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.

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.

Many modeling approaches in engineering are based on physical principles. The input and output relationships are developed using physical laws (e.g., Newton's laws of motion and conservation of mass and energy). However, in many situations, the development of physically-based models requires simplifying assumptions due to the complicated nature of the systems, which could lead to a large degree of uncertainty. In these situations, data can be used to formulate models by detecting relationships between the system’s variables (inputs and outputs) without explicitly knowing the physical behavior of the system. Therefore, there is a paradigm shift from physically-based models to data-driven models. The objective of this study is to develop a drift capacity prediction model for structural walls with special boundary elements using the extreme gradient boosting (XGBoost) machine learning algorithm. The resulting prediction model is compared with the recently developed empirical model presented in literature i.e., the Abdullah & Wallace (2019) model. The results reveal the proposed model’s superior predictive capabilities relative to the empirical model.

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.