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:

Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model
Yimiao Huang, Junfei Zhang, Foo Tze Ann, et al.
Construction and Building Materials (2020) Vol. 260, pp. 120457-120457
Open Access | Times Cited: 84

Showing 1-25 of 84 citing articles:

Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 377
Machine learning in concrete science: applications, challenges, and best practices
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 182
Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices
Arash Teymori Gharah Tapeh, M.Z. Naser
Archives of Computational Methods in Engineering (2022) Vol. 30, Iss. 1, pp. 115-159
Closed Access | Times Cited: 156
Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms
Torkan Shafighfard, Faramarz Bagherzadeh, Rana Abdollahi Rizi, et al.
Journal of Materials Research and Technology (2022) Vol. 21, pp. 3777-3794
Open Access | Times Cited: 93
Intelligent design and manufacturing of ultra-high performance concrete (UHPC) – A review
Dingqiang Fan, Jinyun Zhu, Mengxin Fan, et al.
Construction and Building Materials (2023) Vol. 385, pp. 131495-131495
Closed Access | Times Cited: 77
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 57
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: 48
Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures
Rayed Alyousef, Muhammad Faisal Rehman, Majid Khan, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02418-e02418
Open Access | Times Cited: 42
Review on Solid Wastes Incorporated Cementitious Material using 3D Concrete Printing Technology
Hongyu Zhao, Yufei Wang, Xianda Liu, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03676-e03676
Open Access | Times Cited: 29
Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review
Itzel Nunez, Afshin Marani, Majdi Flah, et al.
Construction and Building Materials (2021) Vol. 310, pp. 125279-125279
Closed Access | Times Cited: 86
Mixture optimization for environmental, economical and mechanical objectives in silica fume concrete: A novel frame-work based on machine learning and a new meta-heuristic algorithm
Junfei Zhang, Yimiao Huang, Guowei Ma, et al.
Resources Conservation and Recycling (2021) Vol. 167, pp. 105395-105395
Open Access | Times Cited: 85
A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm
Jiandong Huang, Panagiotis G. Asteris, Siavash Manafi Khajeh Pasha, et al.
Engineering With Computers (2020) Vol. 38, Iss. 3, pp. 2209-2220
Closed Access | Times Cited: 77
Real-time prediction of rockburst intensity using an integrated CNN-Adam-BO algorithm based on microseismic data and its engineering application
Xin Yin, Quansheng Liu, Xing Huang, et al.
Tunnelling and Underground Space Technology (2021) Vol. 117, pp. 104133-104133
Closed Access | Times Cited: 68
A survey on firefly algorithms
Jun Li, Xiaoyu Wei, Bo Li, et al.
Neurocomputing (2022) Vol. 500, pp. 662-678
Closed Access | Times Cited: 44
Prediction of the frost resistance of high-performance concrete based on RF-REF: A hybrid prediction approach
Xianguo Wu, Shiyi Zheng, Zongbao Feng, et al.
Construction and Building Materials (2022) Vol. 333, pp. 127132-127132
Closed Access | Times Cited: 40
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 12
Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures
Muhammad Faisal Javed, Majid Khan, Moncef L. Nehdi, et al.
Materials Today Communications (2024) Vol. 39, pp. 108832-108832
Closed Access | Times Cited: 11
Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization
Olivia Pfeiffer, Kai Gong, Kristen Severson, et al.
Cement and Concrete Research (2024) Vol. 177, pp. 107406-107406
Closed Access | Times Cited: 9
A framework for low-carbon mix design of recycled aggregate concrete with supplementary cementitious materials using machine learning and optimization algorithms
Emadaldin Mohammadi Golafshani, Ali Behnood, Taehwan Kim, et al.
Structures (2024) Vol. 61, pp. 106143-106143
Open Access | Times Cited: 9
A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance
Xinyuan Ke, Yu Duan
Construction and Building Materials (2020) Vol. 270, pp. 121424-121424
Closed Access | Times Cited: 50
Experimental study and theoretical prediction of mechanical properties of ultra-high-performance concrete incorporated with nanorice husk ash burning at different temperature treatments
Sahar A. Mostafa, Nashaat Ahmed, Ibrahim Almeshal, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 50, pp. 75380-75401
Closed Access | Times Cited: 34
Loading capacity prediction and optimization of cold-formed steel built-up section columns based on machine learning methods
Lei Xiao, Qiuyun Li, Heng Li, et al.
Thin-Walled Structures (2022) Vol. 180, pp. 109826-109826
Closed Access | Times Cited: 32
Developing Hybrid Machine Learning Models to Determine the Dynamic Modulus (E*) of Asphalt Mixtures Using Parameters in Witczak 1-40D Model: A Comparative Study
Wenjuan Xu, Xin Huang, Zhengjun Yang, et al.
Materials (2022) Vol. 15, Iss. 5, pp. 1791-1791
Open Access | Times Cited: 29
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend
Ramin Kazemi
Engineering Reports (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 19
Intelligent multiobjective optimization for high-performance concrete mix proportion design: A hybrid machine learning approach
Sai Yang, Hongyu Chen, Zongbao Feng, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106868-106868
Closed Access | Times Cited: 16
Page 1 - Next Page

Scroll to top