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:

Computing Models to Predict the Compressive Strength of Engineered Cementitious Composites (ECC) at Various Mix Proportions
Kawan Ghafor, Hemn Unis Ahmed, Rabar H. Faraj, et al.
Sustainability (2022) Vol. 14, Iss. 19, pp. 12876-12876
Open Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles
Hemn Unis Ahmed, Ahmed Salih Mohammed, Rabar H. Faraj, et al.
Neural Computing and Applications (2023) Vol. 35, Iss. 17, pp. 12453-12479
Closed Access | Times Cited: 160
RETRACTED: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation
Hemn Unis Ahmed, Ahmed Salih Mohammed, Azad A. Mohammed
Construction and Building Materials (2023) Vol. 394, pp. 132266-132266
Closed Access | Times Cited: 51
The effectiveness of surrogate models in predicting the long-term behavior of varying compressive strength ranges of recycled concrete aggregate for a variety of shapes and sizes of specimens
Aisha Kamal Ibrahim, Hyfaa Yasin Dhahir, Ahmed Salih Mohammed, et al.
Archives of Civil and Mechanical Engineering (2023) Vol. 23, Iss. 1
Closed Access | Times Cited: 37
Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches
T. Vamsi Nagaraju, Sireesha Mantena, Marc Azab, et al.
Results in Engineering (2023) Vol. 17, pp. 100973-100973
Open Access | Times Cited: 37
Effect of short to long hybrid elastic/hyperelastic (Glass/PP) fibers on high-strength engineered cementitious composites (ECC), along with presenting a constitutive model
Naser Shabakhty, Hamid Reza Karimi, Abbas Yeganeh‐Bakhtiary
Journal of Building Engineering (2024) Vol. 89, pp. 109215-109215
Closed Access | Times Cited: 14
Mathematical modeling techniques to predict the compressive strength of pervious concrete modified with waste glass powders
Soran Abdrahman Ahmad, Serwan Rafiq, Hozan Hilmi, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 773-785
Closed Access | Times Cited: 32
Innovative soft computing techniques including artificial neural network and nonlinear regression models to predict the compressive strength of environmentally friendly concrete incorporating waste glass powder
Soran Abdrahman Ahmad, Serwan Khwrshed Rafiq, Hemn Unis Ahmed, et al.
Innovative Infrastructure Solutions (2023) Vol. 8, Iss. 4
Closed Access | Times Cited: 25
Interface behaviors between smart-functional ECC and steel rebar under coupling effect of sustained load and chloride ion erosion: Corrosion resistance, mechanical and self-sensing properties
Jun Tian, Xiaowei Wu, Yang Zuo, et al.
Construction and Building Materials (2024) Vol. 438, pp. 137205-137205
Closed Access | Times Cited: 6
Machine learning approach for predicting compressive strength in foam concrete under varying mix designs and curing periods
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Rafiq, et al.
Smart Construction and Sustainable Cities (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 19
Innovative machine learning approaches to predict the compressive strength of recycled plastic aggregate self-compacting concrete incorporating different waste ashes
Brwa Hamah Saeed Hamah Ali, Rabar H. Faraj, Mariwan Hama Saeed, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2585-2604
Closed Access | Times Cited: 5
Exploring the influence of waste glass granular replacement on compressive strength in concrete mixtures: a normalization and modeling study
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Rafiq, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 5
Smart Predictive Modeling for Compressive Strength in Sisal-Fiber-Reinforced-Concrete Composites: Harnessing SVM, GP, and ANN Techniques
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Khurshid Rafiq, et al.
Multiscale Science and Engineering (2024) Vol. 6, Iss. 1, pp. 95-111
Closed Access | Times Cited: 5
Utilizing multivariable mathematical models to predict maximum dry density and optimum moisture content from physical soil properties
Hunar Farid Hama Ali
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 6, Iss. 4, pp. 603-627
Closed Access | Times Cited: 13
A Comprehensive Exploration on the Effect of Waste Glass Powder as a Partial Replacement of Cement in Mortar: A Review, Analysis, and Modeling Investigation
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Rafiq, et al.
Arabian Journal for Science and Engineering (2023)
Closed Access | Times Cited: 13
Predicting concrete strength with waste glass using statistical evaluations, neural networks, and linear/nonlinear models
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Dler Ali Ahmed, et al.
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3023-3035
Closed Access | Times Cited: 11
A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites
Qingyu Cao, Xiongzhou Yuan, Muhammad Nasir Amin, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133540-133540
Closed Access | Times Cited: 10
Soft Computing Models to Predict the Compaction Characteristics from Physical Soil Properties
Hunar Hama Ali
Engineering and Technology Journal (2023) Vol. 41, Iss. 5, pp. 1-18
Open Access | Times Cited: 6
Modeling for torsional strength prediction of strengthened RC beams
Nasih Habeeb Askandar, Ghazi Bahroz Jumaa, Ghafur H. Ahmed
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2535-2553
Closed Access | Times Cited: 1
Evaluation of the compressive strength of polypropylene fiber reinforced high-strength concrete support with AI-based model
Soran Abdrahman Ahmad, Hemn Unis Ahmed, Serwan Rafiq, et al.
Innovative Infrastructure Solutions (2023) Vol. 8, Iss. 12
Closed Access | Times Cited: 4
Ensemble learning models to predict the compressive strength of geopolymer concrete: a comparative study for geopolymer composition design
Qiong Tian, Zhanlin Su, Nicholas Fiorentini, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 3, pp. 1793-1806
Closed Access | Times Cited: 3
Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites
Y. A. Khan, Adeel Zafar, Muhammad Faisal Rehman, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e21601-e21601
Open Access | Times Cited: 2
Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks
Hunar Farid Hama Ali
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 3, pp. 1737-1753
Closed Access | Times Cited: 2
Machine Learning Models for Predicting Flexural Behavior of FRP-Strengthened RC Beams
Nasih Habeeb Askander
passer (2024) Vol. 6, Iss. 1, pp. 198-213
Open Access
Experimental study of flexural behavior of ECC-CFRP composite reinforced beams with different ECC thicknesses
Longyu Cong, Fangwei Zhang, Qian Yong-jiu
Structures (2024) Vol. 66, pp. 106873-106873
Closed Access
ANN-Based Prediction of Compressive Strength in Glass Wool Reinforced Brick
Yashwanth Pamu, Prasanna SVSNDL
E3S Web of Conferences (2024) Vol. 559, pp. 04006-04006
Open Access
Page 1 - Next Page

Scroll to top