K-Nearest-Neighbours-Based Fuzzy Regression for Modelling Heterogeneous Strength Development in Cement-Based Materials
DOI:
https://doi.org/10.37134/jsml.vol14.3.9.2026Keywords:
Compressive strength , Integrated KNN fuzzy regression, Possibilistic fuzzy regression, Predictive performance, Triangular fuzzy numberAbstract
Compressive strength is a key indicator of cement performance. The 28-day compressive strength is a benchmark for compressive strength development. In most studies, compressive strength data are treated as precise values. However, compressive strength measurements inherently involve uncertainty due to experimental variability, material heterogeneity, and testing conditions. This uncertainty suggests that compressive strength may be appropriately represented as a fuzzy number and analysed using fuzzy regression. This study aims to investigate the use of integrated k-nearest-neighbour fuzzy regression (I-KNNFR) to predict the triangular fuzzy number of the 28-day compressive strength of cement mortar. For each prediction point, the method constructs a possibilistic fuzzy regression model using locally similar observations identified through the nearest-neighbour mechanism. The I-KNNFR was developed to address issues in possibilistic fuzzy regression related to global modelling, outliers, and over-constrained problems. Its predictive performance was evaluated using 10-fold range-preserved controlled cross-validation and compared with that of possibilistic fuzzy regression based on mean squared error (MSE) and mean relative distance (MRD). The best predictive performance was achieved when I-KNNFR used nine nearest neighbours and the Euclidean distance. Compared with possibilistic fuzzy regression, I-KNNFR reduced MSE and MRD by approximately 85% and 83%, respectively, indicating a substantial improvement in predictive performance. In conclusion, I-KNNFR more effectively captures local strength-development patterns through fuzzy modelling. It outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the compressive strength. By using locally selected neighbours, it also represents the uncertainty associated with 28-day compressive-strength prediction.
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Al Biajawi MI, Embong R, Muthusamy K, Ismail N, Obianyo I. (2022). Recycled coal bottom ash as sustainable materials for cement replacement in cementitious composites: A review. Construction and Building Materials, 338, 127624. doi:10.1016/j.conbuildmat.2022.127624
Asanre AA, Ifamuyiwa AS, Abiodun TO. (2024). Dimensions of students’ academic engagement as predictors of senior secondary school mathematics achievement. Journal of Science and Mathematics Letters, 12(1), 27-33. doi:10.37134/jsml.vol12.1.4.2024
Bector CR, Chandra S.(2005). Fuzzy Mathematical Programming and Fuzzy Matrix Games. Springer-Verlag, Germany. doi:10.1007/3-540-32371-6
Bernal SA, Juenger MCG, Ke X, Matthes W, Lothenbach B, De Belie N, Provis JL. (2017). Characterization of supplementary cementitious materials by thermal analysis. Materials and Structures, 50, article 26. doi:10.1617/s11527-016-0909-2
Chaira T. (2019). Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley. USA. p. 1-42. doi:10.1002/9781119544203
Dubois D, Prade H. (1980). Fuzzy Sets and Systems: Theory and Applications. Academic Press, USA. p. 9-11.
Fathabadi A, Seyedian SM, Malekian A. (2022). Comparison of Bayesian, k-nearest neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction. Science of the Total Environment, 818, article 151760. doi:10.1016/j.scitotenv.2021.151760.
Ferraris CF, Garboczi EJ. (2013). Identifying improved standardized tests for measuring cement particle size and surface area. Transportation Research Record: Journal of the Transportation Research Board, 2342(1), 10-16. doi:10.3141/2342-02
Gupta M, Nirola M, Sharma A, Dhungel P, Singh H, Gupta A. (2024). Geospatial analysis of contagious infection growth and cross-boundary transmission in non-vaccinated districts of North-East Indian states during the COVID-19 pandemic. The Lancet Regional Health - Southeast Asia, 28, 100451. doi:10.1016/j.lansea.2024.100451
Hameed MM, AlOmar MK. (2020). Prediction of compressive strength of high-performance concrete: hybrid artificial intelligence technique. In Khalaf M, Al-Jumeily D, Lisitsa A (eds.), Applied Computing to Support Industry: Innovation and Technology, p. 323-335. Sringer, Switzerland. doi:10.1007/978-3-030-38752-5_26
Jia X. (2025). Digital marketing and brand competitiveness: The mediating role of marketing capability and operational capability. Journal of Retailing and Consumer Services, 87, 104429. doi:10.1016/j.jretconser.2025.104429
Kalwa V, Ullegaddi MM, Kittali P, Thottempudi B. (2023). Ordinary least square regression technique for predicting wear rate of EN8 carbon steel. Materials Today: Proceedings, advance online publication. doi:10.1016/j.matpr.2023.03.388
Kim B, Bishu RR. (1998). Evaluation of fuzzy linear regression models by comparing membership functions. Fuzzy Sets and Systems, 100(1-3), 343-352. doi:10.1016/S0165-0114(97)00100-0
Kohli S, Godwin GT, Urolagin S. (2021). Sales Prediction Using Linear and KNN Regression. In Patnaik S, Yang X, Sethi IK. Advances in Machine Learning and Computational Intelligence, p. 321-329. Springer, Singapore. doi:10.1007/978-981-15-5243-4_29
Kumbure MM, Luukka P, Collan M. (2020). A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recognition Letters, 140, 172-178. doi:10.1016/j.patrec.2020.10.005.
Kumbure MM, Luukka P. (2022). A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Computing, 7, 657-671. doi:10.1007/s41066-021-00288-w
Lam J, Broccatelli C, Baxter J. (2023). Diversity of strong and weak ties and loneliness in older adults. Journal of Aging Studies, 64, 101097, doi:10.1016/j.jaging.2022.101097.
Lee GS, Erkinov FB, Aripova MK. (2025). Additional loss on ignition of cement raw mixes at high temperatures. Chemistry and Chemical Engineering, 2025 (3), 15-20. doi:10.70189/1992-9498.1670
Maryoto A, Setijadi R, Widyaningrum A, Waluyo S. (2020). Drying shrinkage of concrete containing calcium stearate, Ca(C₁₈H₃₅O₂)₂, with ordinary Portland cement (OPC) as a binder: Experimental and modelling studies. Molecules, 25(21), Article 4880. doi:10.3390/molecules25214880.
Mohan S, Ajmal M, Drewniok MP. (2025). Prediction of 28-day compressive strength of high-slag concrete by establishing accelerated oven curing regimes for rapid quality control. Journal of Materials Science: Materials in Engineering, 20, 111. doi:10.1186/s40712-025-00333-5
Mouawad F, Homsi F, Geara F, Mina R. (2025). Predicting compressive strength of sustainable concrete using machine learning and artificial neural networks. Construction Materials, 5(3), article 56. doi:10.3390/constrmater5030056
Nelson BR, Jamaludin AA, Shahimi S, Lian CJ, Meilana L, Kumaran JV, Raj Kumar HC, Moqbel RGA, Sharon K, Chowdhury AJK, Sheikh HI. (2026). Outlining probable fundamental doctrine and relations to disasters in the continental crust. Journal of Science and Mathematics Letters, 14(1), 15-28. doi:10.37134/jsml.vol14.1.2.2026
Onjure EO, Kabubo CK, Okumu V. (2023). Use of Plant-Based Accelerator to Enhance Rate of Gain of Strength of Kenyan Blended Cement. Open Journal of Civil Engineering, 13, 399-410. doi:10.4236/ojce.2023.133030
Rahmi I, Efendi R, Yanuar F, Yozza H, Wahyudi M, Burney SMA, Eğrioğlu E. (2025). Essentials of preprocessing data in improving logistic regression performance based on rough sets theory: A case study of stunting in West Sumatra, Indonesia. Journal of Science and Mathematics Letters, 13(1), 124-139. doi:10.37134/jsml.vol13.1.12.2025
Rezaei A, Areshenkoff CN, Gale DJ, De Brouwer AJ, Nashed JY, Flanagan JR, Gallivan JP. (2025). Transfer of motor learning is associated with patterns of activity in the default mode network. Plos Biology, 23(8), e3003268. doi:10.1371/journal.pbio.3003268
Safiuddin M, Raman SN, Salam MA, Jumaat MZ. (2016). Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash. Materials, 9(5), Article 396. doi:10.3390/ma9050396
Saha D, Paul B, Sarkar B. (2025). Revolutionizing sustainable construction through predictive modeling of green concrete. Developments in the Built Environment, 23, 100740. doi:10.1016/j.dibe.2025.100740
Sajedi F. (2012). Strength Development of Cement-Slag Mortars. Journal of Civil Engineering and Architecture, 6(9), 1169-1183. doi:10.17265/1934-7359/2012.09.009
Schack T, Strybny B, Haist M. (2024). Improving the early age strength of eco-efficient mortar with low clinker content considering binder granulometry and chemical additives. Materials, 17(18), 4509. doi:10.3390/ma17184509
Shaaban M, Amin M, Selim S, Riad IM. (2025). Machine learning approaches for forecasting compressive strength of high-strength concrete. Scientific Report, 15, 25567. doi:10.1038/s41598-025-10342-1
Siddique R, Mehta A. (2014). Effect of carbon nanotubes on properties of cement mortars. Construction and Building Materials, 50, 116-129. doi:10.1016/j.conbuildmat.2013.09.019
Škrabánek P, Marek J, Pozdílková A. (2021). Boscovich Fuzzy Regression Line. Mathematics, 9, 685. doi:10.3390/math9060685
Škrabánek P, Martinková N. (2021). Algorithm 1017: fuzzyreg: An R package for fitting fuzzy regression models. ACM Transactions on Mathematical Software, 47(3), Article 29, 1-8. doi:10.1145/3451389
Solano-Vinueza G, Albuja-Sánchez J. (2025). Development and field based validation of a predictive model for concrete compressive strength using fresh state properties in a large scale construction project. Case Studies in Construction Materials, 24, e05705. doi:10.1016/j.cscm.2025.e05705
Song Y, Liang J, Lu J, Zhao X. (2017). An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing, 251, 26-34. doi:10.1016/j.neucom.2017.04.018
Sunar F, Dervisoglu A, Yagmur N, Atabay H, Donertas A. (2024). Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit. Marine Pollution Bulletin, 208, Article 116942. doi:10.1016/j.marpolbul.2024.116942.
Tanaka H, Uejima S, Asai, K. (1982) Linear regression analysis with fuzzy model. IEEE Transaction on System, Man, and Cybernetics, 12(6), 903-907. doi:10.1109/TSMC.1982.4308925
Tsamatsoulis DC, Korologos CA, Tsiftsoglou DV. (2023). Optimizing the sulfate content of cement using neural networks and uncertainty analysis. ChemEngineering, 7, 58. doi:10.3390/chemengineering7040058
Tur VV, Derechennik SS. (2018). Assessment of the Concrete compressive strength in existing structures based on core test results. Solid State Phenomena, 272, 238-243. doi:10.4028/www.scientific.net/ssp.272.238
Türkşen Ö. (2021). A comparison analysis of fuzzy and Bayesian linear model parameter estimates for replicated response measures. Gazi University Journal of Science, 34(1), 251-270. doi:10.35378/gujs.660845
Urbański MK, Wójcicka K, Wójcicki P. (2023). Fuzzy approach with t-norm arithmetic to analyze measurement uncertainty-concept and algorithm. Przegląd Elektrotechniczny, 99(11), 216-220. doi:10.15199/48.2023.11.39
Wahyuni, Abdullah, Mulyati S, Hayati Y. (2025). Performance analysis of ordinary Portland cement and Portland pozzolan cement in improving sea sand and sea water concrete with zeolite powder. Journal of Ecological Engineering, 26(4), 264-274. doi:10.12911/22998993/200278
Yankelevsky DZ. (2024). The uniaxial compressive strength of concrete: revisited. Materials and Structures, 57, Article 144. doi:10.1617/s11527-024-02422-x
Zadeh LA. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/S0019-9958(65)90241-X
Zakaria AS, Shafi MA, Zim MAM, Razali SNAM. (2023). The use of fuzzy linear regression modeling to predict high-risk symptoms of lung cancer in Malaysia. International Journal of Advanced Computer Science and Applications, 14(5), 586-593 doi:10.14569/ijacsa.2023.0140562
Zhang W, Guo J, Ning C, Cheng R, Liu Z. (2024). Prediction of concrete compressive strength using a Deepforest-based model. Scientific Reports, 14, 18918. doi:10.1038/s41598-024-69616-9
Zhang Y, Li J, Li X. (2025). Research on compressive strength prediction and formula optimization based on random forest and response surface method. SSRN preprint, p. 17. doi:10.2139/ssrn.5262134
Zhao N, Zhang H, Xie P, Chen X, Wang X. (2025). Prediction of compressive strength of multiple types of fiber-reinforced concrete based on optimized machine learning models. Engineering Applications of Artificial Intelligence, 152, 110714. doi:10.1016/j.engappai.2025.110714
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Copyright (c) 2026 Hazmira Yozza, Riswan Efendi, Nor Azah Samat, Izzati Rahmi, Ridho Saputra, Asko Mononen, Petrônio Cândido de Lima e Silva
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