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Modeling the compressive strength behavior of concrete reinforced with basalt fiber
(2025-12-01)
Onyelowe, Kennedy C.
;
Ebid, Ahmed M.
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Hanandeh, Shadi
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Kamchoom, Viroon
;
Awoyera, Paul
This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. Basalt Fiber Reinforced Concrete (BFRC) is a composite material that incorporates basalt fibers into traditional concrete to enhance its mechanical and durability properties. The use of basalt fibers, derived from natural volcanic rocks, aligns with sustainability goals due to their eco-friendliness, cost-effectiveness, and high performance. BFRC combines structural excellence with sustainability, making it an ideal material for modern construction practices. Its ability to enhance performance, reduce environmental impact, and ensure long-term durability positions it as a pivotal solution for sustainable infrastructure development. The developed models were used to predict compressive strength of basalt fiber concrete (Cs_bf) using the concrete mixture contents, age, and fiber dimensions. All the developed models were created using “Orange Data Mining” software version 3.36. A total of three hundred and nine (309) records were collected from literature for compressive strength for different mixing ratios of basalt fiber concrete with concrete at different ages. Each record contains the following data: C-Cement content (Kg/m3), FA-Fly ash content (Kg/m3), W-Water content (Kg/m3), SP-Super-plasticizer content (Kg/m3), CAg-Coarse aggregates content (Kg/m3), FAg-Fine aggregates content (Kg/m3), Age-The concrete age at testing (days), L_b-length of basalt fibers (mm), d_bf-Diameter of basalt fibers (µm), V_bf-Volume content of basalt fibers (%) and Cs_bf-Compressive strength of basalt fibre concrete (MPa). The collected records were divided into training set (249 records≈80%) and validation set (60 records≈ 20%). At the end of the process, it can be shown that the present research work outclassed other ML techniques applied in the previous research paper, which reported the utilization of the same size of data entries and basalt reinforced concrete constituents. Taylor chart for measured compressive strength of basalt fiber reinforced concrete predicted with ANN, KNN, SVM, Tree and RF is presented for comparing the performance of predictive models by illustrating three key statistical measures simultaneously: the correlation coefficient (R), the normalized standard deviation (σ), and the root-mean-square error (RMSE). Finally, it can be deduced that after considering the performance indices of the selected ensemble and classification models utilized in this present research paper, all the developed modes have almost the same excellent level of accuracy 95%, but ANN, KNN, and SVR produced R2 of 0.98 each with KNN producing MAE of 1.4 MPa, and MSE of 2.5 MPa to outperform ANN and SVR which produced MAE of 1.55 MPa/MSE of 4.1 MPa and MAE of 1.6 MPa/MSE of 3.85 MPa, respectively. Three techniques were used to estimate the impact of each input on the compressive strength, namely correlation matrix, sensitivity analysis and relative importance chart.
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Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Llamuca Llamuca, José Luis
The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables researchers to derive valuable predictions and optimize critical performance parameters efficiently. In this research work, 235 records were collected from extensive literature search for compressive strength for different mixing ratios of pre-cured metakaolin-based geopolymer concrete with concrete at different ages. Each record contains MK: The content of metakaolin (kg/m3), SHS: Sodium hydroxide solution content (kg/m3), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m3), W: Extra water content (not including the water in alkaline solutions) (kg/m3), W/S: Water to Solid ratio (Total water content / Solid part of activator solutions + MK), Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf>: Sodium oxide to aluminium oxide ratio, SiO<inf>2</inf>/Al<inf>2</inf>O<inf>3</inf>: Silicon oxide to aluminium oxide ratio, H<inf>2</inf>O/Na<inf>2</inf>O: Water to Sodium oxide ratio, CA/FA: Coarse to Fine aggregate ratio, CAg: The content of coarse aggregates (kg/m3), SP: The content of super-plasticizer (kg/m3), PCC: 0 for no pre-curing, 1 for pre-curing at 60 °C, and 2 for pre-curing at 80 °C, CT: Curing temperature (°C), Age: The concrete age at testing (days) and CS: Compressive strength (MPa). The collected records were portioned into training set (180 records≈75%) and validation set (55 records≈ 25%) and modeled with ensemble and symbolic regression methods. At the end of the model work, performance metrics were used to evaluate the models’ ability and Hoffman and Gardener’s sensitivity analysis was used to evaluate the impact of the variables on the compressive strength of the pre-cured geopolymer concrete mixed with metakaolin. GB and KNN models became the decisive models with excellent performance which outclassed others and the sensitivity analysis indicated that SHSM, SSS, W/S, and Na<inf>2</inf>O/Al<inf>2</inf>O<inf>3</inf> are the most influential to the predicted compressive strength.

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