Assessment of beam-column joints in reinforced concrete and precast concrete structures based on CNN
Kim D. Heo J. Lee M. Lee D. Ju H.
10 December 2025Techno-Press
Steel and Composite Structures
2025#57Issue 5405 - 419 pp.
In this study, a CNN (Convolutional Neural Network) based image recognition model is proposed to address the challenges in diagnosis and inspection of deteriorated buildings. With approximately 42.6% of buildings nationwide classified as aging, regular inspections are critical, yet current visual assessments are prone to a shortage of specialized personnel. While existing deep learning studies focus primarily on surface defects, this research targets the failure modes of beam-column joints which are critical elements for overall safety of structural system. Based on data collected from existing literature, a dataset was constructed by classifying the failure modes of beam-column joints in reinforced concrete and precast concrete structures according to the crack patterns at the joints. Using libraries such as TensorFlow and Grad-CAM++, the model was trained, and its performance was evaluated. The classification of joint failure modes based on the ACI 352R-02 code resulted in an accuracy of approximately 64%. In contrast, the 5-fold cross-validation results showed an accuracy of 77% and AUC (Area Under the Curve) of 80%, demonstrating the potential to develop a system that enables even non-experts to easily assess the damaged structures.
beam-column joint , classification , image data , precast concrete , reinforced concrete
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Department of Architecture and Architectural Engineering, Hankyong National University, Jungang-ro 327, Gyeonggi, Anseong, 17579, South Korea
School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
Department of Architectural Engineering, Chungbuk National University, 1 Chungdae-ro, Chungbuk, Cheongju, 28644, South Korea
School of Architecture and Architectural Engineering, Hankyong National University, Jungang-ro 327, Gyeonggi, Anseong, 17579, South Korea
Department of Architecture and Architectural Engineering
School of Engineering and Digital Sciences
Department of Architectural Engineering
School of Architecture and Architectural Engineering
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