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.

在建筑工地捕获的视觉数据是建筑项目日常运营的丰富信息来源。基于深度学习的方法的发展已经证明了它们在分析复杂的视觉数据和推断有价值的见解方面的能力。这些方法最近在施工中的应用也显示出在使施工管理过程更智能方面的良好表现。为了了解当前的研究趋势并突出未来的研究方向，本研究回顾了在建筑项目管理背景下可视化数据分析的最先进深度学习应用。本次深入审查确定了施工管理的六个主要领域和 52 个子领域，其中应用了基于深度学习的可视化数据分析。它还提出了一个通用的工作流程，用于应用基于深度学习的可视化数据分析方法来解决施工管理问题。此外，该研究强调了三个未来的研究方向，其中基于深度学习的视觉数据分析可以应用于相对较少探索的 3D 视觉数据。