International Concrete Abstracts Portal

The ACI Concrete Convention – Spring 2023 was held in San Francisco, CA, USA, April 2-6, 2023. More than 2100 concrete professionals attended with a common interest of advancing the use of concrete knowledge, resulting in the highest attended spring convention and the fourth highest attended convention overall. This convention hosted over 350 committee meetings, close to 70 sessions, and many networking opportunities.

This paper investigates the use of glass fiber reinforced polymer (GFRP) bars to reinforce the jointed precast bridge deck slabs built integrally with steel I-girders. In addition to a cast-in-place slab, three full-size, GFRPreinforced, precast concrete slabs were erected to perform static and fatigue tests under a truck wheel load. Each slab had 200 mm (7.9 in) thickness, 2500 mm (98.4 in) width normal to traffic, and 3500 mm (137.8 in) length in the direction of traffic and was supported over a braced twin-steel girder system. The closure strip between connected precast slabs has a width of 125 mm (4.9 in) with a vertical shear key, filled with ultra-high-performance concrete (UHPC). Sand-coated GFRP bars in the precast slab project into the closure strip with a headed end to provide a 100 mm (3.9 in) embedment length. A static test and two fatigue tests were performed, namely: (i) accelerated variable amplitude cyclic loading and (ii) constant amplitude cyclic loading, followed by static loading to collapse. Test results demonstrated excellent fatigue performance of the developed closure strip details, with the ultimate load-carrying capacity of the slab far greater than the demand. While the failure in the cast-in-place slab was purely punching shear, the failure mode in the jointed precast slabs was punching shear failure with incomplete cone-shape peroration through the UHPC closure strip, combined with a major transverse flexural crack in the UHPC strip. This may be attributed to the fact that the UHPC joint diverted the load distribution pattern towards a flexural mode in the UHPC strip itself close to failure.

The bond between reinforcing bars and concrete is an important property that determines the performance of reinforced concrete structures. Accurate prediction of the bond strength is essential for ensuring the safety and economy of the structures. This paper proposes an artificial neural network for predicting the bond strength between straight deformed reinforcing bars and concrete under tensile load. The neural network was trained using a large dataset of test results from the ACI Committee 408 database. A robust ten-fold cross-validation method was employed for evaluating network performance and finding optimal network parameters. Hyperparameter tuning was carried out to establish the optimal network hyperparameters. The relative impact of the neural network input parameters on the bond strength was evaluated using the SHAP method. The developed neural network with the optimal hyperparameters shows a good agreement with the test results. Its accuracy exceeds the accuracy of the descriptive equations.

The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.

TUBEDECK, a one-way spanning voided composite slab, has been utilized in the construction field over the years to enhance the efficiency, constructability, and environmental performance of structures. TUBEDECK incorporates both cast-in-situ reinforced concrete slabs and profiled steel decks. However, there is a need to clarify the shear resistance capacity in this slab because the shear strength of the member reduces as concrete volume is eliminated to optimize flexural strength. Therefore, this study applied the artificial neural network (ANN) technique to determine the shear strength of TUBEDECK. By varying factors in the ANN features, several ANN models were developed. Out of many models developed, an optimal model was selected, having a maximum/mean relative errors of 5.1% in a dataset.