Cited by Automated Interpretation of Non-Destructive Evaluation Contour Maps Using Large Language Models for Bridge Condition Assessment

English text and video online resource recommendation based on attention mechanism and GNN DOI

Zunlan Xiao,

Zhimin Yang,

Yin Li

et al.

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

This paper introduces an innovative online resource recommendation system tailored for English text and video content, leveraging the power of attention mechanisms graph neural networks. Given exponential growth learning resources, a crucial challenge lies in delivering personalized efficient recommendations to users. Our study strives optimize both accuracy efficiency these by harnessing synergistic effects GNNs. By collecting analyzing large amount user behavior data, we build user-resource interaction graph. not only contains information between users but also incorporates association providing rich context subsequent recommendations. We introduce mechanism handle node edge graphs. assessing significance various nodes edges process, are able capture users’ interests preferences with greater precision. According experimental integration has led notable improvement system’s accuracy, achieving increase approximately 15%. significant enhancement underscores effectiveness effectively capturing interests. Additionally, leverage networks model intricate structural within With convolution operations, potential relationships resources use process. Experimental results show that combined GNN, coverage increased about 20%, more diverse results. The proposed based on GNN achieved improvements diversity In future, will further explore optimization methods provide services.

Language: Английский

Citations

Domain knowledge-driven image captioning for bridge damage description generation DOI

CL Chai, Yan Gao,

Guanyu Xiong

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 174, P. 106116 - 106116

Published: March 14, 2025

Language: Английский

Citations

Seismic risk and loss assessment models for highway continuous girder bridges in large-scale zones DOI

Si-Qi Li,

Peng-Fei Qin,

Peng-Chi Chen

et al.

Soil Dynamics and Earthquake Engineering, Journal Year: 2025, Volume and Issue: 195, P. 109401 - 109401

Published: March 28, 2025

Language: Английский

Citations

Automated Image-Based Condition Assessment of Built Environment: A State-of-the-Art Investigation of Damage Characteristics and Detection Requirements DOI

Leila Farahzadi,

Ibrahim Odeh,

Mahdi Kioumarsi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104978 - 104978

Published: April 1, 2025

Language: Английский

Citations

Lightweight bilateral network of Mura detection on micro-OLED displays DOI

Guobao Zhao, Yuhang Lin, Yijun Lu

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117937 - 117937

Published: May 1, 2025

Language: Английский

Citations

Integrative AI and UAV-based visual recognition with metaheuristics for automated repair cost analysis of bridge structural deterioration DOI

Jui‐Sheng Chou, Jyh‐Ming Lien, Chi‐Yun Liu

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 176, P. 106273 - 106273

Published: May 24, 2025

Language: Английский

Citations

Damage‐level classification considering both correlation between image and text data and confidence of attention map DOI

Keisuke Maeda, Naoki Ogawa, Takahiro Ogawa

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

Abstract In damage‐level classification, deep learning. models are more likely to focus on regions unrelated classification targets because of the complexities inherent in real data, such as diversity damages (e.g., crack, efflorescence, and corrosion). This causes performance degradation. To solve this problem, it is necessary handle data complexity uncertainty. study proposes a multimodal learning model that can damaged using text related damage images, materials components. Furthermore, by adjusting effect attention maps based confidence calculated when estimating these maps, proposed method realizes an accurate classification. Our contribution development with end‐to‐end mechanism simultaneously consider both image map. Finally, experiments images validate effectiveness method.

Language: Английский

Citations

Automated Interpretation of Non-Destructive Evaluation Contour Maps Using Large Language Models for Bridge Condition Assessment DOI

Viraj Nishesh Darji,

Callie C. Liao,

Duoduo Liao

et al.

2021 IEEE International Conference on Big Data (Big Data), Journal Year: 2024, Volume and Issue: unknown, P. 3258 - 3263

Published: Dec. 15, 2024

Language: Английский

Citations