Visual data captured at construction sites is a rich source of information for the day-to-day operation of construction projects. The development of deep-learning-based methods has demonstrated their capabilities in analyzing complex visual data and inferring valuable insights. Recent applications of these methods in construction have also shown promising performance in making the construction management process smarter. To understand the current research trends and to highlight future research directions, this study reviews state-of-the-art deep-learning applications on visual data analytics in the context of construction project management. This in-depth review identifies six major fields and fifty-two subfields of construction management where deep-learning-based visual data analytics have been applied. It also proposes a generalized workflow for applying deep-learning-based visual data analytics methods for solving construction management problems. In addition, the study highlights three future research directions where deep-learning-based visual data analytics can be applied on relatively less explored 3D visual data.（土木系謝尚賢主任提供）
https://doi.org/10.1016/j.autcon.2021.103892
