Fiber-reinforced polymer composites in strengthening reinforced concrete structures: A critical review MZ Naser, RA Hawileh, JA Abdalla Engineering Structures 198, 109542, 2019 | 372 | 2019 |
Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences MZ Naser, AH Alavi Architecture, Structures and Construction 3 (4), 499-517, 2023 | 186 | 2023 |
Finite element simulation of reinforced concrete beams externally strengthened with short-length CFRP plates RA Hawileh, MZ Naser, JA Abdalla Composites Part B: Engineering 45 (1), 1722-1730, 2013 | 152 | 2013 |
Modeling of insulated CFRP-strengthened reinforced concrete T-beam exposed to fire RA Hawileh, M Naser, W Zaidan, HA Rasheed Engineering structures 31 (12), 3072-3079, 2009 | 151 | 2009 |
Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams R Solhmirzaei, H Salehi, V Kodur, MZ Naser Engineering structures 224, 111221, 2020 | 139 | 2020 |
Extraterrestrial construction materials MZ Naser Progress in materials science 105, 100577, 2019 | 125 | 2019 |
An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference MZ Naser Automation in Construction 129, 103821, 2021 | 114 | 2021 |
Finite element modeling of reinforced concrete beams externally strengthened in flexure with side-bonded FRP laminates RA Hawileh, HA Musto, JA Abdalla, MZ Naser Composites Part B: Engineering 173, 106952, 2019 | 103 | 2019 |
Nonlinear finite element modeling of concrete deep beams with openings strengthened with externally-bonded composites RA Hawileh, TA El-Maaddawy, MZ Naser Materials & Design 42, 378-387, 2012 | 102 | 2012 |
Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences MZ Naser Fire Technology, 1-44, 2021 | 96 | 2021 |
Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices ATG Tapeh, MZ Naser Archives of Computational Methods in Engineering 30 (1), 115-159, 2023 | 92 | 2023 |
Evaluating structural response of concrete-filled steel tubular columns through machine learning MZ Naser, S Thai, HT Thai Journal of Building Engineering 34, 101888, 2021 | 90 | 2021 |
Materials and design concepts for space-resilient structures MZ Naser, AI Chehab Progress in Aerospace Sciences 98, 74-90, 2018 | 85 | 2018 |
Importance factor for design of bridges against fire hazard VKR Kodur, MZ Naser Engineering Structures 54, 207-220, 2013 | 83 | 2013 |
Fire resistance evaluation through artificial intelligence - A case for timber structures MZ Naser Mendeley Data, 2019 | 82 | 2019 |
Deriving temperature-dependent material models for structural steel through artificial intelligence MZ Naser Construction and Building Materials 191, 56-68, 2018 | 81 | 2018 |
Structural Fire Engineering V Kodur, M Naser McGraw Hill Professional, 2020 | 80* | 2020 |
Insights into performance fitness and error metrics for machine learning MZ Naser, A Alavi arXiv preprint arXiv:2006.00887, 2020 | 79 | 2020 |
A probabilistic assessment for classification of bridges against fire hazard MZ Naser, VKR Kodur Fire Safety Journal 76, 65-73, 2015 | 73 | 2015 |
Bond behavior of CFRP cured laminates: Experimental and numerical investigation M Naser, R Hawileh, JA Abdalla, A Al-Tamimi | 71 | 2012 |