Panagiotis G. Asteris

15.1k total citations · 15 hit papers
233 papers, 11.8k citations indexed

About

Panagiotis G. Asteris is a scholar working on Civil and Structural Engineering, Mechanical Engineering and Building and Construction. According to data from OpenAlex, Panagiotis G. Asteris has authored 233 papers receiving a total of 11.8k indexed citations (citations by other indexed papers that have themselves been cited), including 188 papers in Civil and Structural Engineering, 44 papers in Mechanical Engineering and 41 papers in Building and Construction. Recurrent topics in Panagiotis G. Asteris's work include Masonry and Concrete Structural Analysis (52 papers), Innovative concrete reinforcement materials (46 papers) and Infrastructure Maintenance and Monitoring (34 papers). Panagiotis G. Asteris is often cited by papers focused on Masonry and Concrete Structural Analysis (52 papers), Innovative concrete reinforcement materials (46 papers) and Infrastructure Maintenance and Monitoring (34 papers). Panagiotis G. Asteris collaborates with scholars based in Greece, Iran and Vietnam. Panagiotis G. Asteris's co-authors include Danial Jahed Armaghani, Liborio Cavaleri, Athanasia D. Skentou, Ahmed Salih Mohammed, Abidhan Bardhan, Christis Ζ. Chrysostomou, Konstantinos G. Kolovos, Maria G. Douvika, Mohammadreza Koopialipoor and Pijush Samui and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Scientific Reports.

In The Last Decade

Panagiotis G. Asteris

220 papers receiving 11.5k citations

Hit Papers

Predicting concrete compr... 2019 2026 2021 2023 2021 2019 2020 2020 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 Panagiotis G. Asteris, Athanasia D. Skentou et al. profile →
  2. Concrete compressive strength using artificial neural networks
    2019 334 citations Panagiotis G. Asteris et al. Neural Computing and Applications profile →
  3. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
    2020 318 citations Danial Jahed Armaghani, Panagiotis G. Asteris Neural Computing and Applications profile →
  4. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
    2020 284 citations Panagiotis G. Asteris et al. profile →
  5. Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO2) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete
    2023 254 citations Ahmed Salih Mohammed, Panagiotis G. Asteris et al. profile →
  6. Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns
    2019 210 citations Panagiotis G. Asteris, Danial Jahed Armaghani et al. profile →
  7. Prediction of cement-based mortars compressive strength using machine learning techniques
    2021 202 citations Panagiotis G. Asteris, Mohammadreza Koopialipoor et al. Neural Computing and Applications profile →
  8. Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate
    2019 202 citations Panagiotis G. Asteris, Danial Jahed Armaghani et al. Applied Sciences profile →
  9. Prediction of concrete materials compressive strength using surrogate models
    2022 199 citations Ahmed Salih Mohammed, Liborio Cavaleri et al. profile →
  10. Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models
    2020 191 citations Panagiotis G. Asteris, Danial Jahed Armaghani et al. profile →
  11. Mapping and holistic design of natural hydraulic lime mortars
    2020 183 citations Panagiotis G. Asteris, Danial Jahed Armaghani et al. profile →
  12. Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives
    2023 124 citations Panagiotis G. Asteris et al. profile →
  13. Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques
    2022 118 citations Panagiotis G. Asteris, Panayiotis C. Roussis et al. profile →
  14. Introducing stacking machine learning approaches for the prediction of rock deformation
    2022 117 citations Mohammadreza Koopialipoor, Panagiotis G. Asteris et al. Transportation Geotechnics profile →
  15. Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data
    2024 87 citations Panagiotis G. Asteris, Μαρία Καρόγλου et al. Ultrasonics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Panagiotis G. Asteris Greece 67 9.5k 3.1k 1.8k 1.5k 1.3k 233 11.8k
Paulo B. Lourénço Portugal 73 18.8k 2.0× 7.9k 2.6× 2.1k 1.2× 1.6k 1.1× 373 0.3× 858 21.7k
Guowei Ma China 75 10.9k 1.2× 6.8k 2.2× 6.9k 3.9× 2.8k 1.9× 1.4k 1.1× 503 20.1k
Andrew Chan Australia 40 3.7k 0.4× 800 0.3× 1.7k 0.9× 979 0.7× 573 0.4× 183 6.0k
Mahmood Md. Tahir Malaysia 45 4.3k 0.5× 1.7k 0.6× 1.2k 0.7× 986 0.7× 547 0.4× 188 5.5k
Zhen‐Yu Yin Hong Kong 71 13.4k 1.4× 801 0.3× 3.0k 1.7× 1.2k 0.8× 4.7k 3.6× 611 17.0k
Jun Li China 51 6.5k 0.7× 2.5k 0.8× 1.3k 0.7× 885 0.6× 541 0.4× 386 8.8k
M. Hesham El Naggar Canada 51 9.6k 1.0× 1.5k 0.5× 783 0.4× 836 0.6× 1.5k 1.2× 517 11.3k
Baoshan Huang United States 74 15.3k 1.6× 4.3k 1.4× 883 0.5× 2.2k 1.5× 838 0.7× 391 17.3k
Chongchong Qi China 50 4.9k 0.5× 1.4k 0.5× 2.9k 1.6× 939 0.6× 635 0.5× 156 7.4k
Hehua Zhu China 52 6.4k 0.7× 1.2k 0.4× 4.0k 2.3× 843 0.6× 3.0k 2.4× 425 10.5k

Countries citing papers authored by Panagiotis G. Asteris

Since Specialization
Citations

This map shows the geographic impact of Panagiotis G. Asteris'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 Panagiotis G. Asteris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Panagiotis G. Asteris more than expected).

Fields of papers citing papers by Panagiotis G. Asteris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Panagiotis G. Asteris. 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 Panagiotis G. Asteris. The network helps show where Panagiotis G. Asteris may publish in the future.

Co-authorship network of co-authors of Panagiotis G. Asteris

This figure shows the co-authorship network connecting the top 25 collaborators of Panagiotis G. Asteris. A scholar is included among the top collaborators of Panagiotis G. Asteris 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 Panagiotis G. Asteris. Panagiotis G. Asteris 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.
Khatti, Jitendra, et al.. (2026). Computational Aspects in Estimating the Bearing Capacity of RC Piles Using Hybrid Machine Learning Models. Transportation Infrastructure Geotechnology. 13(1).
2.
3.
Ahmadi, Fahimeh, et al.. (2025). Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses. Technologies. 13(6). 211–211.
4.
Bardhan, Abidhan, et al.. (2024). An efficient framework of optimized ensemble paradigm for estimating resilient modulus of subgrades. Transportation Geotechnics. 48. 101315–101315. 4 indexed citations
5.
He, Biao, et al.. (2024). A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost. Transportation Geotechnics. 45. 101216–101216. 25 indexed citations
6.
Asteris, Panagiotis G., Μαρία Καρόγλου, Athanasia D. Skentou, et al.. (2024). Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data. Ultrasonics. 141. 107347–107347. 87 indexed citations breakdown →
7.
Ghrici, Mohamed, et al.. (2024). Predicting the shear strength of rectangular RC beams strengthened with externally-bonded FRP composites using constrained monotonic neural networks. Engineering Structures. 313. 118192–118192. 43 indexed citations
8.
Bardhan, Abidhan, et al.. (2024). Hybrid ensemble paradigms for estimating tunnel boring machine penetration rate for the 10-km long Bahce-Nurdagi twin tunnels. Engineering Applications of Artificial Intelligence. 136. 108997–108997. 6 indexed citations
9.
Asteris, Panagiotis G., Konstantinos Daniel Tsavdaridis, Minas E. Lemonis, et al.. (2024). AI-powered GUI for prediction of axial compression capacity in concrete-filled steel tube columns. Neural Computing and Applications. 36(35). 22429–22459. 48 indexed citations
10.
He, Mingming, et al.. (2024). A comparative study of three types of strength criteria for rocks. 2(1). 56–65. 10 indexed citations
11.
Bardhan, Abidhan & Panagiotis G. Asteris. (2023). Application of hybrid ANN paradigms built with nature inspired meta-heuristics for modelling soil compaction parameters. Transportation Geotechnics. 41. 100995–100995. 37 indexed citations
12.
Ghanizadeh, Ali Reza, et al.. (2023). Soft Computing to Predict Earthquake-Induced Soil Liquefaction via CPT Results. Infrastructures. 8(8). 125–125. 14 indexed citations
13.
Mavrouli, Olga, et al.. (2023). Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls. Geosciences. 13(6). 156–156. 2 indexed citations
14.
Maraveas, Chrysanthos, Panagiotis G. Asteris, Konstantinos G. Arvanitis, Thomas Bartzanas, & Dimitrios Loukatos. (2022). Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering. Archives of Computational Methods in Engineering. 30(3). 1979–2012. 45 indexed citations
15.
Liao, Jin‐song, Panagiotis G. Asteris, Liborio Cavaleri, et al.. (2021). Novel Fuzzy-Based Optimization Approaches for the Prediction of Ultimate Axial Load of Circular Concrete-Filled Steel Tubes. Buildings. 11(12). 629–629. 49 indexed citations
16.
Zeng, Jie, Panayiotis C. Roussis, Ahmed Salih Mohammed, et al.. (2021). Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences. 11(8). 3705–3705. 39 indexed citations
17.
Asteris, Panagiotis G., Danial Jahed Armaghani, George D. Hatzigeorgiou, Chris G. Karayannis, & Kypros Pilakoutas. (2019). Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks. Computers and Concrete, an International Journal. 24(5). 469–488. 130 indexed citations
18.
Cavaleri, Liborio, George E. Chatzarakis, Fabio Di Trapani, et al.. (2017). Modeling of surface roughness in electro-discharge machining using artificial neural networks. 6(2). 169–184. 70 indexed citations
19.
Asteris, Panagiotis G.. (2010). A Simple Heuristic Algorithm to Determine the Set of Closed Surfaces of the Cubic Tensor Polynomial. 4(1). 5 indexed citations
20.
Asteris, Panagiotis G., et al.. (2004). FE Analysis of Complex Discontinuous and Jointed Structural Systems: Part 2: Application of the Method – Development of a 3D Model for the Analysis of Unreinforced Masonry Walls. Electronic Journal of Structural Engineering. 4. 93–107. 2 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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