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OpenAlex Citation Counts

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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:

Compressive strength prediction of high-strength oil palm shell lightweight aggregate concrete using machine learning methods
Saeed Ghanbari, Amir Ali Shahmansouri, Habib Akbarzadeh Bengar, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 1, pp. 1096-1115
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners
Usama Asif, Muhammad Faisal Javed, Maher Abuhussain, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03135-e03135
Open Access | Times Cited: 36
Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study
Mohamed Abdellatief, Youssef M. Hassan, Mohamed T. Elnabwy, et al.
Construction and Building Materials (2024) Vol. 436, pp. 136884-136884
Closed Access | Times Cited: 36
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: 41
Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface
W.K.V.J.B. Kulasooriya, R.S.S. Ranasinghe, Udara Sachinthana Perera, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 34
Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation
Maan Habib, Maan Okayli
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 10, pp. 13709-13727
Closed Access | Times Cited: 17
A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector
Siyab Ul Arifeen, Muhammad Nasir Amin, Waqas Ahmad, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133485-133485
Closed Access | Times Cited: 18
Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)
Waleed Bin Inqiad, Elena Valentina Dumitrascu, Robert Alexandru Dobre, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e36841-e36841
Open Access | Times Cited: 8
An interpretable probabilistic machine learning model for forecasting compressive strength of oil palm shell-based lightweight aggregate concrete containing fly ash or silica fume
Yang‐Kook Sun, Han‐Seung Lee
Construction and Building Materials (2024) Vol. 426, pp. 136176-136176
Closed Access | Times Cited: 6
Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features
Muhammad Zeshan Akber
Construction and Building Materials (2024) Vol. 444, pp. 137884-137884
Closed Access | Times Cited: 5
Predicting compressive strength of pervious concrete with fly ash: a machine learning approach and analysis of fly ash compositional influence
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5651-5671
Closed Access | Times Cited: 4
An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes
Ali Ashrafian, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, et al.
Buildings (2022) Vol. 12, Iss. 8, pp. 1280-1280
Open Access | Times Cited: 16
Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network
Ramin Kazemi, Emadaldin Mohammadi Golafshani, Ali Behnood
Structural Concrete (2023) Vol. 25, Iss. 2, pp. 1343-1363
Closed Access | Times Cited: 9
Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN
Vishnupriyan Marimuthu, Annadurai Ramasamy
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 19, pp. 28474-28493
Closed Access | Times Cited: 3
Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste.
Usama Asif, Muhammad Faisal Javed, Deema Mohammed Alsekait, et al.
Case Studies in Construction Materials (2024), pp. e03763-e03763
Open Access | Times Cited: 3
Efficient mix design method for lightweight high strength concrete: A machine learning approach
Mohamed Sifan, Hoang X. Nguyen, Brabha Nagaratnam, et al.
Structures (2023) Vol. 55, pp. 1805-1822
Closed Access | Times Cited: 8
An evolutionary functional link artificial neural network for assessment of compressive strength of concrete structures
Sarat Chandra Nayak, Satchidananda Dehuri, Sung‐Bae Cho
Ain Shams Engineering Journal (2023) Vol. 15, Iss. 3, pp. 102462-102462
Open Access | Times Cited: 6
A study on the synthesis and performance evaluation of fly ash and alccofine as sustainable cementitious materials
Siva Shanmukha Anjaneya Babu Padavala, Venkatesh Noolu, Yeswanth Paluri, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction
T. Senthil Vadivel, Ardra Suseelan, K. Karthick, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Optimizing mix design of concrete with manufactured sand for low embodied carbon and desired strength using machine learning
Qiang Ren, Luchuan Ding, Xiaodi Dai, et al.
Construction and Building Materials (2024) Vol. 457, pp. 139407-139407
Closed Access | Times Cited: 2
Potential of Waste Material as Coarse Aggregates for Lightweight Concrete Production: A Sustainable Approach
Usmani Mohammed Umar, Khairunisa Muthusamy
CONSTRUCTION (2023) Vol. 3, Iss. 1, pp. 87-114
Open Access | Times Cited: 5
Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques
Asad S. Albostami, Rwayda Kh. S. Al‐Hamd, Saif Alzabeebee
Journal of Building Pathology and Rehabilitation (2023) Vol. 8, Iss. 2
Open Access | Times Cited: 4
Multiscale modeling for accurate forecasting of concrete wear depth: a comprehensive study on mixture proportions and environmental factors
Wael Mahmood, Payam Ismael Abdulrahman, Dilshad Kakasor, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5971-5989
Closed Access | Times Cited: 1
Investigating the compressive property of foamcrete and analyzing the feature interaction using modeling approaches
Muhammad Nasir Amin, Roz‐Ud‐Din Nassar, Muhammad Tahir Qadir, et al.
Results in Engineering (2024) Vol. 24, pp. 103305-103305
Open Access | Times Cited: 1
Machine learning models for predicting the compressive strength of cement-based mortar materials: Hyper tuning and optimization
Mana Alyami, Irfan Ullah, Ali H. AlAteah, et al.
Structures (2024) Vol. 71, pp. 107931-107931
Closed Access | Times Cited: 1
Sustainably produced concrete using weathered Linz-Donawitz slag as a fine aggregate Substitute: A comprehensive study with Artificial intelligence approach
Pavitar Singh, Heaven Singh, A.B. Danie Roy
Structures (2023) Vol. 54, pp. 964-980
Closed Access | Times Cited: 2
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