Standout Papers

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation 2022 2026 2023 2024113
  1. Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation (2022)
    Maaz Amjad, Irshad Ahmad et al. Applied Sciences

Immediate Impact

1 from Science/Nature 14 standout
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Citing Papers

Supercapacitors: A promising solution for sustainable energy storage and diverse applications
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Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
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2 intermediate papers

Works of Maaz Amjad being referenced

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation
2022 Standout
Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature
2021

Author Peers

Author Last Decade Papers Cites
Maaz Amjad 70 138 40 44 15 271
Yonas Zewdu Ayele 18 148 23 8 25 304
Adel Mottahedi 20 178 24 5 11 320
Amin Keramati 24 66 4 14 12 286
Benjin Zhu 21 229 6 27 11 327
Enji Sun 7 65 6 21 12 203
Yinhe Zheng 134 82 5 11 17 329
Clement Kweku Arthur 33 106 4 6 12 270
C S Papacostas 7 46 17 14 19 220
Pedram Ghannad 15 90 61 12 19 284
Olga Špačková 8 159 9 7 15 304

All Works

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2026