Virtual prototyping- and transfer learning-enabled module detection for modular integrated construction

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.