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Author: search.filters.author.Khan, Ameer Murad

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    Integrating machine learning with experimental data to predict tensile strength of cement pastes with binary cementitious systems
    (2026-07-01)
    Khan, Ameer Murad
    ;
    Hussain, Qudeer
    ;
    Mohamad, Hisham
    ;
    Jirasakjamroonsri, Amornthep
    ;
    Anotaipaiboon, Weerachai
    This study demonstrates a concerted experimental and machine learning model to determine the direct tensile strength of cement pastes with the addition of supplementary cementitious materials (SCM) used as binary cementitious systems. Five types of SCM were used as single binary substitutes at levels of 5–25 % each to form a complete experimental database of 135 cement paste mixtures at water-cement ratios of 0.30–0.40, respectively, to provide a systematic and applied evaluation of the individual contribution of each material to tensile performance. Briquette tests were used to measure direct tensile strength at the binder level to isolate behaviour. Ten mix design parameters were used to train six machine learning regressors (Decision Tree, Random Forest, Gradient Boosting, XGBoost, CatBoost and Support Vector Regression) to predict tensile strength. Support Vector Regression and XGBoost had the best predictive accuracy with R<sup>2</sup> that reached 0.88 and lowest RMSE of 3.3. Analysis of feature importance and partial dependence indicated that the dominant predictors were the water-cement ratio, cement content, and dosage of fly ash and silica fume had a small optimal replacement range, which is in line with the experimental data. The existence of a correspondence between the trends of machine learning and the known hydration mechanisms proves the physical plausibility of the models. The most successful model was an interactive web based tool, TensAile-Lab, to be used in support of fast, data-driven mix design decisions. The suggested framework illustrates the use of interpretable machine learning to forecast tensile behavior with high reliability and speed up the creation of optimized, low-carbon cementitious systems.
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    Performance of Environmentally Friendly Concrete Containing Fly-Ash and Waste Face Mask Fibers
    (2024-12-01)
    Nawaz, Adnan
    ;
    Khan, Ameer Murad
    ;
    Jirasakjamroonsri, Amorntep
    ;
    Saingam, Panumas
    ;
    Ejaz, Ali
    This work was carried out to explore the potential use of used face masks in concrete to develop sustainable green concrete. In this experimental study, used face masks were cut up, removing the ear stripes and internal nose steel wire, to prepare elongated fibers. These fibers were incorporated in cement fly ash mixtures as an additive to determine the response of M20-grade concrete. The Class F fly ash (FA) was employed as a fractional substitute of cement up to 25% by weight, whereas the addition of face masks occurred at 0%, 0.125%, and 0.25% by volume of concrete. The testing scheme focused on the mechanical and durability characteristics of the cement FA mixtures carried out after 3, 28, and 60 days of curing. The inclusion of FA and face mask fibers reduced the density of concrete specimens. The compressive, splitting tensile, and flexural strengths of mixes were also reduced at an early age; however, the strength characteristics improved at later ages, compared to the control mix. The combination of both materials in concrete mixtures resulted in lower water absorption, lower bulk water sorption, and lower mass loss values against acid attack at later ages. Similarly, the electrical resistance of concrete substantially enhanced by increasing the percentage of both materials. The experimental results demonstrated that processed face masks can be utilized in cement fly ash mixes without significantly compromising the resultant concrete characteristics.