International Concrete Abstracts Portal

Portland limestone cement (PLC) is being more and more specified and used in wet-mix shotcrete construction for ground support in tunnels and mines across the USA and Canada. The most widely used cement of Type GU (general use) in Canada and Type I in the USA is being replaced by Type GUL (general use limestone) in Canada and Type IL in the USA. There is no significant research being conducted on the performance of shotcrete made using PLC, including plastic properties, early age strength development, compressive strength development and, when fibers are added, flexural toughness and residual tensile strength development. This paper presents studies on the properties of wet-mix shotcrete produced with PLC. Results show that PLC requires a higher dosage of alkali-free accelerator (AFA) to achieve similar development of early age compressive strength compared to shotcrete made with PC. Development of compressive strength at 7 and 28 days for shotcrete made with PLC is similar to shotcrete made with PC. When both steel fibers and macrosynthetic fibers are used in wet-mix shotcrete made with PLC, development of residual tensile strength with notched beams and flexural toughness for round determinate panels is also similar to that for wet-mix shotcrete produced with PC. Future research on wet-mix shotcrete with Type GUL cement is also discussed.

Due to global warming, the temperature of earth surface increased by 0.95 to 1.20℃ in the past 4 decades. The increase in temperature has significant effects on the concrete industry, causing alterations in concrete curing conditions and degradation in strength and durability properties. The understanding of changes in concrete properties due to variations in curing conditions from climate change is an imminent task that has to be resolved. Among the durability properties of concrete, freeze-thaw (FT) resistance is most directly affected by climate change. However, in all of the studies conducted on the FT behavior of concrete, the dramatic changes in environmental conditions due to climate change were not considered. Therefore, the focus of this study is to understand the FT performance of concrete from extreme temperature and relative humidity (RH) changes in curing conditions. To find the relationship between the curing condition change and FT resistance levels as a function of time, a 3-D satisfaction surface graph was developed using the Bayesian probabilistic method. Then, an example of drawing the 3-D satisfaction surface diagrams for FT resistance based on the weather conditions in New York City between 2001 and 2100 was shown. Furthermore, considering the reduction rate of the average annual FT cycle due to climate change, this study confirmed that FT resistance performance increased. This approach contributes to a performance-based evaluation (PBE) strategy for concrete exposed to FT cycles under various environmental conditions. The study details and results are discussed in the paper.

To address the autogenous shrinkage issue of ultra-high-performance concrete (UHPC), internal curing technology has shown great potential in resolving this challenge by providing additional moisture. To further improve its curing efficiency, this study proposes an innovative internal curing technology that can significantly reduce autogenous shrinkage without increasing the amount of internal curing water or compromising mechanical strength. This approach utilizes perforated cenospheres (PCs) as internal curing agents while substituting internal curing water with urea solutions. In addition to replenishing water, urea solutions, once released into the cement paste, can react with portlandite. This reaction generates CaCO₃; owing to the intrinsic properties of CaCO₃, it has a larger macroscopic volume and a much higher elastic modulus than portlandite. This approach effectively reduces chemical shrinkage while concurrently increasing the stiffness of the cement paste, thereby achieving a significant reduction in autogenous shrinkage. As a result, replacing water with 3% urea solution in PCs enhances the autogenous shrinkage of UHPC, reducing it from less than 50% to over 90%.

The accurate identification of cracks is crucial in the research and practical use of strain-hardening cementitious composites (SHCC). The rise of deep learning techniques in computer vision has introduced efficient ways to automate crack detection processes. However, creating dataset for training these deep learning models demands a lot of effort and time, a situation worsened by intricate crack patterns. This study introduces a novel method using a hybrid generative adversarial network (HGAN) to simplify the task of detecting complex cracks. HGAN combines the strengths of deep convolutional generative adversarial network (DCGAN) and conditional generative adversarial network (CGAN), offering a solution for evaluating SHCC characterized by dense microcracks and conventional concrete with simpler cracks. Our findings demonstrate the method's effectiveness for SHCC with dense and microcracks, leading to enhanced precision in crack characterization, with an F1 score and intersection-over-union (IOU) for SHCC crack segmentation at 0.982 and 0.980, respectively.

Advancements in AI and computational models have significantly enhanced the predictability of concrete performance by leveraging extensive datasets. Recently, machine learning models have been developed to predict concrete’s compressive strength based on its mixture proportions. However, these models treat supplementary cementitious materials (SCMs) as a categorical (as opposed to quantitative) parameter and do not account for the significant impact of the SCM reactivity on concrete’s strength development. In this study, we assembled a dataset of binary (cement-SCM) mixtures, incorporating SCM reactivity measured by the R3 (ASTM C1897) test. Utilizing a random forest machine learning model, we demonstrated that integrating SCM reactivity significantly enhances the model's predictive performance with the fewest input parameters (w/cm, SCM/cm, SCM R3 heat, Agg/cm, cement CaO%). Further, we implemented a multi-objective Bayesian optimization framework to assist in the mixture proportioning of low-carbon low-cost concrete utilizing cement(s) and SCM(s) available to a concrete producer. This framework proposes concrete mix designs to meet a target 28-day compressive strength while minimizing cost and CO2 emissions, by leveraging SCMs with varied reactivity levels. The proposed mix designs were further validated with experiments. The work demonstrates how to avoid model extrapolation and erroneous predictions by utilizing a multi-dimensional convex envelop algorithm. Overall, the outcomes of this work provide a valuable tool for the concrete industry which can be expanded to predict and incorporate other metrics of concrete performance (e.g., workability, durability) and develop optimized mix designs accordingly.