With the rapid advancement of Unmanned Aerial Vehicle (UAV) technologies in recent years, their uses have been increasingly adopted in the architecture, engineering, and construction industries. To satisfy the needs of various types of construction projects, a considerable amount of research work has been performed to implement and refine the operations, safety, and accuracy of UAVs. This paper presents the findings of a comprehensive literature review that focuses on UAV research in construction management during the timeframe of 2016 to 2021. A total of 95 papers were identified and collected from a list of 21 relevant journals and conference proceedings, and were then categorized by their research topic, sensor types, and targeted structures. The results of 47 exemplary studies were reported in two categories, namely UAV uses and construction uses. The research topics identified for UAV uses include algorithm, applications, operations, framework, and training, while research topics identified for construction use include inspection, surveying, safety, and monitoring. The connection between the research topics, sensor types, targeted structures, and other advanced technologies were also discussed. This paper summarizes the current results of UAV research in construction management, reviews the methodology, benefits, and limitations of the reviewed literature, and provides valuable knowledge for the future trend of UAV applications in the civil, infrastructure, and construction industries.

This paper presents a time- and cost-efficient method for the management of construction and demolition (C&D) debris at construction sites, demolition jobsites, and illegal C&D waste dumping sites. The developed method integrates various drone, deep learning, and geographic information system (GIS) technologies, including C&D debris drone scanning, 3D reconstruction with structure from motion (SfM), image segmentation with fully convolutional network (FCN), and C&D debris information management with georeferenced 2D and 3D as-built. Experiments and parameter analysis led us to conclude that (1) drone photogrammetry using top- and side-view images is effective in the 3D reconstruction of C&D debris (stockpiles); (2) FCNs are effective in C&D debris extraction with point cloud-generated RGB orthoimages with a high intersection over union (IoU) value of 0.9 for concrete debris; and (3) using FCN-generated pixelwise label images, point cloud-converted elevation data for projected area, and volume measurements of C&D debris is both robust and accurate. The developed automatic method provides quantitative and geographic information to support city governments in intelligent information management of C&D debris.

The widespread application of zinc oxide nanoparticles (ZnO-NPs) brings convenience to our lives while also renders threats to public health and ecological environment. The lung has been recognized as a primary target of ZnO-NPs, however, the detrimental effects and mechanism of ZnO-NPs on the respiratory system have not been thoroughly characterized so far. To investigate the effect of ZnO-NPs on acute lung injury (ALI), Sprague Dawley rats were intratracheally instilled with ZnO-NPs suspension at doses of 1, 2, and 4 mg/kg/day for 3 consecutive days. Our study revealed that ZnO-NPs induced ALI in rats characterized by increased airway resistance, excessive inflammatory response and lung histological damage. In addition, we identified several molecular biomarkers related to the potential mechanism of ZnO-NP-induced ALI, including oxidative stress, mitochondrial damage, and NLRP3 inflammasome activation. The results of in vitro experiments showed that the viability of A549 cells decreased with the increase in ZnO-NPs concentration. Meanwhile, it was also found that ZnO-NP treatment induced the production of ROS, the decrease in mitochondrial membrane potential and activation of NLRP3 inflammasome in A549 cells. Furthermore, to explore the underlying molecular mechanisms of ZnO-NP-induced ALI, N-acetyl-L-cysteine (a ROS scavenger), Cyclosporin A (an inhibitor for mitochondrial depolarization) and Glibenclamide (an inhibitor for NLRP3 inflammasome activity) were used to pre-treat A549 cells before ZnO-NPs stimulation in the in vitro experiments, respectively. The results from this study suggested that ZnO-NP-induced ROS production triggered the accumulation of damaged mitochondria and assembly of NLRP3 inflammatory complex, leading to maturation and release of IL-1β. Moreover, ZnO-NP-induced NLRP3 inflammasome activation was partly mediated by mitochondrial damage. Taken together, our study suggested that ZnO-NPs induced ALI through oxidative stress-mediated mitochondrial damage and NLRP3 inflammasome activation and provided insight into the mechanisms of ZnO-NPs-induced ALI.

This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three-dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities.

Using deep learning to recover depth information from a single image has been studied in many situations, but there are no published articles related to the determination of construction site elevations. This paper presents the research results of developing and testing a deep learning model for estimating construction site elevations using a drone-based orthoimage. The proposed method includes an orthoimage-based convolutional neural network (CNN) encoder, an elevation map CNN decoder, and an overlapping orthoimage disassembling and elevation map assembling algorithm. In the convolutional encoder-decoder network model, the max pooling and up-sampling layers link the orthoimage pixel and elevation map pixel in the same coordinate. The experiment data sets are eight orthoimage and elevation map pairs ($\mathrm{1,536}\times \mathrm{1,536}\mathrm{pixels}$ ), which are cropped into 64,800 patch pairs ($128\times 128\mathrm{pixels}$ ). Experimental results indicated that the $128\times 128\text{-}\mathrm{pixel}$ patch had the best model prediction performance. After 100 training epochs, 21.22% of the selected 2,304 points from the testing data set were exactly matched with their ground truth elevation values; and 52.43% points were accurately matched in $\pm 5\mathrm{cm}$ and 66.15% points in $\pm 10\mathrm{cm}$ , less than 10% points exceeded $\pm 25\mathrm{cm}$ . This research project advanced drone applications in construction, evaluated CNNs’ effectiveness in site surveying, and strengthened CNNs to work with large-scale construction site images.