计算机科学
数据科学
知识图
图形
领域(数学分析)
知识管理
风险分析(工程)
人工智能
业务
理论计算机科学
数学分析
数学
作者
Yaodong Jia,Jun Wang,Wenchi Shou,M. Reza Hosseini,Yu Bai
标识
DOI:10.1016/j.autcon.2023.104984
摘要
Graph Neural Networks (GNNs) have emerged as a promising solution for effectively handling non-Euclidean data in construction, including building information models (BIM) and scanned point clouds. However, despite their potential, there is a lack of comprehensive scholarly work providing a holistic understanding of the application of GNNs in the construction domain. This paper addresses this gap by conducting a thorough review of 34 publications on GNNs in construction, presenting a comprehensive overview of the current research landscape. By analyzing the existing literature, this paper aims to identify opportunities and challenges for further advancing the application of GNNs in construction. The findings from this review shed light on diverse approaches for constructing graph data from common construction data types and demonstrate the significant potential of GNNs for the industry. Moreover, this paper contributes to the existing body of knowledge by increasing awareness of the current state of GNNs in the construction industry and offering practical recommendations to overcome challenges in real-world practice.
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