optimization and trajectory analysis of drone’s flying and environmental variables for 3d modelling the construction progress monitoring

The construction professionals attempt to enhance construction site visualization and modeling using unmanned aerial vehicle (uav) technology. however, there is no research covering best flying and environmental variables for this purpose. this research aimed to determine the optimized flying and environmental variables for 2d dronography and 3d modeling for the construction progress monitoring. the research focused on building façade facing east and conducted an experimental study in the eco-home building at the university of technology malaysia. for 2d visualization, 900 photos were captured based on the three setpoints (20 m, 30 m, and 40 m) perpendicular to the building façade. flying operation features of the employed drone (i.e., dji phantom4) enforced to set the first setpoint at 20 m perpendicular to the façade to get the whole façade in the required 80% of the photos. the 30 m and 40 m setpoints were defined based on the horizontal circles making the angle of 12° from the drone’s z-ax to x-ax perpendicular to the façade. the images were analyzed using image color summarizer (ics) software. the research found that the best distance is 30 m, and noontime is the best time for dronography. the proper temperature, humidity, lux, and wind speed to quality 2d image are; 38.1°, 55.5% rh, 38,107 lx, and 0.1 m/s. the ics software results were validated by applying the image processing toolbox of matlab, particularly the thresholding-based image rgb pixel analyzing technique. the regression analysis showed 93% accuracy and completeness of the results. a trajectory optimization study has also been conducted, which determined that the drone could considerably control trajectories at the target positions and referenced velocity. the findings can significantly help the construction professionals to calibrate their drone’s flying variables for quality 3d modeling the construction progress monitoring. © 2021, this is a u.s. government work and not under copyright protection in the u.s.; foreign copyright protection may apply.