International Concrete Abstracts Portal

The production of ordinary portland cement (OPC) is a major contributor to carbon emissions. One immediate and viable solution is the use of optimized concrete mixtures that employ a decreased quantity of cement and increased dosage of high-range water- reducing (HRWR) admixtures. This study investigates five different concrete mixtures with varying water-cement ratios (w/c) (0.37 to 0.42) and reduced cement contents. The mixtures with “low- cement + high-dosage HRWR admixture” content had over 30% increase in mechanical strength and presented 40% lower water absorption, as well as 68 to 97% higher formation factor, indicating enhanced durability. The optimized concrete mixtures with reduced cement and lower w/c have a service life increase of up to 117% and a life-cycle cost reduction of 29%. The application of low-cement + high-dosage HRWR admixture mixtures can improve the sustainability of concrete mixtures by reducing cement and water contents and increasing the service life of concrete in severe environments.

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 cost and embodied CO₂ emissions. A curated dataset 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, RMSE = 1.37 MPa, MAE = 1.09 MPa), supported by residual analysis confirming minimal prediction bias and robust generalization. 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 NSGA-III algorithm 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 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.

Despite the advantageous features of earthen construction for sustainability, certain limitations arise, notably the time-intensive nature of the construction process. Some efforts have been made to achieve self-consolidating earth concrete (SCEC) by overcoming the presence of fine particles to achieve adequate rheology. The impacts of cement, metakaolin, and limestone filler on dry flowability characteristics, rheology, workability, and compressive strength of self-consolidating earth paste (SCEP) mixtures were assessed in this study. The investigated mixtures were proportioned with different clay compositions and polycarboxylate ether (PCE), with and without the initial addition of sodium hexametaphosphate (NaHMP) as a clay dispersant. It was revealed that the addition of NaHMP and metakaolin to the mixtures consisting of finer clay particles significantly increased the static yield stress, build-up index, critical shear strain, and storage modulus evolution. Finally, the contribution of dry flowability characteristics of the powders to the rheological properties of the SCEP mixtures was investigated to facilitate the selection process.

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, such as coating, oxidation, silane, plasma, and radiation, to enhance the compatibility of recycled plastic aggregates in cementitious composites. Coating with materials such as waterglass, 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%–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° within 30 seconds. Radiation treatment using gamma rays and microwaves increased surface roughness and strength, with gamma irradiation at 100–200 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 (ANN) to predict CP in concrete structures. The model was trained on a small but carefully controlled experimental dataset of 10 concrete mixtures, considering four key parameters: water-to-cementing materials ratio, silica fume content, cementing materials content, and air content. Despite the limited dataset 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.