World's Best Scientists 2026 revealed!
Mohammadreza Koopialipoor

Mohammadreza Koopialipoor

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
41
Citations
4235
World Ranking
630
National Ranking
29

Engineering and Technology

D-Index
43
Citations
4930
World Ranking
6343
National Ranking
89

Mohammadreza Koopialipoor publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Mohammadreza Koopialipoor sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 63 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Mohammadreza Koopialipoor D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Mohammadreza Koopialipoor sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 43 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Mohammadreza Koopialipoor is affiliated with Amirkabir University of Technology in Iran and has contributed to research predominantly within the field of engineering, with a focus on civil and structural engineering, ocean engineering, mechanics of materials, mechanical engineering, and safety, risk, reliability, and quality.

Their recent publications include work on machine learning applications in geotechnical and structural contexts. Notable papers include:

  • Prediction of cement-based mortars compressive strength using machine learning techniques, 2021, published in Neural Computing and Applications
  • Introducing stacking machine learning approaches for the prediction of rock deformation, 2022, published in Transportation Geotechnics
  • A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling, 2020, published in Transportation Geotechnics
  • A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs, 2020, published in Materials
  • Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques, 2022, published in Applied Sciences

The topics addressed in their research often revolve around:

  • Rock mechanics and modeling
  • Tunneling and rock mechanics
  • Drilling and well engineering
  • Landslides and related hazards
  • Mineral processing and grinding
  • Geotechnical engineering and analysis
  • Dam engineering and safety

Mohammadreza Koopialipoor regularly publishes in the following venues:

  • Applied Sciences
  • Transportation Geotechnics
  • Bulletin of Engineering Geology and the Environment
  • Natural Resources Research
  • Engineering With Computers

Their frequent coauthors include:

  • Danial Jahed Armaghani
  • Panagiotis G. Asteris
  • Jian Zhou
  • Binh Thai Pham
  • Ahmed Salih Mohammed

Overall, the scientist's work integrates computational techniques such as stacking machine learning, Adaboost, decision trees, and artificial neural networks to address complex problems in materials science and geotechnical engineering. Their research outputs reflect multidisciplinary approaches within engineering, focusing on the application of advanced modeling for structural and geotechnical analysis.

Best Publications

  • Prediction of cement-based mortars compressive strength using machine learning techniques

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

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

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

  • 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

  • Introducing stacking machine learning approaches for the prediction of rock deformation

    Unknown

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

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

  • Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

    Mohammadreza Koopialipoor;Ahmad Fahimifar;Ebrahim Noroozi Ghaleini;Mohammadreza Momenzadeh

  • 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

  • A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

    Mohammadreza Koopialipoor;Danial Jahed Armaghani;Mojtaba Haghighi;Ebrahim Noroozi Ghaleini

  • Deep neural network and whale optimization algorithm to assess flyrock induced by blasting

    Hongquan Guo;Jian Zhou;Mohammadreza Koopialipoor;Danial Jahed Armaghani

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

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

  • A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

    Jian Zhou;Nasim Aghili;Nasim Aghili;Ebrahim Noroozi Ghaleini;Dieu Tien Bui

  • A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls

    Ebrahim Noroozi Ghaleini;Mohammadreza Koopialipoor;Mohammadreza Momenzadeh;Mehdi Esfandi Sarafraz

  • Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

    Lei Huang;Panagiotis G. Asteris;Mohammadreza Koopialipoor;Danial Jahed Armaghani

  • A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

    Shasha Lu;Mohammadreza Koopialipoor;Panagiotis G. Asteris;Maziyar Bahri

  • A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling

    Binh Thai Pham;Manh Duc Nguyen;Trung Nguyen-Thoi;Lanh Si Ho

  • Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks

    Panagiotis G. Asteris;Anna Mamou;Mohsen Hajihassani;Mahdi Hasanipanah

  • A new methodology for optimization and prediction of rate of penetration during drilling operations

    Yanru Zhao;Amin Noorbakhsh;Mohammadreza Koopialipoor;Aydin Azizi

  • Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

    Jian Zhou;Hongquan Guo;Mohammadreza Koopialipoor;Danial Jahed Armaghani

  • Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

    Mohammadreza Koopialipoor;Ebrahim Noroozi Ghaleini;Mojtaba Haghighi;Sujith Kanagarajan

  • Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system

    Jian Zhou;Mohammadreza Koopialipoor;Enming Li;Danial Jahed Armaghani

  • Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

    Behrouz Gordan;Mohammadreza Koopialipoor;A. Clementking;Hossein Tootoonchi

Frequent Co-Authors

Danial Jahed Armaghani
Danial Jahed Armaghani University of Technology Sydney
Mahmood Md. Tahir
Mahmood Md. Tahir University of Technology Malaysia
Edy Tonnizam Mohamad
Edy Tonnizam Mohamad University of Technology Malaysia
Panagiotis G. Asteris
Panagiotis G. Asteris School of Pedagogical and Technological Education
Aminaton Marto
Aminaton Marto University of Technology Malaysia
Mahdi Hasanipanah
Mahdi Hasanipanah Duy Tan University
Mohsen Hajihassani
Mohsen Hajihassani Urmia University
Paulo B. Lourenço
Paulo B. Lourenço University of Minho
Manoj Khandelwal
Manoj Khandelwal Federation University Australia
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway

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