1589-1597, 2010.
• 57. Moon, J., Kim, J. J., Lee, T. H. and Lee, H. E., 'Prediction of axial load capacity of stub circular concrete-filled steel tube using fuzzy logic', J Constr Steel Res., Vol. 101, Pages 184-191, 2014.
• 58. Güler, K., Demir, F. and Pakdamar, F., 'Stress–strain modelling of high strength concrete by fuzzy logic approach', Constr Build Mater., Vol. 37, Pages 680-684, 2012.
• 59. Tanyildizi, H., 'Fuzzy logic model for prediction of mechanical properties of lightweight concrete exposed to high temperature', Mater Des., Vol. 30, Issue 6, Pages 2205-2210, 2009.
• 60. Golafshani, E. M., Rahai, A., Sebt, M. H. and Akbarpour, H., 'Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic', Constr Build Mater., Vol. 36, Pages 411-418, 2012.
• 61. Özcan, F., Atiş, C. D., Karahan, O., Uncuoǧlu, E. and Tanyildizi, H., 'Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete', Adv Eng Softw., Vol. 40, Issue 9, Pages 856-863, 2009.
• 62. Saridemir, M., Topçu, I. B., Özcan, F. and Severcan, M. H., 'Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic', Constr Build Mater., Vol. 23, Issue 3, Pages 1279-1286, 2009.
• 63. Topçu, I. B. and Saridemir, M., 'Prediction of rubberized concrete properties using artificial neural network and fuzzy logic', Constr Build Mater. Vol. 22, Issue 4, Pages 532-540, 2008.
• 64. Topçu, I. B. and Saridemir, M., 'Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic', Comput Mater Sci., Vol. 41, Issue 3, Pages 305-311, 2008. 65. Cao, Y., Zandi, Y., Rahimi, A., et al., 'Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm', Structures. Vol. 34, Pages 3750-3756, 2021.
• 66. Qi, C,. Fourie, A. and Chen, Q., 'Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill', Constr Build Mater., Vol. 159, Pages 473-478, 2018.
• 67. Kaplanvural, İ., 'Volumetric water content estimation of concrete by particle swarm optimization of GPR data', Constr Build Mater., Vol. 375, Pages 130995, 2023.
• 68. Hanoon, A. N., Jaafar, M. S., Hejazi, F. and Aziz F. N. A. A., 'Strut-and-tie model for externally bonded CFRP-strengthened reinforced concrete deep beams based on particle swarm optimization algorithm: CFRP debonding and rupture', Constr Build Mater., Vol. 147, Pages 428-447, 2017.
• 69. Jiang, G., Keller, J., Bond, P. L. and Yuan, Z., 'Predicting concrete corrosion of sewers using artificial neural network', Water Res., Vol. 92, Pages 52-60, 2016.
• 70. Wen, Z., Zhou, R. and Su, H., 'MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam', Expert Syst Appl., Vol. 201, Pages 117272, 2022.
• 71. Yilmaz, I. and Yuksek, G., 'Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models', Int J Rock Mech Min Sci., Vol. 46, Issue 4, Pages 803-810, 2009. 72. Yilmaz, I. and Kaynar, O., 'Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils', Expert Syst Appl., Vol. 38, Issue 5, Pages 5958-5966, 2011.
• 73. Gaya, M. S. A., Abdu, A. M. T., Abubakar, I. S., Mubarak, A. E. P. and Wahab, N. A., 'Estimation of water quality index using artificial intelligence approaches and multi-linear regression', Int J Artif Intell., Vol. 9, Issue 1, Pages 126-134, 2020.
• 74. Uzundurukan, S. and Saplıoğlu, K., 'Konsol İstinat Duvarlarında Yükseklik Maliyet İlişkisinin Parçacık Sürü Algoritması İle İncelenmesi' Düzce Üniversitesi Bilim ve Teknol Derg., Vol. 8, Issue 4, Pages 2544-2554, 2020.
• 75. Acar, R. and Saplıoğlu, K., 'Etkili Girdi Parametrelerinin Çoklu Regresyon ile Belirlendiği Su Sertliğinin ANFIS Yöntemi ile Tahmin Edilmesi', Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilim Derg., Vol. 22, Issue 6, Pages 1413-1424, 2022.
• 76. Al-Adhaileh, M. H. and Alsaade, F. W., 'Modelling and Prediction of Water Quality by Using Artificial Intelligence', Sustainability. Vol. 13, Issue 8, Pages 4259, 2021.
• 77. Acar, R. and Saplıoğlu, K., 'Akarsulardaki Sediment Taşınımının Yapay Sinir Ağları Ve Anfıs Yöntemleri Kullanılarak Tespiti', Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Vol. 9, Issue 1, Pages 437-450, 2020.
• 78. Acar, R. and Saplioglu, K., 'Using the Particle Swarm Optimization (PSO) Algorithm for Baseflow Separation and Determining the Trends for the Yesilirmak River (North Turkey)', Russian Meteorology and Hydrology., Vol. 49, Issue 1, Pages 40-51, 2024.
• 79. Lee, H. and Kang, K., 'Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling', Hindawi Publishing Corporation Advances in Meteorology., Vol. 2015, Article ID 935868, 12 Pages, 2015.
• 80. Acar, R., 'A comparison of the performance of different innovative trend assessment approaches for air temperature and precipitation data: an application to Elazığ Province (Turkey)', Journal of Water and Climate Change, Volume 15, Issue 3, Pages 1417-1437, 2024
• 81. Wilcoxon F., 'Individual comparisons by ranking methods. In: Breakthroughs in Statistics: Methodology and Distribution New York' (Kotz, S. & Johnson, N. L., eds.). Springer, New York, Pages 196–202, 1992.