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A deep learning approach to concrete water-cement ratio prediction · Peerford

Article · Journal · 2022

A deep learning approach to concrete water-cement ratio prediction

Sururah A. Bello, Lukumon O. Oyedele, Olakunle Kazeem Olaitan, Kolawole Olonade, Akinropo Musiliu Olajumoke, Anuoluwapo O. Ajayi and 4 more

Results in Materials

Open access · Gold
International · 2 countries

Read it 10.1016/j.rinma.2022.100300

Citations: 24from other works
Field-weighted impact: 2.151.00 is the world average for its field and year
Among its field: 86th percentileby citations, against works of its field and year
Authors: 10from 4 institutions

Authors

10 authors across 4 institutions in 2 countries

Sururah A. BelloFirst authorCorresponding
as printed: Sururah Apinke Bello
Lukumon O. Oyedele
as printed: Lukumon Oyedele
Olakunle Kazeem Olaitan
- Obafemi Awolowo University (NG)
Kolawole Olonade
- University of Lagos (NG)
as printed: Kolawole Adisa Olonade
Akinropo Musiliu Olajumoke
- Obafemi Awolowo University (NG)
Anuoluwapo O. Ajayi
as printed: Anuoluwapo Ajayi

Olugbenga Akinade

Mistura Laide Sanni

Obafemi Awolowo University (NG)

Abdul-Lateef BelloSenior author

Development goals

15Life on land

Funded by

Engineering and Physical Sciences Research Council

Countries on it

United Kingdom
Nigeria

Also on it

Teesside Universityeducation · GB
University of the West of Englandeducation · GB

Fields

A work can sit in several, each with a score

Infrastructure Maintenance and MonitoringTopic
Innovative concrete reinforcement materialsTopic
Concrete and Cement Materials ResearchTopic