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:

A new approach for constructing two Bayesian network models for predicting the liquefaction of gravelly soil
Jilei Hu
Computers and Geotechnics (2021) Vol. 137, pp. 104304-104304
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Explainable machine learning model for liquefaction potential assessment of soils using XGBoost-SHAP
Kaushik Jas, G. R. Dodagoudar
Soil Dynamics and Earthquake Engineering (2022) Vol. 165, pp. 107662-107662
Closed Access | Times Cited: 78
Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model
Kaushik Jas, Sujith Mangalathu, G. R. Dodagoudar
Computers and Geotechnics (2024) Vol. 167, pp. 106051-106051
Closed Access | Times Cited: 19
Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks
Huixing Meng, Mengqian Hu, Zihan Kong, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110294-110294
Closed Access | Times Cited: 17
Machine learning approaches for prediction of fine-grained soils liquefaction
Mustafa Özsağır, Caner Erden, Ertan Bol, et al.
Computers and Geotechnics (2022) Vol. 152, pp. 105014-105014
Closed Access | Times Cited: 53
A data-driven Bayesian network model integrating physical knowledge for prioritization of risk influencing factors
Huixing Meng, Xu An, Jinduo Xing
Process Safety and Environmental Protection (2022) Vol. 160, pp. 434-449
Open Access | Times Cited: 44
CPT-based fully probabilistic seismic liquefaction potential assessment to reduce uncertainty: Integrating XGBoost algorithm with Bayesian theorem
Zening Zhao, Wei Duan, Guojun Cai, et al.
Computers and Geotechnics (2022) Vol. 149, pp. 104868-104868
Closed Access | Times Cited: 43
Liquefaction Potential Assessment of Soils Using Machine Learning Techniques: A State-of-the-Art Review from 1994–2021
Kaushik Jas, G. R. Dodagoudar
International Journal of Geomechanics (2023) Vol. 23, Iss. 7
Closed Access | Times Cited: 25
Data-Driven Dynamic Bayesian Network Model for Safety Resilience Evaluation of Prefabricated Building Construction
Junwu Wang, Zhao Chen, Yinghui Song, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 570-570
Open Access | Times Cited: 7
Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches
Jitendra Khatti, Yewuhalashet Fissha, Kamaldeep Singh Grover, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3841-3864
Closed Access | Times Cited: 5
A novel approach for assessment of seismic induced liquefaction susceptibility of soil
Divesh Ranjan Kumar, Pijush Samui, Avijit Burman, et al.
Journal of Earth System Science (2024) Vol. 133, Iss. 3
Closed Access | Times Cited: 5
Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations
Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Suitability assessment of the best liquefaction analysis procedure based on SPT data
Divesh Ranjan Kumar, Pijush Samui, Avijit Burman
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 6, Iss. 2, pp. 319-329
Closed Access | Times Cited: 11
Influence of data quality on the performance of supervised classification models for predicting gravelly soil liquefaction
Jilei Hu, Jing Wang
Engineering Geology (2023) Vol. 324, pp. 107254-107254
Closed Access | Times Cited: 11
Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions
Arsham Moayedi Far, Masoud Zare Naghadehi
Civil Engineering Design (2025)
Open Access
Incorporating modelling uncertainty and prior knowledge into landslide susceptibility mapping using Bayesian neural networks
Chongzhi Chen, Zhangquan Shen, Luming Fang, et al.
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024), pp. 1-20
Closed Access | Times Cited: 2
A novel soil liquefaction prediction model with intellectual feature extraction and classification
Nerusupalli Dinesh Kumar Reddy, Ashok Kumar Gupta, Anil Kumar Sahu
Advances in Engineering Software (2022) Vol. 173, pp. 103233-103233
Closed Access | Times Cited: 12
Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests
Eduardo Martínez García, Marcos García Alberti, Antonio Alfonso Arcos Álvarez
Applied Sciences (2022) Vol. 12, Iss. 9, pp. 4565-4565
Open Access | Times Cited: 10
DPT-based seismic liquefaction triggering assessment in gravelly soils based on expanded case history dataset
Nima Pirhadi, Xusheng Wan, Jianguo Lu, et al.
Engineering Geology (2022) Vol. 311, pp. 106894-106894
Closed Access | Times Cited: 8
Prediction of liquefaction of gravelly soils based on a cost-sensitive Bayesian network combined with rough set weighting
Jilei Hu, Jing Wang
Gondwana Research (2024) Vol. 131, pp. 57-68
Closed Access | Times Cited: 1
Optimized ensemble-classification for prediction of soil liquefaction with improved features
Nerusupalli Dinesh Kumar Reddy, Ashok Kumar Gupta, Anil Kumar Sahu
Multimedia Tools and Applications (2023) Vol. 82, Iss. 20, pp. 31467-31486
Closed Access | Times Cited: 4
Assessment of soil liquefaction potential using genetic programming using a probability-based approach
Nerusupalli Dinesh Kumar Reddy, Ashok Kumar Gupta, Anil Kumar Sahu
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Probabilistic identification of debris flow source areas in the Wenchuan earthquake-affected region of China based on Bayesian geomorphology
Xudong Hu, Jing Wang, Jilei Hu, et al.
Environmental Earth Sciences (2024) Vol. 83, Iss. 18
Closed Access | Times Cited: 1
Leveraging Bayesian methods for addressing multi-uncertainty in data-driven seismic liquefaction assessment
Zhihui Wang, Roberto Cudmani, Andrés Alfonso Peña Olarte, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 1
Assessing soil liquefaction due to large-magnitude subduction earthquakes
Yrene Santiago, Christian Ledezma, Juan Carlos Tiznado
Soil Dynamics and Earthquake Engineering (2024) Vol. 188, pp. 109069-109069
Closed Access | Times Cited: 1
Continuous-discrete hybrid Bayesian network models for predicting earthquake-induced liquefaction based on the Vs database
Jilei Hu, Jing Wang, Zheng Zhang, et al.
Computers & Geosciences (2022) Vol. 169, pp. 105231-105231
Closed Access | Times Cited: 7
Page 1 - Next Page

Scroll to top