Considering high risk, long construction period, high construction and maintenance costs, bridge management is asynchronous, unintelligent and inefficient. The purpose of this research is to investigate a new approach with its supporting building information modelling (BIM) and Internet of Things (IoT) tool to enhance the smart management in bridge life cycle. BIM provides detailed geometric and semantic information and IoT contains the management and analysis of the actual condition of the bridge. An example of a super bridge under construction in Xinjiang is used to illustrate the developed system. Results show that the developed system can significantly grasp the quality of the bridge in time, reduce the construction risk and construction period. The developed BIM/IoT-based system is also effective and practical in the quality and risk management among multiple locations, such as smart buildings, smart tunnels, and even smart cities.
The comprehensive utilization of prefabricated components (PCs) is one of the features of industrial construction. Trial assembly is imperative for PCs used in high-rise buildings and large bridges. Virtual trial assembly (VTA) is a preassembly process for PCs in a virtual environment that can avoid the time-consuming and economic challenges in physical trial assembly. In this study, a general framework for VTA that is performed between a point cloud, a building information model (BIM), and the finite element method is proposed. In obtaining point clouds via terrestrial laser scanning, the registration accuracy of point clouds is the key to building an accurate digital model of PCs. Accordingly, an accurate registration method based on triangular pyramid markers is proposed. This method can enable the general registration accuracy of point clouds to reach the submillimeter scale. Two algorithms for curved members and bolt holes are developed for PCs with bolt assembly to reconstruct a precise BIM that can be used directly in finite element analysis. Furthermore, an efficient simulation method for accurately predicting the elastic deformation and initial stress caused by forced assembly is proposed and verified. The proposed VTA method is verified on a reduced-scale steel pipe arch bridge. Experimental results show that the geometric prediction deviation of VTA is less than 1/1800 of the experimental bridge span, and the mean stress predicted via VTA is 90% of the measured mean stress. In general, this research may help improve the industrialization level of building construction.
The embedded part (EMP) is a structural form in a building and plays an important role in the fixation of a structural or a non-structural member. After the initial installation, the popular detection method of the EMPs is to use the total station for single-point diagnosis. Due to the difficulties in point-by-point measurement and feature points selecting, it is sophisticated to ensure accuracy and efficiency in the detection process of large-scale EMPs. In order to solve the above issues, this paper proposes an automatic panel detection method for EMPs: (1) 3D laser scanning is used to obtain the actual coordinates of EMPs; (2) Developed algorithm is made to match the coordinates between design and measured data to calculate construction error; (3) Further use of the optimization algorithm to adjust the attitude of the point cloud, minimize the construction error and give the adjustment guidance of EMPs. A case study indicates that the efficiency is improved by about five times compared to the total station technique, and the number of EMPs need to be adjusted is greatly reduced. This method is of great significance for the high-volume component precision installation project.
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