A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section “Intelligent Sensors”.
Deadline for manuscript submissions: 31 October 2021.
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Recently, there has been a significant demand for sensing technology in the building and construction field. Timely collection and analysis of heterogeneous sensing data are essential to make wise decisions efficiently for safety management, health monitoring, performance management, remote operation, predictive maintenance, etc. However, built environment and construction projects are complex and dynamic with multiple stakeholders (users, maintainers, managers, engineers, etc.), equipment, and even robots involved, making it difficult to directly adopt existing methods and tools for collecting and mining sensing data. Therefore, enhanced data sensing and mining approaches are required to discover useful knowledge and patterns from multi-source sensing data with consideration of characteristics of the building and construction domain. Moreover, rich domain knowledge is embedded in physical and behavioral models and domain-specific knowledge graphs and it is worth exploring new methods to explore the value of sensing data with the assistance of model- and knowledge-driven approaches.
This Special Issue will collect state-of-the-art research findings on the latest developments and challenges of smart sensing in the building and construction field. High-quality reviews and original research papers that present current research gaps, theoretical frameworks, methodologies, and approaches are welcome.
Potential topics include but are not limited to the following:
Reviews or surveys on state-of-the-art of sensing-related topics in the building and construction field
Methods and tools for sensing data collection and processing, including internet of things, laser scanning, photogrammetry, thermal imaging, virtual sensing, crowdsourcing, multi-source data fusion, and design and development of new sensors
Technologies and approaches to extract complex patterns or knowledge from sensing data, i.e., computer vision, machine learning, and deep learning
Integration of sensing data mining and model- and knowledge-driven approaches to understand the complex and dynamic nature of buildings and construction projects
Real-world applications of remote sensing data mining, i.e., building performance monitoring, structural health monitoring, damage assessment, disaster monitoring, as-built model reconstruction, geometric quality inspection, construction process monitoring, etc.
Dr. Jia-Rui Lin Dr. Zhen-Zhong Hu Dr. Jérôme Frisch Dr. Qian Wang Dr. Yichuan Deng Dr. Yi Tan Guest Editors