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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

An Overview of Machine Learning Approaches for Predicting Marshall Parameters in Modified Asphalt Mixtures (MAM)

2026International Journal of Research and Innovation in Applied ScienceJournalOpen access

10.51584/ijrias.2026.11010066 Full text0 citations

Authors

6 authors across 3 institutions in 3 countries.

William Rupert WabokeFirst author
- University of KwaZulu-Natal (ZA)
Tope Ojuawo
- Kano State University of Technology (NG)
Ashiru Sani
- Kano State University of Technology (NG)
Serestina Viriri
- University of KwaZulu-Natal (ZA)
Obunike Arinze Ubadike
Omotayo Paul AleSenior author

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Asphalt Pavement Performance Evaluationlevel 3 · 0.76
Infrastructure Maintenance and Monitoringlevel 3 · 0.10
Hydrological Forecasting Using AIlevel 3 · 0.02

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2026 · International Journal of Research and Innovation in Applied Science · 0 citations
https://openalex.org/W7128293165

An Overview of Machine Learning Approaches for Predicting Marshall Parameters in Modified Asphalt Mixtures (MAM) · Sankore