World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
48
Citations
8174
World Ranking
4614
National Ranking
50

Overview

Edy Tonnizam Mohamad is affiliated with the University of Technology Malaysia in Malaysia. Their research intersects various domains within engineering, earth and planetary sciences, and environmental science.

The scientist's research has predominantly focused on a set of core topics including:

  • Rock Mechanics and Modeling
  • Tunneling and Rock Mechanics
  • Mineral Processing and Grinding
  • Landslides and related hazards
  • Drilling and Well Engineering
  • Geotechnical Engineering and Analysis
  • Seismic Imaging and Inversion Techniques

Their primary fields of study are engineering (114 publications), earth and planetary sciences (31 publications), and environmental science (23 publications). Within these broad fields, their work extensively covers subfields such as:

  • Mechanics of Materials
  • Civil and Structural Engineering
  • Mechanical Engineering
  • Geophysics
  • Management, Monitoring, Policy and Law

Frequent collaborators in their research include:

  • Danial Jahed Armaghani
  • Ramesh Murlidhar Bhatawdekar
  • Mariatul Kiftiah Ahmad Legiman
  • Eka Kusmawati Suparmanto
  • Vynotdni Rathinasamy

The scientist has published multiple papers in journals and venues such as:

  • Natural Resources Research
  • Applied Sciences
  • Mathematics
  • Sustainability
  • Research Square (Research Square)

Recent notable publications include:

  • "Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization," 2020, Underground Space
  • "Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance," 2021, Engineering With Computers
  • "Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network," 2021, Journal of Rock Mechanics and Geotechnical Engineering
  • "A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock," 2020, Natural Resources Research
  • "The effects of particle swarm optimisation and genetic algorithm on ANN results in predicting pile bearing capacity," 2020, International Journal of Hydromechatronics

Best Publications

  • Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

    Danial Jahed Armaghani;Edy Tonnizam Mohamad;Mogana Sundaram Narayanasamy;Nobuya Narita

  • Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization

    D. Jahed Armaghani;M. Hajihassani;E. Tonnizam Mohamad;A. Marto

  • Improvement of Problematic Soils with Biopolymer—An Environmentally Friendly Soil Stabilizer

    Nima Latifi;Suksun Horpibulsuk;Christopher L. Meehan;Muhd Zaimi Abd Majid

  • Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm

    Mohsen Hajihassani;Danial Jahed Armaghani;Aminaton Marto;Edy Tonnizam Mohamad

  • Rock strength estimation: a PSO-based BP approach

    Unknown

  • Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

    Jian Zhou;Yingui Qiu;Shuangli Zhu;Danial Jahed Armaghani

  • Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach

    Edy Tonnizam Mohamad;Danial Jahed Armaghani;Ehsan Momeni;Seyed Vahid Alavi Nezhad Khalil Abad

  • Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

    M. Hajihassani;D. Jahed Armaghani;H. Sohaei;E. Tonnizam Mohamad

  • 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

  • An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

    Danial Jahed Armaghani;Edy Tonnizam Mohamad;Ehsan Momeni;Mogana Sundaram Narayanasamy

  • Application of ANFIS technique on performance of C and L shaped angle shear connectors

    Yadollah Sedghi;Yousef Zandi;Mahdi Shariati;Ebrahim Ahmadi

  • Prediction of the strength and elasticity modulus of granite through an expert artificial neural network

    Danial Jahed Armaghani;Edy Tonnizam Mohamad;Ehsan Momeni;Masoud Monjezi

  • Three hybrid intelligent models in estimating flyrock distance resulting from blasting

    Mohammadreza Koopialipoor;Ali Fallah;Danial Jahed Armaghani;Aydin Azizi

  • A review on pavement porous concrete using recycled waste materials

    Ali Toghroli;Mahdi Shariati;Fathollah Sajedi;Zainah Ibrahim

  • A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network.

    Aminaton Marto;Mohsen Hajihassani;Danial Jahed Armaghani;Edy Tonnizam Mohamad

  • Predicting tunnel boring machine performance through a new model based on the group method of data handling

    Mohammadreza Koopialipoor;Sayed Sepehr Nikouei;Aminaton Marto;Ahmad Fahimifar

  • Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

    Danial Jahed Armaghani;Edy Tonnizam Mohamad;Mohsen Hajihassani;Saffet Yagiz

  • A combination of the ICA-ANN model to predict air-overpressure resulting from blasting

    Danial Jahed Armaghani;Mahdi Hasanipanah;Edy Tonnizam Mohamad

  • Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods

    D. Jahed Armaghani;E. Tonnizam Mohamad;M. Hajihassani;S. V. Alavi Nezhad Khalil Abad

  • Application of deep neural networks in predicting the penetration rate of tunnel boring machines

    Mohammadreza Koopialipoor;Hossein Tootoonchi;Danial Jahed Armaghani;Edy Tonnizam Mohamad

  • Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network

    Danial Jahed Armaghani;Mohsen Hajihassani;Behnam Yazdani Bejarbaneh;Aminaton Marto

Frequent Co-Authors

Danial Jahed Armaghani
Danial Jahed Armaghani University of Technology Sydney
Mohsen Hajihassani
Mohsen Hajihassani Urmia University
Aminaton Marto
Aminaton Marto University of Technology Malaysia
Mohammadreza Koopialipoor
Mohammadreza Koopialipoor Amirkabir University of Technology
Mahdi Shariati
Mahdi Shariati Duy Tan University
Masoud Monjezi
Masoud Monjezi Tarbiat Modares University
Muhd Zaimi Abd Majid
Muhd Zaimi Abd Majid University of Technology Malaysia
T. N. Singh
T. N. Singh Indian Institute of Technology Bombay
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Mahdi Hasanipanah
Mahdi Hasanipanah Duy Tan University

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