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.

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.

This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.

The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the problem, economic strains limit the resources available for the repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proven to be a cost-effective method for the detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of SHM varies depending on the availability and experience of qualified personnel and largely qualitative damage evaluations. Simply supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. A feedforward ANN utilizing backpropagation learning algorithms was then trained on the FE model database with beam properties and nodal stiffness ratios serving as inputs for the neural network model. The outputs consisted of the predicted parameters of location, depth, and width of up to five cracks. This inverse problem is very difficult or impossible to solve with the training done by the Artificial Neural Network. One ANN was trained to predict the parameters of the cracks using the full database of FE simulations. The damage prediction ANN achieved fair prediction accuracies, with coefficients of determination (R2) equal to 0.42. This result was the outcome of the no uniqueness in the prediction of this inverse analysis. Nevertheless, this ANN model provides a rough estimate of the cracking type and damage content in bridge girders once the nodal stiffness ratios are measured by applying a field vehicle loading and measuring the deflection using a theodolite. A touch-enabled user interface was developed to allow the ANN model to predict the crack configurations. The application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring.