A
Aminaton Marto
Researcher at Universiti Teknologi Malaysia
Publications - 174
Citations - 4207
Aminaton Marto is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Soil water & Compressive strength. The author has an hindex of 30, co-authored 171 publications receiving 3205 citations. Previous affiliations of Aminaton Marto include Universiti Tun Hussein Onn Malaysia.
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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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Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
TL;DR: In this article, a hybrid artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) was proposed to predict peak particle velocity (PPV) resulting from quarry blasting.
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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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Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions
Mohammadreza Koopialipoor,Danial Jahed Armaghani,Danial Jahed Armaghani,Ahmadreza Hedayat,Aminaton Marto,Behrouz Gordan +5 more
TL;DR: Although all predictive models are able to approximate slope SF values, PSO-ANN predictive model can perform better compared to others, and a new system of ranking, i.e., the color intensity rating, was developed, as a result.
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Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach
Mohsen Hajihassani,Danial Jahed Armaghani,Masoud Monjezi,Edy Tonnizam Mohamad,Aminaton Marto +4 more
TL;DR: In this paper, a hybrid model of an artificial neural network and a particle swarm optimization algorithm was implemented to predict ground vibration and air overpressure induced by blasting in a granite quarry site in Malaysia.