A5079177172- Structural Health Monitoring Techniques
- Structural Behavior of Reinforced Concrete
- Geotechnical Engineering and Soil Mechanics
- Structural Load-Bearing Analysis
- Geotechnical Engineering and Analysis
- Geotechnical Engineering and Underground Structures
- Infrastructure Maintenance and Monitoring
- Rock Mechanics and Modeling
- Geotechnical Engineering and Soil Stabilization
- Landslides and related hazards
- Concrete Corrosion and Durability
Beijing Jiaotong University
2024
City University of Hong Kong
2011
Tongji University
2010
Abstract In the calculation of reinforced concrete (RC) flat slabs with transverse reinforcement, punching shear resistance is one most critical factors. It true that design provisions may be implemented, but they often result in significant biases and deviations from expectations. This study aims to present an optimized machine learning (ML) algorithm for estimating resistance. Four algorithms (SVR, DT, RF, XGBoost) Bayesian optimization (BO) are presented this paper provide accurate...
Particle crushing plays an important role on the mechanical behavior of crushable granular soils. In this paper, macro- and micro-mechanical behaviors dense soils composed agglomerates in plane strain compression test are investigated using Discrete Element Method (DEM). A detailed study effects particle soil is facilitated by a comparison between simulation results uncrushable specimens. The DEM show strong dependency confining stress level. It found that under low stresses, insignificant...
This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on mechanism. A comparison conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting (LightGBM), extreme (XGBoost) two hybrid optimized algorithms: grey wolf...
The purpose of this study is to estimate the bond strength between steel rebars and concrete using machine learning (ML) algorithms with Bayesian optimization (BO). It important conduct beam tests determine since it affected by stress fields. A approach for based on 401 six impact factors presented in paper. model composed three standard algorithms, including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), combined BO technique. Compared empirical...