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Modeling the compressive strength behavior of concrete reinforced with basalt fiber
(2025-12-01)
Onyelowe, Kennedy C.
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Ebid, Ahmed M.
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Hanandeh, Shadi
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Kamchoom, Viroon
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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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Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Hanandeh, Shadi
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Ebid, Ahmed M.
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Viñan Villagran, Janneth Alejandra
The self-compacting concrete (SCC) mixes were developed using lightweight expandable clay aggregate (LECA) as a partial substitute for coarse aggregate, ground granulated blast-furnace slag (GGBS) as a partial replacement for cement, and combusted bio-medical waste ash (BMWA) as a partial replacement for fine aggregate. The substitution levels for LECA, GGBS, and BMWA were set at 10%, 20%, and 30% of coarse aggregate, cement, and fine aggregate, respectively. M30-grade SCC mixes were designed with two different water-to-binder ratios—0.40 and 0.45—and their compressive strength (CS) was experimentally evaluated. The data entries from the above mix designs and experiments were collected in this research which deals with evaluating the impact of lightweight expandable clay aggregate, metallurgical slag, and combusted bio-medical waste ash on self-compacting concrete. An extensive literature search was used in this project and this produced a global representative database collected from literature. The collected 384 records were divided into training set (300 records = 80%) and validation set (84 records = 20%) in line with the requirements of a more reliable data partitioning. Six advanced machine learning methods such as the Artificial Neural Network (ANN), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGB), Random Forest (RF), and Adaptive Boosting (AdaBoost) were used to model the concrete behavior. All models were created using “Orange Data Mining” software version 3.36. A combination of error metrics, efficiency metrics and determination/correlation metrics were used to test the models performance and accuracy. Also, the Hoffman and Gardener’s method was used to evaluate the sensitivity analysis of the model variables. At the end of the model work, AdaBoost and KNN excel in predictive accuracy with 97.5%, reducing the margin of error and ensuring precise mix designs for SCC. SVR, XGB, and RF also exhibit strong accuracy (96.5–97%), supporting reliable material selection and proportions. AdaBoost and KNN demonstrate the lowest errors (MAE: 0.65 MPa, RMSE: 0.75 MPa), indicating precise performance, minimizing overdesign or underperformance risks, and optimizing material usage. The Hoffman/Gardener’s sensitivity analysis produced produced GGBS of 31% and Dens of 26% as the highest impact and this is followed by LECA of 21% and BMWA of 20%. This research enables the optimization of self-compacting concrete mix designs using machine learning, reducing experimental trials, enhancing material efficiency, lowering environmental impact, and promoting sustainable construction through the effective reuse of industrial by-products.
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Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning
(2025-12-01)
Onyelowe, Kennedy C.
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Kamchoom, Viroon
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Hanandeh, Shadi
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Anandha Kumar, S.
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Zabala Vizuete, Rolando Fabián
Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R2 of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.

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