# Virtual Scene Construction for Seismic Damage of Building Ceilings and Furniture
## 5 Conclusions  
A method for the construction of a virtual scene representing the seismic damage to suspended ceilings and moveable furniture is proposed and validated by the experiments. Using the proposed method, an integrated indoor virtual earthquake scene of a six-story building is constructed and applied in an earthquake safety drill. Some conclusions are made as follows:  
1. The designed BIM-based modeling solution can efficiently create the consistent structural analysis and VR models of a building.
2. The solution based on structural seismic THA and FEMA P-58 can provide detailed seismic damage results required to construct the earthquake scene for nonstructural components.
3. Physical models and corresponding physics-engine-based implementation of suspended ceilings and moveable furniture are created, allowing the movement of these components to be simulated reasonably.
4. The proposed method constructs a well-founded scene representing the seismic damage to indoor nonstructural components, which can be used to perform virtual indoor earthquake safety drills and thus improve the earthquake survival ability of occupants.  
Note that FEMA P-58 includes the fragility data of various nonstructural components. Therefore, the proposed virtual scene construction method based on FEMA P-58 and a physics engine can be used for other nonstructural components if the corresponding physical models are established. In addition, the propose method is not limited to the office building in Case study and can be used in other buildings, such as hospital buildings. It is also noted that the fragility curve of movable furniture in FEMA P-58 is not adopted, thus the uncertainty of the damage of movable furniture is not fully considered. It is recommended to use a Monte Carlo approach together with the proposed physical model to be able to consider such uncertainty.  
The proposed method in this study can produce numerous indoor earthquake scenes induced by nonstructural components, which provides sufficient training data (e.g., images and videos) for the identification of the potential nonstructural seismic damage based on artificial intelligence (AI). Such AI-based identification can be implemented on smartphones as an application. This application will identify structural and nonstructural components in real-time through object detection and predict the possible seismic motion of nonstructural components based on a predefined intensity. Such applications would be used by the public and based on their own home furniture. The above research based on AI will be deeply investigated in the future.  
Figure 17: The second drill with the awareness of nonstructural damage, (**a**) response; (**b**) consequence.  
**Author Contributions:** Conceptualization, Z.X.; Methodology, H.Z.; Validation, W.W.; Writing--original draft, Z.X.; Writing--review & editing, Z.Y.  
**Funding:** The authors are grateful for the financial support received from the National Key R&D Program of China (no. 2018 YFC0809900), the National Natural Science Foundation of China (no. 51978049) and the Fundamental Research Funds for the Central Universities (no. FRF-BD-18-007A).  
**Conflicts of Interest:** The authors declare no conflict of interest.