The construction of prefabricated concrete structures involves many different types of precast concrete (PC) components. Improper arrangements of the transportation, storage, and hoisting of the PC components could lead to unnecessary relocation of PC components, causing a delay in construction progress. To address this problem, this paper presented a dynamic optimization method for PC component transportation and storage based on real‐time scheduling and tracking. The real‐time schedule can be extracted from a 4D building information model (BIM), and the position of PC components during the whole transportation process is tracked by a proposed tracking system which integrates the global navigation satellite system (GNSS) and the radio frequency identification (RFID) technology. A transportation optimization model was built to obtain a reasonable transportation plan based on real‐time construction progress. A cyclic operation network (CYCLONE) simulation model for component storing and hoisting was proposed to calculate the on‐site transportation time. A storage optimization model was proposed by considering the real‐time transportation information to optimize the storage mode and storage position in the yard. The proposed models were solved in the context of a case study, which indicates that our method can reduce the on‐site transportation time by 37% and effectively control the relocation times.

Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.