The concept of city information modeling (CIM) has become increasingly popular in recent years. A literature review of previous CIM studies is presented in this paper. First, a bibliometric analysis of the current global CIM research is described, revealing that CIM has become a significant research hotspot. Next, three main research areas of the current CIM technique, namely data collection, integration, and visualization, are summarized to describe the characteristics of CIM research. Furthermore, some widely used CIM platforms are compared, and typical application cases of the CIM technique at different stages of the city life cycle are summarized. Finally, the current issues in CIM research are discussed, and future development directions are proposed. The findings of this study are expected to help researchers understand the current state of CIM and identify future development directions, thereby promoting CIM research development.
Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein. Specifically, using the training data of 230,683 buildings in Tangshan city, China, a supervised ML solution based on a decision forest model was designed for the prediction. The scale sensitivity and regional applicability of the designed solution are discussed, respectively, and the results show that the supervised ML solution can maintain high accuracy for different scales; however, it is only suitable for cities similar to the sample city. For wide applicability for various cities, a semi-supervised ML solution was designed based on sampling investigation and self-training procedures. The downtowns of Daxing and Tongzhou districts in Beijing were selected as a case study for the designed semi-supervised ML solution. The overall prediction accuracies of structural types for Daxing and Tongzhou downtowns can reach 94.8% and 99.5%, respectively, which are acceptable for seismic damage simulations. Based on the predicted results, the distributions of seismic damage in Daxing and Tongzhou downtown were output. This study provides a smart and efficient method for obtaining structural types for a city-scale seismic damage simulation.
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