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2025

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Rising Stars

D-Index
33
Citations
5018
World Ranking
924
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152

Engineering and Technology

D-Index
38
Citations
7062
World Ranking
7957
National Ranking
2185

Sujith Mangalathu 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 Sujith Mangalathu 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: 116 publications — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Sujith Mangalathu 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 Sujith Mangalathu 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: 38 D-Index — 20th percentile

20% 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

Sujith Mangalathu is affiliated with the Georgia Institute of Technology in the United States. Their research primarily focuses on engineering, with a specialization in civil and structural engineering. Their scholarly output covers a range of topics related to structural health monitoring, seismic performance, infrastructure maintenance, and the structural behavior of reinforced concrete.

The scientist has contributed significantly to the understanding of structural health monitoring techniques and seismic performance and analysis. Their research interests extend into infrastructure maintenance and monitoring, concrete corrosion and durability, as well as responses of structures to dynamic loads and dam engineering and safety.

Frequent co-authors in their collaborative work include:

  • Jong-Su Jeon
  • Muhamed Safeer Pandikkadavath
  • Robin Davis
  • A. Anisha
  • S. Somala

They have published extensively in several academic journals, with notable recurring publication venues including:

  • Engineering Structures
  • Structures
  • Earthquake Engineering & Structural Dynamics
  • Journal of Building Engineering
  • Journal of Structural Engineering

The following recent papers exemplify their research contributions:

  • Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach, 2020, Engineering Structures
  • Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls, 2020, Engineering Structures

Other notable papers relevant to their research domain, including coauthor works, are:

  • Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls, 2021, Journal of Structural Engineering
  • Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach, 2021, Engineering Structures
  • Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements, 2021, Engineering Structures

Best Publications

  • Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

    Sujith Mangalathu;Seong Hoon Hwang;Jong Su Jeon

  • Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls

    De-Cheng Feng;Wen-Jie Wang;Sujith Mangalathu;Ertugrul Taciroglu

  • Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques

    Sujith Mangalathu;Jong-Su Jeon

  • Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

    Jesika Rahman;Jesika Rahman;Khondaker Sakil Ahmed;Nafiz Imtiaz Khan;Kamrul Islam;Kamrul Islam

  • Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

    Sujith Mangalathu;Hansol Jang;Seong Hoon Hwang;Jong Su Jeon

  • Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes

    Sujith Mangalathu;Gwanghee Heo;Jong Su Jeon

  • Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

    De-Cheng Feng;Wen-Jie Wang;Sujith Mangalathu;Gang Hu

  • Classifying Earthquake Damage to Buildings Using Machine Learning

    Sujith Mangalathu;Han Sun;Chukwuebuka C. Nweke;Zhengxiang Yi

  • Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study

    Sujith Mangalathu;Jong-Su Jeon

  • Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

    Sujith Mangalathu;Seong-Hoon Hwang;Eunsoo Choi;Jong-Su Jeon

  • Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression

    Sujith Mangalathu;Jong Su Jeon;Reginald DesRoches

  • Machine learning-based approaches for seismic demand and collapse of ductile reinforced concrete building frames

    Seong Hoon Hwang;Sujith Mangalathu;Jiuk Shin;Jong Su Jeon

  • Data‐driven rapid damage evaluation for life‐cycle seismic assessment of regional reinforced concrete bridges

    Unknown

  • Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement

    Sujith Mangalathu;Hanbyeol Shin;Eunsoo Choi;Jong Su Jeon

  • Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

    Sujith Mangalathu;Karthika Karthikeyan;De-Cheng Feng;Jong-Su Jeon

  • Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques

    Sujith Mangalathu;Jong Su Jeon

  • Predicting the dissolution kinetics of silicate glasses using machine learning

    N. M. Anoop Krishnan;Sujith Mangalathu;Morten Mattrup Smedskjær;Adama Tandia

  • Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions

    Sujith Mangalathu;Henry V. Burton

  • Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation

    Jong-Su Jeon;Sujith Mangalathu;Junho Song;Reginald Desroches

  • Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns

    Unknown

  • Review of strength models for masonry spandrels

    Katrin Beyer;Sujith Mangalathu

  • Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement

    Pengpeng Ni;Pengpeng Ni;Sujith Mangalathu

  • ANCOVA-based grouping of bridge classes for seismic fragility assessment

    Sujith Mangalathu;Jong-Su Jeon;Jamie E. Padgett;Reginald DesRoches

  • Bridge classes for regional seismic risk assessment: Improving HAZUS models

    Sujith Mangalathu;Farahnaz Soleimani;Jong-Su Jeon

Frequent Co-Authors

Jong-Su Jeon
Jong-Su Jeon Hanyang University
Pengpeng Ni
Pengpeng Ni Sun Yat-sen University
Reginald DesRoches
Reginald DesRoches Rice University
Jamie E. Padgett
Jamie E. Padgett Rice University
De-Cheng Feng
De-Cheng Feng Southeast University
Mathieu Bauchy
Mathieu Bauchy University of California, Los Angeles
Morten Mattrup Smedskjær
Morten Mattrup Smedskjær Aalborg University
Yaolin Yi
Yaolin Yi Nanyang Technological University
Junho Song
Junho Song Seoul National University
Ertugrul Taciroglu
Ertugrul Taciroglu University of California, Los Angeles

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