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:

Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm
Li Hong, Jiajian Lin, Xiaobao Lei, et al.
Materials Today Communications (2022) Vol. 30, pp. 103117-103117
Closed Access | Times Cited: 112

Showing 1-25 of 112 citing articles:

Contribution of urban functional zones to the spatial distribution of urban thermal environment
Yang Chen, Jun Yang, Ruxin Yang, et al.
Building and Environment (2022) Vol. 216, pp. 109000-109000
Closed Access | Times Cited: 155
Research on different types of fiber reinforced concrete in recent years: An overview
Chenggong Zhao, Zhiyuan Wang, Zhenyu Zhu, et al.
Construction and Building Materials (2022) Vol. 365, pp. 130075-130075
Closed Access | Times Cited: 154
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Yanqi Wu, Yisong Zhou
Construction and Building Materials (2022) Vol. 330, pp. 127298-127298
Closed Access | Times Cited: 139
Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
Farzin Kazemi, Torkan Shafighfard, Doo‐Yeol Yoo
Archives of Computational Methods in Engineering (2024) Vol. 31, Iss. 4, pp. 2049-2078
Closed Access | Times Cited: 56
Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)
Md Nasir Uddin, Junhong Ye, Bo-Yu Deng, et al.
Journal of Building Engineering (2023) Vol. 72, pp. 106648-106648
Closed Access | Times Cited: 49
Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach
Celal Çakıroğlu, Yaren Aydın, Gebrai̇l Bekdaş, et al.
Materials (2023) Vol. 16, Iss. 13, pp. 4578-4578
Open Access | Times Cited: 46
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
Long-term tracking of urban structure and analysis of its impact on urban heat stress: a case study of Xi’an, China
Kaipeng Huo, Rui Qin, Jingyuan Zhao, et al.
Ecological Indicators (2025) Vol. 174, pp. 113418-113418
Closed Access | Times Cited: 2
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
Hong‐Gen Chen, Xin Li, Yanqi Wu, et al.
Buildings (2022) Vol. 12, Iss. 3, pp. 302-302
Open Access | Times Cited: 65
Formulation of estimation models for the compressive strength of concrete mixed with nanosilica and carbon nanotubes
Sohaib Nazar, Jian Yang, Muhammad Nasir Amin, et al.
Developments in the Built Environment (2022) Vol. 13, pp. 100113-100113
Open Access | Times Cited: 57
An interpretable forecasting framework for energy consumption and CO2 emissions
Serkan Aras, M. Hanifi Van
Applied Energy (2022) Vol. 328, pp. 120163-120163
Closed Access | Times Cited: 46
Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations
Yanqi Wu, Yisong Zhou
Mechanics of Advanced Materials and Structures (2022) Vol. 30, Iss. 15, pp. 3086-3096
Closed Access | Times Cited: 42
Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach
Josephine D. German, Ardvin Kester S. Ong, Anak Agung Ngurah Perwira Redi, et al.
Heliyon (2022) Vol. 8, Iss. 11, pp. e11382-e11382
Open Access | Times Cited: 40
Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques
Sohaib Nazar, Jian Yang, Waqas Ahmad, et al.
Buildings (2022) Vol. 12, Iss. 12, pp. 2160-2160
Open Access | Times Cited: 39
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
Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm
Xiaoyi Wu, Yuanbao Song, Wei Chen, et al.
Sustainability (2023) Vol. 15, Iss. 5, pp. 4328-4328
Open Access | Times Cited: 26
Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder
Muhammad Sarmad Mahmood, Ayub Elahi, Osama Zaid, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02557-e02557
Open Access | Times Cited: 25
Prediction of building energy performance using mathematical gene-expression programming for a selected region of dry-summer climate
Majed Alzara, Muhammad Faisal Rehman, Furqan Farooq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106958-106958
Closed Access | Times Cited: 24
Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage
Grant Charles Mwakipunda, Norga Alloyce Komba, Allou Koffi Franck Kouassi, et al.
International Journal of Hydrogen Energy (2024) Vol. 87, pp. 373-388
Closed Access | Times Cited: 14
Investigating the applicability of deep learning and machine learning models in predicting the structural performance of V-shaped RC folded plates
Metin Katlav, Faruk Ergen, Kâzım Türk, et al.
Materials Today Communications (2024) Vol. 38, pp. 108141-108141
Closed Access | Times Cited: 12
Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites
Binbin Yin, Jiasheng Huang, Weikang Sun
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 431, pp. 117309-117309
Closed Access | Times Cited: 11
Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
Muhammad Arif, Faizullah Jan, A. Rezzoug, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03935-e03935
Open Access | Times Cited: 11
Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials
Eka Oktavia Kurniati, Hang Zeng, Marat I. Latypov, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03373-e03373
Open Access | Times Cited: 10
Integrating morphology and vitality to quantify seasonal contributions of urban functional zones to thermal environment
Lei Wang, Ruonan Li, Jia Jia, et al.
Sustainable Cities and Society (2025), pp. 106136-106136
Closed Access | Times Cited: 1
Page 1 - Next Page

Scroll to top