To reduce accidents caused by slope failures, disaster assessments and displacement measurements of steep slopes are conducted. Measurement values can vary greatly, depending on the technician’s proficiency. Moreover, land creep and minute displacements are difficult to detect. In this study, we attempted to solve this problem using unmanned aerial vehicles (UAVs) and aerial triangulation. After a review of previous studies, a measurement precision of 10 cm for daily monitoring and sub-centimeters for precise monitoring were set. Twelve checkpoints were used, and the comparison results between the measured values using the total station and the aerial triangulation using the UAV were as follows. A virtual reference station (VRS) and three ground control points (GCPs) yielded 11.58 mm for the 3D (root-mean-square error) RMSE, and post-processed kinematic (PPK) and three GCPs generated 9.71 mm. By differentiating the results of multiple observations, the PPK processing results could achieve sub-centimeter accuracy. We verified that direct georeferencing using a VRS is sufficient for daily monitoring, and precise measurements require at least three GCPs. Geotechnicians can use VRS observations for economical slope monitoring in normal times, and accurate observations with GCPs when precise measurements are required.

Despite advancements in the industry, safety investments at small construction sites remain underinvested due to budget constraints that limit the employment of safety managers. The results of this study explore a cost-effective solution for accurate positioning of workers in hazardous areas using off-the-shelf smartphones and Ultra-Wide Band (UWB) technology, with a focus on small and medium-sized construction sites. We targeted a Three-Dimensional (3D) positioning error of less than 35 cm in an indoor space of 25–50 m². The results show that the spatial configuration of anchors and Mobile Terminals (MTs) has a significant impact on 3D accuracy, and in particular, deployment strategies with increasing height differences show a clearer reduction in error than differences in positional accuracy caused by differences between general least square algorithms. This highlights the importance of optimal anchor-MT placement, similar to Global Navigation Satellite System (GNSS) standards, to minimize Position Dilution of Precision (PDOP) values and improve positioning accuracy. We also suggest that adjusting the tetrahedral volume formed by anchors and MTs can solve complex site problems,improve worker positioning, and benefit safety management. This approach presents a practical and scalable way to improve safety protocols on small construction sites.

When surveying a large target area with a real-time kinematic unmanned aerial vehicle (RTK-UAV), the RTK signal tends to be disconnected when city canyons or macrocells are included. Thus, the accuracy is reduced due to the lack of RTK signal or the fact that RTK signal is not available in certain areas. The available methods to solve this problem are costly. Therefore, we used one GCP and performed post-process kinematics (PPK) to verify whether the accuracy reduction caused by the lack of RTK signal in certain areas could be solved. A data set detailing the percentage of time during which the RTK signal was received (100%, 90%, 5%, and 0%) was obtained, and ATs were conducted both with and without PPK using GCPs located at the four corners and center. In 40 experiments, the trend of root mean square error (RMSE) values based on the distance between the GCP used and the 41 check points (CPs) was analyzed. In the absence of PPK, the error tended to increase depending on the distance between the GCP and CPs, but there was no significant difference after PPK as up to 10 cm horizontal error and up to 20 cm vertical error were observed within a 1 km radius of the GCP. As a result, even if the RTK signal is disconnected during shooting, it is possible to achieve an accuracy within 3 GSD up to a radius of 1 km from the GCP.

This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously.