Abstract
Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.
Highlights
The construction industry has made a significant contribution to the development and maintenance of buildings and civil infrastructure
The results show that a total of 13 types of artificial neural network (ANN) were employed in the construction management (CM) res BPNN has captured the most attention and accounts for 45.1% of the total
ANN offers a suitable approach to address CM problems because can adapt to capture and learn significant information structures in historical data. It has gained significant attention and has been increasingly applied in the research area of CM during the last two decades, but few papers attempt to draw up a holistic review of the existing ANN studies for CM
Summary
The construction industry has made a significant contribution to the development and maintenance of buildings and civil infrastructure. As a data-intensive industry, the construction industry is experiencing unprecedented growth in data volume [1]. As a set of technologies that can automatically detect patterns in data, brings a significant added value to saving time and maximizing computing resources, especially when processing large amounts of data [5]. It has shown excellent performance in many continuously expanding areas of construction, such as building structure design and performance evaluation, prediction of residual value of construction equipment, and vulnerability analysis of existing buildings [6,7,8]. Intelligent technology is urgently needed, with the artificial neural network (ANN) being one of the most promising ones to handle the rapid growth of data generation in CM
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