OPTIMIZATION THE HEAT AND MOISTURE TRANSFER IN BUILDING MATERIALS USING MACHINE LEARNING

Optimizing the heat and moisture transfer in building materials is crucial for enhancing energy efficiency, occupant comfort, and durability of buildings. Traditional approaches to modelling and simulating these transfer processes often rely on complex mathematical equations and experimental data. However, these methods can be time-consuming and computationally expensive. This abstract presents a novel approach that leverages machine learning techniques to optimize the heat and moisture transfer in building materials. The proposed approach utilizes machine learning algorithms, such as artificial neural networks, to learn the complex relationships between material properties, environmental conditions, and heat and moisture transfer in building materials. By training the machine learning models on available data, the models can accurately predict the heat and moisture transfer behaviour of materials under different conditions. These trained models are then integrated into optimization algorithms to find optimal material configurations that maximize energy efficiency and minimize moisture-related issues. The use of machine learning in optimizing heat and moisture transfer offers several advantages. It allows for efficient and rapid analysis of multiple material configurations, considering a wide range of design variables. Additionally, the machine learning models can capture non-linear and dynamic relationships, enabling accurate predictions even for complex material behaviour. This approach reduces the need for extensive experimental testing and costly simulations, leading to significant time and cost savings in the design process. Different building materials are analysed, and their heat and moisture transfer characteristics are optimized using the integrated machine learning and optimization framework. The results show improved energy efficiency, reduced moisture-related issues, and enhanced overall performance of the materials. The application of this approach has significant implications for the design and construction industry. Optimized heat and moisture transfer in building materials can lead to improved thermal comfort, reduced energy consumption, and increased durability of buildings. By leveraging machine learning, designers and engineers can quickly explore a wide range of design options and identify optimal material configurations to achieve desired performance goals. The novel approach for optimizing the heat and moisture transfer in building materials using machine learning. By integrating machine learning algorithms with optimization techniques, the proposed approach enables efficient analysis and optimization of material configurations. The results demonstrate the potential of machine learning in improving energy efficiency and moisture management in buildings, contributing to sustainable and resilient built environments