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| 1 | Shear Buckling Strength of Trapezoidally Corrugated Steel Webs for Bridges显示文摘 | Heungbae Gil Seungrok Lee Jongwon Lee Hakeun Lee | | 0，，： | 1 |
| 2 | Cable Erection Test at Splay Band for Spatial Suspension Bridge显示文摘 | Heungbae Gil Youngjae Choi | 2002 | Journal of Bridge Engineering2002，，910： | 1 |
| 3 | Interactive Shear Buckling Behavior of Trapezoidally Corrugated Steel Webs显示文摘 | Yi Jongwon Gil Heungbae Youm Kwangsoo | 2008 | Engineering Structures2008，30，： | 1 |
| 4 | Interactive shear buckling of trapezoidally corrugatedsteel webs显示文摘 | Yi Jongwon Gil Heungbae Youm Kwangsoo | 2008 | Engineering Structures2008，30，16： | 1 |
| 5 | Bracing requirement of inelastic columns 显示文摘 | Heungbae Gil Yura Joseph A | 1999 | Journal of Construction of Steel Research1999，51，： | 1 |
| 6 | Bracing requirement of inelastic columns 显示文摘 | Heungbae Gil Yura Joseph A | 1999 | Journal of Construction of Steel Research1999，51，： | 1 |
| 7 | Interactive shear buckling behavior of trapezoidally corrugated steel webs显示文摘 | Jongwon Yi Heungbae Gil Kwangsoo Youmc Hakeun Leed | | 0，，30： | 1 |
| 8 | Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map显示文摘Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models. | Hyunkyu Shin Yonghan Ahn Mihwa Song Heungbae Gil Jungsik Choi Sanghyo Lee | 2023 | Computers, Materials & Continua2023，，6： | 1 |
| 9 | Cable Erection Test at Splay Band for Spatial Suspension Bridge显示文摘 | Heungbae Gil Youngjae Choi | 2002 | Journal of Bridge Engineering2002，7，5： | 1 |
| 10 | Interactive shear buckling behavior of trapezoidally corrugated steel webs 显示文摘 | Jongwon Yi Heungbae Gil Kwangsoo Youm | 2008 | Engineering Structures2008，30，： | 1 |
| 11 | Interactive Shear Buckling Behavior of Trapezoidally Corrugated Steel Webs 显示文摘 | JONGWON YI HEUNGBAE GIL KWANGSOO YOUM HAKEUN LEE | 2008 | Engineering Structures2008，30，： | 1 |
| 12 | Yura,Bracing requirements of inelastic columns显示文摘 | Joseph A | 1999 | Journal of Constructional steel research1999，51，： | 1 |
| 13 | Bracing requirements of inelastic columns显示文摘 | HEUNGBAE Gil JOSEPH A Yura | 1999 | Journal of Constructional Steel Research1999，51，： | 1 |
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