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<p>The construction industry’s growing concern for sustainable practices has led to the exploration of alternative materials to replace traditional components in concrete production. One such promising material is glass powder, a byproduct of the glass industry. This study proposes a finite element method (FEM) and deep learning (DL)–based model to assess the structural and mechanical properties of concrete when incorporating waste glass powder (WGP) as a partial replacement for traditional cementitious materials. The research begins by characterizing the physical properties of WGP to understand its potential as a cement substitute. Subsequently, a comprehensive FEM model is developed to simulate the behavior of WGP as a partial replacement of cement in concrete under various loading conditions. The FEM and DL model considers factors such as material heterogeneity, interfacial bonding, and the influence of WGP on the microstructure of the composite material. Through systematic analyses, the study aims to investigate the impact of different proportions of WGP on the compressive strength of concrete. Additionally, the FEM simulations will provide insights into the deformation patterns, stress distribution, and failure mechanisms within this sustainable concrete. Furthermore, the DL model will provide the best prediction input parameters for the prediction of the compression strength of concrete with WGP. The proposed model offers a platform for optimizing the mix design by considering the mechanical performance and sustainability aspects of waste glass powder-replaced concrete. Notably, FEA results show that the best compressive strength is achieved when 10% of cement is replaced with WGP; these findings are corroborated by experimental lab results. Reliability is demonstrated by the convolutional neural network (CNN) used in the DL model, which predicts the compressive strength of WGP concrete with an accuracy of more than 92% both in the training and testing stages. Research findings support using WGP in construction for eco-friendly concrete, offering guidelines to reduce environmental impact and promote sustainability in the industry.</p>

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Integrated modeling approach for assessing sustainable concrete incorporating waste glass powder: a finite element and deep learning perspective

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Megha Gupta,
  • Ishwor Thapa,
  • Sufyan Ghani,
  • Satya Prakash

摘要

The construction industry’s growing concern for sustainable practices has led to the exploration of alternative materials to replace traditional components in concrete production. One such promising material is glass powder, a byproduct of the glass industry. This study proposes a finite element method (FEM) and deep learning (DL)–based model to assess the structural and mechanical properties of concrete when incorporating waste glass powder (WGP) as a partial replacement for traditional cementitious materials. The research begins by characterizing the physical properties of WGP to understand its potential as a cement substitute. Subsequently, a comprehensive FEM model is developed to simulate the behavior of WGP as a partial replacement of cement in concrete under various loading conditions. The FEM and DL model considers factors such as material heterogeneity, interfacial bonding, and the influence of WGP on the microstructure of the composite material. Through systematic analyses, the study aims to investigate the impact of different proportions of WGP on the compressive strength of concrete. Additionally, the FEM simulations will provide insights into the deformation patterns, stress distribution, and failure mechanisms within this sustainable concrete. Furthermore, the DL model will provide the best prediction input parameters for the prediction of the compression strength of concrete with WGP. The proposed model offers a platform for optimizing the mix design by considering the mechanical performance and sustainability aspects of waste glass powder-replaced concrete. Notably, FEA results show that the best compressive strength is achieved when 10% of cement is replaced with WGP; these findings are corroborated by experimental lab results. Reliability is demonstrated by the convolutional neural network (CNN) used in the DL model, which predicts the compressive strength of WGP concrete with an accuracy of more than 92% both in the training and testing stages. Research findings support using WGP in construction for eco-friendly concrete, offering guidelines to reduce environmental impact and promote sustainability in the industry.