Pin-Chan Lee

The nudge theory has been applied to improve safety behavior in various industries. We implemented the nudge theory in the construction industry to improve worker safety. Nudges for construction safety were grouped into three categories and used in a project. The application of nudges improved workers’ safety behavior, particularly in highlighting control measures. The background analysis results reveal that the implementation of nudges did not vary by gender, nationality, or age, demonstrating its high universality. The results of this study offer a reference for stakeholders in the construction industry who are aiming to bolster worker safety.

Building information modeling (BIM) not only can be a medium for 3D display and simulation but also can be embedded in the knowledge ontology to connect more comprehensive information. Despite the advantages of the BIM‐embedded knowledge ontology that has been explored in engineering practices, it is still less discussed in engineering education. Therefore, this paper aims to establish a BIM‐embedded knowledge‐sharing platform and its corresponding learning community model to improve learning outcomes. The BIM‐embedded knowledge‐sharing platform is established by the MediaWiki to connect the knowledge entries with the related BIM components to expand the BIM‐embedded knowledge ontology. The learning community model is developed on problem‐based learning and project‐based learning to encourage students to build their own knowledge by creating, sharing, and modifying knowledge entries. In the experimental teaching, twenty students are invited to participate in the learning community for a class on prefabricated building design. Students are guided to build and share their knowledge entries and the related BIM components by following the learning community model. Besides this, they also use the BIM‐embedded knowledge‐sharing platform to complete a real design project. Results show the proposed BIM‐embedded knowledge‐sharing platform and the learning community model are beneficial for the learning outcomes.

Several natural and human factors are responsible for the defacement of the external walls and tiles of buildings, and the related deterioration can be a public safety hazard. Therefore, active building maintenance and repair processes are essential for ensuring building sustainability. However, conventional inspection methods are time‐, cost‐, and labor‐intensive processes. Therefore, herein, this study proposes a convolutional neural network (CNN) model for image‐based automated detection and localization of key building defects (efflorescence, spalling, cracking, and defacement). Based on a pretrained CNN VGG‐16 classifier, this model applies class activation mapping for object localization. After identifying its limitations in real‐life applications, this study determined the model’s robustness and ability to accurately detect and localize defects in the external wall tiles of buildings. For real‐time detection and localization, this study applied this model by using mobile devices and drones. The results show that the application of deep learning with UAV can effectively detect various kinds of external wall defects and improve the detection efficiency.