D.P.P. Meddage

1.9k total citations · 5 hit papers
40 papers, 1.2k citations indexed

About

D.P.P. Meddage is a scholar working on Civil and Structural Engineering, Environmental Engineering and Building and Construction. According to data from OpenAlex, D.P.P. Meddage has authored 40 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Civil and Structural Engineering, 14 papers in Environmental Engineering and 9 papers in Building and Construction. Recurrent topics in D.P.P. Meddage's work include Building Energy and Comfort Optimization (6 papers), Hydrological Forecasting Using AI (6 papers) and Wind and Air Flow Studies (6 papers). D.P.P. Meddage is often cited by papers focused on Building Energy and Comfort Optimization (6 papers), Hydrological Forecasting Using AI (6 papers) and Wind and Air Flow Studies (6 papers). D.P.P. Meddage collaborates with scholars based in Australia, Sri Lanka and Ireland. D.P.P. Meddage's co-authors include Upaka Rathnayake, I.U. Ekanayake, Damith Mohotti, Kasun Wijesooriya, Hazi Mohammad Azamathulla, A.U. Weerasuriya, Namal Rathnayake, C.K. Lee, Thilini N. Jayasinghe and Shagufta Henna and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of Hydrology.

In The Last Decade

D.P.P. Meddage

38 papers receiving 1.2k citations

Hit Papers

A novel approach to explain the black-box nature of machi... 2022 2026 2023 2024 2022 2024 2024 2024 2024 100 200 300
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. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
    2022 312 citations I.U. Ekanayake, D.P.P. Meddage et al. profile →
  2. Adapting cities to the surge: A comprehensive review of climate-induced urban flooding
    2024 80 citations Upaka Rathnayake, D.P.P. Meddage et al. Results in Engineering profile →
  3. Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature
    2024 75 citations D.P.P. Meddage, Upaka Rathnayake et al. Results in Engineering profile →
  4. A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing)
    2024 63 citations D.P.P. Meddage et al. Materials Today Communications profile →
  5. A novel machine learning approach for diagnosing diabetes with a self-explainable interface
    2024 51 citations Thilini N. Jayasinghe, D.P.P. Meddage et al. SHILAP Revista de lepidopterología profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
D.P.P. Meddage Australia 19 377 279 190 189 173 40 1.2k
I.U. Ekanayake Sri Lanka 12 254 0.7× 140 0.5× 110 0.6× 80 0.4× 126 0.7× 20 725
Debaditya Chakraborty United States 18 226 0.6× 283 1.0× 381 2.0× 142 0.8× 160 0.9× 34 1.2k
Vuong Minh Le Vietnam 13 649 1.7× 163 0.6× 207 1.1× 53 0.3× 88 0.5× 14 1.1k
Nehal Elshaboury Egypt 18 154 0.4× 222 0.8× 264 1.4× 168 0.9× 75 0.4× 93 917
Lanh Si Ho Vietnam 23 1.3k 3.3× 263 0.9× 451 2.4× 99 0.5× 149 0.9× 60 2.2k
Mohammed Majeed Hameed Iraq 18 298 0.8× 580 2.1× 109 0.6× 410 2.2× 172 1.0× 47 1.2k
Chongzhi Wu China 21 1.7k 4.5× 185 0.7× 135 0.7× 71 0.4× 169 1.0× 48 2.8k
Li Ma China 23 159 0.4× 127 0.5× 117 0.6× 27 0.1× 190 1.1× 108 2.3k
Seong‐Hoon Hwang South Korea 14 1.1k 2.9× 129 0.5× 341 1.8× 40 0.2× 132 0.8× 28 1.6k

Countries citing papers authored by D.P.P. Meddage

Since Specialization
Citations

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

Fields of papers citing papers by D.P.P. Meddage

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D.P.P. Meddage. 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 D.P.P. Meddage. The network helps show where D.P.P. Meddage may publish in the future.

Co-authorship network of co-authors of D.P.P. Meddage

This figure shows the co-authorship network connecting the top 25 collaborators of D.P.P. Meddage. A scholar is included among the top collaborators of D.P.P. Meddage 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 D.P.P. Meddage. D.P.P. Meddage 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.
Mohotti, Damith, et al.. (2025). Future challenges in hydrogen storage infrastructure design: Mitigating accidental explosion hazards. International Journal of Protective Structures.
2.
Mohotti, Damith, et al.. (2025). Explainable machine learning-based prediction of blast loads on structural surfaces in two-dimensional spatial coordinates. Results in Engineering. 26. 104979–104979. 1 indexed citations
3.
Alawatugoda, Janaka, et al.. (2025). A review of machine learning and internet-of-things on the water quality assessment: Methods, applications and future trends. Results in Engineering. 26. 105182–105182. 11 indexed citations
4.
Meddage, D.P.P., et al.. (2025). Predicting moment-rotation behaviour of beam to column extended endplate bolted connections: An explainable machine learning approach. Materials Today Communications. 47. 113086–113086. 1 indexed citations
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Meddage, D.P.P., et al.. (2024). An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete. Construction and Building Materials. 449. 138346–138346. 37 indexed citations
11.
Gatheeshgar, Perampalam, et al.. (2024). Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations. Structures. 71. 108079–108079. 4 indexed citations
12.
Ekanayake, I.U., et al.. (2024). Effect of endogenous and anthropogenic factors on the alkalinisation and salinisation of freshwater in United States by using explainable machine learning. Case Studies in Chemical and Environmental Engineering. 10. 100919–100919. 3 indexed citations
13.
Meddage, D.P.P., Damith Mohotti, Kasun Wijesooriya, C.K. Lee, & K.C.S. Kwok. (2024). Interpolating wind pressure time-histories around a tall building - A deep learning-based approach. Journal of Wind Engineering and Industrial Aerodynamics. 256. 105968–105968. 6 indexed citations
14.
Meddage, D.P.P., Damith Mohotti, & Kasun Wijesooriya. (2024). Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning. Journal of Building Engineering. 85. 108725–108725. 37 indexed citations
15.
Meddage, D.P.P., et al.. (2024). A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing). Materials Today Communications. 41. 110294–110294. 63 indexed citations breakdown →
16.
Rathnayake, Upaka, et al.. (2024). Integrating explainable machine learning and user-centric model for diagnosing cardiovascular disease: A novel approach. Intelligent Systems with Applications. 23. 200428–200428. 9 indexed citations
17.
Okasha, Nader M., et al.. (2024). Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes. Developments in the Built Environment. 19. 100494–100494. 15 indexed citations
18.
Jayasinghe, Thilini N., et al.. (2024). A novel machine learning approach for diagnosing diabetes with a self-explainable interface. SHILAP Revista de lepidopterología. 5. 100301–100301. 51 indexed citations breakdown →
19.
Ekanayake, I.U., et al.. (2023). Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface. Scientific Reports. 13(1). 13138–13138. 43 indexed citations
20.
Ekanayake, I.U., et al.. (2023). Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations. Materials Today Communications. 36. 106545–106545. 23 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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