E
E. Tonnizam Mohamad
Researcher at Universiti Teknologi Malaysia
Publications - 5
Citations - 709
E. Tonnizam Mohamad is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Soil water & Geogrid. The author has an hindex of 5, co-authored 5 publications receiving 514 citations.
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Journal ArticleDOI
Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization
D. Jahed Armaghani,Mohsen Hajihassani,E. Tonnizam Mohamad,Aminaton Marto,Seyed Ahmad Noorani +4 more
TL;DR: A novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN and the results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy.
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Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
TL;DR: A new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting is presented and it is suggested that the PSO-based ANN model outperforms the other predictive models.
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Rock strength estimation: a PSO-based BP approach
E. Tonnizam Mohamad,D. Jahed Armaghani,Ehsan Momeni,Ehsan Momeni,Amir Hossein Yazdavar,Monireh Ebrahimi +5 more
TL;DR: Insight is given into development of a hybrid PSO–BP predictive model of uniaxial compressive strength (UCS) of rocks using back-propagation (BP) artificial neural network (ANN) and results showed that PSO-BP model performs well in predicting UCS.
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Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods
D. Jahed Armaghani,E. Tonnizam Mohamad,Mohsen Hajihassani,S. V. Alavi Nezhad Khalil Abad,Aminaton Marto,Mohammad Reza R. Moghaddam +5 more
TL;DR: It was found that the ANFIS model can predict flyrock with higher performance capacity compared to ANN predictive model and R2 values of testing datasets are 0.925 and 0.964, suggesting the superiority of the AnFIS technique in predicting flyrock.
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Uplift resistance of buried pipelines enhanced by geogrid
TL;DR: In this article, the authors investigated the effect of pipe diameter, burial depth, as well as length and number of geogrid layers on the uplift resistance of sandy soils.