• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.133-134
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• 2022

This study proposed vision artificial intelligence-based automated supervision technology for external insulation and finishing system, and basic research was conducted for it. The automated supervision technology proposed in this study consists of the object detection model (YOLOv5) and the part that derives necessary information based on the object detection result and then determines whether the external insulation-related adhesion regulations are complied with. As a result of a test, the judgement accuracy of the proposed model showed about 70%. The results of this study are expected to contribute to securing the external insulation quality and further contributing to the realization of energy-saving eco-friendly buildings. As further research, it is necessary to develop a technology that can improve the accuracy of the object detection model by supplementing the number of data for model training and determine additional related regulations such as the adhesive area ratio.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.165-173
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• 2023

This research proposed a model for automatically monitoring the quality of insulation adhesive application in external insulation construction. Upon case implementation, the area segmentation model demonstrated a 92.3% accuracy, while the area and distance calculation accuracies of the proposed model were 98.8% and 96.7%, respectively. These findings suggest that the model can effectively prevent the most common insulation defect, insulation failure, while simultaneously minimizing the need for on-site supervisory personnel during external insulation construction. This, in turn, contributes to the enhancement of the external insulation system. Moving forward, we plan to gather construction images of various external insulation methods to refine the image segmentation model's performance and develop a model capable of automatically monitoring scenarios with a considerable number of insulation materials in the image.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.401-412
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• 2020

This study analyzed the causes of warehouse fires using a creative problem-solving technique called TRIZ. It identified preventive measures by applying 4M. The results are as follows. First, this study examined the inconsistency among the causes of warehouse fires using TRIZ. Second, it analyzed human factors and fire prevention measures in warehouses such as safety standards for managers, and methods for the promotion of safety consciousness among workers, and for the reinforcement of construction technology for sandwich panel workers. Third, it identified the mechanical and facility factors and fire prevention measures in warehouses such as safety facilities, the expanded installation of safety devices, the adoption and development of fire suppression equipment, and the deployment of methods to improve the fire resistance of sandwich panels. Fourth, it presented working and environmental factors and fire prevention measures in warehouses such as the tightening of safety precautions and the supervision of working methods, and setting fire partitions both in loading places and based on performance-based design. Finally, it proposed managerial factors and fire prevention measures in warehouses such as specific targeting for firefighting with low fire hazards, reviewing the material quality regulations of non-combustible or higher for sandwich panels in the specific target of firefighting that cannot apply fire safety standards, installing sprinklers in cold storage, and mandating the installation of automated facilities with retroactive application regardless of the floor area in the warehouse with a sandwich panel structure.