Topic Editors

State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
College of Engineering and Technology, Southwest University, Chongqing 400715, China
Dr. Fengbo Wu
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Dr. Runchuan Xia
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Dr. Yonghui Fan
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China

New Developments in Intelligent Construction and Operation of Infrastructures

Abstract submission deadline
30 September 2025
Manuscript submission deadline
30 November 2025
Viewed by
1606

Topic Information

Dear Colleagues,

Infrastructure plays an important role in human activities. Improving the safety and durability of infrastructure becomes a major challenge in the economic development process. The combination of artificial intelligence with engineering construction, structural health monitoring, condition assessment, and performance improvement is an essential way to realize the intelligent construction and operation of infrastructure, which has become a research hotspot and difficulty involving multiple disciplines. At present, with the rapid development of the Internet of Things, big data, artificial intelligence, and remote sensing, it is inevitable to explore new technologies covering the intelligent construction and operation of infrastructures in the life cycle. For this reason, this topic aims to boost knowledge and development in the intelligent construction and operation of infrastructures through multi-disciplinary works.

The potential topics include (but are not limited to):

  • Intelligent structural design;
  • Intelligent construction;
  • Intelligent operation and maintenance;
  • Intelligent disaster prevention;
  • Structural earthquake and wind engineering;
  • New technologies in the life cycle of infrastructures.

Prof. Dr. Jingzhou Xin
Dr. Yan Jiang
Dr. Fengbo Wu
Dr. Runchuan Xia
Prof. Dr. Simon X. Yang
Dr. Qizhi Tang
Dr. Yonghui Fan
Topic Editors

Keywords

  • non-destructive testing
  • structural health monitoring
  • construction
  • artificial intelligence
  • disaster prevention
  • smart materials
  • structural design
  • structural durability
  • structural reinforcement
  • marine and ocean structures/infrastructures

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
1.8 4.1 2008 18.9 Days CHF 1600 Submit
Buildings
buildings
3.1 3.4 2011 15.3 Days CHF 2600 Submit
Sensors
sensors
3.4 7.3 2001 18.6 Days CHF 2600 Submit
CivilEng
civileng
- 2.8 2020 24.4 Days CHF 1200 Submit
Journal of Marine Science and Engineering
jmse
2.7 4.4 2013 16.4 Days CHF 2600 Submit

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Published Papers (1 paper)

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18 pages, 3218 KiB  
Article
CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer
by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu and Yuantong Xu
Buildings 2025, 15(2), 197; https://doi.org/10.3390/buildings15020197 - 10 Jan 2025
Viewed by 459
Abstract
Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature [...] Read more.
Lining cracking is among the most prevalent forms of tunnel distress, posing significant threats to tunnel operations and vehicular safety. The segmentation of tunnel lining cracks is often hindered by the influence of complex environmental factors, which makes relying solely on local feature extraction insufficient for achieving high segmentation accuracy. To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). By fostering information exchange among local features, the model enhances comprehension of the global structural patterns of cracks and improves inference capabilities in recognizing intricate crack configurations. This approach effectively addresses the challenge of modeling contextual information in crack feature extraction. Additionally, the Detailed-Macro Feature Fusion (DMFF) module enables multi-scale feature integration by combining detailed and coarse-grained features, mitigating the significant feature loss encountered during the encoding and decoding stages, and further improving segmentation precision. To overcome the limitations of existing public datasets, which often feature a narrow range of crack types and simplistic backgrounds, this study introduces TunnelCrackDB, a dataset encompassing diverse crack types and complex backgrounds.Experimental evaluations on both the public Crack dataset and the newly developed TunnelCrackDB demonstrate the efficacy of CGV-Net. On the Crack dataset, CGV-Net achieves accuracy, recall, and F1 scores of 73.27% and 57.32%, respectively. On TunnelCrackDB, CGV-Net attains accuracy, recall, and F1 scores of 81.15%, 83.54%, and 82.33%, respectively, showcasing its superior performance in challenging segmentation tasks. Full article
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