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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:

Prediction and uncertainty quantification of compressive strength of high‐strength concrete using optimized machine learning algorithms
Bing Han, Yanqi Wu, Lulu Liu
Structural Concrete (2022) Vol. 23, Iss. 6, pp. 3772-3785
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

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
Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques
Yanqi Wu, Yisong Zhou
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 59, pp. 89198-89209
Closed Access | Times Cited: 46
Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material
Xiancheng Mei, Zhen Cui, Qian Sheng, et al.
Materials (2023) Vol. 16, Iss. 3, pp. 1286-1286
Open Access | Times Cited: 24
Integrated deep learning and Bayesian optimization approach for enhanced prediction of high-performance concrete strength
Rupesh Kumar Tipu, Archna Goyal, Digvijay Singh, et al.
Asian Journal of Civil Engineering (2025)
Closed Access | Times Cited: 1
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: 21
Compressive Strength Prediction of Rice Husk Ash Concrete Using a Hybrid Artificial Neural Network Model
Chuanqi Li, Xiancheng Mei, Daniel Dias, et al.
Materials (2023) Vol. 16, Iss. 8, pp. 3135-3135
Open Access | Times Cited: 17
Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning
Ehsan Mansouri, Maeve Manfredi, Jong Wan Hu
Sustainability (2022) Vol. 14, Iss. 20, pp. 12990-12990
Open Access | Times Cited: 28
Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques
Yimin Jiang, Hangyu Li, Yisong Zhou
Buildings (2022) Vol. 12, Iss. 5, pp. 690-690
Open Access | Times Cited: 27
Comparative analysis of cement grade and cement strength as input features for machine learning-based concrete strength prediction
Jeonghyun Kim, Donwoo Lee, Andrzej Ubysz
Case Studies in Construction Materials (2024) Vol. 21, pp. e03557-e03557
Open Access | Times Cited: 5
Prediction of the concrete compressive strength using improved random forest algorithm
Mohammad Khodaparasti, Ali Alijamaat, Majid Pouraminian
Journal of Building Pathology and Rehabilitation (2023) Vol. 8, Iss. 2
Closed Access | Times Cited: 12
Optimizing compressive strength in sustainable concrete: a machine learning approach with iron waste integration
Rupesh Kumar Tipu, Vandna Batra, Suman Suman, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 6, pp. 4487-4512
Closed Access | Times Cited: 4
Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models
Qinyi Chen, Guo Hu, Jun Wu
Case Studies in Construction Materials (2024) Vol. 21, pp. e03439-e03439
Open Access | Times Cited: 4
Machine learning to estimate the bond strength of the corroded steel bar‐concrete
Kai‐Lai Wang, Jingyi Li, Li Li, et al.
Structural Concrete (2023) Vol. 25, Iss. 1, pp. 696-715
Closed Access | Times Cited: 10
RF Optimizer Model for Predicting Compressive Strength of Recycled Concrete
Lin Liu, Liuyan Wang, Hui Wang, et al.
Journal of Wuhan University of Technology-Mater Sci Ed (2025) Vol. 40, Iss. 1, pp. 215-223
Closed Access
Tensile behavior evaluation of two-stage concrete using an innovative model optimization approach
Muhammad Nasir Amin, Faizullah Jan, Kaffayatullah Khan, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Prediction of ultimate strength for high strength concrete (HSC) using machine learning approaches - optimized by PSO technique
P. Ruba, S. Aarthi, P. Bhuvaneshwari
Deleted Journal (2025) Vol. 28, Iss. 1
Open Access
Variance‐Based Sensitivity of Seismic Damage of the Containment Building Using Efficient Bayesian Additive Regression Trees
Md Samdani Azad, Duy‐Duan Nguyen, Bidhek Thusa, et al.
The Structural Design of Tall and Special Buildings (2025) Vol. 34, Iss. 6
Open Access
Application of a Hybrid Machine Learning Model for the Prediction of Compressive Strength and Elastic Modulus of Rocks
Xiaoliang Jin, Rui Zhao, Yulin Ma
Minerals (2022) Vol. 12, Iss. 12, pp. 1506-1506
Open Access | Times Cited: 16
Compressive strength prediction of high-strength concrete using machine learning
Manan Davawala, Tanmay Joshi, Manan Shah
Emergent Materials (2022) Vol. 6, Iss. 1, pp. 321-335
Closed Access | Times Cited: 13
A optimum prediction model of chloride ion diffusion coefficient of machine-made sand concrete based on different machine learning methods
Wei Zheng, Jiqi Cai
Construction and Building Materials (2023) Vol. 411, pp. 134414-134414
Closed Access | Times Cited: 8
Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation
Xuerui Liu, Yanqi Wu, Yisong Zhou
Buildings (2022) Vol. 12, Iss. 5, pp. 698-698
Open Access | Times Cited: 10
Estimating the Bond Strength of FRP Bars Using a Hybrid Machine Learning Model
Ran Li, Lulu Liu, Ming Cheng
Buildings (2022) Vol. 12, Iss. 10, pp. 1654-1654
Open Access | Times Cited: 10
Optimal boosting method of HPC concrete compressive and tensile strength prediction
Chao Xu
Structural Concrete (2023) Vol. 25, Iss. 1, pp. 283-302
Open Access | Times Cited: 4
Comparing Machine Learning Regression Models for Early-Age Compressive Strength Prediction of Recycled Aggregate Concrete
Muhammed Ulucan, Güngör Yıldırım, Bilal Alataş, et al.
Fırat Üniversitesi Mühendislik Bilimleri Dergisi (2024) Vol. 36, Iss. 2, pp. 563-580
Open Access | Times Cited: 1
Concrete strength and durability prediction through deep learning and artificial neural networks
Maedeh Hosseinzadeh, Hojjat Samadvand, Alireza Hosseinzadeh, et al.
Frontiers of Structural and Civil Engineering (2024) Vol. 18, Iss. 10, pp. 1540-1555
Closed Access | Times Cited: 1
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