World's Best Scientists 2026 revealed!
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Rising Stars
2025
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Engineering and Technology
Malaysia
2022

D-Index & Metrics

Rising Stars

D-Index
85
Citations
18680
World Ranking
15
National Ranking
2

Engineering and Technology

D-Index
91
Citations
21470
World Ranking
255
National Ranking
19

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award
  • 2022 - Research.com Engineering and Technology in Malaysia Leader Award

Overview

Danial Jahed Armaghani is affiliated with the University of Technology Sydney in Australia. Their research primarily focuses on the field of engineering, with a specialization in civil and structural engineering. Their work extends to several subfields, including mechanics of materials, mechanical engineering, ocean engineering, and safety, risk, reliability, and quality.

The scientist's main research topics include rock mechanics and modeling, tunneling and rock mechanics, mineral processing and grinding, drilling and well engineering, geotechnical engineering and analysis, landslides and related hazards, and dam engineering and safety.

Frequent coauthors collaborating with Danial Jahed Armaghani include Panagiotis G. Asteris, Jian Zhou, Edy Tonnizam Mohamad, Mohammadreza Koopialipoor, and Ahmed Salih Mohammed.

They have published extensively in several venues such as Applied Sciences, Engineering With Computers, Natural Resources Research, Transportation Geotechnics, and Sustainability.

Notable recent papers include:

  • A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength (2020) published in Neural Computing and Applications
  • Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate (2020) published in Engineering Applications of Artificial Intelligence
  • Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques (2020) published in Geoscience Frontiers
  • Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization (2020) published in Underground Space
  • Prediction of cement-based mortars compressive strength using machine learning techniques (2021) published in Neural Computing and Applications

They have contributed to book publications through publishers such as Springer Nature and Emerging Trends in Mechatronics. Titles include "Applications of Artificial Intelligence in Tunnelling and Underground Space Technology" (2021), "Environmental Issues of Blasting" (2021), and "Artificial Intelligence in Mechatronics and Civil Engineering" (2023).

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

  • A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

    Danial Jahed Armaghani;Panagiotis G. Asteris

  • Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

    Ehsan Momeni;Danial Jahed Armaghani;Mohsen Hajihassani;Mohd For Mohd Amin

  • Prediction of seismic slope stability through combination of particle swarm optimization and neural network

    Behrouz Gordan;Danial Jahed Armaghani;Mohsen Hajihassani;Masoud Monjezi

  • Feasibility of indirect determination of blast induced ground vibration based on support vector machine

    Mahdi Hasanipanah;Masoud Monjezi;Azam Shahnazar;Danial Jahed Armaghani

  • Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

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

  • 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

  • Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

    Mahdi Hasanipanah;Majid Noorian-Bidgoli;Danial Jahed Armaghani;Hossein Khamesi

  • Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns

    Payam Sarir;Jun Chen;Panagiotis G. Asteris;Danial Jahed Armaghani

  • Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

    Jian Zhou;Yingui Qiu;Danial Jahed Armaghani;Wengang Zhang

  • 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 cement-based mortars compressive strength using machine learning techniques

    Panagiotis G. Asteris;Mohammadreza Koopialipoor;Danial Jahed Armaghani;Evgenios A. Kotsonis

  • 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 and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

    Ebrahim Ebrahimi;Masoud Monjezi;Mohammad Reza Khalesi;Danial Jahed Armaghani

  • Application of several optimization techniques for estimating TBM advance rate in granitic rocks

    Danial Jahed Armaghani;Mohammadreza Koopialipoor;Aminaton Marto;Saffet Yagiz

  • Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials

    Jian Zhou;Enming Li;Haixia Wei;Chuanqi Li

  • Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models

    Jian Zhou;Panagiotis G. Asteris;Danial Jahed Armaghani;Binh Thai Pham

  • Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate

    Hai Xu;Jian Zhou;Panagiotis G. Asteris;Danial Jahed Armaghani

  • 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

  • Developing a hybrid PSO---ANN model for estimating the ultimate bearing capacity of rock-socketed piles

    Danial Jahed Armaghani;Raja Shahrom Shoib;Koohyar Faizi;Ahmad Safuan Rashid

  • 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

Frequent Co-Authors

Edy Tonnizam Mohamad
Edy Tonnizam Mohamad University of Technology Malaysia
Mohammadreza Koopialipoor
Mohammadreza Koopialipoor Amirkabir University of Technology
Mahdi Hasanipanah
Mahdi Hasanipanah Duy Tan University
Masoud Monjezi
Masoud Monjezi Tarbiat Modares University
Panagiotis G. Asteris
Panagiotis G. Asteris School of Pedagogical and Technological Education
Mahmood Md. Tahir
Mahmood Md. Tahir University of Technology Malaysia
Aminaton Marto
Aminaton Marto University of Technology Malaysia
Manoj Khandelwal
Manoj Khandelwal Federation University Australia
Mohsen Hajihassani
Mohsen Hajihassani Urmia University
Muhd Zaimi Abd Majid
Muhd Zaimi Abd Majid University of Technology Malaysia

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