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Pipeline and Underground Space Technology
Topic Information
Dear Colleagues,
The development of underground space technology meets the needs of rapid urban development, such as easing traffic pressure, promoting economic development, and realizing resource sharing. With an increase in the age of service, underground engineering occurs a variety of damages, such as deformation, water gushing, collapse, etc. These damages cause huge economic losses and casualties. Therefore, the detection and monitoring of the long-term performance in addition to the timely repair of underground engineering become a major concern. This Topic aims to collect the latest advances and trends in the field of intelligent detection and trenchless technology of underground engineering.
In this Topic, we invite you to contribute original research articles and reviews on any aspects related to the intelligent detection and trenchless technology of underground engineering, including, but not limited to:
AI-based intelligent sensing and monitoring, damage detection, diagnosis and characterization of pipeline damages, in situ field test methods, nondestructive techniques, laboratory tests, and analysis.
The geoinvestigation, geomechanical analysis, design and modeling, construction and monitoring, and maintenance as well as rehabilitation of pipelines in addition to large underground and earth-sheltered structures.
The planning, development, and operation of underground space and underground space environments, such as architecture, safety and comfort, and human–space interaction.
The installation and rehabilitation of underground pipelines, ducts, and cables using pipe jacking, microtunneling, and other forms of trenchless technologies.
Prof. Dr. Hongyuan Fang
Prof. Dr. Baosong Ma
Prof. Dr. Qunfang Hu
Prof. Dr. Xin Feng
Prof. Dr. Niannian Wang
Dr. Cong Zeng
Prof. Dr. Hongfang Lu
Topic Editors
Keywords
- pipeline
- underground space technology
- intelligent detection
- trenchless technology
- performance evaluation
Participating Journals
Journal Name | Impact Factor | CiteScore | Launched Year | First Decision (median) | APC |
---|---|---|---|---|---|
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Applied Sciences
|
2.5 | 5.3 | 2011 | 18.4 Days | CHF 2400 |
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Buildings
|
3.1 | 3.4 | 2011 | 15.3 Days | CHF 2600 |
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Geosciences
|
2.4 | 5.3 | 2011 | 23.5 Days | CHF 1800 |
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Machine Learning and Knowledge Extraction
|
4.0 | 6.3 | 2019 | 20.8 Days | CHF 1800 |
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Materials
|
3.1 | 5.8 | 2008 | 13.9 Days | CHF 2600 |
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Sensors
|
3.4 | 7.3 | 2001 | 18.6 Days | CHF 2600 |
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