Abstract
On 17-21 June 2019, the Conseil International du Bâtiment (CIB) World Building Congress 2019 (WBC 2019) on Constructing Smart Cities took place in The Hong Kong Polytechnic University with the host being the Department of Building and Real Estate. This triennial international congress facilitated the in-depth exchange of research ideas on the aspects of smart construction using information technologies, including “smart transportation and mobility”, and “smart planning, design, construction”. Besides, the CIB Working Commission Group WC78 emphasised the importance of using the up-to-date information technologies for smart city development. The fruitful discussions fostered the close collaboration between academia and practitioners. Built upon the research works presented in the CIB WBC 2019, outstanding conference papers were selected and invited for enriching the contents which were accepted for publication after rigorous peer review in this special issue of “Journal of Information Technology in Construction” which aims to promote the research interest of the information technologies in smart city development. Highlights of each paper is provided below for readers’ reference.
Highlights
An integrated data assessment and modelling (IDAM) method which allows the generation of information-rich as-built BIM model was newly proposed based on the integration of two sensors
The case study results proved that the usage of ground penetrating radar (GPR) sensors can accurately and smartly detect the material compositions of building elements and can be combined with its corresponding geometric information of the buildings
The proposed IDAM with the three improvements is expected to be popularly applied in existing buildings thanks to the integration of laser scanner and GPR sensor
Summary
The opening paper was presented by Uggla and Horemuz (2021) They introduced a sensor-fused segmentation algorithm integrating camera captured images and laser scanned point clouds to detect the game fences of roadside, which improved a nearly invisible roadside object detection performance for practical smart infrastructure maintenance. Geometric processing and refinement are applied to the identified points to filter out further and recognize game fence regions. They have explored the algorithm performance through practical mobile laser scanning and imaging data collected by a moving vehicle on the road. The proposed image-based point cloud segmentation outperformed conventional PointNet processing with higher accuracy and efficiency. A future extension is set to generalise the object detection type for extracting holistic smart semantic infrastructure information
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