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Mahmud Muhammad Jibril

Kano University of Science and Technology

Wudil, 23464

Nigeria

  • Kano University of Science and Technology

SCHOLARLY PAPERS

5

DOWNLOADS

350

TOTAL CITATIONS

0

Scholarly Papers (5)

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1.

Interfacial Tension of the H2-Brine Modelling Effect on Energy Transition of Hydrogen Geological Storage: Artificial Intelligence Predictive Insights

Number of pages: 33 Posted: 11 Aug 2023
Babatunde A. Salami, Afeez Gbadamosi, Haruna Adamu, Jamilu Usman, A. G. Usman, Mahmud Muhammad Jibril, Ganiyu Kayode Otukogbe and S. I. Abba
King Fahd University of Petroleum and Minerals (KFUPM), affiliation not provided to SSRN, affiliation not provided to SSRN, King Fahd University of Petroleum and Minerals, Near East University, Kano University of Science and Technology, Cardiff Metropolitan University and affiliation not provided to SSRN
Downloads 121 (600,462)

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Machine Learning, Hydrogen, Hydrogen storage, interfacial tension

2.

Multi-Input Attributes Scenarios Using Machine Learning Models: A Prediction of the Elastic Modulus of Concrete Containing Sawdust Ash Cured in NaCl Solution

Number of pages: 31 Posted: 01 Jul 2023
Salim Idris Malami, Isyaku Auwal Alhassan, Auwal Alhassan Musa, Babatunde A. Salami, Usman U. Aliyu, A. G. Usman, Abba Bashir, Mahmud Muhammad Jibril, Umar Jibrin Muhammad, I. A. Adam and S.I. Abba
Heriot-Watt University, Kano University of Science and Technology, Kano University of Science and Technology, King Fahd University of Petroleum and Minerals (KFUPM), Federal University Dutsin-Ma, Near East University, Federal University Dutsin-Ma, Kano University of Science and Technology, Bayero University, Federal University Dutse and King Fahd University of Petroleum and Minerals (KFUPM)
Downloads 75 (837,727)

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Artificial Intelligence, Back propagation neural network, Elastic Modulus, Sodium Chloride Solution, Support Vector Regression

3.

Improving Structural Integrity in Reinforced Concrete Footings: A Novel Approach to Compressive Strength Prediction with an Insight into Machine Learning Models

Number of pages: 31 Posted: 10 Nov 2023
Mahmud Muhammad Jibril, Umar Jibrin Muhammad, Abubakar Aisar, Abubakar S. Ado, Yusuf haruna Muhammad, Salim Idris Malami, isma'il isma'il ameenu, Abdullahi garba Shehu, A. G. Usman and S.I. Abba
Kano University of Science and Technology, Bayero University, Bayero University, Bayero University, Bayero University, Heriot-Watt University, Heriot-Watt University, Bayero University, Near East University and King Fahd University of Petroleum and Minerals (KFUPM)
Downloads 61 (951,658)

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Concrete footing, punching shear, Bilayer neural network, Medium neural network, narrow neural network, tri-layer neural network, Wide neural network

4.

Implementation of Nonlinear Computing Models and Classical Regression for Predicting Compressive Strength of High-Performance Concrete

Number of pages: 29 Posted: 04 Apr 2023
Mahmud Muhammad Jibril, Muhammad Abubakar zayyan, Salim Idris Malami, A. G. Usman, Babatunde A. Salami, Abdulazeez Rotimi and Sani Isah Abba
Kano University of Science and Technology, Federal University Dutsin-Ma, Heriot-Watt University, Near East University, King Fahd University of Petroleum and Minerals (KFUPM), Baze University and King Fahd University of Petroleum and Minerals (KFUPM)
Downloads 48 (1,086,013)

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High-performance concrete, Feedforward neural network, Elman neural network, Support vector machine, Multilinear regression

5.

A Novel Approach for Compressive Strength Prediction in Fly-Ash Geopolymer Concrete: Leveraging Optimization Algorithms, Ensemble Model and Shap Interpretation

Number of pages: 35 Posted: 27 Feb 2024
Mahmud Muhammad Jibril, Umar Jibrin Muhammad, Ahmad sani, Hussaini Abdullahi, Isma'il Mahmoud, S.I. Abba and Salim Idris Malami
Kano University of Science and Technology, Bayero University, affiliation not provided to SSRN, Kano University of Science and Technology, Kano University of Science and Technology, King Fahd University of Petroleum and Minerals (KFUPM) and Heriot-Watt University
Downloads 45 (1,133,231)

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Keywords: Fly-Ash Geopolymer concrete, Artificial intelligent, Machine learning, Compressive strength