Pijush Samui

3.6k total citations · 2 hit papers
91 papers, 2.6k citations indexed

About

Pijush Samui is a scholar working on Civil and Structural Engineering, Safety, Risk, Reliability and Quality and Management, Monitoring, Policy and Law. According to data from OpenAlex, Pijush Samui has authored 91 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Civil and Structural Engineering, 37 papers in Safety, Risk, Reliability and Quality and 14 papers in Management, Monitoring, Policy and Law. Recurrent topics in Pijush Samui's work include Geotechnical Engineering and Analysis (37 papers), Dam Engineering and Safety (23 papers) and Geotechnical Engineering and Underground Structures (19 papers). Pijush Samui is often cited by papers focused on Geotechnical Engineering and Analysis (37 papers), Dam Engineering and Safety (23 papers) and Geotechnical Engineering and Underground Structures (19 papers). Pijush Samui collaborates with scholars based in India, Australia and South Korea. Pijush Samui's co-authors include Abidhan Bardhan, Panagiotis G. Asteris, Athanasia D. Skentou, Baboo Rai, Kypros Pilakoutas, Rakesh Kumar, Rahul Biswas, Mosbeh R. Kaloop, Navid Kardani and Jong Wan Hu and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Scientific Reports and Cement and Concrete Research.

In The Last Decade

Pijush Samui

84 papers receiving 2.5k citations

Hit Papers

Predicting concrete compressive strength using hybrid ens... 2021 2026 2022 2024 2021 2023 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
    2021 445 citations Abidhan Bardhan, Pijush Samui et al. profile →
  2. A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
    2023 129 citations Pijush Samui et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pijush Samui India 29 1.9k 548 522 335 259 91 2.6k
Abidhan Bardhan India 28 1.8k 0.9× 457 0.8× 511 1.0× 197 0.6× 261 1.0× 54 2.3k
Mohammad Amin Hariri‐Ardebili United States 31 2.7k 1.4× 275 0.5× 434 0.8× 258 0.8× 180 0.7× 146 3.2k
Lanh Si Ho Vietnam 23 1.3k 0.7× 451 0.8× 225 0.4× 337 1.0× 215 0.8× 60 2.2k
Abdülkadir Çevik Türkiye 34 2.8k 1.4× 1.0k 1.9× 238 0.5× 140 0.4× 382 1.5× 107 3.3k
Tien-Thinh Le Vietnam 34 2.1k 1.1× 742 1.4× 230 0.4× 117 0.3× 424 1.6× 70 3.0k
Huaizhi Su China 33 3.1k 1.6× 373 0.7× 491 0.9× 501 1.5× 379 1.5× 193 4.2k
Navid Kardani Australia 19 882 0.5× 167 0.3× 360 0.7× 224 0.7× 217 0.8× 26 1.4k
Junling Qiu China 32 1.9k 1.0× 318 0.6× 1.4k 2.6× 527 1.6× 166 0.6× 70 2.7k
Ahmed Elgamal United States 44 6.0k 3.1× 258 0.5× 515 1.0× 378 1.1× 212 0.8× 192 6.4k
Van Quan Tran Vietnam 27 1.7k 0.9× 599 1.1× 205 0.4× 74 0.2× 200 0.8× 62 2.6k

Countries citing papers authored by Pijush Samui

Since Specialization
Citations

This map shows the geographic impact of Pijush Samui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pijush Samui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pijush Samui more than expected).

Fields of papers citing papers by Pijush Samui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pijush Samui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pijush Samui. The network helps show where Pijush Samui may publish in the future.

Co-authorship network of co-authors of Pijush Samui

This figure shows the co-authorship network connecting the top 25 collaborators of Pijush Samui. A scholar is included among the top collaborators of Pijush Samui based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pijush Samui. Pijush Samui is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Armaghani, Danial Jahed, et al.. (2025). Optimizing Twin Tunnel Excavation: Machine Learning and Algorithmic Solutions for Surface Settlement Reduction. Indian geotechnical journal. 56(2). 624–643. 3 indexed citations
3.
Kumar, Rakesh, Divesh Ranjan Kumar, Warit Wipulanusat, et al.. (2024). Estimation of the compressive strength of ultrahigh performance concrete using machine learning models. Intelligent Systems with Applications. 25. 200471–200471. 19 indexed citations
4.
5.
Samui, Pijush, et al.. (2024). Machine Learning-Aided Monte Carlo Simulation and Subset Simulation. Transportation Research Record Journal of the Transportation Research Board. 2678(12). 864–886. 5 indexed citations
6.
Kumar, Rakesh, Pijush Samui, & Baboo Rai. (2024). Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning. Iranian Journal of Science and Technology Transactions of Civil Engineering. 48(5). 3717–3734. 22 indexed citations
7.
Kumar, Rakesh, Baboo Rai, & Pijush Samui. (2024). Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review. Structural Concrete. 26(2). 1309–1337. 30 indexed citations
8.
Kumar, Rakesh, et al.. (2024). Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques. Construction and Building Materials. 438. 136933–136933. 31 indexed citations
9.
10.
Ibrahim, Syed Muhammad, et al.. (2024). Experimental and Computational Analysis of lime-treated geogrid-reinforced Silty Sand Beneath Circular Footings. Iranian Journal of Science and Technology Transactions of Civil Engineering. 48(6). 4617–4638. 1 indexed citations
11.
Xue, Xingsi, et al.. (2023). Machine Learning Approach for Prediction of Lateral Confinement Coefficient of CFRP-Wrapped RC Columns. Symmetry. 15(2). 545–545. 15 indexed citations
12.
Samui, Pijush, et al.. (2023). Optimization of an Artificial Neural Network Using Three Novel Meta-heuristic Algorithms for Predicting the Shear Strength of Soil. Transportation Infrastructure Geotechnology. 11(4). 1708–1729. 18 indexed citations
13.
Samui, Pijush, et al.. (2023). Optimized ANN-based approach for estimation of shear strength of soil. Asian Journal of Civil Engineering. 24(8). 3627–3640. 24 indexed citations
14.
Samui, Pijush, et al.. (2023). Implementing ensemble learning models for the prediction of shear strength of soil. Asian Journal of Civil Engineering. 24(7). 2103–2119. 31 indexed citations
15.
Samui, Pijush, et al.. (2022). A novel hybrid model of augmented grey wolf optimizer and artificial neural network for predicting shear strength of soil. Modeling Earth Systems and Environment. 9(2). 2327–2347. 27 indexed citations
16.
Kumar, Manish, et al.. (2022). A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features. Sustainability. 14(21). 14049–14049. 2 indexed citations
17.
Nhu, Viet‐Ha, Nhat‐Duc Hoang, Mahdis Amiri, et al.. (2021). An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas. Environmental Earth Sciences. 80(7). 1 indexed citations
18.
Kardani, Navid, T. Pradeep, Pijush Samui, Dookie Kim, & Annan Zhou. (2021). Smart phase behavior modeling of asphaltene precipitation using advanced computational frameworks: ENN, GMDH, and MPMR. Petroleum Science and Technology. 39(19-20). 804–825. 15 indexed citations
19.
Kaloop, Mosbeh R., et al.. (2021). Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height. Survey Review. 54(387). 505–518. 11 indexed citations
20.
Biswas, Rahul, et al.. (2021). Efficient soft computing techniques for the prediction of compressive strength of geopolymer concrete. Computers and Concrete, an International Journal. 28(2). 221. 46 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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