Abstract: Tracking construction machines in videos is a fundamental step in the automated surveillance of construction productivity, safety, and project progress. However, existing vision-based tracking methods are not able to achieve ...

Abstract: Deep learning object detection algorithms have proven their capacity to identify a variety of objects from images and videos in near real-time speed. The construction industry can potentially benefit from this machine ...

Abstract: Deep learning image captioning methods are able to generate one or several natural sentences to describe the contents of construction images. By deconstructing these sentences, the construction object and activity information ...

Abstract: Training deep learning models for vision-based monitoring of construction sites usually requires a large amount of labeled data. Semisupervised learning methods can efficiently obtain unlabeled data with substantial cost ...

Abstract: Collaboration in design decision-making is a critical factor in enhancing the quality of built environments. However, several factors, such as the lack of clarity in the negotiation process, the diverse disciplinary ...

Abstract: A simple thermal model is developed for solar chimney thermal power system and validated by the comparison between the simulated results and those simulated by Schlaich et al. Based on a proposed

Abstract: The construction industry is increasingly digital and dependent on extensive use of information technologies. However, data exchange in a digital environment makes construction data more vulnerable to cyber risks. For ...

Abstract: Visual data comprising images and videos has become an integral aspect of construction management, potentially supplanting traditional paper-based site documentation. With the vast amount of image data generated in ...