Traditional inspection methods are difficult to achieve the accurate inspection of the quailty of prefabricated buildings. To solve the problem above, this paper proposes an automatic extraction method of geometric dimensions of prefabricated components of buildings based on point cloud model. The paper conducts this method under the following steps: firstly, the typical prefabricated component point cloud is separated from the overall building point cloud by taking advantage of the spatial location relationship and geometric characteristics of the building structure. Meanwhile, feature extraction of component point cloud is used to measure dimension information and the perpendicularity of the wall is detected by calculating the included angle between the fitting plane normal vector and the vertical direction unit vector. The perpendicularity of the column is detected by extracting the central axis of the column from the point cloud slice. Moreover, the flatness of the board is represented by the mean square error from each point to the fitting plane. The research takes the point cloud data collected from the construction site of a prefabricated building as an example, removes and segments their outliers, after which it can manage the automatic extraction and measurement of point cloud data features. The measurement accuracy, above 95%, verifies the feasibility and effectiveness of the method, which provides a reference for automatic segmentation, dimension measurement and parametric management of typical prefabricated components.

With the technology developing fast, more and more designers begin to concentrate on the digital ways of controlling the shape of architecture. While in the process of practice, designers always find that real situation and the original assumption are out of sync when using digital ways. It's easy to get stuck in specific things and lose sight of the overall aim. In order to solve this problem, we have worked out the integrity of digital construction method with logic optimization model based on designers'thinking.

With the development of smart cities, the requirement of sampling spatial characteristics of building regarding to efficiencies and accuracies are becoming much higher.Focusing on those limitations of current available technologies in AEC market, which is poor of flexibilities, lack of intelligences, and high of labor cost, this paper has proposed a new solution based on UAV(Unmanned Aerial Vehicle)scanning and YOLO(You Only Look Once)recognition to enable the real-time identification and extraction of window components from as-built projects. In this paper, the algorithm of YOLOv3 is being optimized to train the algorithm model by using integrated dataset. Nginx is being used to build RTMP(Real Time Messaging Protocol)for receiving the data from UAV scanning through streaming server for displaying the live results in ground station. The proposed solution has improved the efficiencies of recognition and has reduced the delays of streaming, which reflects the advantages of UAV technologies from the aspects of simultaneous, efficiencies, and intelligences for satisfying those demands in building components detection.

For most free forms, we usually start from the logical structure of mathematics. However, it is difficult to control the building form from the overall perspective, and as a result, we often have a deviation from the overall control of the design trend. In order to solve this design problem, according to the practical experience of TIENS University Sports Center project, we present a set of digital construction and logic optimization experience. We hope to bring some useful inspiration to the design of such projects.