Asbestos is a class 1 carcinogen, and it has become clear that it harms the human body. Its use has been banned in many countries, and now the investigation and removal of installed asbestos has become a very important social issue. Accordingly, many social costs are expected to occur, and an efficient asbestos investigation method is required. So far, the examination of asbestos slates was performed through visual inspection. With recent advances in deep learning technology, it is possible to distinguish objects by discovering patterns in numerous training data. In this study, we propose the use of drone images and a faster region-based convolutional neural network (Faster R-CNN) to identify asbestos slates in target sites. Furthermore, the locations of detected asbestos slates were estimated using orthoimages and compiled cadastral maps. A total of 91 asbestos slates were detected in the target sites, and 91 locations were estimated from a total of 45 addresses. To verify the estimated locations, an on-site survey was conducted, and the location estimation method obtained an accuracy of 98.9%. The study findings indicate that the proposed method could be a useful research method for identifying asbestos slate roofs.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24–84 mm and 8–48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95–91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

In Republic of Korea, cracks in concrete structures are considered to be objective structural defects, and the constant maintenance of deteriorating facilities leads to substantial social costs. Thus, it is important to develop technologies that enable economical and efficient building safety inspection. Recently, the application of UAVs and deep learning is attracting attention for efficient safety inspection. However, the currently developed technology has limitations in defining structural cracks that can seriously affect the stability of buildings. This study proposes a method to define structural cracks on the outer wall of a concrete building by merging the orthoimage layer and the structural drawing layer with the UAV and deep learning that were previously applied during a safety inspection. First, we acquired data from UAV-based aerial photography and detected cracks through deep learning. Structural and non-structural cracks were defined using detected crack layer, design drawing layer defined the structural part, and the orthoimage layer was based on UAV images. According to the analysis results, 116 structural parts cracks and 149 non-structural parts cracks were defined out of a total of 265 cracks. In the future, the proposed method is expected to greatly contribute to safety inspections by being able to determine the quality and risk of cracks.

As technology advances, the use of unmanned aerial vehicles (UAVs) and image sensors for structural monitoring and diagnostics is becoming increasingly critical. This approach enables the efficient inspection and assessment of structural conditions. Furthermore, the integration of deep learning techniques has been proven to be highly effective in detecting damage from structural images, as demonstrated in our study. To enable effective learning by deep learning models, a substantial volume of data is crucial, but collecting appropriate instances of structural damage from real-world scenarios poses challenges and demands specialized knowledge, as well as significant time and resources for labeling. In this study, we propose a methodology that utilizes a generative adversarial network (GAN) for image-to-image translation, with the objective of generating synthetic structural damage data to augment the dataset. Initially, a GAN-based image generation model was trained using paired datasets. When provided with a mask image, this model generated an RGB image based on the annotations. The subsequent step generated domain-specific mask images, a critical task that improved the data augmentation process. These mask images were designed based on prior knowledge to suit the specific characteristics and requirements of the structural damage dataset. These generated masks were then used by the GAN model to produce new RGB image data incorporating various types of damage. In the experimental validation conducted across the three datasets to assess the image generation for data augmentation, our results demonstrated that the generated images closely resembled actual images while effectively conveying information about the newly introduced damage. Furthermore, the experimental validation of damage detection with augmented data entailed a comparative analysis between the performance achieved solely with the original dataset and that attained with the incorporation of additional augmented data. The results for damage detection consistently demonstrated that the utilization of augmented data enhanced performance when compared to relying solely on the original images.

Currently, firefighter drones in Republic of Korea underperform due to the lack of take-off site reservations in advance. In order to address this issue, this study proposes a GIS-based multi-criteria model for selecting take-off and landing sites for firefighter drones in urban areas. Seven criteria were set for the selection of take-off and landing sites based on building roofs. Buildings at 318 sites in the target area that satisfy all seven criteria were extracted and grouped according to the geographical location. Among the grouped buildings, 11 sites were reselected through network analysis and central feature methods. In addition, two more sites were selected through the relaxation of criteria for take-off and landing sites for firefighter drones. Validation was performed using the data of building fires that occurred in the target area in the past. The results confirmed the effectiveness of the method applied in this study, as potential responses could be verified for ≥95% of the buildings with a past fire incidence. By introducing a simple methodology in which a multi-criteria model is built through spatial information, this study contributes to the literature on improving operational firefighting strategies and provides practitioners and policymakers with valuable insights to support decision-making.

Prefabrication, one of the methods to increase productivity by moving construction activity to factory work, has evolved into multi-trade prefabrication. Although previous studies have introduced the merits and directions of multi-trade prefabrication technology, various design and installation techniques must be developed for the economical application of multi-trade prefabrication. This study aims to provide an economical design for prefabricated mechanical, electrical, and plumbing (MEP) rack (PMR) structural framing. We proposed five types of PMR structural framing techniques and analyzed their required channel material and labor inputs through a pilot test. The efficiency of PMR structural frames was examined by adjusting the supporting interval, moving the duct outside, and removing the upper framing. Economics and productivity analysis revealed that removing the upper framing method was the most effective when the coordination period was secured. Adjusting the supporting intervals is also an economical design option. The findings of this study can help enhance the economic feasibility of prefabrication and modularization of construction and their widespread utilization.