Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
I.U. Ekanayake, D.P.P. Meddage, Upaka Rathnayake
Case Studies in Construction Materials (2022) Vol. 16, pp. e01059-e01059
Open Access | Times Cited: 260

Showing 1-25 of 260 citing articles:

Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches
Ali Aldrees, Majid Khan, Abubakr Taha Bakheit Taha, et al.
Journal of Water Process Engineering (2024) Vol. 58, pp. 104789-104789
Closed Access | Times Cited: 70
Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses
Rezaul Karim, Md. Hamidul Islam, Shuvo Dip Datta, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02828-e02828
Open Access | Times Cited: 65
Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 58
Adapting cities to the surge: A comprehensive review of climate-induced urban flooding
Gangani Dharmarathne, Anushka Osadhi Waduge, Madhusha Bogahawaththa, et al.
Results in Engineering (2024) Vol. 22, pp. 102123-102123
Open Access | Times Cited: 53
Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations
Pobithra Das, Abul Kashem
Case Studies in Construction Materials (2023) Vol. 20, pp. e02723-e02723
Open Access | Times Cited: 48
Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm
M. Aminul Haque, Bing Chen, Abul Kashem, et al.
Materials Today Communications (2023) Vol. 35, pp. 105547-105547
Closed Access | Times Cited: 47
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 39
A novel machine learning approach for diagnosing diabetes with a self-explainable interface
Gangani Dharmarathne, Thilini N. Jayasinghe, Madhusha Bogahawaththa, et al.
Healthcare Analytics (2024) Vol. 5, pp. 100301-100301
Open Access | Times Cited: 35
Machine learning-based prediction of outdoor thermal comfort: Combining Bayesian optimization and the SHAP model
Ruiqi Guo, Bin Yang, Yuyao Guo, et al.
Building and Environment (2024) Vol. 254, pp. 111301-111301
Closed Access | Times Cited: 35
Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning
D.P.P. Meddage, Damith Mohotti, Kasun Wijesooriya
Journal of Building Engineering (2024) Vol. 85, pp. 108725-108725
Open Access | Times Cited: 29
Sustainable mix design of recycled aggregate concrete using artificial intelligence
Emadaldin Mohammadi Golafshani, Taehwan Kim, Ali Behnood, et al.
Journal of Cleaner Production (2024) Vol. 442, pp. 140994-140994
Open Access | Times Cited: 25
Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques
Charuni I. Madhushani, K. G. S. Dananjaya, I.U. Ekanayake, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130846-130846
Closed Access | Times Cited: 24
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
R.S.S. Ranasinghe, W.K.V.J.B. Kulasooriya, Udara Sachinthana Perera, et al.
Results in Engineering (2024) Vol. 23, pp. 102503-102503
Open Access | Times Cited: 22
Machine learning-based optimization of catalytic hydrodeoxygenation of biomass pyrolysis oil
Xiangmeng Chen, Alireza Shafizadeh, Hossein Shahbeik, et al.
Journal of Cleaner Production (2024) Vol. 437, pp. 140738-140738
Closed Access | Times Cited: 21
Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Bilal Siddiq, Kennedy C. Onyelowe, et al.
Results in Engineering (2024) Vol. 23, pp. 102637-102637
Open Access | Times Cited: 21
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 21
Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming
Majid Khan, Mujahid Ali, Taoufik Najeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 20
A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI
U.A.K.K. Perera, D.T.S. Coralage, I.U. Ekanayake, et al.
Results in Engineering (2024) Vol. 21, pp. 101920-101920
Open Access | Times Cited: 19
A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
Pobithra Das, Abul Kashem, Imrul Hasan, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 4, pp. 3301-3316
Closed Access | Times Cited: 18
Data-driven Approach to Estimate Urban Heat Island Impacts on Building Energy Consumption
Alireza Attarhay Tehrani, Saeideh Sobhaninia, Niloofar Nikookar, et al.
Energy (2025) Vol. 316, pp. 134508-134508
Closed Access | Times Cited: 3
Transfer Learning Framework for the Wind Pressure Prediction of High-Rise Building Surfaces Using Wind Tunnel Experiments and Machine Learning
Jingyu Wei, Tzung-Sz Shen, Kun Wang, et al.
Building and Environment (2025), pp. 112620-112620
Closed Access | Times Cited: 3
Super learner machine‐learning algorithms for compressive strength prediction of high performance concrete
Seunghye Lee, Ngoc‐Hien Nguyen, Armağan Karamanlı, et al.
Structural Concrete (2022) Vol. 24, Iss. 2, pp. 2208-2228
Closed Access | Times Cited: 52
Concrete compressive strength prediction using an explainable boosting machine model
Gaoyang Liu, Bochao Sun
Case Studies in Construction Materials (2023) Vol. 18, pp. e01845-e01845
Open Access | Times Cited: 43
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, Harinda Aththanayake, D.P.P. Meddage, et al.
Results in Engineering (2023) Vol. 19, pp. 101388-101388
Open Access | Times Cited: 42
Towards sustainable construction: Machine learning based predictive models for strength and durability characteristics of blended cement concrete
Majid Khan, Muhammad Faisal Javed
Materials Today Communications (2023) Vol. 37, pp. 107428-107428
Closed Access | Times Cited: 42
Page 1 - Next Page

Scroll to top