Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of manpower. The dataset used for training a deep-learning-based network contains actual residential building façade images. Various building designs in these raw images make it difficult to detect defects because of their various types and complex backgrounds. We employed the faster regions with convolutional neural network (Faster R-CNN) structure for more accurate defect detection in such environments, achieving an average precision (intersection over union (IoU) = 0.5) of 62.7% for all types of trained defects. As it is difficult to detect defects in a training environment, it is necessary to improve the performance of the network. However, the object detection network employed in this study yields an excellent performance in complex real-world images, indicating the possibility of developing a system that would detect defects in more types of building façades.

Existing structural safety diagnosis methods are time-consuming due to personnel-oriented measurement methods and have a limitation that it is difficult to obtain consistent research results. In order to overcome these limitations, this study proposes a structural safety diagnosis method using laser scanning and BIM. In spite of the various studies related to laser scanning and BIM, it is difficult to find a study that verifies the effect of shortening the service period and cost reduction in terms of project management. Therefore, in this study, case analysis of structural safety diagnosis of large-scale civil infrastructure was conducted. In the structural safety diagnosis, the laser scanning data and the BIM model were compared and analyzed to determine the degree of deformation of pipe rack (e.g., truss, column). Laser scanning data reflects the deformation state of large-scale civil infrastructure. On the other hand, the BIM model was constructed by reflecting the state before the transformation with reference to the laser scanning data. Finally, proposed method of structural safety diagnosis saved four months. In terms of manpower saving, 125 man-month was saved. The research findings can provide a quantitative basis for the introduction of laser scanning and BIM technology in the structural safety diagnosis of aging large-scale civil infrastructures. However, the limitations of this study have not been analyzed economically by considering the investment cost (e.g., hardware, software, training, etc.) of laser scanning and BIM technology and the cost saving effect of technology introduction.

This study investigated defect risks in residential buildings using the Loss Distribution Approach (LDA), a method of identifying and quantifying operational risks in economic terms. Analysis was performed on 7554 defects in 48 residential buildings where defect disputes occurred between 2008 and 2017. Defects were classified into eight types: affected functionality, broken items, corrosion, detachment, incorrect installation, missing task, surface appearance, and water problems. Work types were classified into seven groups: reinforced concrete (RC), masonry, finish, mechanical, electrical, and plumbing (MEP), door and windows, furniture, and miscellaneous. Using a risk matrix from these categories, the frequency distribution and severity distribution for each matrix cell was used to calculate loss distributions; these were combined to find the total loss distribution. The defect risks centered on RC and MEP. For RC, broken items and water leaks due to cracks or damage represented the most severe defects. For MEP, severe defects occurred owing to malfunctions in products and installation problems. Loss distributions can be used to create scenarios and corresponding response plans; thus, when a defect dispute occurs, the cost can be assessed. Furthermore, residential buildings’ loss distributions for each cell can be used to evaluate the types of work where defects occur and to verify relevant subcontractor’s abilities.

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

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.

This study analyzes and evaluates building maintenance costs by applying the loss-distribution approach (LDA) normally used by financial organizations to assess operational risks to 7,267 historical maintenance data items for 1,915 apartment housing complexes in South Korea. These maintenance activities are classified into six categories and the period after construction into four categories in order to create a risk matrix, after which the severity and frequency distribution for each cell is calculated and the results combined to derive the total maintenance cost distribution. Maintenance activities incurred 11 to 20 years post-construction were found to have the greatest impact on cost and highest occurrence, likely because facilities such as fire safety and electrical equipment require replacement during that period and the painting is renewed. Adapting an LDA for maintenance cost evaluation, therefore, allows both the total cost distribution and the specific cost distribution incurred by particular circumstances to be analyzed, making it possible to allocate sufficient maintenance funding to properly maintain the building structure and systems and ensure adequate housing quality for occupants.