Experimental and Statistical Evaluation of Mechanical Properties of Green Cement Concrete - Taguchi Integrated Supervised Learning Approach

Balakrishna Maddodi, P. Radhika Bhandary, Vivek Sharma, Jitendra Singh Yadav, Smaranika Mohapatra, Asha Udaya Rao, Prasanna M. Kumar, Prakash Rao Gurpur, Shivani Chougule, Dhanaraj Bharathi Narasimha

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Globally, rapid infrastructure development and environmental challenges associated with the higher carbon footprints of ordinary Portland cement (OPC) based concrete have increased the usage of green cement-based concrete (GCC) to reduce energy consumption and provide a sustainable option. Even though GCC is a superior alternative to OPC, only a few publications have addressed optimizing process parameters in GCC manufacturing to optimize mechanical properties. The Taguchi method is well-known as one of the most effective methods for optimizing predictors to get the desired level of response. Additionally, in modern era, data-driven supervised machine learning approaches have been used extensively to develop mathematical models to establish relationships between the variables. As a result, the Taguchi method was used in this study to obtain the best mix design targeting a compressive strength of greater than 40 MPa. Numerous design combinations have been tested, and a process for selecting the most effective combination has been established. The analysis aided in comprehending the individual contributions of the major components to the mechanism of strength gain. The observations confirmed the Taguchi method's ability to predict the design mix proportions of the GCC and the ability of machine learning to relate the variables mathematically.

Original languageEnglish
Pages (from-to)148-158
Number of pages11
JournalEngineered Science
Volume18
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Physical and Theoretical Chemistry
  • Chemistry (miscellaneous)
  • General Materials Science
  • Energy Engineering and Power Technology
  • Artificial Intelligence
  • Applied Mathematics

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