Zhigang Wang 编辑 at IgMin Research
We strive to catalyze dialogues that encourage collaborative progress and innovation.
简历
Wang Zhigang, a PhD holder from Beijing University of Technology, is an expert in disaster prevention and mitigation for underground structures that traverse active fracture zones. He has an impressive publication record, including 10 papers as the first or corresponding author, with 4 SCI papers (RMRE, ES, ksce, etc.) and 4 EI papers (Journal of Geotechnical Engineering and Journal of Civil Engineering, etc.).
The candidate's extensive experience and expertise in research and academia are highlighted by their leadership and contributions to multiple successful research projects, including two National Key Research and Development Programmes, two National Natural Science Foundations of China, and several university-enterprise cooperation projects.
研究兴趣
Disaster prevention and mitigation for tunnels crossing active fault zones
Editor
工作详情
Doctor
Beijing University of Technology
Faculty of Architecture, Civil And Transportation Engineering
China
0000-0001-9715-3414 Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
现在哪些文章在热门？
研究文章
- A New Modification of Classification of Traumatic Patients with Pelvic Fracture
- The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach
- Lunar-Derived Propellants for Fueling Mars-Bound Spacecraft in Cis-Lunar Space
- LC-MS and HPLC-UV for Detecting Uremic Toxins: Two Validated Methods with Simultaneous Sensitivity and Specificity Evaluation
- Efficacy of Different Concentrations of Insect Growth Regulators (IGRs) on Maize Stem Borer Infestation
- A Machine Learning-based Method for COVID-19 and Pneumonia Detection
