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<p>The present paper deals with the optimization of geopolymer concrete (GPC) using a blend of 50% ground granulated blast furnace slag (GGBS) and 50% fly ash (FA), drawing on the sustainable construction materials with a low carbon footprint. Advanced deep learning techniques, namely artificial neural networks (ANN), were used to predict mechanical properties and optimize mix proportions. This study also adopted microstructural analysis using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) to determine the morphological characteristics and chemical composition of concrete. It was observed that the results showed dramatic improvements in compressive strength (CS) and split tensile strength (STS) for concrete with optimum curing methods. It provided accurate predictions, and integration with ANN showed enhanced understanding of the relationships among material composition, curing conditions, and mechanical performance, pointing toward substantial potential for developing sustainable, high-strength construction materials.</p>

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Comparative study on the mechanical properties of geopolymer concrete containing GGBS and fly ash using experimental analysis and artificial neural network

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • B. R. Kavya,
  • S. M. Naveen Kumar,
  • N. Rahul,
  • A. S. Shrikanth

摘要

The present paper deals with the optimization of geopolymer concrete (GPC) using a blend of 50% ground granulated blast furnace slag (GGBS) and 50% fly ash (FA), drawing on the sustainable construction materials with a low carbon footprint. Advanced deep learning techniques, namely artificial neural networks (ANN), were used to predict mechanical properties and optimize mix proportions. This study also adopted microstructural analysis using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) to determine the morphological characteristics and chemical composition of concrete. It was observed that the results showed dramatic improvements in compressive strength (CS) and split tensile strength (STS) for concrete with optimum curing methods. It provided accurate predictions, and integration with ANN showed enhanced understanding of the relationships among material composition, curing conditions, and mechanical performance, pointing toward substantial potential for developing sustainable, high-strength construction materials.