A5029814958- Flood Risk Assessment and Management
- Landslides and related hazards
- Hydrology and Watershed Management Studies
- Fire effects on ecosystems
- Dam Engineering and Safety
- Geotechnical Engineering and Analysis
- Infrastructure Maintenance and Monitoring
- Tree Root and Stability Studies
- Groundwater and Watershed Analysis
- Hydrology and Drought Analysis
- Hydrological Forecasting Using AI
- Soil and Unsaturated Flow
- Grouting, Rheology, and Soil Mechanics
- Innovative concrete reinforcement materials
- Asphalt Pavement Performance Evaluation
- Cryospheric studies and observations
- Structural Health Monitoring Techniques
- Soil erosion and sediment transport
- Hydrology and Sediment Transport Processes
- Rock Mechanics and Modeling
- Structural Load-Bearing Analysis
- Geotechnical Engineering and Soil Mechanics
- Structural Behavior of Reinforced Concrete
- Superconducting Materials and Applications
- Concrete Corrosion and Durability
University Of Transport Technology
2016-2025
Hiroshima University
2020-2023
Shanghai Jiao Tong University
2022
Geological Survey of India
2022
Vietnam Academy of Science and Technology
2021-2022
King Fahd University of Petroleum and Minerals
2022
Duy Tan University
2018-2021
Institute of Materials Science
2021
Institute of Marine Geology and Geophysics
2021
University of Economics Ho Chi Minh City
2021
The main objective of this study is to evaluate and compare the performance different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Boosting Trees (Boosted) considering influence various training testing ratios in predicting soil shear strength, one most critical geotechnical engineering properties civil design construction. For aim, a database 538 samples collected from Long Phu 1 power plant project, Vietnam, was utilized...
Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement (PCC) in various construction applications. In this paper, two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and neural network (ANN), were to predict the compressive strength GPC, where coarse fine waste steel slag aggregates. The prepared mixtures contained fly ash, sodium hydroxide solid state, silicate solution, aggregates well water, which four variables (fly...
Floods are some of the most destructive and catastrophic disasters worldwide. Development management plans needs a deep understanding likelihood magnitude future flood events. The purpose this research was to estimate flash susceptibility in Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), quadratic discriminant analysis (QDA). A geospatial database including...
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of Bayes Network (BN), Naïve (NB), Decision Tree (DT), Multivariate Logistic Regression (MLP) machine learning methods for prediction across Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing information from 57 historical fires set nine spatially explicit explanatory variables, namely elevation,...
The quality of digital elevation models (DEMs), as well their spatial resolution, are important issues in geomorphic studies. However, influence on landslide susceptibility mapping (LSM) remains poorly constrained. This work determined the scale dependency DEM-derived geomorphometric factors LSM using a 5 m LiDAR DEM, resampled 30 and ASTER DEM. To verify validity our approach, we first compiled an inventory map comprising 267 landslides for Sihjhong watershed, Taiwan, from 2004 to 2014....
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, can cause social upheaval loss of life. As a result, many scientists study the phenomenon, some them have focused on producing landslide susceptibility maps that be used by land-use managers to reduce injury damage. This paper contributes this effort comparing power effectiveness five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Artificial Neural...