Z
Zainab Hasan Ali
Researcher at University of Diyala
Publications - 12
Citations - 275
Zainab Hasan Ali is an academic researcher from University of Diyala. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 4, co-authored 8 publications receiving 127 citations.
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Journal ArticleDOI
Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model
Abeer A. Al-Musawi,Afrah Abdulelah Hamzah Alwanas,Sinan Q. Salih,Zainab Hasan Ali,Minh Tung Tran,Zaher Mundher Yaseen +5 more
TL;DR: New hybrid artificial intelligence model called integrative support vector regression with firefly optimization algorithm (SVR-FFA) for shear strength prediction of steel fiber-reinforced concrete beam (SFRCB) is attempted.
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Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model
TL;DR: The developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error, and indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.
Journal ArticleDOI
Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model
Guangnan Zhang,Zainab Hasan Ali,Mohammed Suleman Aldlemy,Mohamed H. Mussa,Sinan Q. Salih,Sinan Q. Salih,Mohammed Majeed Hameed,Zainab S. Al-Khafaji,Zaher Mundher Yaseen +8 more
TL;DR: The proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.
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Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
Hai Tao,Ali Omran Al-Sulttani,Ameen Mohammed Salih Ameen,Zainab Hasan Ali,Nadhir Al-Ansari,Sinan Q. Salih,Reham R. Mostafa +6 more
TL;DR: The hydrological process has a dynamic nature characterised by randomness and complex phenomena, and the application of machine learning (ML) models in forecasting river flow has grown rapidly.
Journal ArticleDOI
Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction
Jamal Abdulrazzaq Khalaf,Abeer A. Majeed,Mohammed Suleman Aldlemy,Zainab Hasan Ali,Ahmed W. Al Zand,S. Adarsh,Aissa Bouaissi,Mohammed Majeed Hameed,Zaher Mundher Yaseen +8 more
TL;DR: In this article, the capacity of deep learning neural network (DLNN) for shear strength prediction of Perfobond Rib Shear Strength Connector (PRSC) is investigated, and the proposed DLNN model is validated against support vector regression (SVR), ANN, and M5 tree model.