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Article summary:
1. Machine Learning (ML) can be used to predict material properties and optimize manufacturing processes in building materials.
2. This paper reviews the literature on how ML models are used to predict properties of buildings’ materials (thermal, mechanical, and optical) and optimize production lines for various materials.
3. ML-driven approaches for materials’ properties prediction in buildings and process optimization have grown rapidly, providing information that can be utilized to maximize efficiency while reducing CO2 emissions.
Article analysis:
The article is generally reliable and trustworthy as it provides a comprehensive review of the literature on how machine learning models are used to predict material properties and optimize production lines for various building materials. The article is well-researched, with references provided for each claim made throughout the text. The article also provides an up-to-date review of the literature on this topic, which adds to its credibility.
However, there are some potential biases in the article that should be noted. For example, the article does not explore any counterarguments or alternative views on using machine learning models for predicting material properties or optimizing production lines. Additionally, the article does not discuss any possible risks associated with using machine learning models in this context, such as potential errors or inaccuracies in predictions due to incomplete data sets or incorrect assumptions about material behavior. Furthermore, the article does not present both sides of the argument equally; instead it focuses mainly on the benefits of using machine learning models for predicting material properties and optimizing production lines without exploring any potential drawbacks or limitations of this approach.
In conclusion, while this article is generally reliable and trustworthy due to its comprehensive review of relevant literature on this topic, there are some potential biases that should be noted when evaluating its trustworthiness and reliability.