World's Best Scientists 2026 revealed!
Mahdi Hasanipanah

Mahdi Hasanipanah

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
52
Citations
6403
World Ranking
283
National Ranking
2

Engineering and Technology

D-Index
56
Citations
7247
World Ranking
2916
National Ranking
5

Mahdi Hasanipanah 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 Mahdi Hasanipanah 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: 94 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Mahdi Hasanipanah 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 Mahdi Hasanipanah 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: 56 D-Index — 72nd percentile

72% 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

Mahdi Hasanipanah is affiliated with Duy Tan University in Vietnam and specializes in engineering with a focus on civil and structural engineering. Their research spans multiple subfields including mechanics of materials, mechanical engineering, safety, risk, reliability and quality, as well as ocean engineering.

The primary areas of study in their research include rock mechanics and modeling, tunneling and rock mechanics, mineral processing and grinding, drilling and well engineering, dam engineering and safety, geoscience and mining technology, and geotechnical engineering and analysis.

Frequent publication venues for Mahdi Hasanipanah's work are:

  • Engineering With Computers
  • Natural Resources Research
  • Sustainability
  • Applied Sciences
  • Transportation Geotechnics

Significant recent papers authored by or involving Hasanipanah include:

  • A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young's modulus and unconfined compressive strength of rock, 2021, Engineering With Computers
  • Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks, 2021, Transportation Geotechnics
  • Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness, 2020, Sustainability
  • On the Use of Neuro-Swarm System to Forecast the Pile Settlement, 2020, Applied Sciences
  • A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm, 2020, Engineering With Computers

Collaborations have been frequent with several researchers, notably:

  • Danial Jahed Armaghani
  • Ahmed Salih Mohammed
  • Panagiotis G. Asteris
  • Menad Nait Amar
  • Xiaohua Ding

The work conducted by Hasanipanah addresses various engineering challenges related to rock mechanics, modeling, and material strength prediction. The combination of computational methods such as XGBoost-firefly algorithms, soft computing, and swarm optimization in their research highlights the integration of advanced computational intelligence approaches to engineering problems.

Best Publications

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

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

  • Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

    Masoud Monjezi;Mahdi Hasanipanah;Manoj Khandelwal

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

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

  • Forecasting blast-induced ground vibration developing a CART model

    Mahdi Hasanipanah;Roohollah Shirani Faradonbeh;Hassan Bakhshandeh Amnieh;Danial Jahed Armaghani

  • Airblast prediction through a hybrid genetic algorithm-ANN model

    Danial Jahed Armaghani;Mahdi Hasanipanah;Amir Mahdiyar;Muhd Zaimi Abd Majid

  • Application of PSO to develop a powerful equation for prediction of flyrock due to blasting

    Mahdi Hasanipanah;Danial Jahed Armaghani;Hassan Bakhshandeh Amnieh;Muhd Zaimi Abd Majid

  • A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

    Maryam Amiri;Hassan Bakhshandeh Amnieh;Mahdi Hasanipanah;Leyli Mohammad Khanli

  • Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting

    Mahdi Hasanipanah;Hassan Bakhshandeh Amnieh;Hossein Arab;Mohammad Saber Zamzam

  • Prediction of air-overpressure caused by mine blasting using a new hybrid PSO---SVR model

    Mahdi Hasanipanah;Azam Shahnazar;Hassan Bakhshandeh Amnieh;Danial Jahed Armaghani

  • A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration

    Khalil Taheri;Mahdi Hasanipanah;Saeid Bagheri Golzar;Muhd Zaimi Majid

  • Prediction of blast-produced ground vibration using particle swarm optimization

    Mahdi Hasanipanah;Reyhaneh Naderi;Javad Kashir;Seyed Ahmad Noorani

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

    Danial Jahed Armaghani;Mahdi Hasanipanah;Edy Tonnizam Mohamad

  • Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting

    Jian Zhou;Chuanqi Li;Chelang A. Arslan;Mahdi Hasanipanah

  • Feasibility of ICA in approximating ground vibration resulting from mine blasting

    Danial Jahed Armaghani;Mahdi Hasanipanah;Hassan Bakhshandeh Amnieh;Edy Tonnizam Mohamad

  • Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

    Haiqing Yang;Mahdi Hasanipanah;M. M. Tahir;Dieu Tien Bui

  • Several non-linear models in estimating air-overpressure resulting from mine blasting

    Mahdi Hasanipanah;Danial Jahed Armaghani;Hossein Khamesi;Hassan Bakhshandeh Amnieh

  • Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system

    Mahdi Hasanipanah;Danial Jahed Armaghani;Masoud Monjezi;Samira Shams

  • A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

    Wusi Chen;Mahdi Hasanipanah;Hima Nikafshan Rad;Danial Jahed Armaghani

  • Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

    Danial Jahed Armaghani;Amir Mahdiyar;Mahdi Hasanipanah;Roohollah Shirani Faradonbeh

  • A novel systematic and evolved approach based on XGBoost-firefly algorithm to predict Young’s modulus and unconfined compressive strength of rock

    Jing Cao;Juncheng Gao;Hima Nikafshan Rad;Ahmed Salih Mohammed

  • Developing a least squares support vector machine for estimating the blast-induced flyrock

    Hima Nikafshan Rad;Mahdi Hasanipanah;Mohammad Rezaei;Amin Lotfi Eghlim

  • Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

    Edy Tonnizam Mohamad;Danial Jahed Armaghani;Mahdi Hasanipanah;Bhatawdekar Ramesh Murlidhar

Frequent Co-Authors

Danial Jahed Armaghani
Danial Jahed Armaghani University of Technology Sydney
Mahmood Md. Tahir
Mahmood Md. Tahir University of Technology Malaysia
Panagiotis G. Asteris
Panagiotis G. Asteris School of Pedagogical and Technological Education
Masoud Monjezi
Masoud Monjezi Tarbiat Modares University
Manoj Khandelwal
Manoj Khandelwal Federation University Australia
Muhd Zaimi Abd Majid
Muhd Zaimi Abd Majid University of Technology Malaysia
Mohammadreza Koopialipoor
Mohammadreza Koopialipoor Amirkabir University of Technology
Behrooz Keshtegar
Behrooz Keshtegar Zabol University
Edy Tonnizam Mohamad
Edy Tonnizam Mohamad University of Technology Malaysia
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway

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