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 swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam)
Dieu Tien Bui, Nhat‐Duc Hoang, Viet‐Ha Nhu
Engineering With Computers (2018) Vol. 35, Iss. 3, pp. 955-965
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen, Hai‐Bang Ly, Lanh Si Ho, et al.
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 448
Estimating compressive strength of coral sand aggregate concrete in marine environment by combining physical experiments and machine learning-based techniques
Zhiming Chao, Zhikang Li, Youkou Dong, et al.
Ocean Engineering (2024) Vol. 308, pp. 118320-118320
Closed Access | Times Cited: 23
Predictive modelling of cohesion and friction angle of soil using gene expression programming: a step towards smart and sustainable construction
Muhammad Naqeeb Nawaz, Badee Alshameri, Zain Maqsood, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 18, pp. 10545-10566
Closed Access | Times Cited: 18
35 Years of (AI) in Geotechnical Engineering: State of the Art
Ahmed M. Ebid
Geotechnical and Geological Engineering (2020) Vol. 39, Iss. 2, pp. 637-690
Closed Access | Times Cited: 113
Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers
Binh Thai Pham, Tran Van Phong, T. Nguyen‐Thoi, et al.
Geocarto International (2020) Vol. 37, Iss. 3, pp. 735-757
Closed Access | Times Cited: 91
Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
Dang An Tran, Maki Tsujimura, Nam Thang Ha, et al.
Ecological Indicators (2021) Vol. 127, pp. 107790-107790
Open Access | Times Cited: 89
Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
Navid Kardani, Abidhan Bardhan, Pijush Samui, et al.
International Journal of Thermal Sciences (2021) Vol. 173, pp. 107427-107427
Closed Access | Times Cited: 79
Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling
Muhammad Nouman Amjad Raja, Syed Taseer Abbas Jaffar, Abidhan Bardhan, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 15, Iss. 3, pp. 773-788
Open Access | Times Cited: 42
Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions
José García, Gabriel Villavicencio, Francisco Altimiras, et al.
Automation in Construction (2022) Vol. 142, pp. 104532-104532
Closed Access | Times Cited: 42
Implementing ensemble learning models for the prediction of shear strength of soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 7, pp. 2103-2119
Closed Access | Times Cited: 25
Utilization of Tree-Based Ensemble Models for Predicting the Shear Strength of Soil
Ahsan Rabbani, Jan Afzal Muslih, Mukul Saxena, et al.
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 4, pp. 2382-2405
Closed Access | Times Cited: 11
A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)
Viet‐Ha Nhu, Nhat‐Duc Hoang, Binh Van Duong, et al.
Engineering With Computers (2019) Vol. 36, Iss. 2, pp. 603-616
Closed Access | Times Cited: 55
A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering
Sparsh Sharma, Suhaib Ahmed, Mohd Naseem, et al.
Sensors (2021) Vol. 21, Iss. 2, pp. 463-463
Open Access | Times Cited: 54
Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination
Binh Thai Pham, T. Nguyen‐Thoi, Hai‐Bang Ly, et al.
Sustainability (2020) Vol. 12, Iss. 6, pp. 2339-2339
Open Access | Times Cited: 53
Advanced industrial informatics towards smart, safe and sustainable roads: A state of the art
Hui Yao, Zijin Xu, Yue Hou, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2023) Vol. 10, Iss. 2, pp. 143-158
Open Access | Times Cited: 18
Optimized ANN-based approach for estimation of shear strength of soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3627-3640
Closed Access | Times Cited: 16
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
Predicting Mechanical Properties of Concrete Structures Using Metaheuristic-Optimization-Based Machine Learning Models
Ngoc‐Mai Nguyen
Applied Soft Computing (2025), pp. 112893-112893
Closed Access
Improved prediction of soil shear strength using machine learning algorithms: interpretability analysis using SHapley Additive exPlanations
Mahmood Ahmad, Mohammad Al-Zubi, Hamad Almujibah, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
Backpropagation Neural Network-Based Machine Learning Model for Prediction of Soil Friction Angle
Thuy‐Anh Nguyen, Hai‐Bang Ly, Binh Thai Pham
Mathematical Problems in Engineering (2020) Vol. 2020, pp. 1-11
Open Access | Times Cited: 44
Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength
Hossein Moayedi, Mesut Gör, Mahdy Khari, et al.
Measurement (2020) Vol. 156, pp. 107576-107576
Closed Access | Times Cited: 43
Ultrasonic Characterization of Compacted Salty Kaolin–Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning
Abolfazl Baghbani, Hossam Abuel-Naga, Roohollah Shirani Faradonbeh, et al.
Geotechnical and Geological Engineering (2023) Vol. 41, Iss. 5, pp. 2987-3012
Closed Access | Times Cited: 15
Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil
Hossein Moayedi, Dieu Tien Bui, Anastasios Dounis, et al.
Applied Sciences (2019) Vol. 9, Iss. 22, pp. 4738-4738
Open Access | Times Cited: 42
A novel hybrid model of augmented grey wolf optimizer and artificial neural network for predicting shear strength of soil
Ahsan Rabbani, Pijush Samui, Sunita Kumari
Modeling Earth Systems and Environment (2022) Vol. 9, Iss. 2, pp. 2327-2347
Closed Access | Times Cited: 19
Artificial intelligence (AI) development in the Vietnam's energy and economic systems: A critical review
Pham Thu Hien, Duy Nong, Paul Simshauser, et al.
Journal of Cleaner Production (2024) Vol. 438, pp. 140692-140692
Open Access | Times Cited: 3
Page 1 - Next Page

Scroll to top