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.

Concrete and cement-based materials inherently possess an autogenous self-healing capacity, which is even higher in High- and Ultra-High-Performance Concrete (HPC, UHPC) because of the high content of cement and supplementary cementitious materials (SCM) and low water/binder ratios. In this study, quantitative correlation through statistical models have been investigated based on the meta-data analysis. The employed approaches aim at establishing a correlation between the mix proportions, exposure type, and time and width of the initial crack against suitably defined self-healing indices. This study provides a holistic investigation of the autogenous self-healing capacity of cement-based materials based on extensive literature data mining. This is also intended to pave the way towards consistent incorporation of self-healing concepts into durability-based design approaches for reinforced concrete structures. The study has shown that the exposure type and duration, crack width size, and chemical admixtures have the most significant promotion on self-healing indices. However, other parameters, such as fibers and mineral admixtures have less impact on the autogenous self-healing of UHPC. The study also proposes suitably built design charts to quickly predict and evaluate the self-healing efficiency of cement-based materials which can significantly reduce, in the design stage, the time and efforts of laboratory investigation.

Soft computing applications through artificial intelligence (AI) are becoming increasingly popular in civil engineering. From concrete technology to structural engineering, AIs have provided successful solutions to various problems and greatly reduced the computational costs while achieving excellent prediction accuracy. In this study, a review of the main artificial neural network (ANN) types used in civil engineering is presented. Each ANN type is described, and example applications are provided. As a new research contribution, a deep feedforward neural network (FFNN) is developed to predict the load capacities of post-installed adhesive anchors installed in cracked concrete, which is challenging and computationally expensive to achieve with conventional methods. The development of this FFNN is discussed, the influence of several parameters on its performance is demonstrated, and optimum parameter values are selected. In addition, a hybrid methodology that combines 2D nonlinear finite element (NLFE) techniques with the developed FFNN is briefly presented to account for real-life adverse effects in anchor analysis, including concrete cracking, wind-induced beam bending, and elevated temperatures. The results show that the developed network and methodology can rapidly and efficiently predict the load capacities of adhesive anchors installed into cracked concrete, accounting for the damage caused by the cracks, with high accuracies.

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.