Session Details
構造物の設計/維持管理におけるAI/DX(8)
Fri. Sep 4, 2026 1:30 PM - 2:50 PM JST
Fri. Sep 4, 2026 4:30 AM - 5:50 AM UTC
Fri. Sep 4, 2026 4:30 AM - 5:50 AM UTC
Multi-Purpose Hall, 5th Floor, Building 2 (Hokkai School of Commerce)
[CS11-57]Adaptation of Data Assimilation to improve the Accuracy of Damage Estimation for Components during Earthquake.
*Kengo Matsumoto1, Yasushi Tanaka1, Noa Igarashi2, Keigo Tamano2, Naoki Sogabe2 (1. KANAZAWA Institute of Technology, 2. KAJIMA Corporation)
[CS11-58]Fine-Tuning Pre-trained Deep Learning for Bridge Displacement Estimation from Acceleration
*Ririko Fujita Fujita1, Masaru Kitahara2, Tomonori Nagayama1, Kai Xue1 (1. University of Tokyo, 2. Hokkaido University)
[CS11-59]Validity for Condition Assessment of Bridge during Earthquake by using UAV measurement data
*Nao Hidaka1, Kouki Tsubouchi1, Chie Seya2, Shouhei Yoshida2 (1. Nagoya Institute of Technology, 2. Nagoya Expressway Public Corporation)
[CS11-60]Seismic retrofit design of a two-span continuous steel through truss bridge modeled in three dimensions
*Tomoki Oguri1, Nagoya Kazushi1, Horiuchi Shin1, Onda Toshihide1, shimokawara suguru2, Murata kousuke2 (1. Yachiyo Engineering Co., Ltd., 2. East Nippon Expressway Company Limited)
[CS11-61]Fundamental Study on the Application of Bayesian Optimization to Traffic Camera Pole Based on Nonlinear Dynamic Analysis
*Shoya Hirano1, Yoshihiko Toda2, Weisung Lin2, Katsuhiro Tanaka2, Seiya Yamaki1, Sota Sasawaki1, Masahiro Hattori1 (1. Hanshin Expressway Research Institute for Advanced Technology, 2. JIP Techno Science Corporation)
[CS11-62]Development of a Method for Estimating Scour Depth and Performing Probabilistic Risk Assessment of River Bridge Piers Using Dynamic Bayesian Updating
*Yukito Hanabata1, Daigo Kawabe1, Chulwoo Kim1 (1. Graduate School of Kyoto University)
[CS11-63]ANN-BASED PREDICTION OF STRESS CONCENTRATION FACTORS IN CORRUGATED STEEL WEB GIRDER WELDED JOINTS
*Lingjie Chen1, Shozo Nakamura1, Takafumi Nishikawa1, Kohei Yamaguchi1 (1. Nagasaki University)
[CS11-64]Visually-Driven Synthetic Image Generation and Deep Learning for Classification of Local Buckling Severity in Steel Bridge Piers
*Lama Hajmousa1, Ji Dang1 (1. Saitma University)
