Abstract Modular integrated construction is one of the most advanced off-site construction technologies and involves the repetitive process of installing prefabricated prefinished volumetric modules. Automatic detection of location and movement of modules should facilitate progress monitoring and safety management. However, automatic module detection has not been implemented previously. Hence, virtual prototyping and transfer-learning techniques were combined in this study to develop a module-detection model based on mask regions with convolutional neural network (Mask R-CNN). The developed model was trained with datasets comprising both virtual and real images, and it was applied to two modular construction projects for automatic progress monitoring. The results indicate the effectiveness of the developed model in module detection. The proposed method using virtual prototyping and transfer learning not only facilitates the development of automation in modular construction, but also provides a new approach for deep learning in the construction industry.

中文翻译：

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用于模块化集成结构的虚拟原型和迁移学习模块检测

摘要 模块化集成施工是最先进的异地施工技术之一，涉及安装预制预制体积模块的重复过程。模块位置和移动的自动检测应有助于进度监控和安全管理。但是，之前还没有实现自动模块检测。因此，本研究将虚拟原型和迁移学习技术相结合，以开发基于带有卷积神经网络（Mask R-CNN）的掩码区域的模块检测模型。开发的模型使用包含虚拟和真实图像的数据集进行训练，并将其应用于两个模块化建设项目以进行自动进度监控。结果表明所开发模型在模块检测中的有效性。