International Concrete Abstracts Portal

Carbon dioxide emissions from cement production pose major environmental concerns. This study investigates the combined incorporation of glass powder (GP) as a partial cement replacement and dog hair (DH) as a natural fiber reinforcement in cement paste. GP was incorporated with replacement levels of 5%, 10%, and 15%, whereas DH was incorporated with dosages of 0.25% and 0.5% by weight of cement. Both fresh and hardened properties were evaluated for a duration of up to 90 days. GP enhanced workability, increasing mini-slump by approximately 21% at 15% GP, whereas DH with 0.25% reduced workability by up to 6%. At 90 days, compressive strength improved by 26.6%, 17.6%, and 16.5% for GP5, GP10, and GP15, respectively. Flexural strength was improved by a maximum of 8.9% with the addition of DH. The porosity of all the modified mixes was reduced to a minimum of 20.4% in the GP15-0.25DH mix compared to the control mix of 28.0%. Sustainability analysis showed CO₂ emission reductions ranging from 4.06% to 16.07%, and material cost decreased to a maximum of 15.95% for GP15. These results clearly show the potential of GP and DH to enhance performance while improving economic and environmental sustainability.

The two-way shear equation in ACI 440.11 was originally developed nearly two decades ago using experimental data from early FRP materials, most of which are no longer representative of modern GFRP reinforcement. With current GFRP bars exhibiting significantly improved mechanical and surface properties, the validity of the existing equation requires reassessment to ensure practical and economical design. This study evaluates the ACI 440.11 two-way shear provisions using a comprehensive database of 49 GFRP-RC interior slabs and 14 edge column connections. The current code equation was found to be highly conservative, yielding an average test-to-predicted ratio of 2.13. Updated equations are proposed for both interior and edge conditions, reducing the ratio to 1.02 and 1.04, respectively, while maintaining acceptable statistical variation. Additionally, symbolic regression (SR) is used to develop machine-learning-based expressions, which show high predictive accuracy. The proposed models provide reliable, physically grounded, and less conservative predictions of punching shear capacity, supporting broader implementation of GFRP reinforcement in structural concrete applications.

This study presents the development of a novel three-dimensional (3-D) assembled retaining wall block system, in which individual blocks are interconnected in the upper-lower, left-right, and front-back directions. Unlike conventional segmental block gravity walls that rely solely on horizontal shear keys between upper and lower blocks, the proposed 3-D system is designed to resist shear forces across the entire block cross-section through comprehensive mechanical interlocking. To evaluate its structural performance, direct shear tests were conducted, focusing on two key parameters: the block arrangement between the front and back sides, and the frictional resistance between the block and the foundation concrete. Experimental results demonstrated that the proposed system exhibits significantly enhanced shear strength compared to conventional retaining wall systems. Based on these findings, shear strength estimation formulas were developed to support structural design and stability assessment. The proposed 3-D block system not only improves the mechanical integrity of retaining walls but also holds potential for enhanced resilience against complex geotechnical challenges due to climate change. These results suggest that the new system provides a reliable and robust alternative for the design of segmental retaining walls requiring high shear resistance and long-term stability.

The aging reserve of bridges in the United States needs load rating assessment to ensure sufficient load-carrying capacity and safety. Bridges without sufficient capacity to carry the legal loads are load posted. These load limits reroute traffic that may result in traffic congestion and longer routes and, thus, impose inconvenience to travelers and significant cost to society. This paper investigates the potential for improvement in the load rating process for simple-span concrete slab bridges. Such bridges are load rated by the Texas Department of Transportation using simplified load rating procedures, which are intended to be conservative and can have varying degrees of accuracy compared to the actual behavior of bridges. Finite element modeling was conducted to simulate the expected behavior of a representative concrete slab bridge, and the model was calibrated using experimental test data. The equivalent width results were compared with estimates from established design specifications and empirical guidelines. The methods developed for concrete slab bridges with integral curbs provided accurate estimates of moment demand for curb sections. In addition, an established analytical approach in the literature accurately predicted the moment demand for interior slab sections under one-lane loading, while the equations in current design specifications performed well for the two-lane loading case.

Repeated vehicle loading causes a decrease in transverse joint stiffness in concrete pavements due to damage accumulation around dowel bars. The relationship between key design parameters and damage accumulation is not well established due to limited faulting performance data and a lack of experimental data from expensive full-slab testing. A novel laboratory test setup was developed to characterize damage development caused by repeated vehicle loads. This setup was used to characterize damage for a range of key parameters at a lower cost and level of effort compared to full-scale slab testing. The concept of beam deflection energy, DE_Beam, is also introduced. Experimental results were used to develop a DE_Beam prediction model. The novel test setup developed in this study enables the rapid evaluation of a variety of dowel materials and geometries, and experimental results can be used to improve current faulting prediction performance.