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 modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 441-452
Open Access | Times Cited: 106

Showing 26-50 of 106 citing articles:

Automatic control of groundwater balance to combat dewatering during construction of a metro system
Shui‐Long Shen, Hai‐Min Lyu, Annan Zhou, et al.
Automation in Construction (2021) Vol. 123, pp. 103536-103536
Closed Access | Times Cited: 49
Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time‐dependent behavior and its application
Pin Zhang, Yin‐Fu Jin, Zhen‐Yu Yin
International Journal for Numerical and Analytical Methods in Geomechanics (2021) Vol. 45, Iss. 11, pp. 1588-1602
Open Access | Times Cited: 47
Estimating unconfined compressive strength of unsaturated cemented soils using alternative evolutionary approaches
Navid Kardani, Annan Zhou, Shui‐Long Shen, et al.
Transportation Geotechnics (2021) Vol. 29, pp. 100591-100591
Closed Access | Times Cited: 46
Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin, et al.
Acta Geotechnica (2021) Vol. 17, Iss. 4, pp. 1403-1422
Closed Access | Times Cited: 46
Predicting clay compressibility using a novel Manta ray foraging optimization-based extreme learning machine model
Panagiotis G. Asteris, Anna Mamou, Maria Ferentinou, et al.
Transportation Geotechnics (2022) Vol. 37, pp. 100861-100861
Closed Access | Times Cited: 29
Multifidelity-based Gaussian process for quasi-site-specific probabilistic prediction of soil properties
Geng‐Fu He, Pin Zhang, Zhen‐Yu Yin, et al.
Canadian Geotechnical Journal (2024) Vol. 61, Iss. 10, pp. 2304-2322
Closed Access | Times Cited: 5
State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering
Hongchen Liu, Huaizhi Su, Lizhi Sun, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 8
Open Access | Times Cited: 5
Uncertainty quantification in data-driven modelling with application to soil properties prediction
Geng‐Fu He, Zhen‐Yu Yin, Pin Zhang
Acta Geotechnica (2025)
Closed Access
1DCNN-based prediction methods for subsequent settlement of subgrade with limited monitoring data
Senlin Xie, Anfeng Hu, Meihui Wang, et al.
European Journal of Environmental and Civil engineering (2025), pp. 1-26
Closed Access
Explainable Artificial Intelligence Estimation of Maximum Dry Density in Soil Compaction Based on Basic Soil Properties and Compaction Energy
Rodney Ewusi-Wilson, Jerome Anabannye Yendaw, Sylvanus Sebbeh-Newton, et al.
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 2
Closed Access
Evaluation of compression index of red mud by machine learning interpretability methods
Fan Yang, Jieya Zhang, Mingxing Xie, et al.
Computers and Geotechnics (2025) Vol. 181, pp. 107130-107130
Closed Access
Predicting Energy Consumption of Residential Buildings Using Metaheuristic-optimized Artificial Neural Network Technique in Early Design Stage
Mosbeh R. Kaloop, Furquan Ahmad, Pijush Samui, et al.
Building and Environment (2025), pp. 112749-112749
Closed Access
Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea)
S. Ryu, Jin Kim, Hyoung In Choi, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2757-2757
Open Access
Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction
Geng‐Fu He, Pin Zhang, Zhen‐Yu Yin
Data-Centric Engineering (2025) Vol. 6
Open Access
Prediction of Unconfined Compressive Strength of Cemented Tailings Backfill Containing Coarse Aggregate Using a Hybrid Model Based on Extreme Gradient Boosting
Jinping Guo, Zechen Li, Xiaolin Wang, et al.
International Journal for Numerical and Analytical Methods in Geomechanics (2025)
Closed Access
Big data and machine learning in geoscience and geoengineering: Introduction
Wengang Zhang, Jianye Ching, Anthony T.C. Goh, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 327-329
Open Access | Times Cited: 42
Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms
Abdelhalim Azam, Abidhan Bardhan, Mosbeh R. Kaloop, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 25
Modeling of frozen soil-structure interface shear behavior by supervised deep learning
Weihang Chen, Qiang Luo, Jiankun Liu, et al.
Cold Regions Science and Technology (2022) Vol. 200, pp. 103589-103589
Closed Access | Times Cited: 22
Ensemble Learning Methods for Shear Strength Prediction of Fly Ash-Amended Soils with Lignin Reinforcement
Weihang Chen, Shujun Qu, Luo-bin Lin, et al.
Journal of Materials in Civil Engineering (2023) Vol. 35, Iss. 4
Closed Access | Times Cited: 12
An advanced machine learning-based mathematical equation for predicting compression index from readily-available soil physical properties
Ba-Quang-Vinh Nguyen, Tan-Hung Nguyen, Tu Van Le
Geomechanics and Geoengineering (2025), pp. 1-17
Closed Access
A framework for compression index prediction considering geographical information and feature missing
Yuan-en Pang, Xu Li, Jingmin Xin, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110192-110192
Closed Access
Random forest based artificial intelligent model for predicting failure envelopes of caisson foundations in sand
Pin Zhang, Yin-Fu Jin, Zhen‐Yu Yin, et al.
Applied Ocean Research (2020) Vol. 101, pp. 102223-102223
Open Access | Times Cited: 36
A LSTM surrogate modelling approach for caisson foundations
Pin Zhang, Zhen‐Yu Yin, Yuanyuan Zheng, et al.
Ocean Engineering (2020) Vol. 204, pp. 107263-107263
Open Access | Times Cited: 35
A multi-objective optimization algorithm for forecasting the compressive strength of RAC with pozzolanic materials
Wafaa Mohamed Shaban, Khalid Elbaz, Jian Yang, et al.
Journal of Cleaner Production (2021) Vol. 327, pp. 129355-129355
Closed Access | Times Cited: 29
Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction
Jie Yang, Saffet Yağız, Yingjing Liu, et al.
Underground Space (2021) Vol. 7, Iss. 1, pp. 37-49
Open Access | Times Cited: 27

Scroll to top