Jinjun Xu

ORCID: 0000-0003-4701-5771
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About
Contact & Profiles
Research Areas
  • Structural Behavior of Reinforced Concrete
  • Recycled Aggregate Concrete Performance
  • Innovative concrete reinforcement materials
  • Structural Load-Bearing Analysis
  • Innovations in Concrete and Construction Materials
  • Concrete Corrosion and Durability
  • Structural Response to Dynamic Loads
  • Bamboo properties and applications
  • Natural Fiber Reinforced Composites
  • Fire effects on concrete materials
  • Concrete and Cement Materials Research
  • Infrastructure Maintenance and Monitoring
  • Tree Root and Stability Studies
  • Seismic Performance and Analysis
  • Mechanical Behavior of Composites
  • Structural Health Monitoring Techniques
  • Geotechnical Engineering and Soil Stabilization
  • Epoxy Resin Curing Processes
  • High-Velocity Impact and Material Behavior
  • Wood Treatment and Properties
  • Transportation Safety and Impact Analysis
  • Asphalt Pavement Performance Evaluation
  • Advanced machining processes and optimization
  • Metal Forming Simulation Techniques
  • Injection Molding Process and Properties

Nanjing Tech University
2016-2024

Xi'an University of Architecture and Technology
2020

Guangxi University
2014-2018

Ministry of Education of the People's Republic of China
2018

University of California, Santa Barbara
1995

The Ohio State University
1992

In recent years crushing waste brick to produce recycled aggregates (RBAs) has become a viable solution for reducing environmental pollution and addressing the natural resource shortage in civil engineering. To promote widespread use of aggregate concrete (RBAC) construction, this study analyzes existing test results on attributes RBAs compressive mechanical behaviors RBAC. The review indicate significant differences variabilities characteristics compared coarse aggregates. have highest...

10.1016/j.cscm.2023.e02184 article EN cc-by-nc-nd Case Studies in Construction Materials 2023-06-02

Existing semi-empirical formulas for predicting punching shear capacity in FRP bar reinforced concrete flat slabs without reinforcement often prove inaccurate and unstable. This is primarily due to limited modeling data, inadequate consideration of key variables neglect complex nonlinear relationships. To address these challenges, this study delves into the utilization advanced machine learning (ML) algorithms offer precise dependable estimates such structural components. The initially...

10.1016/j.cscm.2024.e03162 article EN cc-by Case Studies in Construction Materials 2024-04-18
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