D
D. Jahed Armaghani
Researcher at Universiti Teknologi Malaysia
Publications - 12
Citations - 1445
D. Jahed Armaghani is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Artificial neural network & Particle swarm optimization. The author has an hindex of 11, co-authored 12 publications receiving 1048 citations. Previous affiliations of D. Jahed Armaghani include Amirkabir University of Technology.
Papers
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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 pile bearing capacity using a hybrid genetic algorithm-based ANN
TL;DR: Results indicate that implementation of GA-based ANN models as a highly-reliable, efficient and practical tool in predicting the pile bearing capacity is of advantage.
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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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Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction
R. Shirani Faradonbeh,D. Jahed Armaghani,M. Z. Abd. Majid,Mahmood Md Tahir,B. Ramesh Murlidhar,Masoud Monjezi,H. M. Wong +6 more
TL;DR: In this article, a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia is presented, which is able to predict blast-induced ground vibration more accurately than other developed technique.