Abstract
Empirical evidence has recently highlighted a significant size effect on the shear strength of slender concrete beams reinforced with corrosion-resistant glass fiber-reinforced polymer (GFRP) bars. Existing nominal strength algorithms are based on fundamentally different hypotheses on the governing mechanisms. Advanced numerical models can aid with understanding the role of size-dependent mechanisms. To this end, this paper demonstrates the validation of a Lattice Discrete Particle Model (LDPM) for concrete. The LDPM represents the physical heterogeneity of concrete, and incorporates constitutive laws that are suitable to simulate meso-scale friction and fracture damage mechanisms. The calibrated LDPM was used to model slender GFRP-reinforced concrete beams with effective depth of 146 and 292 mm, for which load tests revealed a size effect up to 62%. The simulations yielded accurate predictions, which were used to better understand the contribution of aggregate interlocking and shear-compression fracture mechanisms to strength and size effect.
| Original language | English |
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| Title of host publication | Advances in Engineering Materials, Structures and Systems |
| Subtitle of host publication | Innovations, Mechanics and Applications - Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation, 2019 |
| Editors | Alphose Zingoni |
| Publisher | CRC Press/Balkema |
| Pages | 1509-1514 |
| Number of pages | 6 |
| ISBN (Print) | 9781138386969 |
| DOIs | |
| State | Published - 2019 |
| Event | 7th International Conference on Structural Engineering, Mechanics and Computation, 2019 - Cape Town, South Africa Duration: Sep 2 2019 → Sep 4 2019 |
Publication series
| Name | Advances in Engineering Materials, Structures and Systems: Innovations, Mechanics and Applications - Proceedings of the 7th International Conference on Structural Engineering, Mechanics and Computation, 2019 |
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Conference
| Conference | 7th International Conference on Structural Engineering, Mechanics and Computation, 2019 |
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| Country/Territory | South Africa |
| City | Cape Town |
| Period | 09/2/19 → 09/4/19 |
Funding
The authors gratefully acknowledge the support of the University of South Carolina, Columbia (USC), and Rensselaer Polytechnic Institute. Special thanks are extended to Engineering and Software System Solutions (ES3), Inc., and USC Research Cyberin-frastructure (Division of Information Technology), for technical assistance with the computational software MARS.