| A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP) IU Ekanayake, DPP Meddage, U Rathnayake Case Studies in Construction Materials 16, e01059, 2022 | 257 | 2022 |
| Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings DPP Meddage, IU Ekanayake, AU Weerasuriya, CS Lewangamage, ... Journal of Wind Engineering and Industrial Aerodynamics 226, 105027, 2022 | 57 | 2022 |
| 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 P Thisovithan, H Aththanayake, DPP Meddage, IU Ekanayake, ... Results in Engineering 19, 101388, 2023 | 47 | 2023 |
| A novel machine learning approach for diagnosing diabetes with a self-explainable interface G Dharmarathne, TN Jayasinghe, M Bogahawaththa, DPP Meddage, ... Healthcare Analytics 5, 100301, 2024 | 46 | 2024 |
| A simplified mathematical formulation for water quality index (WQI): A case study in the Kelani River Basin, Sri Lanka R Makubura, DPP Meddage, HM Azamathulla, M Pandey, U Rathnayake Fluids 7 (5), 147, 2022 | 41 | 2022 |
| Adapting cities to the surge: A comprehensive review of climate-induced urban flooding G Dharmarathne, AO Waduge, M Bogahawaththa, U Rathnayake, ... Results in Engineering, 102123, 2024 | 40 | 2024 |
| Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers JPSS Madushani, RMK Sandamal, DPP Meddage, HR Pasindu, ... Transportation Engineering 13, 100190, 2023 | 39 | 2023 |
| Interpretation of machine-learning-based (black-box) wind pressure predictions for low-rise gable-roofed buildings using Shapley additive explanations (SHAP) P Meddage, I Ekanayake, US Perera, HM Azamathulla, MA Md Said, ... Buildings 12 (6), 734, 2022 | 38 | 2022 |
| Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface W Kulasooriya, RSS Ranasinghe, US Perera, P Thisovithan, ... Scientific Reports 13 (1), 13138, 2023 | 34 | 2023 |
| Exploring the applicability of expanded polystyrene (EPS) based concrete panels as roof slab insulation in the tropics DPP Meddage, A Chadee, MTR Jayasinghe, U Rathnayake Case Studies in Construction Materials 17, e01361, 2022 | 34 | 2022 |
| Predicting bulk average velocity with rigid vegetation in open channels using tree-based machine learning: a novel approach using explainable artificial intelligence DPP Meddage, IU Ekanayake, S Herath, R Gobirahavan, N Muttil, ... Sensors 22 (12), 4398, 2022 | 32 | 2022 |
| Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations IU Ekanayake, S Palitha, S Gamage, DPP Meddage, K Wijesooriya, ... Materials Today Communications 36, 106545, 2023 | 30 | 2023 |
| Tree-based regression models for predicting external wind pressure of a building with an unconventional configuration DPP Meddage, IU Ekanayake, AU Weerasuriya, CS Lewangamage 2021 Moratuwa Engineering Research Conference (MERCon), 257-262, 2021 | 28 | 2021 |
| Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning DPP Meddage, D Mohotti, K Wijesooriya Journal of Building Engineering 85, 108725, 2024 | 27 | 2024 |
| Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques C Madhushani, K Dananjaya, IU Ekanayake, DPP Meddage, ... Journal of Hydrology 631, 130846, 2024 | 22 | 2024 |
| Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning RSS Ranasinghe, W Kulasooriya, US Perera, IU Ekanayake, ... Results in Engineering 23, 102503, 2024 | 19 | 2024 |
| Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive … RK Makumbura, L Mampitiya, N Rathnayake, DPP Meddage, S Henna, ... Results in Engineering 23, 102831, 2024 | 17 | 2024 |
| A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI U Perera, DTS Coralage, IU Ekanayake, J Alawatugoda, DPP Meddage Results in Engineering 21, 101920, 2024 | 17 | 2024 |
| On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence G Dharmarathne, M Bogahawaththa, M McAfee, U Rathnayake, ... Intelligent Systems with Applications, 200397, 2024 | 12 | 2024 |
| A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP), Case Stud … IU Ekanayake, DPP Meddage, U Rathnayake | 12 | |
Upaka Rathnayake (Top 2% researcher)Professor, Atlantic Technological University, Sligo IrelandVerified email at atu.ie
