BIM(Building Information Modeling) is a salient technology for influential innovation in the construction industry. The patent network analysis is useful for suggesting the direction of technology development and exploring the research and development field. Therefore, the purpose of this study is to analyze the BIM technology structure and core technologies according to the convergence of BIM technology and market expansion. In this study, social network analysis was conducted by establishing a co-classification IPC network for the United States BIM patent. In particular, the characteristics of the major technical areas in the BIM technology network were identified through centrality analysis. G06F017/00, digital computing or data processing method, is a core technology field in the BIM network. Arrangements, apparatus or systems for transmission of digital information, H04L029/00 is an influential technology across the network. B25J009/00 for program controlled manipulators is an intermediary technology field and G06T019/00, manipulating 3D models or images for computer graphics, is an important field for technological development competitiveness.

Recently, interest in BIM technology has been growing in the domestic construction market as the number of construction projects requiring the application of BIM technology has increased. The use of BIM allows the use of various visualization functions through 3D models as well as drawing interference review, quantity calculation and integrated management of information. In particular, estimates of construction costs based on BIM can increase work efficiency by reducing the burden of calculating construction costs and managing them. In order to increase the efficiency of BIM-based cost estimates, it is necessary to improve the correlation between the BoQ(Bill of Quantity) information and the BIM model. For this purpose, the ratio of the amount and item to the detailed construction of the current BoQ was analyzed, especially the problem of the calculation of construction cost in the window work in this study. In addition, we would like to suggest a method for systematically linking the BoQ information with the BIM model by improving the BoQ information structure of the window work. It is expected that this will increase the efficiency of the quantity calculation and analysis work of construction cost of window work. In addition, it is expected that this method will help to analyze and predict changes in construction cost of windows and doors by facility characteristics and type.

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.