Experimental and Machine learning investigation of Potential strength of recycled aggregate concrete

Authors

  • Ali Akber Khan Department of Environmental management, NCBA&E Lahore Pakistan.
  • Iftekhar Ahmed Department of Environment Management NCBA&E Lahore,
  • Tahreem Aqsa Department of Environment Management NCBA&E Lahore, Pakistan.
  • Houda Javed the Department of Environment Management NCBA&E Lahore, Pakistan.

DOI:

https://doi.org/10.62019/abbdm.v4i1.120

Keywords:

Recycled Aggregate Concrete, Sustainable Construction, Experimental Analysis, Machine Learning

Abstract

Recycled Aggregate Concrete (RAC) offers a promising perspective for sustainable construction through the use of recycled materials to minimize environmental damage. This work uses a dual approach that integrates experimental analysis and machine learning to comprehensively explore the potential effectiveness of RAC. The experimental research process involves the formulation of concrete mixes using recycled aggregate followed by comprehensive testing to assess its compressive strength, flexural strength and durability. A machine learning model is developed to predict concrete strength using data-driven techniques to improve understanding. The study is to identify and evaluate the viability of using RAC as a long-term and environmentally friendly building material through the integration of traditional testing techniques and contemporary predictive analysis. (Citation: Chandra, S., Berntsson, L., 2002; Tam, V.W.Y. et al., 2007; Silva, R.V. et al., 2014).

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Published

2024-02-29

How to Cite

Khan, A. A., Ahmed , I., Aqsa , T., & Javed, H. (2024). Experimental and Machine learning investigation of Potential strength of recycled aggregate concrete. The Asian Bulletin of Big Data Management, 4(1). https://doi.org/10.62019/abbdm.v4i1.120

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