###### 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. artificial neural network; construction management; content analysis; challenges; review 11209616  
17 September 2021  
Accepted: 6 October 2021  
15 October 2021  
**Subject:** MDPI MDPI is currently one of the worst-performing industries in terms of digitalization [3]. Meanwhile, data in the construction industry are far from being fully utilized. It is estimated that site managers 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]. Therefore, intelligent technology is urgently needed, with the artificial neural network (ANNANN is a mathematical model inspired by the biological brain to acquire the knowledge hidden in historical data for information-processing and computation purposes [9]. As a branch of artificial intelligence (AI) technology, ANN performs well in dealing with complex nonlinear problems without assuming the relationships between variables, because of the self-learning, self-organizing functions, and high-speed computing capabilities [10]. For these reasons, ANN is particularly suitable for solving practical CM problems, which is difficult for classical mathematics and traditional modeling [9]. ANN can play roles in the prediction, optimization, classification, and decision-making in the practice of CM and has been used in CM since the early 1990s [11]. For instance, [PERSON] et al. [12] proposed a predictive model for fast cost analyses and conceptual estimates in the planning stage. The crack detection method of wavelet neural network was proposed by [PERSON] et al. [13] to minimize the possibility of facility failure. Since ANN is the most commonly used AI method in the architecture, engineering and construction industry [14], a growing body of literature reviewing ANN applications has been published. For instance, [PERSON] et al. [15] systematically reviewed the application of convolutional neural networks in structural state assessment. These reviews on ANN focused on a specific application instead of targeting CM. [PERSON] and [PERSON] [16] reviewed AI in CM in which ANN is mentioned limitedly in some paragraphs. [PERSON] [17] reviewed the application status of ANN from 1989 to 2000 in CM which was limited to research before 2000. It can be seen there is no thorough review and systematic analysis of the existing literature on ANN in CM for the past 20 years.