Scholarlink

<p>As the world moves towards rapid urbanization, there arises a huge need for lightweight, high-strength, and low-cost prefabricated wall panels. Traditional cement-based systems show drawbacks with extremely high porosity along with limited early-age performance and poor microstructural control, especially with the incorporation of supplementary cementitious materials. Most optimization methods deal with strength only, without simultaneous control of perforation, microstructure, and practical constraints such as workability and cost. There is little understanding of microstructure-property relationships in terms of ternary blends modified with silica nanoparticles. The proposed work presents a complete, data-driven, multi-scale modeling framework for designing and optimizing cement-fly ash-lime wall panels augmented with silica nanoparticles. The hybrid machine learning-finite element surrogate modeling (ML-FEM-SM) approach combines the finite element simulation of microstructural stress and porosity evolution with machine learning regression to allow efficient prediction of compressive strength and pore distribution (R² ≈ 0.94, porosity error &lt; 5). This is complemented by MD-MDFMBE where multimodal data fusion entails the integration of FTIR spectra, thermal curing images, and early mechanical data from transformer networks for non-destructive early prediction of strength and shrinkage with ± 1.5&#xa0;MPa accuracy. Microstructure GAN production (µGAN) synthetic SEM images are of high fidelity for virtual mix validation (SSIM &gt; 0.92). Constrained Multi-Objective Bayesian Optimization (MOBO-C) identified Pareto-optimal mixes under cost and flowability restrictions. Persistent Homology-Based Clustering (PHMC) is now classifying microstructural images into strength-correlated topological clusters (R² ≈ 0.89). The merged framework significantly improves the capabilities of mixing design for pre-casting quality control, deeper microstructure understanding, and performance-driven classification into advanced prefab materials.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated data-driven optimization and microstructural modeling of nano-silica enhanced cement–fly ash–lime wall panels for prefabricated construction

  • 期刊论文

  • 发表日期：

    2025年7月31日

查看全文
  • Shradhesh R. Marve,
  • Sumit R. Punam,
  • Shady Gomaa Abdulaziz,
  • Lowlesh N. Yadav,
  • Sanket G. Padishalwar,
  • Tejas R. Patil,
  • Nischal Puri,
  • Rohit Pawar,
  • Amit Pimpalkar,
  • Mayuri A. Chandak

摘要

As the world moves towards rapid urbanization, there arises a huge need for lightweight, high-strength, and low-cost prefabricated wall panels. Traditional cement-based systems show drawbacks with extremely high porosity along with limited early-age performance and poor microstructural control, especially with the incorporation of supplementary cementitious materials. Most optimization methods deal with strength only, without simultaneous control of perforation, microstructure, and practical constraints such as workability and cost. There is little understanding of microstructure-property relationships in terms of ternary blends modified with silica nanoparticles. The proposed work presents a complete, data-driven, multi-scale modeling framework for designing and optimizing cement-fly ash-lime wall panels augmented with silica nanoparticles. The hybrid machine learning-finite element surrogate modeling (ML-FEM-SM) approach combines the finite element simulation of microstructural stress and porosity evolution with machine learning regression to allow efficient prediction of compressive strength and pore distribution (R² ≈ 0.94, porosity error < 5). This is complemented by MD-MDFMBE where multimodal data fusion entails the integration of FTIR spectra, thermal curing images, and early mechanical data from transformer networks for non-destructive early prediction of strength and shrinkage with ± 1.5 MPa accuracy. Microstructure GAN production (µGAN) synthetic SEM images are of high fidelity for virtual mix validation (SSIM > 0.92). Constrained Multi-Objective Bayesian Optimization (MOBO-C) identified Pareto-optimal mixes under cost and flowability restrictions. Persistent Homology-Based Clustering (PHMC) is now classifying microstructural images into strength-correlated topological clusters (R² ≈ 0.89). The merged framework significantly improves the capabilities of mixing design for pre-casting quality control, deeper microstructure understanding, and performance-driven classification into advanced prefab materials.