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Zainah Ibrahim

Researcher at University of Malaya

Publications -  150
Citations -  2962

Zainah Ibrahim is an academic researcher from University of Malaya. The author has contributed to research in topics: Damper & Finite element method. The author has an hindex of 24, co-authored 137 publications receiving 1914 citations. Previous affiliations of Zainah Ibrahim include Universiti Tun Hussein Onn Malaysia.

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Nondestructive test methods for concrete bridges: A review

TL;DR: NDT methods applicable to concrete bridges are reviewed in this paper, where a flow chart based on damage level along with NDT methods and potential remedial measures are proposed for periodic health monitoring of structures.
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Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

TL;DR: In this paper, the shear strength of steel-concrete composite beams was analyzed based on the respective variable parameters and the methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) was adopted for this purpose.
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Prediction of shear capacity of channel shear connectors using the ANFIS model

TL;DR: The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the classical linear regressions.
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Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

TL;DR: Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models and indicate that on the whole, the newflanged algorithm creates good generalization presentation.
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RETRACTED ARTICLE: Analysis of influential factors forpredicting the shear strength of a V-shaped angle shear connector in composite beamsusing an adaptive neuro-fuzzy technique

TL;DR: The results exhibited that the proposed shear connector (V-shaped angle) contained the potentiality to be used practically after several improvements, and might be the improvement of the testing process for different predictive models with more input variables that will improve the predictive power of the created models.