Revolutionizing Material Selection in Construction 4.0: A Comparative Analysis of Artificial Intelligence Technologies

The construction industry is rapidly evolving with the advent of Industry 4.0, and one significant area of transformation is material selection. Choosing the right construction materials is a crucial factor in achieving high-performance and sustainable buildings. However, with the sheer number of available materials, it can be a challenging task to select the most suitable one for a specific application. This study explores how artificial intelligence (AI) technologies can revolutionize material selection in Construction 4.0. We conduct a comparative analysis of AI-based approaches such as supervised and unsupervised learning, decision trees, neural networks, and K-means clustering algorithms, as well as principal component analysis. We examine their benefits, drawbacks, and suitability for different material selection scenarios. Furthermore, we provide a mathematical justification for their use in material selection. Neural networks can learn nonlinear models to identify patterns in a database and suggest optimal construction materials based on identified attributes and boundary conditions. Clustering algorithms can help in creating innovative designs by grouping constituents with similar functionality or working conditions. Our study highlights the potential of AI-based approaches to improve material selection in the construction industry by reducing time, costs, and errors associated with manual selection methods. With the use of AI technologies, developers can make informed decisions about selecting the best resources for their construction projects, ultimately leading to the creation of high-performance, sustainable buildings.