In process of creating BIM model for tunnel, many user use still CAD, so the workflow for BIM design is not perfect. Therefore, in this paper, we proposes a method to automatically create BIM model without converting 2D drawings in tunnel design. It can allow engineers to design BIM-based tunnel with maximum use of linear information and modify the BIM model whenever there is changed linear information. To do this, we use Dynamo, which can reduce the time required for creating and modifying BIM models during design changes, saving time wasted for BIM conversion design.

Recently, there is an increase in fire incidents in building structures. Due to this, the importance of fire-damaged buildings' safety diagnosis and evaluation after fire is growing. However, the existing fire-damaged safety diagnosis and evaluation methods are personnel-oriented, so the diagnostic results are intervened by investigators' subjectivity and unquantified. Thus, improper repair and reinforcement can result in secondary damage accidents and economic losses. In order to overcome these limitations, this study proposes using 3D laser scanning technology. The case analysis of fire-damaged building structures was conducted to verify the effectiveness of accuracy and manpowering by comparing the existing method and the proposed method. The proposed method using 3D laser scanning technology to obtain point cloud data of fire-damaged field. The point cloud data and BIM model is combined to inspect the fire-damaged area and depth. From inspection, quantified repair and reinforcement quantity take-off can be acquired. Also, the proposed method saves half of the manpowering within same time period compared to the existing method. Therefore, it seems that using 3D laser scanning technology in fire-damaged safety diagnosis and evaluation will improve in accuracy and saving time and manpowering.

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

As population in Sarasota and Manatee Counties, Florida in the United States is projected to increase, land use changes from land development happen continuously. The more land development means the more impervious surfaces and stormwater runoff to Sarasota Bay, which causes critical impact on the resiliency of the ecosystem. In order to decrease its impact on water quality and the ecosystem function of Sarasota Bay, it is important to assess the resilient status of communities that create negative impacts on the ecosystem. Three types of guiding principles of resiliency for Sarasota Bay watershed are suggested. To assess resiliency status, three indexes - vulnerability index, socio-economic index, and ecological index are developed and analyzed by using geographic information system for each census tract in the two counties. Since each indicator for vulnerability index, socio-economic index, and ecological index is measured with different metrics, statistical standardizing method - distance from the best and worst performers is used for this study to directly compare and combine them all to show total resilience score for each census tract. Also, the ten most and the ten least scores for the total resilience index scores are spatially distributed for better understanding which census tracts are most or least resilient. As Sarasota Watershed boundary is also overlaid, it is easy to understand how each census tract attains its resilience and how each census tract impacts to Sarasota Bay ecosystem. Based on results of the resiliency assessment several recommendations, guidelines, or policies for attaining or enhancing resiliency are suggested.

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.