Pijush Samui

8.9k total citations · 2 hit papers
245 papers, 7.0k 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 245 papers receiving a total of 7.0k indexed citations (citations by other indexed papers that have themselves been cited), including 171 papers in Civil and Structural Engineering, 84 papers in Safety, Risk, Reliability and Quality and 41 papers in Management, Monitoring, Policy and Law. Recurrent topics in Pijush Samui's work include Geotechnical Engineering and Analysis (82 papers), Dam Engineering and Safety (70 papers) and Geotechnical Engineering and Underground Structures (60 papers). Pijush Samui is often cited by papers focused on Geotechnical Engineering and Analysis (82 papers), Dam Engineering and Safety (70 papers) and Geotechnical Engineering and Underground Structures (60 papers). Pijush Samui collaborates with scholars based in India, South Korea and Australia. Pijush Samui's co-authors include T. G. Sitharam, Dookie Kim, Nhat‐Duc Hoang, Abidhan Bardhan, Dieu Tien Bui, Phuong Thao Thi Ngo, Sarat Kumar Das, Deepak Kumar, Avijit Burman and Pham Viet Hoa and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Journal of Hydrology.

In The Last Decade

Pijush Samui

243 papers receiving 6.8k citations

Hit Papers

align trajectories

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.

A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area

2019 309 citations Nhat‐Duc Hoang, Phuong Thao Thi Ngo et al. profile →
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

2019 250 citations Dieu Tien Bui, Phuong Thao Thi Ngo et al. CATENA profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pijush Samui India	46	3.7k	1.8k	1.2k	1.2k	1.1k	245	7.0k
Hoang Nguyen Vietnam	56	3.0k 0.8×	1.4k 0.8×	1.2k 1.0×	1.1k 0.9×	1.3k 1.2×	158	8.5k
Annan Zhou China	64	9.0k 2.4×	2.9k 1.6×	995 0.8×	1.5k 1.3×	2.0k 1.8×	385	13.7k
Nhat‐Duc Hoang Vietnam	47	3.1k 0.8×	665 0.4×	1.6k 1.3×	643 0.5×	1.3k 1.1×	137	6.1k
Hai‐Bang Ly Vietnam	40	2.9k 0.8×	515 0.3×	1.0k 0.8×	673 0.6×	621 0.5×	119	5.4k
Shaojun Li China	46	2.3k 0.6×	1.5k 0.8×	1.9k 1.5×	759 0.6×	3.0k 2.6×	182	7.3k
Hai‐Min Lyu China	34	1.8k 0.5×	1.1k 0.6×	1.3k 1.0×	616 0.5×	446 0.4×	64	4.3k
Chongchong Qi China	50	4.9k 1.3×	635 0.3×	481 0.4×	614 0.5×	689 0.6×	156	7.4k
Işık Yılmaz Türkiye	32	1.8k 0.5×	1.0k 0.6×	1.2k 1.0×	505 0.4×	2.5k 2.2×	136	5.3k
Wengang Zhang China	58	7.9k 2.1×	5.2k 2.8×	910 0.7×	719 0.6×	3.3k 2.9×	374	13.1k
Ye‐Shuang Xu China	45	3.5k 0.9×	2.1k 1.1×	381 0.3×	892 0.7×	766 0.7×	98	5.3k

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

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20 of 20 papers shown

Kaloop, Mosbeh R., et al.. (2025). Predicting energy consumption of residential buildings using metaheuristic-optimized artificial neural network technique in early design stage. Building and Environment. 274. 112749–112749. 10 indexed citations

Samui, Pijush, et al.. (2024). Machine learning approach for evaluating soil liquefaction probability based on reliability method. Natural Hazards. 121(3). 3313–3342. 3 indexed citations

Kumar, Divesh Ranjan, et al.. (2024). Machine Learning Approaches for the Prediction of the Seismic Stability of Unsupported Rectangular Excavation. Engineered Science. 12 indexed citations

Feng, Bin, Shahab Hosseini, Jie Chen, et al.. (2024). Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites. Infrastructures. 9(10). 181–181. 12 indexed citations

Baghbani, Abolfazl, Roohollah Shirani Faradonbeh, Yi Lu, et al.. (2024). Enhancing earth dam slope stability prediction with integrated AI and statistical models. Applied Soft Computing. 164. 111999–111999. 9 indexed citations

Samui, Pijush, et al.. (2024). Intelligent computing hybrid models on estimating the consolidation settlement of shallow foundations. Multiscale and Multidisciplinary Modeling Experiments and Design. 7(4). 3579–3596. 2 indexed citations

He, Biao, Danial Jahed Armaghani, Sai Hin Lai, Pijush Samui, & Edy Tonnizam Mohamad. (2023). Applying data augmentation technique on blast-induced overbreak prediction: Resolving the problem of data shortage and data imbalance. Expert Systems with Applications. 237. 121616–121616. 21 indexed citations

Kumar, Divesh Ranjan, et al.. (2023). Bearing Capacity of Eccentrically Loaded Footings on Rock Masses Using Soft Computing Techniques. Engineered Science. 15 indexed citations

Onyelowe, Kennedy C., Ahmed M. Ebid, Pijush Samui, et al.. (2022). Evaluation of the Compressive Strength of CFRP-Wrapped Circular Concrete Columns Using Artificial Intelligence Techniques. Designs. 6(6). 112–112. 13 indexed citations

10.

Onyelowe, Kennedy C., Ahmed M. Ebid, Hisham A. Mahdi, et al.. (2022). Modeling the confined compressive strength of CFRP-jacketed noncircular concrete columns using artificial intelligence techniques. Cogent Engineering. 9(1). 16 indexed citations

11.

Guo, Deping, et al.. (2021). Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model. Acta Geotechnica. 17(4). 1183–1205. 52 indexed citations

12.

Kaloop, Mosbeh R., et al.. (2021). Hybrid-ANFIS approaches for compressive strength prediction of cementitious mortar and paste employing magnetic water. Smart Structures and Systems. 27(4). 651. 2 indexed citations

13.

Nhu, Viet‐Ha, Nhat‐Duc Hoang, Hieu Nguyen, et al.. (2020). Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. CATENA. 188. 104458–104458. 129 indexed citations

14.

Samui, Pijush, Nhat‐Duc Hoang, Viet‐Ha Nhu, et al.. (2019). A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project. Applied Sciences. 9(22). 4912–4912. 16 indexed citations

15.

Samui, Pijush, et al.. (2019). Reliability analysis of simply supported beam using GRNN, ELM and GPR. STRUCTURAL ENGINEERING AND MECHANICS. 71(6). 739–749. 9 indexed citations

16.

Roshni, Thendiyath, et al.. (2017). Potential of regression models in projecting sea level variability due to climate change at Haldia Port, India. 7(4). 319. 2 indexed citations

17.

Murthy, A. Ramachandra, et al.. (2014). ANN Model to Predict Fracture Characteristics of HighStrength and Ultra High Strength Concrete Beams. Cmc-computers Materials & Continua. 41(3). 193–213. 3 indexed citations

18.

Samui, Pijush, et al.. (2014). Machine Learning Techniques Applied to Uniaxial Compressive Strength of Oporto Granite. International Journal of Performability Engineering. 10(2). 189. 3 indexed citations

19.

Samui, Pijush, et al.. (2014). Prediction of Fracture Parameters of High Strength andUltra-High Strength Concrete Beams using MinimaxProbability Machine Regression and Extreme LearningMachine. Cmc-computers Materials & Continua. 44(2). 73–84. 5 indexed citations

20.

Murthy, A. Ramachandra, et al.. (2013). Multivariate Adaptive Regression Splines Model to PredictFracture Characteristics of High Strength and Ultra HighStrength Concrete Beams. Cmc-computers Materials & Continua. 36(1). 73–97. 9 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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