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<p>Recent need for enhanced mechanical properties in cementitious composites poses a demand for high performance along with functional infrastructure, particularly hybrid concrete reinforced with carbon nanotubes (CNTs) and nano-SiO<sub>2</sub> particles. Such demands require improvements to be made in mechanical properties under modern requirements. This work develops a comprehensive multiscale hybrid model that is data-driven by incorporating stochasticity to grasp complex behavior in hybrid concrete materials under a wide array of loading conditions. As such, the model fuses Stochastic Kriging-based Hybrid Multiscale Model (SK-HMM) that encompass random field models of CNT and nano-SiO<sub>2</sub> dispersion coupled with finite element analysis, thereby making it Viable for the probabilistic predictions of the effective Young’s modulus and tensile as well as compressive strength accurately. Bayesian neural networks (BNNs) with Monte Carlo Dropout are exploited for uncertainty quantification so that the outputs are probabilistic with uncertainty bounds quantified. When it comes to finding solutions to problems and making judgments, it is an essential and essential component. As a further point of interest, the constitutive models of stress-strain behavior adhere to scientific laws, such as the conservation of momentum and energy in physics-informed neural networks (PINNs). Because of this, it is possible to generate fairly accurate predictions regarding stress and strain even when the behavior is not linear. At the end of the day, the nonlinear response surface for crack growth rate and compressive strength can be modeled using gaussian process regression (GPR) with heteroscedastic noise. This allows for input diversity. It is far simpler to make an educated prediction regarding the tensile strength, fracture toughness, and wear life of a material than it would be otherwise. The purpose of this study is to develop a robust framework for the enhancement of hybrid concrete composites by making careful use of randomization, physical laws, and uncertainty quantification respectively. It is possible that these composites will result in structures that are more durable and better building materials that are more resistant to damages.</p>

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Stochastic interpretable machine learning-based multi-scale modeling of nanomaterial reinforced concrete with carbon nanotubes and nano-silica for enhanced structural performance

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Boskey V. Bahoria,
  • Nilesh Mendhe,
  • Shradhesh R. Marve,
  • Prashant Hiwase,
  • Lowlesh N. Yadav,
  • Nilesh Shelke,
  • Latika Pinjarkar,
  • Rakesh Patel,
  • P. Jagadesh,
  • Aseel Smerat,
  • Vikrant S. Vairagade

摘要

Recent need for enhanced mechanical properties in cementitious composites poses a demand for high performance along with functional infrastructure, particularly hybrid concrete reinforced with carbon nanotubes (CNTs) and nano-SiO2 particles. Such demands require improvements to be made in mechanical properties under modern requirements. This work develops a comprehensive multiscale hybrid model that is data-driven by incorporating stochasticity to grasp complex behavior in hybrid concrete materials under a wide array of loading conditions. As such, the model fuses Stochastic Kriging-based Hybrid Multiscale Model (SK-HMM) that encompass random field models of CNT and nano-SiO2 dispersion coupled with finite element analysis, thereby making it Viable for the probabilistic predictions of the effective Young’s modulus and tensile as well as compressive strength accurately. Bayesian neural networks (BNNs) with Monte Carlo Dropout are exploited for uncertainty quantification so that the outputs are probabilistic with uncertainty bounds quantified. When it comes to finding solutions to problems and making judgments, it is an essential and essential component. As a further point of interest, the constitutive models of stress-strain behavior adhere to scientific laws, such as the conservation of momentum and energy in physics-informed neural networks (PINNs). Because of this, it is possible to generate fairly accurate predictions regarding stress and strain even when the behavior is not linear. At the end of the day, the nonlinear response surface for crack growth rate and compressive strength can be modeled using gaussian process regression (GPR) with heteroscedastic noise. This allows for input diversity. It is far simpler to make an educated prediction regarding the tensile strength, fracture toughness, and wear life of a material than it would be otherwise. The purpose of this study is to develop a robust framework for the enhancement of hybrid concrete composites by making careful use of randomization, physical laws, and uncertainty quantification respectively. It is possible that these composites will result in structures that are more durable and better building materials that are more resistant to damages.