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Updated: Sep 10, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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An AI-based approach to thermal bridge analysis.

Marta Pomada1, Krzysztof Cpałka1, Piotr Lacki1

  • 1Czestochowa University of Technology, 69 Dabrowskiego St., Czestochowa, 42-201, Poland.

Scientific Reports
|August 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial intelligence (AI) approach using a fuzzy system (FS) to analyze thermal bridges in buildings. The AI model accurately estimates heat loss coefficients, reducing the need for complex traditional analyses.

Keywords:
Artificial intelligence (AI)Construction materialsFuzzy system (FS)Heat transfer coefficientLinear heat transmittance coefficientWindow installation

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Area of Science:

  • Building Science
  • Artificial Intelligence
  • Energy Efficiency

Background:

  • Buildings are major contributors to global energy consumption and CO2 emissions.
  • Addressing building energy efficiency is crucial for climate change mitigation.
  • Thermal bridges in building envelopes, like window-to-wall connections, represent significant heat loss pathways.

Purpose of the Study:

  • To propose and evaluate an artificial intelligence (AI) based method for analyzing thermal bridges in window-to-wall connections.
  • To utilize a fuzzy system (FS) as a universal approximator for estimating linear heat transmittance coefficients (Ψ).
  • To demonstrate the AI approach's ability to generalize and predict Ψ values for novel cases.

Main Methods:

  • Development of a fuzzy system (FS) trained on data from conventional thermal bridge analysis using the TRISCO program.
  • Application of the trained FS to estimate linear heat transmittance coefficients (Ψ).
  • Validation of the FS model's performance and generalization capabilities on unseen data.

Main Results:

  • The AI-driven fuzzy system achieved excellent performance in estimating linear heat transmittance coefficients.
  • The trained FS accurately predicted Ψ values for window-to-wall connections not included in the initial training dataset.
  • The AI approach demonstrated superior interpretability compared to other AI models.

Conclusions:

  • The proposed AI approach offers an efficient and accurate method for thermal bridge analysis in buildings.
  • This AI-driven technique can significantly reduce the reliance on time-consuming traditional calculations and experiments.
  • Implementing this AI approach can streamline the process for designers to select optimal building solutions for energy efficiency.