Abstract
In the domain of construction engineering, the compressive strength of concrete stands as a critical parameter of immense significance. Nevertheless, the traditional concrete production entails significant physical labor and the consumption of finite natural resources. Furthermore, achieving the requisite compressive strength necessitates a prolonged curing period of up to 365 days. To mitigate these challenges, curtail cement consumption, and address production issues, diverse industrial and agricultural waste materials have been integrated into concrete formulations.
To surmount these limitations, the application of machine learning (ML) has garnered substantial attention in contemporary settings for predicting essential output parameters. This research centers on predicting the compressive strength of red mud (RM) based concrete through the deployment of five distinct machine learning (ML) models, encompassing 14 input parameters, utilizing a dataset of 400 data points. Comparative analysis of the model outputs has been conducted employing a plethora of analytical techniques, such as visual descriptive statistics, error evaluation, scatter plots, R2 coefficient, feature importance (FI), and Taylor's diagram. From a comprehensive evaluation of the study's findings, it was deduced that the decision tree (DT) and extra tree (ET) models exhibited the most favorable fit. This deduction was based on their minimal error rates and higher R2 values when compared to other utilized models.
Microstructural analysis and leaching tests were carried out, attesting to the adherence of red mud-incorporated concrete to safety and toxicity standards. In conclusion, the amalgamation of red mud into concrete formulations holds substantial potential for the development of eco-friendly construction materials and sustainable waste management, especially suited for low traffic or rural road construction applications.
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SS: software. SB: methodology, investigation. VS: writing—original draft. AS: writing—review and editing. AK: writing—review and editing. SNS: writing—review and editing.
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Singh, S., Bano, S., Singh, V. et al. An investigative inquiry into harnessing the capabilities of machine learning for the assessment of compressive strength in red mud-based concrete enriched with fly ash as a viable road construction constituent. Asian J Civ Eng 25, 1571–1585 (2024). https://doi.org/10.1007/s42107-023-00862-4
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DOI: https://doi.org/10.1007/s42107-023-00862-4