Journal paper (Reviewed)

• Takao Dantsuji, Yuki Takayama：Hypercongestion, autonomous vehicles, and urban spatial structure,, Transportation Science, in press. English. https://doi.org/10.1287/trsc.2024.0519
• Taichi Ishikawa and Riki Honda：Deep Learning-based interpretation method of bridge point cloud data considering uncertainty, Journal of JSCE Special issue（Applied Mechanics）, No. 15, 2024. (in Japanese).
• Kosuke Kinoshita, Yukihiro Yano, Takahiro Kumura, Takashi Miyamoto, Pang-jo Chun：Comparative analysis of time-series InSAR bridge deformation analysis and structural simulation for a hinged PC bridge, Artificial Intelligence and Data Science, Vol. 5, No. 3, pp. in press, 2024. (in Japanese).
• Naomichi Katayama, Pang-jo Chun：Potential of digital twin in seismic emergency inspection of road bridges, Artificial Intelligence and Data Science, Vol. 5, No. 3, pp. in press, 2024. (in Japanese).
• Kenta Itakura, Takuya Hayashi, Yuto Kamiwaki, Pang-jo Chun：Semantic segmentation and sensor fusion in point cloud processing for estimation of bridge structural parameters, Artificial Intelligence and Data Science, Vol. 5, No. 3, pp. in press, 2024. (in Japanese).
• Kenta Itakura, Takuya Hayashi, Yuto Kamiwaki, Pang-jo Chun：Noise Removal from Point Clouds through Sensor Fusion of LiDAR and Camera, Artificial Intelligence and Data Science, Vol. 5, No. 3, pp. in press, 2024. (in Japanese).
• Pang-jo Chun, Honghu Chu, Kota Shitara, Tatsuro Yamane, Yu Maemura：Implementation of explanatory texts output for bridge damage in a bridge inspection web system, Advances in Engineering Software, Vol. 195, pp. 103706, 2024. English.
• "Pang‐jo Chun, Toshiya Kikuta：Self-training with Bayesian neural networks and spatial priors for unsupervised domain adaptation in crack segmentation, Computer‐Aided Civil and Infrastructure Engineering, Vol. 39, No. 17, pp. 2642-2661， 2024. English. "
• Takashi Miyamoto：Reconstructing complex 3D surface of roadways from point cloud data, Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 5453-5456， 2024. English. https://doi.org/10.1109/IGARSS53475.2024.10642114
• Kinoshita Kosuke, Yano Yukihiro, Kumura Takahiro：Comparison of anomalous displacement detectability of bridges between X and C band InSAR data: Case study on a collapse of water pipe bridge, European Workshop on Structural Health Monitoring (EWSHM 2024), Potsdam, Germany, 2024. English. https://doi.org/10.58286/29761
• Pang-jo Chun：Current Status and Prospects of Artificial Intelligence Technologies for Further Utilization of Urban 3D Digital Twins, Journal of structures and materials in civil engineering, Vol. 39, pp. 19-28， 2024. (in Japanese). https://doi.org/10.60345/kabse.39.0_19
• Pang-jo Chun：AI-Driven 3D Digital Twins for Smart Cities, Concrete Journal, Vol. 62, No. 9, pp. 732-737， 2024. (in Japanese).
• Kumura Takahiro, Kinoshita Kosuke, Yano Yukihiro：Towards the Development of a Bridge Displacement Analysis System Using Remote Sensing, Vol. 58, No. 8, pp. 115-118， 2024. (in Japanese).

International conference (Reviewed)

• Yano Yukihiro, Kinoshita Kosuke, Kumura Takahiro：Physics-informed decomposition of LOS displacement for infrastructure monitoring, SPIE Remote Sensing 2024, United Kingdum, Edinburgh, 2024. English. https://doi.org/10.1117/12.3031328
• Yano Yukihiro, Kinoshita Kosuke, Kumura Takahiro：Detailed analysis based on vertical and longitudinal deformations estimated from InSAR displacement by data assimilation, European Workshop on Structural Health Monitoring (EWSHM 2024), Potsdam, Germany, 2024. English. https://doi.org/10.58286/29757
• Nishio Mayuko, Sawayama Runa, Kinoshita Kosuke, Kumura Takahiro：Correlation analysis of InSAR displacement data and inspection data towards multiple bridge monitoring in transportation network, European Workshop on Structural Health Monitoring (EWSHM 2024), Potsdam, Germany, 2024. English. https://doi.org/10.58286/29755
• Pang-jo Chun, Tatsuro Yamane, Shreejan Maharjan：Advancing Infrastructure Inspection with AI and 3D Data Platform Integration for Improved Damage Assessment and Enhanced Modeling , University-Industry Collaborations for Sustainable Development (ICSD) 2024, Colombo, pp. ICSD100, 2024. English.
• Pang-jo Chun, Tatsuro Yamane, Shreejan Maharjan, Shogo Inadomi, Chao Lin, Shitao Zheng and Xianfeng Li：Integrating AI and 3D Data Platform for Advancing Infrastructure Inspection including Enhanced Damage Assessment and Modeling, The 18th East Asia-Pacific Conference on Structural Engineering & Construction (EASEC-18), Thailand, 2024. English.

Domestic conference (Reviewed)

• Kinoshita Kosuke, Yano Yukihiro, Kumura Takahiro, Takashi Miyamoto, Pang-jo Chun：Comparative analysis of time-series InSAR bridge deformation analysis and structural simulation for a hinged PC bridge, 2024. (in Japanese).
• Kumura Takahiro, Kinoshita Kosuke, Yano Yukihiro：Satellite-based Bridge Anomaly Detection as a Case Study of 2024 Noto Peninsula Earthquake, 2024. (in Japanese).

International conference (non-Reviewed)

Domestic conference (Non Reviewed)

• Taichi Ishikawa and Riki Honda：3D bridge model generation method based on interpretation of point cloud data using deep learning, Japan Society of Civil Engineers 2024 Annual Meeting, Tohoku University, 2024. (in Japanese).
• Shuuhei Abe, Pang-jo Chun：Point Cloud Semantic Segmentation by Machine Learning Using Information of Bridges, Japan Society of Civil Engineers 2024 Annual Meeting, pp. CS11-05, 2024. (in Japanese).
• Taichi Ishikawa and Riki Honda：Proposal of deep learning-based interpretation method of bridge point cloud data considering uncertainty, The 27th Applied Mechanics Symposium, Okayama University, 2024. (in Japanese).
• Riki Honda：Development of Digital Twin Environment for Smart Infrastructure Management(Proceedings of Symposium on Applied Mechanics), The 27th Applied Mechanics Symposium, Okayama University, 2024. (in Japanese).

Others

• Shuuhei Abe, Pang-jo Chun, Wakana Asano：POINT CLOUD SEMANTIC SEGMENTATION CONSIDERING INFORMATION OF BRIDGES, Proceeding of the i-Construction Symposium, Vol. 6, pp. 113-116， 2024. (in Japanese).