# UAV Application for Typhoon Damage Assessment in Construction Sites
## 6 Conclusions  
The study developed a natural disaster analysis model and a warning flow based on disaster theory and field investigation of the construction site. AHP, as a quantitative analysis method, was applied to calculate the weight ratios of different influence factors in the theoretical model. In the criterion layer of the model, we set five indicators, including risk, exposure, vulnerability, the capability of disaster prevention, and the capability of disaster mitigation. Respectively, at the factor level, we again decomposed 35 influencing indicators. The results show that the intensity of typhoons, the management level of the construction project leader, and the rationality of measures are key factors in the natural disaster model on the construction site. Different factors determine the impact degree of the typhoon. We proposed a more reasonable early warning flow through data analysis and formulated disaster prevention and reduction measures through data analysis. In the case investigation of the construction site, we used UAV to evaluate cracks in the periphery of some structures and the status of structural joints. We found that using the maneuverability of UAVs can improve the identification speed and significantly save precious time for pre-disaster prevention. As for the information on disaster level warnings, we developed corresponding measures that can improve the management ability of the construction site and lay a theoretical foundation for the prevention and reduction of typhoons. Given the wide range and complex terrain of construction sites, it is impossible to conduct all-around detection when using UAVs for pre-disaster inspection. Therefore, further studies are recommended to seek potential in improving UAV detection comprehensiveness by optimizing path planning and collecting parameters. The paper systematically introduces and analyzes establishing an early warning process and making disaster prevention and reduction measures. Although the study does not cover all types of typhoons and all periods of construction sites, it provides feasible ideas for similar studies. It also helps to extend the application of UAVs in the early warning detection of the construction industry during a natural disaster to realize the development of industrial automation technology. Therefore, the future development direction of this research is to achieve intelligent detection and automated collection and analysis of construction site safety information. Conceptualization, C.W., Y.T., M.A.K. and Z.C. ; methodology, C.W. and Y.T. ; software, C.W., Y.T. and Z.C. ; validation, C.W., Y.T. and Z.C. ; formal analysis, C.W., Y.T. and Z.C. ; investigation C.W. and Y.T. ; resources, C.W. and Y.T. ; data curation, C.W., Y.T. and M.A.K. ; writing--original draft preparation, C.W. and Y.T. ; writing--review and editing, C.W. and M.A.K. ; and; visualization, C.W., Y.T. and Z.C. ; supervision, C.W. and M.A.K. ; project administration, C.W., Y.T. and M.A.K. ; funding acquisition, C.W. and Y.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Fujian Province Science and Technology Department of China (grant number 2021I0014); the Quanzhou Tongjiang Scholar Special Fund for financial support through grant number (600005-Z17X0234); Huaqiao University through grant number (17 BS201), and Universiti Teknologi Malaysia (UTM) Research Grant Vot No: (05E79). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data sets during and/or analyzed during the current study are available from the corresponding author upon reasonable request.