Machine Learning Applied to Building Information Models
3. MACHINE LEARNING IN AEC INDUSTRY In the context of C4.0, as it has been reviewed, significant improvements have been recorded in the data management process. Technological development, particularly increasing BIM usage, assists the facilitation of data storage and collection processes and improves interoperability. This study suggests that Machine Learning (ML) techniques can improve and accelerate data management in the AEC industry. Machine Learning can be identified as a subfield of Artificial Intelligence (AI), a big field that generally can be described as an approach for adopting human cognitive thinking abilities. The concept of AI was first developed in the middle of the 20th century and started its development with various stages; the adoption of AI as a scientific method started to increase from the beginning of the recent 21st century. It was also triggered by the availability of an enormous amount of data, which made the use of AI possible in practical areas with various disciplines (Russell and Norvig, 2011). Figure 7 illustrates the relation between (and a hierarchy) Data Science, Artificial Intelligence, Machine Learning and Deep Learning as a general understanding resulting from the complete literature review performed for this chapter. Basic principles of those subfields of Data Science will be explored further in this chapter.
Figure 7 – The relation between Data Science, Artificial Intelligence, Machine Learning and Deep Learning 3.1.
Overview on Machine Learning
Machine Learning is a technique to teach computers to imitate human learning ability following various algorithms and learn from the available data. Additionally, ML can be defined "…as computational methods using experience to improve performance or to make accurate predictions" (Mehyar et al., 2018, p. 1). Erasmus Mundus Joint Master Degree Programme – ERASMUS+ European Master in Building Information Modelling BIM A+
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