KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Arunachalam, Krishna PrakashSubject: search.filters.subject.Sustainable construction

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete
    (2025-12-01)
    Onyelowe, Kennedy C.
    ;
    Hanandeh, Shadi
    ;
    Kamchoom, Viroon
    ;
    Ebid, Ahmed M.
    ;
    Imran, Hamza
    This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R<sup>2</sup>, and KGE. Sensitivity analysis was conducted using Hoffman and Gardener’s method as well as the SHAP technique to determine the most influential parameter in the prediction process. Results indicate that the Firefly and Wolf algorithms exhibited the highest prediction accuracy across all four properties, with Wolf emerging as the overall best-performing model due to its superior generalization ability, lower error rates, and high correlation with experimental results. Among the input parameters, the water-to-binder ratio was identified as the most influential factor affecting the mechanical properties of waste glass aggregate concrete, as demonstrated by both sensitivity analysis methods. This highlights the critical role of optimal water content in achieving desirable strength and workability in sustainable concrete mixtures. The study’s novelty lies in the comparative assessment of multiple optimization algorithms applied to waste-based concrete, an approach that has not been extensively explored in previous research. Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. The practical implications of this research extend to sustainable machine learning-based concrete design, where AI-driven optimization can help reduce the reliance on conventional trial-and-error methods. By utilizing waste glass aggregates, the study supports circular economy initiatives in construction, reducing environmental impact while maintaining structural performance. The proposed models can be implemented in real-world scenarios to optimize mix designs for large-scale applications, leading to cost-effective and eco-friendly construction materials. This research advances the field of smart construction by demonstrating the effectiveness of machine learning in sustainable material engineering, paving the way for future AI-assisted innovations in the industry.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning
    (2025-12-01)
    Onyelowe, Kennedy C.
    ;
    Kamchoom, Viroon
    ;
    Hanandeh, Shadi
    ;
    Anandha Kumar, S.
    ;
    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 R<sup>2</sup> 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.