International Concrete Abstracts Portal

This study evaluated the effect of class F fly ash (5, 10, 15, and 20%) and silica fume (20%) as partial cement replacements on bacterial crack healing. Concrete cylinders were prepared, cracked into one-inch disks, and submerged in fresh water. Healing progress was monitored over 18 weeks using microscopy and quantified through a healing index. Results showed that bacterial activity substantially improved healing compared to natural hydration in control specimens. Fly ash replacement did not prevent healing, and several disks across all percentages achieved complete crack closure. However, higher fly ash levels shortened the duration of bacterial activity, indicating sensitivity to calcium availability. At 20% fly ash, healing progressed more slowly but remained active at 18 weeks. In contrast, specimens containing 20% silica exhibited significantly lower healing efficiency, with few disks achieving full closure and overall lower healing indices. These results confirm that bacteria-based self-healing concrete remains effective with fly ash but is constrained by high silica fume content due to very low to zero calcium content in silica fume. The findings indicated that lower calcium content in supplementary cementitious material (SCM) replacement, either due to higher fly ash content with lower calcium compared to OPC or with silica fume with almost zero calcium content, with bacteria, may have a significant effect on the healing progress.

This study aimed to evaluate the effect of isolated silica fume (SF) and SF combined with three contents of crystallizing admixture (CA) in the self-healing of engineered cementitious composites (ECCs) with different polymeric fibers. Self-healing was evaluated in coupon specimens subjected to bending to produce cracking. Healing products were evaluated in the cracks within 84 days. Exposure conditions for self-healing were water-saturated (SAT) and wetting-and-drying (WD) cycles. The results showed that the composites with isolated SF presented a continuous layer of healing product, covering widths of up to 100 μm. The final widths for these composites were 40 μm for different conditions. In composites with CA, the volume of product generated (gel) was considerably greater, causing it to leak out of the microcracks existing in the ECC, impairing healing. Thus, the results showed that the use of SF + CA reduced the ECC healing potential. Healing from the CA was spot-wise only, decreasing its healing potential. The performance of the crystallizing additive was impaired under WD conditions. Leaching was observed both under SAT and WD exposure conditions. More leaching was observed from WD, while SAT formed a more uniform product layer.

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.

The growing generation of construction and demolition waste necessitates the development of effective recycling strategies to address environmental concerns. This study investigated the replacement of natural fine aggregate (NFA) with recycled fine aggregate (RFA) at 0, 50, and 100% using two treatment methods: (i) sodium silicate (SS)–silica fume (SF) pre-soaking treatment (SS-T) and (ii) organic treatment (OA-T) with bio-additives derived from Persea macranta, Haritaki, and Ciccus glauca roxb. A quantitative comparison of the aggregate and mortar quality was conducted for each method. The combined application of SST and OT demonstrated an 85% improvement in workability and a 68% reduction in water absorption for RFA. Mortar experiments revealed up to 76% improvement in compressive and flexural strengths compared with untreated RFA mortar. Microstructural analyses (SEM, EDS, XRD, and FT-IR) confirmed the enhanced bond strength and mineral composition. This study highlights the potential of SST and OT to produce durable, high-performance RFA mortars using locally available, economical bio-additives.

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.