BS (hon) in Civil Engineering, Ho Chi Minh city University of Technology (HCMUT, Vietnam
MS in Geotechnical Engineering, Ho Chi Minh city University of Technology (HCMUT, Vietnam
PhD in Geotechnical Engineering, Kyoto University, Japan
Identifying suitable intensity measures for developing seismic fragility curves of horseshoe tunnels Van-Quang Nguyen, Tan Hung Nguyen, Hoang D. Nguyen International Journal of Geo Engineering, 2026 Abstract This study presents a comprehensive framework for selecting optimal ground motion intensity measures (IMs) and developing seismic fragility curves for horseshoe tunnels. To do so, a total of 20 candidate IMs were evaluated through nonlinear time history analyses. The two-dimensional finite difference models considering soil-tunnel interaction were developed and validated using the FLAC2D program. The model incorporated varying tunnel embedment depths (10 m, 20 m, and 30 m) and different site conditions (site classes B, C, and D), with 100 input ground motions representing a wide range of seismic characteristics. The performance of each IM was examined based on four statistical criteria: goodness of fit, efficiency, practicality, and proficiency. The findings show that for stiff soil conditions (site B), the most effective intensity measures are peak ground acceleration (PGA), acceleration spectrum intensity, effective design acceleration, and the A95 parameter. In contrast, for tunnels constructed in medium to soft soils (sites C and D), velocity spectrum intensity, Housner intensity, peak ground velocity (PGV), and Arias intensity provide a better correlation with the seismic response. In comparison, predominant period, mean period, and the PGV/PGA ratio consistently exhibited weak correlation, high variability, and limited practical value, rendering them unsuitable for fragility analysis. Fragility curves were then developed based on the proposed optimal IMs. The results reveal that increasing embedment depth significantly reduces seismic vulnerability, and tunnels in soft soils exhibit higher damage probabilities under the same seismic demand. Moreover, comparisons between different tunnel shapes show that rectangular tunnels are the most vulnerable, followed by horseshoe tunnels, while circular tunnels demonstrate the highest seismic resistance. The proposed framework provides a useful reference for performance-based seismic design and risk assessment of underground structures.
Prediction of bearing capacity of ring footings on cohesive frictional soils using Terzaghi stability factors and Kolmogorov Arnold networks Tran Vu-Hoang, Tan Nguyen, Jim Shiau, Duy Ly-Khuong, Hung-Thinh Pham-Tran Scientific Reports, 2026 , based on Terzaghi's principle of superposition. These factors are governed by two key inputs: the soil's internal friction angle (ϕ) and the geometric ratio of the inner radius to the footing width (r₁/B). Advanced predictive models are developed by integrating finite element limit analysis (FELA) with an adaptive meshing technique and a data-driven Kolmogorov-Arnold Network (KAN). Building upon recent applications of KAN in geotechnical modeling, this study advances its use by demonstrating superior predictive accuracy and interpretability compared with traditional Artificial Neural Networks. A closed-form representation of the stability factors is derived from the trained KAN model, offering an efficient and transparent means for estimating bearing capacity. The optimized KAN framework achieved high coefficients of determination and low root mean square errors for both training and testing phases. Sensitivity and feature-importance analyses confirmed that ϕ exerts the dominant influence on stability, whereas r₁/B has a secondary effect. The results enhance the mechanistic understanding of ring-footing behavior on frictional-cohesive soils under surcharge and provide practical guidance for foundation design across diverse soil conditions.
Tunnel Design in Rock Masses Under Uncertainty With Reliability Constraints and Natural Gradient Boosting-Based Surrogates Tran Vu‐Hoang, Tan Nguyen, Hung‐Thinh Pham‐Tran, Duy Ly‐Khuong, Tuan A. Pham International Journal for Numerical and Analytical Methods in Geomechanics, 2026 This study develops a reliability‐based framework for predicting and optimizing tunnel stability in rock masses under surcharge loading while explicitly accounting for both aleatory and epistemic uncertainties. A unified dataset for twin circular and square tunnels is generated using Adaptive Finite Element Limit Analysis under the generalized Hoek–Brown criterion. The results demonstrate that probabilistic predictions obtained using Natural Gradient Boosting provide accurate stability estimates together with well‐calibrated uncertainty bounds, consistently outperforming multiple baseline machine‐learning models. Validation against more than 300 independent Optum G2 simulations confirms strong agreement with numerical benchmarks. A dedicated uncertainty decomposition analysis further shows that neglecting either input uncertainty or model uncertainty can lead to misleading and potentially unsafe reliability estimates, underscoring the necessity of joint uncertainty propagation. Overall, the proposed framework enables robust, uncertainty‐aware tunnel design under reliability constraints and provides a practical decision‐support tool for rock engineering applications.
Group efficiency and load transfer mechanisms of semi-rigid soil–cement piles: integrated experimental, 3D numerical, and analytical evaluation Tuan A. Pham, Abdollah Tabaroei, Bayram Ateş, Tan Nguyen Computers and Geotechnics, 2026 Understanding the group behavior of semi-rigid soil–cement piles remains a major challenge in deep-mixed foundation engineering. Unlike conventional displacement piles, soil–cement columns exhibit transitional stiffness and composite interaction with the surrounding ground, leading to settlement-dependent mechanisms that are not captured by existing group-efficiency approaches. This study integrates rare full-scale load tests, carefully calibrated three-dimensional finite-element analysis (3D FEA), and systematic analytical benchmarking to establish a mechanistic basis for evaluating group efficiency in soil–cement pile groups. Instrumented field tests on single, three-pile, and five-pile groups (S/D = 2) reveal pronounced stress overlap, non-uniform shaft mobilisation, and significant reductions in per-pile capacity. A high-fidelity 3D FEA model, incorporating a physically justified transitional zone and enhanced interface stiffness, reproduces both the load–settlement response and axial force transfer with high accuracy. Parametric analyses over a wide range of spacings and group sizes demonstrate that group efficiency is not a constant parameter but increases with settlement due to progressive mobilisation of shaft resistance and pile–cap–soil interaction. Benchmarking against eight widely used empirical equations confirms that traditional rigid–pile formulations systematically misrepresent the behavior of semi–rigid pile groups. Motivated by these findings, a new settlement–dependent analytical expression for group efficiency is proposed, combining a geometry-based interaction term with a nonlinear mobilisation function. The model reproduces numerical trends with an average error of only 6.8 % and captures the physical behavior observed in both field and numerical results. The study provides a unified, experimentally validated framework for interpreting soil–cement pile group behavior and offers improved guidance for serviceability-based design of deep-mixed foundations.
A wavelet-enhanced deep residual neural network for vibration-based health assessment of steel frames DC Nguyen, HV Le, TN Pham, T Nguyen, TM Tran, VT Huynh Structures 89, 112110 , 2026 2026
Group efficiency and load transfer mechanisms of semi-rigid soil–cement piles: integrated experimental, 3D numerical, and analytical evaluation TA Pham, A Tabaroei, B Ateş, T Nguyen Computers and Geotechnics 192, 107888 , 2026 2026 Citations: 5
Estimating the Bearing Capacity of Strip Footings Resting on Bi-Layered Soil Profiles Using Neural Networks Optimized by Harris Hawks Optimization H La, T Nguyen-Minh, T Nguyen Arabian Journal for Science and Engineering, 1-38 , 2026 2026
Deep Ensemble–Based Probabilistic Modeling of Torsional Capacity in Reinforced Concrete Beams with Uncertainty Decomposition NV Luat, H La, T Nguyen Applied Mathematical Modelling, 116856 , 2026 2026 Citations: 1
Probability integrated hybrid machine learning models for predicting surface settlement of PVD-treated soft soil T Nguyen, KD Ly, TT Nguyen, PT Tran, TD Nguyen Journal of Rock Mechanics and Geotechnical Engineering , 2026 2026 Citations: 1
Uncertainty-aware prediction of shear strength in reinforced concrete deep beams using quantile machine learning and explainable artificial intelligence H La, NV Luat, K Lee, T Nguyen Structures 84, 110907 , 2026 2026 Citations: 2
Tunnel Design in Rock Masses Under Uncertainty With Reliability Constraints and Natural Gradient Boosting‐Based Surrogates T Vu‐Hoang, T Nguyen, HT Pham‐Tran, D Ly‐Khuong, TA Pham International Journal for Numerical and Analytical Methods in Geomechanics , 2026 2026 Citations: 1
Prediction of bearing capacity of ring footings on cohesive frictional soils using Terzaghi stability factors and Kolmogorov Arnold networks T Vu-Hoang, T Nguyen, J Shiau, D Ly-Khuong, HT Pham-Tran Scientific Reports , 2025 2025
Symbolic Distillation of a Fully Tokenized Transformer for Settlement Prediction in Pre-bored Grouted Planted Nodular Piles in Stratified Soils H La, T Nguyen Knowledge-Based Systems, 115186 , 2025 2025 Citations: 1
Reliability-informed inverse design of dual tunnels with deep evidential regression H La, T Le-Thanh, T Nguyen Reliability Engineering & System Safety, 112134 , 2025 2025 Citations: 4
Closed-form seismic earth pressure solutions via adaptive limit analysis and hybrid learning models T Nguyen, J Shiau, T Bui-Ngoc Acta Geotechnica, 1-25 , 2025 2025
Reliable inverse optimization of rice husk ash concrete via copula-based design spaces and conformal quantile surrogates H La, M Uddin, T Nguyen Expert Systems with Applications, 130582 , 2025 2025 Citations: 8
TPE-Optimized Neural Network Framework for Predicting Settlement H La, T Nguyen, KQ Tran 4th International Conference on Structural Health Monitoring and Engineering … , 2025 2025
A novel hybrid metaheuristic-Bayesian machine learning model for accurate load-displacement prediction of pile foundations H La, T Nguyen, TA Pham Engineering Structures 343, 121131 , 2025 2025 Citations: 18
High-order Chebyshev finite element for efficient stability assessment of tunnel structures TN Bui, DK Ly, T Nguyen, T Nguyen-Thoi Engineering Analysis with Boundary Elements 180, 106512 , 2025 2025 Citations: 2
Hybrid NSGA-III and Surrogate Model Framework for Sustainable Concrete Mix Design: Balancing Strength, Energy, and Carbon H La, M Uddin, NHT Nguyen, T Nguyen Applied Soft Computing, 114018 , 2025 2025 Citations: 7
Flexural behavior of corroded recycled aggregate concrete beams strengthened with CFRP sheets HT Vuong, TH Nguyen, T Nguyen Structures 80, 109831 , 2025 2025
Interpretable and optimized TabNet-TPE for predicting bond strength in corroded reinforced concrete using a global database H La, T Nguyen Structures 80, 109707 , 2025 2025 Citations: 8
Advanced numerical modeling for nonlinear responses of sandwich multiphase composite plates with viscoelastic damping core HN Vu, DK Ly, U Topal, T Nguyen, T Nguyen-Thoi Advances in Engineering Software 208, 103958 , 2025 2025 Citations: 1
Optimized prediction of tunnel stability using advanced machine learning and an ANN-based analytical expression H La, T Nguyen-Minh, T Nguyen Tunnelling and Underground Space Technology 164, 106778 , 2025 2025 Citations: 18
MOST CITED SCHOLAR PUBLICATIONS
Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network T Nguyen, KD Ly, T Nguyen-Thoi, BP Nguyen, NP Doan Soils and Foundations 62 (5), 26 , 2022 2022 Citations: 65
Soft computing for determining base resistance of super-long piles in soft soil: A coupled SPBO-XGBoost approach T Nguyen, DK Ly, TQ Huynh, TT Nguyen Computers and Geotechnics 162, 105707 , 2023 2023 Citations: 53
A novel direct SPT method to accurately estimate ultimate axial bearing capacity of bored PHC nodular piles with 81 case studies in Vietnam VH Huynh, T Nguyen, DP Nguyen, TS Nguyen, TC Nguyen Soils and Foundations 62 (4), 101163 , 2022 2022 Citations: 43
Optimizing load-displacement prediction for bored piles with the 3mSOS algorithm and neural networks T Nguyen, DK Ly, J Shiau, P Nguyen-Dinh Ocean Engineering 304, 117758 , 2024 2024 Citations: 39
Coupling isogeometric analysis with deep learning for stability evaluation of rectangular tunnels T Nguyen-Minh, T Bui-Ngoc, J Shiau, T Nguyen, T Nguyen-Thoi Tunnelling and Underground Space Technology 140, 105330 , 2023 2023 Citations: 32
Corrosion effect on bond behavior between rebar and concrete using Bayesian regularized feed-forward neural network TH Nguyen, T Nguyen, TT Truong, DTV Doan, DH Tran Structures 51, 1525-1538 , 2023 2023 Citations: 32
Evaluation of residual flexural strength of corroded reinforced concrete beams using convolutional long short-term memory neural networks T Nguyen, TT Truong, T Nguyen-Thoi, LVH Bui, TH Nguyen Structures 46, 899-912 , 2022 2022 Citations: 28
Predicting load–displacement of driven PHC pipe piles using stacking ensemble with Pareto optimization T Bui-Ngoc, T Nguyen, MT Nguyen-Quang, J Shiau Engineering Structures 316, 118574 , 2024 2024 Citations: 24
Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm M Van Tran, DK Ly, T Nguyen, N Tran Construction and Building Materials 431, 136470 , 2024 2024 Citations: 24
Undrained sinkhole stability of circular cavity: a comprehensive approach based on isogeometric analysis coupled with machine learning T Nguyen-Minh, T Bui-Ngoc, J Shiau, T Nguyen, T Nguyen-Thoi Acta Geotechnica, 1-23 , 2024 2024 Citations: 23
An exact solution of active earth pressures based on a statically admissible stress field T Nguyen Computers and Geotechnics 153, 105066 , 2023 2023 Citations: 20
Hybrid machine learning for predicting hydration heat in pipe-cooled mass concrete structures M Van Tran, H La, T Nguyen Construction and Building Materials 481, 141558 , 2025 2025 Citations: 19
Sustainable foundation design: Hybrid TLBO-XGB model with confidence interval enhanced load–displacement prediction for PGPN piles T Bui-Ngoc, DK Ly, T Nguyen, T Nguyen-Thoi Advanced Engineering Informatics 65, 103288 , 2025 2025 Citations: 19
A novel hybrid metaheuristic-Bayesian machine learning model for accurate load-displacement prediction of pile foundations H La, T Nguyen, TA Pham Engineering Structures 343, 121131 , 2025 2025 Citations: 18
Optimized prediction of tunnel stability using advanced machine learning and an ANN-based analytical expression H La, T Nguyen-Minh, T Nguyen Tunnelling and Underground Space Technology 164, 106778 , 2025 2025 Citations: 18
Passive earth pressures with sloping backfill based on a statically admissible stress field T Nguyen Computers and Geotechnics 149, 104857 , 2022 2022 Citations: 18
Synergistic integration of isogeometric analysis and data-driven modeling for enhanced strip footing design on two-layered clays: Advancing geotechnical engineering practices T Nguyen-Minh, T Bui-Ngoc, J Shiau, T Nguyen, T Nguyen-Thoi Engineering Analysis with Boundary Elements 167, 105880 , 2024 2024 Citations: 16
Enhanced earth pressure determination with negative wall-soil friction using soft computing T Nguyen, J Shiau, DK Ly Computers and Geotechnics 167, 106086 , 2024 2024 Citations: 15
Analytical model for consolidation and bearing capacity of soft soil stabilized by combined PVD-deep cement mixing columns BP Nguyen, TT Nguyen, T Nguyen, W Guo Bulletin of Engineering Geology and the Environment 82 (7), 286 , 2023 2023 Citations: 15
Stability of rectangular tunnels in cohesive-frictional soil under surcharge loading using isogeometric analysis and Bayesian neural networks MT Nguyen, TN Bui, J Shiau, T Nguyen, TT Nguyen Advances in Engineering Software 201, 103861 , 2025 2025 Citations: 14
