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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:

Artificial Intelligence in Civil Engineering
Pengzhen Lu, Shengyong Chen, Yu‐Jun Zheng
Mathematical Problems in Engineering (2012) Vol. 2012, Iss. 1
Open Access | Times Cited: 209

Showing 1-25 of 209 citing articles:

Emerging artificial intelligence methods in structural engineering
Hadi Salehi, Rigoberto Burgueño
Engineering Structures (2018) Vol. 171, pp. 170-189
Open Access | Times Cited: 799
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
Yazhou Xie, Majid Ebad Sichani, Jamie E. Padgett, et al.
Earthquake Spectra (2020) Vol. 36, Iss. 4, pp. 1769-1801
Open Access | Times Cited: 356
Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity – A review
Ana M. Jiménez‐Carvelo, Antonio González‐Casado, M. Gracia Bagur-González, et al.
Food Research International (2019) Vol. 122, pp. 25-39
Closed Access | Times Cited: 264
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105444-105444
Open Access | Times Cited: 225
Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System
Imran Ali Lakhiar, Jianmin Gao, Tabinda Naz Syed, et al.
Journal of Sensors (2018) Vol. 2018, pp. 1-18
Open Access | Times Cited: 185
A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
Hossein Moayedi, Mansour Mosallanezhad, Ahmad Safuan A. Rashid, et al.
Neural Computing and Applications (2019) Vol. 32, Iss. 2, pp. 495-518
Closed Access | Times Cited: 162
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: 155
Review of artificial intelligence applications in engineering design perspective
Nurullah Yüksel, Hüseyin Rıza Börklü, Hüseyin Kürşad Sezer, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105697-105697
Closed Access | Times Cited: 120
Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry
Nitin Rane
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 54
Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry
Hamed Nabizadeh Rafsanjani, Amir Hossein Nabizadeh
Computers in Human Behavior Reports (2023) Vol. 11, pp. 100319-100319
Open Access | Times Cited: 43
Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning
Rakesh Kumar, Pijush Samui, Baboo Rai
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024) Vol. 48, Iss. 5, pp. 3717-3734
Closed Access | Times Cited: 15
Utilizing Emerging Technologies for Construction Safety Risk Mitigation
Ifeanyi Okpala, Chukwuma Nnaji, Ali A. Karakhan
Practice Periodical on Structural Design and Construction (2020) Vol. 25, Iss. 2
Closed Access | Times Cited: 98
Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls
Ahmad Siam, Mohamed Ezzeldin, Wael El‐Dakhakhni
Structures (2019) Vol. 22, pp. 252-265
Closed Access | Times Cited: 84
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: 84
StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains
M.Z. Naser, Venkatesh Kodur, Huu‐Tai Thai, et al.
Journal of Building Engineering (2021) Vol. 44, pp. 102977-102977
Closed Access | Times Cited: 71
Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review
Bilal Manzoor, Idris Othman, Serdar Durdyev, et al.
Applied System Innovation (2021) Vol. 4, Iss. 3, pp. 52-52
Open Access | Times Cited: 63
Machine learning-driven seismic failure mode identification of reinforced concrete shear walls based on PCA feature extraction
Qingsong Xiong, Haibei Xiong, Qingzhao Kong, et al.
Structures (2022) Vol. 44, pp. 1429-1442
Closed Access | Times Cited: 37
Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment
Ke Wang, Ziyi Ying, Shankha Shubhra Goswami, et al.
Sustainability (2023) Vol. 15, Iss. 15, pp. 11848-11848
Open Access | Times Cited: 34
Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete
Majid Khan, Roz‐Ud‐Din Nassar, Asad U. Khan, et al.
Results in Engineering (2023) Vol. 20, pp. 101625-101625
Open Access | Times Cited: 33
A review of deep learning and machine learning techniques for hydrological inflow forecasting
Sarmad Dashti Latif, Ali Najah Ahmed
Environment Development and Sustainability (2023) Vol. 25, Iss. 11, pp. 12189-12216
Closed Access | Times Cited: 30
Compressive strength prediction of ternary-blended concrete using deep neural network with tuned hyperparameters
J. S. Choi, Dongyoun Kim, Minsam Ko, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 107004-107004
Closed Access | Times Cited: 23
A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete
Akshita Bassi, Aditya Manchanda, Rajwinder Singh, et al.
Natural Hazards (2023) Vol. 118, Iss. 1, pp. 209-238
Closed Access | Times Cited: 22
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: 10
Predictive Modelling of Flexural Strength in Recycled Aggregate-Based Concrete: A Comprehensive Approach with Machine Learning and Global Sensitivity Analysis
Rajwinder Singh, Rupesh Kumar Tipu, Ajaz Ahmad Mir, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024)
Closed Access | Times Cited: 8
A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings
James Ogundiran, Ehsan Asadi, Manuel Gameiro da Silva
Sustainability (2024) Vol. 16, Iss. 9, pp. 3627-3627
Open Access | Times Cited: 7
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