International Concrete Abstracts Portal

This research aims to prepare porous ceramsite with low thermal conductivity. The porous ceramsite was also used as fine aggregate to substitute the river sand in pumice concrete. Its impact on improving the thermal insulation performance of pumice concrete was thoroughly investigated. The experimental method included high-temperature calcination, transient planar heat source analysis, as well as the use of X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Mercury-Intrusion Porosimetry (MIP) techniques. The investigation revealed that the best calcination parameters were a preheating temperature of 400°C, a preheating duration of 25 minutes, a calcination temperature of 125°C, and a calcination duration of 25 minutes. Under these conditions, the crushing index of the porous ceramsite was determined to be 29.1%, with a thermal conductivity of 0.138 W/(m·K). It is worth noting that an increase in calcination temperature promotes the hole content in ceramsite, leading to a 52.19% increase in macropore volume and a corresponding decrease in thermal conductivity. Furthermore, as the replacement rate of ceramic aggregate increases, the thermal conductivity of pumice concrete gradually decreases, with values ranging from 18% to 34.8%. This reduction occurs because the replacement elevates the volume of coarse capillary pores and non-capillary pores in pumice concrete, increasing by 13.9 to 91.3% and 63.1 to 128.5%, respectively. Additionally, a prediction model for the thermal conductivity of pumice concrete has been established using the Mori-Tanaka homogenization method. The model's verification accuracy falls within an error range of 5%, demonstrating its effectiveness in accurately predicting the thermal conductivity of pumice concrete.

This study employed a mixed microbial culture (MB) comprising Bacillus subtilis (BS), Bacillus polymyxa (PM), and nitrate-reducing bacteria (NRB) in equal proportions. The mixed microbial culture was used to enhance recycled brick aggregate (RBA) through the microbial-induced carbonate precipitation (MICP) method, investigating the effects of this enhancement on both the aggregate and recycled mortar properties. Results indicate that the mineralization activity of the mixed culture significantly exceeded that of individual strains, achieving an 84.64% mineralization rate after 14 days. MICP-enhanced RBA demonstrated markedly improved performance. The compressive strength of the reinforced recycled mortar increased by 32.62% at 3 days and 22.6% at 28 days, with the 28-day compressive strength approaching that of cement mortar using natural aggregates. The interfacial transition zone properties were significantly improved, with its width reduced from 30-wenzhong35 μm to 20-25 μm. This study provides experimental evidence for recycled brick aggregate reinforcement technology while offering technical support for the resource utilization of recycled brick aggregates.

The durability of manufactured sand concrete is substantially influenced by variations in parent rock lithology, fineness modulus, and stone powder content of the manufactured sand. This study develops a predictive model for the relative dynamic elastic modulus of manufactured sand concrete using six machine learning algorithms. The results demonstrate that the CPO (crested porcupine optimizer)-optimized XGBoost model exhibits superior prediction accuracy and stability. The algorithm-based optimization reveals that manufactured sand produced from limestone, iron ore tailings, and quartzite demonstrates improved frost resistance in concrete. The optimal fineness modulus range was found to be 2.6 to 2.86; stone powder content should be maintained between 3 and 12% for optimal performance. The study further proposes a mixture ratio optimization scheme that takes into account frost resistance, material cost, and carbon emissions, so that the cost and carbon emissions of single concrete are reduced, and the frost resistance is further improved.

A twin-screw extruder was employed to melt-blend polylactic acid (PLA), bamboo fibers (BF), aluminum diethyl pyrophosphate (ADP), melamine cyanurate (MCA), and polyethylene glycol (PEG). In the PLA/BF/PEG ternary composite, increasing PEG dosage reduced mechanical properties. Conversely, in the PLA/BF/ADP/MCA/PEG multicomposite, higher PEG content enhanced mechanical performance. Compared with PLA/BF composites, the addition of ADP, MCA, and PEG increased the melt flow index by over 15-fold, with MCA-containing composites showing a 24-fold improvement. Both PEG-containing and non-PEG PLA/BF/ADP/MC composites achieved UL94 V-0 flame retardancy ratings, with oxygen barriers ranging between 24 and 26 vol%. Importantly, while maintaining the UL94 V-0 rating, the introduction of PEG improved mechanical properties through more uniform dispersion of bamboo fibers.

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.