International Concrete Abstracts Portal

Use of blended cement, where portland cement is substituted by supplementary cementitious materials (SCMs), represents a key strategy for enhancing concrete sustainability and durability. This study examined fresh, mechanical, and durability properties of blended binder concrete produced with U.S. regional calcined clays and Type IL and IP cements, with and without additional limestone powder and gypsum, called CCIL blends. All concrete mixtures studied had nearly the same proportions but different CCIL blends. Most blends contained 30% calcined clay by weight of total binder, except one with 20% calcined clay due to the consideration of the binder flowability. The results indicate that despite demanding more superplasticizer to reach a designed slump, all CCIL binder concrete mixtures studied achieved compressive, splitting tensile, and flexural strengths that were comparable to or higher than concrete mixtures with neat IL cement, especially at the later ages (after 28 days). All tested CCIL binder concrete mixtures displayed increased surface resistivity, reduced initial sorptivity, and comparable freeze-thaw (FT) resistance, suggesting that the CCIL blends are well-suited for sustainable and long-lasting infrastructure. This study supports the design of robust and durable concrete systems compatible with diverse raw materials, facilitating the wider adoption of emerging cement technologies.

This study presents a machine learning-driven framework for the sustainable design of ultra-high-performance concrete (UHPC) mixtures with a focus on maximizing flexural strength while minimizing material costs and embodied CO2 emissions. A curated data set of 333 UHPC mixtures was developed, incorporating 13 input features, including binder composition, steel fiber dosage, and curing parameters. A Bayesian neural network (BNN) was trained to predict flexural strength with high accuracy (R2 = 0.936, root mean square error [RMSE] = 1.37 MPa, and mean absolute error [MAE] = 1.09 MPa), supported by residual analysis confirming minimal prediction bias and robust generalization. SHapley Additive exPlanations (SHAP) analysis was used to interpret model predictions and identify key drivers of flexural behavior—namely, curing time, steel fiber dosage, and silica fume content. The BNN was coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to perform multi-objective optimization and generate Pareto-optimal UHPC mixtures. A utility-based scoring method was introduced to select designs aligned with different project priorities, enabling the identification of fiber-rich, high-strength mixtures as well as low-emission, cost-efficient alternatives. The framework supports field-level implementation and is well-suited for integration with sustainability rating systems such as Leadership in Energy and Environmental Design (LEED) or Envision. It provides a transparent, generalizable, and industry-ready tool for intelligent UHPC mixture optimization, advancing data-driven design practices for green infrastructure applications.

Carbon dioxide (CO2) mineralization in concrete enhances cement hydration by reacting with calcium-rich materials, forming nano-scale calcium carbonate that fills micropores. This study explores CO2-mineralized concrete performance produced using a two-step mineralization process. Concrete with 0.2% CO2 by cement weight exhibited significantly higher compressive strength, increasing by 18.78%, 19.27%, and 20.63% at 7, 28, and 56 days, respectively. Isothermal calorimetric analysis confirmed increased heat evolution in CO2-mineralized cement paste, while X-ray diffraction and scanning electron microscopy revealed calcium carbonate formation and more ettringite volume. The higher strength gain due to CO2 mineralization is used to leverage the cement content. A comparative study reveals that CO2-mineralized concrete with 7.5% reduced cement content achieves equivalent strength and durability to conventional concrete, reducing carbon emissions by 8% while significantly lowering cost per unit strength and enhancing sustainability and performance.

The use of recycled plastic aggregate in cement-based materials has emerged as a promising strategy to reduce plastic waste and promote sustainable construction. However, the inherent hydrophobicity of plastic surfaces poses a significant challenge by limiting their bonding with the cement matrix. This review critically examines five major surface treatment methods—coating, oxidation, silane, plasma, and radiation—to enhance the compatibility of recycled plastic aggregates in cementitious composites. Coating with materials such as water glass, slag powder, or acrylic resins improved compressive strength by up to 78%, depending on the coating type. Oxidation using hydrogen peroxide or calcium hypochlorite increased hydrophilicity and improved strength by approximately 10 to 30%, while excessive treatment with NaOH- hypochlorite mixtures reduced strength by up to 60%. Silane treatment significantly enhanced surface bonding, resulting in improved mechanical properties. Plasma treatment demonstrated high efficiency, reducing contact angles from ~108 to 44.0 degrees within 30 seconds. Radiation treatment using gamma rays and microwaves increased surface roughness and strength, with gamma irradiation at 100 to 200 kGy leading to substantial improvements in compressive strength and surface morphology. To the authors’ knowledge, this is the first review to systematically compare the effectiveness, mechanisms, and limitations of these surface treatments specifically for recycled plastic aggregates in cement-based materials. This review also highlights the practical challenges of scaling such treatments, including energy demand, chemical handling, and cost, and identifies future directions, such as bio-based coatings and nanomaterial functionalization. The findings provide critical insight into optimizing surface treatments to improve the mechanical performance, durability, and sustainability of concrete incorporating plastic aggregates, supporting their broader adoption in sustainable construction practices.

The durability of reinforced concrete is often compromised by chloride penetration, leading to corrosion of reinforcing steel and reduced structural strength. To improve the sustainability and longevity of concrete structures, it is crucial to model and predict chloride permeability (CP) accurately, thereby minimizing the time and resources required for extensive experimental testing. This paper presents a proof-of-concept study applying artificial neural networks (ANNs) to predict CP in concrete structures. The model was trained on a small but carefully controlled experimental data set of 10 concrete mixtures, considering four key parameters: water-cementitious materials ratio, silica fume content, cementitious materials content, and air content. Despite the limited data set size, which constrains generalizability and statistical robustness, the ANN captured nonlinear relationships among the input parameters and CP. The comparison between experimental and simulated CP values showed reasonable agreement, with errors ranging between –242 and 420 coulombs. These results establish the trustworthiness and reliability of the proposed model, providing a valuable tool for predicting CP and informing the design of durable and sustainable concrete structures.