Geotechnical Engineering and Artificial Intelligence (GEOAI) Research Group
Geotechnical Engineering and Artificial Intelligence (GEOAI) Research Group is a specialized scientific research group established under the initiative of University of Transport Technology (UTT) to pioneer the integration of Artificial Intelligence (AI) technologies with Geotechnical Engineering research and applications. The group was officially launched on March 30, 2018, at the Scientific Conference on Artificial Intelligence in Geotechnical Analysis held at UTT, marking a significant milestone in advancing interdisciplinary research in Vietnam.
(Source: internet)
Geotechnical engineering plays a crucial role in infrastructure development, addressing complex problems in soil and rock mechanics, foundation engineering, and ground stability to ensure safe and sustainable construction. Concurrently, AI has emerged as a transformative tool in engineering, offering advanced predictive models, optimization techniques, and data-driven decision support systems that significantly enhance analytical capabilities across scientific disciplines.
The establishment of GEOAI responds to the urgent need for innovative solutions that leverage AI to tackle practical challenges in geotechnical engineering. By combining expertise from both fields, the group aims to push the boundaries of research, promote high-impact scientific publications, and develop cutting-edge methodologies applicable to engineering practice in the era of Industry 4.0.
The mission of the GEOAI research group includes:
- Conducting high-quality research and disseminating findings in reputable international and national scientific outlets.
- Developing and applying AI techniques to predict material properties, assess soil behavior, and enhance the reliability of geotechnical analysis.
- Creating technological solutions and intelligent systems for geotechnical monitoring, hazard forecasting, and risk mitigation.
- Facilitating multidisciplinary collaboration among academic institutions, industry partners, and research organizations to strengthen scientific impact and practical relevance.
Main research directions:
• Develop and apply artificial intelligence technologies to predict the properties of construction materials, the behavior and stability of foundation structures, as well as geohazards affecting natural soil–rock environments and engineering constructions.
• Develop and implement advancements in measurement technologies, sensing systems, and telecommunications electronics to support monitoring, forecasting, and early warning of geotechnical problems and geohazards in natural soil–rock environments and engineering structures.
• Apply numerical methods and simulation techniques in the analysis of geotechnical engineering problems.
• Establish and develop experimental models for various geotechnical engineering problems.
• Apply geospatial analysis technologies, geographic information systems (GIS), and remote sensing for forecasting and early warning of geohazards in natural soil–rock environments and engineering constructions.
• Develop intelligent computational models based on artificial intelligence and the Internet of Things (IoT) for applications in geotechnical engineering.
Members:
|
No |
Name |
Prof/PhD/MSc |
Affiliation |
Role |
|
Note |
|
1 |
Phạm Thái Bình |
Assoc. Prof. PhD |
University of Transport Technology (UTT) |
Team Leader |
binhpt@utt.edu.vn |
|
|
2 |
Bùi Tiến Diệu |
Prof. PhD |
|
|
|
|
|
3 |
Indra Prakash |
Prof. PhD |
University of Transport Technology (UTT) |
Co-Leader |
|
|
|
4 |
Nguyễn Trung Kiên |
PhD |
University of Transport Technology (UTT) |
Scientific Secretary |
|
|
|
5 |
Phan Trọng Trịnh |
Prof. PhD |
Vietnam Academy of Social Sciences |
Key Member |
|
|
|
6 |
Lê Hoàng Sơn |
Assoc. Prof. PhD |
Vietnam National University |
Key Member |
|
|
|
7 |
Nguyễn Đức Mạnh |
Assoc. Prof. PhD |
University of Transport and Communications (UTC) |
Key Member |
|
|
|
8 |
Bùi Thị Kiên Trinh |
Assoc. Prof. PhD |
Thuyloi University |
Key Member |
|
|
|
9 |
Trần Văn Phong |
Msc |
Vietnam Academy of Social Sciences |
Key Member |
|
|
|
10 |
Ngô Quốc Trinh |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
11 |
Ngô Thị Thanh Hương |
Assoc. Prof. PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
12 |
Trần Trung Hiếu |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
13 |
Phạm Quang Dũng |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
14 |
Đỗ Minh Ngọc |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
15 |
Nguyễn Thị Bích Hạnh |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
16 |
Hoàng Nguyễn Đức Chí |
PhD |
University of Transport Technology (UTT) |
Key Member |
|
|
|
17 |
Lê Văn Hiệp |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
|
|
18 |
Nguyễn Đức Đảm |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
|
|
19 |
Vũ Anh Tuấn |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
|
|
20 |
Nguyễn Đức Sơn |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
|
|
21 |
Vũ Quang Dũng |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
|
|
22 |
Nguyễn Thanh Tuấn |
Msc |
University of Transport Technology (UTT) |
Key Member |
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|
23 |
Nguyễn Trọng Giáp |
Msc |
University of Transport Technology (UTT) |
Key Member |
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|
|
24 |
Giáp Văn Lợi |
Msc |
University of Transport Technology (UTT) |
Key Member |
|
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Main outcomes:
- Projects funded
|
No |
Title |
Project level/ Funders |
PI |
Beginning year |
Ending year |
Funding (USD) |
Note |
|
1 |
Application of Advance Artificial Intelligence methods of Industry Revolution 4.0 in prediction of Geo-environment in Hai Phong - Ninh Binh coastal road project |
Ministry of Transport, DT184081 |
Binh Thai Pham |
2018 |
2019 |
12500 |
|
|
2 |
Building Big Data and development of machine learning models integrated with optimization techniques for prediction of soil shear strength parameters for construction of transportation projects |
Ministry of Transport, DT203029 |
Binh Thai Pham |
2020 |
2021 |
16700 |
|
|
3 |
Using artificial intelligence and optimization techniques to predict the bearing capacity of bored piles used for construction of transportation projects |
Ministry of Transport, DT214012 |
Binh Thai Pham |
2021 |
2022 |
18800 |
|
|
4 |
Development of Natural Hazards Assessment and Prediction Models based on Advanced Artificial Intelligence Algorithms and Optimization Techniques Using Geo-Spatial Technology at Quang Nam and Dien Bien Areas |
NAFOSTED, 105.08-2019.03 |
Binh Thai Pham |
2019 |
2024 |
122000 |
|
|
5 |
Application of artificial intelligence techniques in the quantitative assessment of multi-hazard risks (landslides, flash floods, and erosion) affecting agricultural ecosystems in Đắk Lắk Province |
Dak Lak province |
Binh Thai Pham |
2024 |
2026 |
51625 |
|
- Publications
|
Pham, BT, Bui, KTT, Prakash, I, & Ly, HB (2023). Novel hybrid computational intelligence approaches for predicting daily solar radiation. Acta Geophysica, 1-15. |
|
Pham, BT, Jaafari, A., Nguyen, D. D., Bayat, M., & Nguyen, H. B. T. (2022). Development of multiclass alternating decision trees based models for landslide susceptibility mapping. Physics and Chemistry of the Earth, Parts A/B/C, 128, 103235. |
|
Pham, BT, Van Dao, D., Acharya, T. D., Van Phong, T., Costache, R., Van Le, H., ... & Prakash, I. (2021). Performance assessment of artificial neural network using chi-square and backward elimination feature selection methods for landslide susceptibility analysis. Environmental Earth Sciences, 80, 1-13. |
|
Pham, BT, Vu, V. D., Costache, R., Phong, T. V., Ngo, T. Q., Tran, T. H., ... & Prakash, I. (2022). Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto International, 37(18), 5175-5200. |
|
Pham, BT, Jaafari, A., Van Phong, T., Mafi-Gholami, D., Amiri, M., Van Tao, N., ... & Prakash, I. (2021). Naïve Bayes ensemble models for groundwater potential mapping. Ecological Informatics, 64, 101389. |
|
Pham, BT, Luu, C., Van Phong, T., Nguyen, H. D., Van Le, H., Tran, T. Q., … Prakash I (2021). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology, 592, 125815. https://www.sciencedirect.com/science/article/abs/pii/S0022169420312762 |
|
Pham, BT, Luu, C., Van Dao, D., Van Phong, T., Nguyen, H. D., Van Le, H., … Prakash I (2021). Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems, 106899. https://www.sciencedirect.com/science/article/abs/pii/S0950705121001623 |
|
Pham, BT, Luu, C., Van Phong, T., Trinh, P. T., Shirzadi, A., Renoud, S., et al. (2021). Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? Journal of Hydrology, 592, 125615. https://www.sciencedirect.com/science/article/abs/pii/S0022169420310763 |
|
Pham, BT, Jaafari, A., Van Phong, T., Yen, H. P. H., Tuyen, T. T., Van Luong, V., et al. (2021). Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques. Geoscience Frontiers, 12(3), 101105. https://www.sciencedirect.com/science/article/pii/S1674987120302450 |
|
Pham BT, Le LM, Le T-T, Bui K-TT, Le VM, Ly H-B, Prakash I (2020) Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research:104845. https://www.sciencedirect.com/science/article/pii/S0169809519311238 |
|
Pham BT, Nguyen M.D, Dao D.V, Prakash I, Ly H.-B, Le T.-T, Ho L.S, Nguyen K.T, Ngo T.Q, Hoang V, Son L.H, Ngo H.T.T, Tran H.T, Do N.M, Van Le H, Ho H.L, Tien Bui D (2019) Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Science of The Total Environment 679, 172–184. https://www.sciencedirect.com/science/article/pii/S004896971932073X |
|
Pham BT, Bui DT, Prakash I (2019) Landslide susceptibility modelling using different advanced decision trees methods. Civil Engineering and Environmental Systems:1-19. https://iahr.tandfonline.com/doi/abs/10.1080/10286608.2019.1568418 |
|
Pham BT (2018) A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility. Journal of the Geological Society of India 91(3):355-62. |
|
Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Trung Tran H, Minh Le T, Tran VP, Kim Khoi D, Shirzadi A (2018) A Novel Hybrid Approach of Landslide Susceptibility Modeling Using Rotation Forest Ensemble and Different Base Classifiers. Geocarto International:1-38. https://www.tandfonline.com/doi/abs/10.1080/10106049.2018.1559885 |
|
Pham BT, Prakash I, Jaafari A, Bui DT (2018) Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier. Journal of the Indian Society of Remote Sensing:1-14. |
|
Pham BT, Prakash I, Khosravi K, Chapi K, Trong P, Trinh TQN, Hosseini SV, Bui DT (2018) A Comparison of Support Vector Machines and Bayesian Algorithms for Landslide Susceptibility Modeling. Geocarto International:1-36. https://www.tandfonline.com/doi/abs/10.1080/10106049.2018.1489422 |
|
Pham BT, Hoang TA, Nguyen D-M, Bui DT (2018) Prediction of shear strength of soft soil using machine learning methods. CATENA 166, 181-191. https://www.sciencedirect.com/science/article/abs/pii/S034181621830119X |
|
Pham BT, Prakash I (2017) A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment. Bulletin of Engineering Geology and the Environment: 1-15 |
|
Pham BT, Prakash I (2017) Evaluation and Comparison of LogitBoost Ensemble, Fisher's Linear Discriminant Analysis, Logistic Regression, and Support Vector Machines Methods for Landslide Susceptibility Mapping. Geocarto International https://www.tandfonline.com/doi/abs/10.1080/10106049.2017.1404141 |
|
Pham BT, Shirzadi A, Bui DT, Prakash I, Dholakia M (2017) A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: a case study in the Himalayan area, India. International Journal of Sediment Research https://www.sciencedirect.com/science/article/pii/S1001627916301329 |
|
Pham BT, Tien Bui D, Indra P, Nguyen LH, Dholakia MB (2017) A Comparative Study of Sequential Minimal Optimization Based Support Vector Machines, Vote Feature Intervals and Logistic Regression in Landslide Susceptibility Assessment Using GIS. Environmental Earth Sciences 76:371 |
|
Pham BT, Bui DT, Dholakia MB, Prakash I, Pham HV, Mehmood K, Le HQ (2016) A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics, Natural Hazards and Risk:1-23 https://www.tandfonline.com/doi/full/10.1080/19475705.2016.1255667 |
|
Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149, Part 1:52-63 https://www.sciencedirect.com/science/article/abs/pii/S034181621630368X |
|
Pham BT, Tien Bui D, Prakash I, Dholakia MB (2016) Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Natural Hazards83:1-31 |
|
Pham BT, Tien Bui D, Pham HV, Le HQ, Prakash I, Dholakia MB (2016) Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen Bai Province (Viet Nam). Journal of the Indian Society of Remote Sensing 1-11 |
|
Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling & Software 84:240-250 https://www.sciencedirect.com/science/article/pii/S1364815216303139 |
|
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2015) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve Bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology 122:1-19 https://link.springer.com/article/10.1007/s00704-015-1702-9 |
- PhD/Master
|
No |
Name |
Project title |
PhD/Master |
Year of Graduation |
Note |
|
1 |
Nguyễn Quang Hung |
Using Artificial Neural Network (ANN) Techniques in Predictive Analysis of the Compressibility Index of Road Foundation Soil. |
Master |
2019-2020 |
Successfully defended |
|
2 |
Dang Quang Thanh |
|
Master |
2019-2020
|
Successfully defended |
|
3 |
Nguyen Cong Chau |
Predicting the Soil Consolidation Coefficient Using Support Vector Machines (SVM) |
Master |
2019-2020 |
|
|
4 |
Nguyen Van Binh
|
Application of UAV Photogrammetry Technology in Establishing Topographic Maps of Thuan Thanh District, Bac Ninh Province for Road Design |
Master |
2020 - 2021 |
Successfully defended |
|
5 |
Do Cong Thanh |
Assessing and zoning landslide susceptibility for road construction activities using Weight of Evidence (WOE) model based on Geographic Information System (GIS) |
Master |
2021-2022 |
Successfully defended |
|
6 |
Vu Van Truong |
Application of decision tree model in spatial prediction of landslides serving planning and design of traffic works |
Master |
2021-2022 |
Successfully defended |
|
7 |
Nguyen Van Tuan
|
Forecasting the construction price index of road works in Son La province using artificial intelligence models |
Master |
2022-2023
|
Successfully defended |
|
8 |
Nguyen Phu Hai
|
Predicting the Bearing Capacity of Driven Precast Centrifugal Piles Using Adaptive Neuro-Fuzzy Inference Systems Based on Large-Deformation Pile Testing Results |
Master |
2022-2023
|
Successfully defended |
|
9 |
Nguyen Duc Dam
|
Assessing Landslide Risk Along Newly Constructed Mountainous Road Alignments Using Artificial Intelligence Models and Proposing Mitigation Solutions |
PhD |
2023-2025 |
Successfully defended |
|
10 |
Tran Van Phong
|
Assessing Geological Multi-Hazards in Quảng Nam Province Based on the Application of Artificial Intelligence |
PhD |
2023-2026 |
Ongoing |
|
11 |
Le Van Hiep |
Improving Unsuitable Soils for Use as Road Embankment Materials in the Cần Thơ and Hậu Giang Areas of the Mekong Delta. |
PhD |
2024-2026 |
Ongoing |
|
12 |
Nguyen Thanh Tuan |
Predicting and Mapping Land Subsidence Hazard Zonation Using Machine Learning and Geospatial Techniques. |
PhD |
2024-2026 |
Ongoing |
|
13 |
Vu Anh Tuan |
Predicting the Settlement of Pre-Consolidation-Treated Soft Ground Using Machine Learning Models in Road Construction |
PhD |
2025-2027 |
Ongoing |
