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Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques
(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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Llamuca Llamuca, José Luis
Basalt fiber-reinforced concrete (BFRC) mixed with fly ash, combined with advanced machine learning techniques, offers a practical, cost-effective, and less time-consuming alternative to traditional experimental methods. Conventional approaches to evaluating mechanical properties, such as compressive and splitting tensile strengths, typically require sophisticated equipment, meticulous sample preparation, and extended testing periods. These methods demand substantial financial resources, specialized labor, and considerable time for data collection and analysis. The integration of machine learning provides a transformative solution by enabling accurate prediction of concrete properties with minimal experimental data. The methods of data collection from literature and analysis were used and 121 records were collected from experimentally tested basalt fiber reinforced concrete samples measuring the compressive and splitting tensile strengths of the concrete. Eleven (11) critical factors have been considered as constituents of the studied concrete to predict the Fc-Compressive strength (MPa) and Fsp-Splitting tensile strength (MPa), which are the output parameters. The collected records were divided into training set (96 records = 80%) and validation set (25 records = 20%) following the requirements for data partitioning for sustainable machine learning application. Seven (7) selected machine learning techniques are applied in the prediction. Further, performance evaluation indices were used to compare the models’ abilities and lastly, the Hoffman and Gardener’s technique was used to evaluate the sensitivity of the parameters on the concrete strengths. At the end of the exercise, results were collated. In predicting the compressive strength (Fc), AdaBoost similarly excels, matching XGBoosting’s validation performance with R2 of 0.98 and the same MAE values. This shows the effectiveness of boosting techniques for predictive modeling in concrete strength estimation. For splitting tensile strength (Fsp), AdaBoost also outperforms most models, achieving an R2 of 0.96 for training and validation phases. Its exceptionally low validation MAE of 0.124 MPa underscores its excellent generalization capabilities. Overall, XGBoosting and AdaBoost consistently demonstrate superior performance for both compressive and splitting tensile strength predictions, followed closely by KNN. These models benefit from advanced ensemble techniques that efficiently handle non-linear patterns and noise. SVR also performs admirably, whereas GEP and GMDHNN exhibit weaker predictive capabilities due to limitations in handling complex data dynamics. For the sensitivity analysis, the Hoffman and Gardener’s method of sensitivity analysis proves instrumental in identifying key drivers of strength in fiber-reinforced concrete, guiding informed decision-making for material optimization and sustainable construction practices.
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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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