Pengfei Ma: h-index, Total Citations, and Citation Map
Pengfei Ma's h-index is 8 (7 i10-index, 192+ total citations across 15+ publications) according to Google Scholar as of June 2026. Pengfei Ma is affiliated with Missouri University of Science and Technology, Michael Baker International.
Pengfei Ma is a researcher affiliated with Missouri University of Science and Technology, Michael Baker International, specializing in Concrete Durability, Optic fiber sensor, Structural Health Monitoring. Their work has been cited 192 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Pengfei Ma's Citation Metrics
Bibliometric impact based on 15 indexed publications. Of these, 14 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 8
- i10-Index
- 7
- Total Citations
- 192
- Citing Countries
- 32
As of June 2026.
Pengfei Ma has an h-index of 8 and 192 total citations across 15 publications, with research cited by institutions in 32 countries.
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Global Impact Map
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Top Cited Works
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Hyperspectral reflectance for determination of steel rebar corrosion and Cl− concentration
202344
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
5 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered nondestructive structural health monitoring by establishing hyperspectral reflectance for rebar corrosion detection and extending spectral methods to ASR characterization.
The researcher developed a hyperspectral imaging framework to identify and discriminate natural gas-induced vegetation stress for pipeline leakage detection.
The researcher developed a deep learning framework using hyperspectral vegetation indicators for the early detection of natural gas pipeline leaks.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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