Open AccessResearch Trends on Machine Learning in Construction Management: A Scientometric Analysis
Author(s) -
Tam Nguyen Van,
Toan Nguyen Quoc
Publication year - 2021
Publication title -
journal of applied science and technology trends
Language(s) - English
Resource type - Journals
ISSN - 2708-0757
DOI - 10.38094/jastt203105
Subject(s) - scopus , schedule , computer science , quality (philosophy) , domain (mathematical analysis) , construction management , productivity , knowledge management , data science , project management , artificial intelligence , engineering , engineering management , systems engineering , mathematical analysis , philosophy , civil engineering , mathematics , medline , epistemology , political science , law , economics , macroeconomics , operating system
Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.