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Author: search.filters.author.Allauca Palta, José Luis

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    Developing advanced datadriven framework to predict the bearing capacity of piles on rock
    (2025-12-01)
    Onyelowe, Kennedy C.
    ;
    Hanandeh, Shadi
    ;
    Kamchoom, Viroon
    ;
    Ebid, Ahmed M.
    ;
    Reyes Silva, Fabián Danilo
    Developing accurate predictive models for pile bearing capacity on rock is crucial for optimizing foundation design and ensuring structural stability. This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. A comprehensive dataset comprising key influencing factors such as pile dimensions, geological characteristics, and penetration resistance was utilized to train and validate various models, including Kstar, M5Rules, ElasticNet, XNV, and Decision Trees. The Taylor diagram and statistical evaluations demonstrated the superiority of the proposed models in capturing complex nonlinear relationships, with high correlation coefficients and low root mean square errors indicating robust predictive capabilities. Sensitivity analyses using Hoffman and Gardener’s approach and SHAP values identified the most influential parameters, revealing that penetration resistance, pile embedment depth, and geological conditions significantly impact pile capacity. The findings underscore the effectiveness of machine learning in geotechnical engineering applications, offering a reliable and efficient alternative to traditional empirical and analytical methods. The developed framework provides engineers and practitioners with a powerful tool for improving pile design accuracy, reducing uncertainties, and optimizing construction practices. Future research should focus on expanding the dataset with diverse geological conditions and exploring hybrid modeling techniques to enhance prediction accuracy further.
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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.
    ;
    Kamchoom, Viroon
    ;
    Ebid, Ahmed M.
    ;
    Hanandeh, Shadi
    ;
    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/m<sup>3</sup>), SHS: Sodium hydroxide solution content (kg/m<sup>3</sup>), SHSM: Sodium hydroxide solution molarity (Mole), SSS: Sodium silicate solution content (kg/m<sup>3</sup>), W: Extra water content (not including the water in alkaline solutions) (kg/m<sup>3</sup>), 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/m<sup>3</sup>), SP: The content of super-plasticizer (kg/m<sup>3</sup>), 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.