I.U. Ekanayake

1.0k total citations · 1 hit paper
20 papers, 725 citations indexed

About

I.U. Ekanayake is a scholar working on Civil and Structural Engineering, Environmental Engineering and Water Science and Technology. According to data from OpenAlex, I.U. Ekanayake has authored 20 papers receiving a total of 725 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Civil and Structural Engineering, 7 papers in Environmental Engineering and 4 papers in Water Science and Technology. Recurrent topics in I.U. Ekanayake's work include Infrastructure Maintenance and Monitoring (5 papers), Hydrological Forecasting Using AI (4 papers) and Building Energy and Comfort Optimization (3 papers). I.U. Ekanayake is often cited by papers focused on Infrastructure Maintenance and Monitoring (5 papers), Hydrological Forecasting Using AI (4 papers) and Building Energy and Comfort Optimization (3 papers). I.U. Ekanayake collaborates with scholars based in Sri Lanka, Australia and Ireland. I.U. Ekanayake's co-authors include D.P.P. Meddage, Upaka Rathnayake, Damayanthi Herath, A.U. Weerasuriya, K.T. Tse, Damith Mohotti, Hazi Mohammad Azamathulla, Md Azlin Md Said, Komali Kantamaneni and Janaka Alawatugoda and has published in prestigious journals such as Scientific Reports, Journal of Hydrology and IEEE Access.

In The Last Decade

I.U. Ekanayake

16 papers receiving 702 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 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. Case Studies in Construction Materials profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
I.U. Ekanayake Sri Lanka	12	254	140	126	110	80	20	725
D.P.P. Meddage Australia	19	377 1.5×	279 2.0×	173 1.4×	190 1.7×	189 2.4×	40	1.2k
Bishwajit Roy India	14	270 1.1×	132 0.9×	140 1.1×	42 0.4×	62 0.8×	21	657
Adeel Zafar Pakistan	17	624 2.5×	49 0.3×	124 1.0×	377 3.4×	26 0.3×	51	1.0k
Vuong Minh Le Vietnam	13	649 2.6×	163 1.2×	88 0.7×	207 1.9×	53 0.7×	14	1.1k
George Kopsiaftis Greece	12	100 0.4×	127 0.9×	98 0.8×	26 0.2×	67 0.8×	24	601
Faramarz Bagherzadeh Poland	9	260 1.0×	158 1.1×	83 0.7×	125 1.1×	253 3.2×	19	816
Seong‐Hoon Hwang South Korea	14	1.1k 4.3×	129 0.9×	132 1.0×	341 3.1×	40 0.5×	28	1.6k
Quang Hung Nguyen Vietnam	5	209 0.8×	44 0.3×	83 0.7×	73 0.7×	27 0.3×	10	574
S. Madeh Piryonesi Canada	9	267 1.1×	36 0.3×	81 0.6×	120 1.1×	15 0.2×	11	586
Manh Duc Nguyen Vietnam	12	381 1.5×	108 0.8×	68 0.5×	57 0.5×	42 0.5×	22	742

Countries citing papers authored by I.U. Ekanayake

Since Specialization

Citations

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

Fields of papers citing papers by I.U. Ekanayake

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I.U. Ekanayake. 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 I.U. Ekanayake. The network helps show where I.U. Ekanayake may publish in the future.

Co-authorship network of co-authors of I.U. Ekanayake

This figure shows the co-authorship network connecting the top 25 collaborators of I.U. Ekanayake. A scholar is included among the top collaborators of I.U. Ekanayake 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 I.U. Ekanayake. I.U. Ekanayake 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

Rathnayake, Namal, et al.. (2025). A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation. Smart Agricultural Technology. 11. 100879–100879. 11 indexed citations

Ekanayake, I.U., et al.. (2025). Code-compliant optimal design of reinforced concrete slab beam systems for low to mid-rise buildings using heterogeneous graph neural networks with attention layers. Journal of Building Engineering. 117. 114599–114599.

Alawatugoda, Janaka, et al.. (2025). Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods. Discover Applied Sciences. 7(5). 1 indexed citations

Alawatugoda, Janaka, et al.. (2025). Prediction of the strength characteristics of basalt fibre reinforced concrete using explainable machine learning models. Discover Applied Sciences. 7(8).

Ekanayake, I.U., et al.. (2025). Prediction of Dengue Outbreaks in Sri Lanka Using Machine Learning Techniques. 34(1). 15–26.

Ekanayake, I.U., et al.. (2024). Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques. Journal of Hydrology. 631. 130846–130846. 33 indexed citations

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

Ekanayake, I.U., et al.. (2024). Navigating the Ethical Landscape of ChatGPT Integration in Scientific Research: Review of Challenges and Recommendations. 3(4). 360–372.

Ekanayake, I.U., et al.. (2024). Overall Survival Predictions of GBM Patients Using Radiomics: An Explainable AI Approach Using SHAP. IEEE Access. 12. 145234–145253. 3 indexed citations

10.

Ekanayake, I.U., et al.. (2024). Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning. Results in Engineering. 23. 102503–102503. 32 indexed citations

11.

Ekanayake, I.U., et al.. (2024). A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI. Results in Engineering. 21. 101920–101920. 27 indexed citations

12.

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

13.

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

14.

Meddage, D.P.P., et al.. (2023). A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine learning techniques. Results in Engineering. 19. 101388–101388. 48 indexed citations

15.

Meddage, D.P.P., et al.. (2022). Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings. Journal of Wind Engineering and Industrial Aerodynamics. 226. 105027–105027. 64 indexed citations

16.

Ekanayake, I.U., D.P.P. Meddage, & Upaka Rathnayake. (2022). A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Studies in Construction Materials. 16. e01059–e01059. 312 indexed citations breakdown →

17.

Meddage, D.P.P., et al.. (2022). Interpretation of Machine-Learning-Based (Black-box) Wind Pressure Predictions for Low-Rise Gable-Roofed Buildings Using Shapley Additive Explanations (SHAP). Buildings. 12(6). 734–734. 40 indexed citations

18.

Meddage, D.P.P., et al.. (2022). Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence. Sensors. 22(12). 4398–4398. 24 indexed citations

19.

Meddage, D.P.P., et al.. (2021). Tree-based Regression Models for Predicting External Wind Pressure of a Building with an Unconventional Configuration. 257–262. 10 indexed citations

20.

Ekanayake, I.U. & Damayanthi Herath. (2020). Chronic Kidney Disease Prediction Using Machine Learning Methods. 260–265. 51 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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