South Korea aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 and over 80% by 2050, with buildings accounting for a significant portion of emissions. Aging structures, comprising 41% of the nation's buildings, suffer from poor insulation, increasing energy consumption and emissions. This study proposes a drone-based method using thermal imaging cameras to assess building insulation performance. Drones efficiently inspect large and high-rise buildings, ensuring consistent data collection. Using advanced software like Agisoft Metashape, thermal images are precisely mapped onto 3D mesh models, addressing issues of inconsistency in traditional methods. By transitioning from analog to digital techniques, this framework enhances accuracy, visualizes energy loss, and supports sustainable building management. The approach contributes to achieving national carbon neutrality goals and provides a scalable solution for reducing building-related GHG emissions.

This study proposes an automated three-dimensional (3D)-modeling method that combines convolutional neural networks (CNNs) with unmanned aerial vehicle (UAV) technology for the efficient management of thermal anomalies in building exteriors. Conventional 3D-modeling methods for thermal imaging management either require the processing of large volumes of data due to the use of thermal distribution information from entire image regions or involve increased processing time when architectural drawings are unavailable. In this study, RGB and infrared (IR) thermal images collected via UAVs were used to automatically detect windows and thermal anomalies using a CNN-based object detection model (YOLOv5). Subsequently, Global Navigation Satellite System (GNSS)-based coordinate data and image metadata were used to convert the resolution coordinates into actual spatial coordinates, which were then vectorized to automatically generate a 3D model. The resulting 3D model demonstrated high similarity to the actual building, accurately representing the locations of thermal anomalies. This method enabled faster, more objective, and more cost-effective maintenance compared to conventional methods, making it especially beneficial for efficiently managing difficult-to-access high-rise buildings.

This study examines the dynamic relationship between changes in construction contract amounts across the diversified business areas within the portfolios of Korean construction firms and their overall business performance using a vector error correction model. It aims to provide a detailed evaluation of the effectiveness and characteristics of diversification strategies in the construction industry. This analysis employs key variables, including the debt ratio, return on total assets, diversification index, and construction contract amounts in domestic and overseas building, civil engineering, and plant construction projects. Two distinct models are used: Model A investigates the relationship between the debt ratio and diversification while Model B explores the relationship between the return on total assets and diversification. The time series data for the analysis spans from Q1 2002 to Q4 2021 on a quarterly basis. The results indicate that Korean construction firms have actively expanded into overseas markets to enhance their financial soundness. However, while such diversification efforts facilitate short-term capital acquisition, they have a negative impact on long-term business performance. When technological capabilities remain constant, lowering prices to increase contract volume may ultimately erode long-term profitability. Therefore, rather than focusing solely on expanding contract volumes through diversification, it is essential to first objectively assess the strengths of each business sector and focus on strengthening core competencies and expertise before pursuing further diversification.

Improving the energy efficiency of buildings is an important element of the effort to address global warming. The thermal performance of building envelopes is the most important thermal and physical property affecting energy performance. Therefore, identifying the thermal performance of a building envelope is essential to applying effective energy-saving measures. The U-value is a quantitative indicator of the thermal performance of the building envelope quantitatively. Methods for determining the U-value are largely classified into passive methods, which use building information without measurement campaigns, and active methods, which conduct in situ measurements. This paper reviews and evaluates the most commonly used methods and experimental results of previous studies to determine the actual U-value of a building envelope. Accordingly, this paper focuses solely on field measurement studies, excluding laboratory measurements. Comparing the existing methods used to determine the U-value can help researchers choose appropriate field measurement methods and future research directions.

Over the past decade, extensive research has been conducted on employing deep learning techniques to detect visual defects in structural facades during inspection. Although these models have shown accuracy in identifying defects from visual data, they encounter limitations in practical applications. This includes uncertainty with data that fall outside the trained distribution and their lack of explanation of detection results. In addition, owing to their extensive parameters, these models require substantial computational resources, which is impractical for visual inspection. These limitations impede immediate defect checking and misjudgment of the deep learning model. The study aims to address these challenges by optimizing a deep-learning-based defect recognition model using a selective layer attention network (SAN). This utilizes a selective feature extraction method to provide essential visual defect information through feature maps within a deep learning model. SAN can effectively represent defect information from building surface images across each layer using the gradient-weighted class activation-mapping visualization technique. These findings demonstrate that the SAN-based model offers clear visual information while significantly reducing the usage of computational resources by 90% compared with the original network, maintaining an equivalent performance level.

Continuous inspections and observations are required to preserve the safety and condition of buildings. Although the number of deteriorated buildings has increased over the years, traditional inspection methods are still used. However, this approach is time-consuming, costly, and carries the risk of poor inspection owing to the subjective intervention of the inspector. To overcome these limitations, many recent studies have developed advanced inspection methods by integrating unmanned aerial vehicles (UAVs) and artificial intelligence (AI) methods during the visual inspection stage. However, the inspection approach using UAV and AI can vary in operation and data acquisition methods depending on the building structures. Notably, in the case of residential buildings, it is necessary to consider how to operate UAVs and how to apply AI due to privacy issues of residents and various exterior contour shapes. Thus, an empirical case study was adopted in this study to explore the integration of UAVs and artificial intelligence (AI) technology to inspect the condition of structures, focusing on residential buildings. As a result, this study proposed the field-adopted UAV operation method and AI-based defect detection model for adopting the residential buildings. Moreover, the lessons learned from holistic and descriptive analyses, which include drone application limitations, points of improvement of data collection, and items to be considered when AI and UAV based inspection for residential buildings, are summarized in this paper. The discussed problems and results derived from this study can contribute to future AI- and UAV-based building inspections.