To reduce environmental impact and CO₂ footprint, ground granulated blast furnace slag (GGBS) is extensively used to replace cement in cementitious materials. High-volume GGBS systems enhance chloride transport resistance but significantly reduce early-age strength, limiting application in chloride-laden environments. This paper aims to enhance early strength and chloride resistance in high-volume GGBS composites through novel nano-SiO₂@CLDH core-shell additives. Nano-SiO₂@CLDH core-shell material was synthesized via in situ coprecipitation of layered double hydroxide (LDH) on nano-SiO₂ followed by calcination. Results show SiO₂@CLDH exhibits greater specific surface area than pure CLDH. Compared to individual nano-SiO₂ or CLDH, SiO₂@CLDH incorporation more significantly enhances early strength in high-volume GGBS composites. Chloride migration coefficients are also more substantially reduced. This enhancement mechanism involves accelerated cement hydration, improved chloride-binding capacity, and microstructural densification induced by SiO₂@CLDH.

Limestone calcined clay cement (LC³) typically exhibits low early-age strength, limiting its practical use in construction. To address this, the present study performs a comparative assessment of three waste-derived siliceous additives; rice husk ash (RHA), rice husk biochar (RHB), and rice straw biochar (RSB), to enhance early strength development. A comprehensive experimental program evaluated their effects on consistency, compressive strength, ultrasonic pulse velocity (UPV), microstructure, and hydration products using TGA/DTG, FTIR, XRD, and SEM analyses. Results showed that LC³-RHA significantly improved compressive strength at 7 and 28 days. It has been observed that LC³-RSB exhibited the highest early strength gain (3 d), attributed to enhanced pozzolanic activity, pore refinement, and internal curing. The hybrid analytical approach, based on qualitative and quantitative analysis, has been employed and it was observed that LC³-RSB supersede the LC³ strength by 29%, 14% and 19%, at 3, 7, and 28 days, respectively. And have marginal difference form the ordinary Portland cement (OPC) strength. TGA and FTIR analyses confirmed increased carbonate formation, evidenced by mass retention in the 700 to 850 °C range and characteristic bands at 1410 to 1500 cm⁻¹, indicating CO₂ capture via physical adsorption and mineralization. RSB outperformed RHB in carbonation potential due to its higher porosity and favorable ash composition. Overall, biochar-modified LC³ offers a sustainable, carbon-negative binder solution by valorizing agro-waste, reducing clinker content, and improving environmental performance.

This paper presents experimental and numerical investigations on the structural performance of six steel tube-encased concrete (SC) columns confined by bolted thin-walled circular steel tubes. All specimens were circular columns with a 300 mm diameter, incorporating an encased circular steel tube of 200 mm in outer diameter. Reversed cyclic lateral force and constant axial compression were applied to these specimens. Key experimental parameters included axial load ratio, presence of infilled concrete and the rank of the encased steel tube. Test results demonstrated that confinement by thin-walled circular steel tubes ensured sufficient deformation capacity up to 0.06 rad drift for SC columns with FB-rank and higher encased steel tube, even without concrete infilled and under high axial compression. Test results also indicated that infilling concrete into the FC-rank encased steel tube enhanced the deformability up to 0.06 rad drift. Parallel to the experimental work, a simple evaluation method for the ultimate flexural strength of the SC column was proposed, and a numerical analysis was conducted to predict the overall structural behavior of the SC columns. Both calculated strength and numerical prediction showed close agreement with experimental results.

Long-term shrinkage strain is one of the most critical mechanical characteristics for assessing the long-term performance of concrete structures. This study aims to enhance machine learning methods for accurately predicting this phenomenon. A comprehensive and widely referenced dataset of concrete shrinkage, comprising 1869 shrinkage curves and 32318 individual shrinkage strain measurements from a diverse range of samples worldwide, is employed. The analysis uses the full database for model training, while curve-level evaluation emphasizes long-term total shrinkage. The advanced XGBoost algorithm, recognized as one of the most effective machine learning models for tabular data, is used to improve predictive performance. Short-term shrinkage strains measured at standard early ages of 1, 10 or 100 days are included as input features to efficiently predict long- to ultra-long-term shrinkage strains up to 30 years. To avoid data leakage, the dataset is split by shrinkage curves rather than individual measurement points for training and testing. This approach, achieving a testing R² score of up to 0.97, significantly outperforms conventional machine techniques that exclude early-age shrinkage data. The improved performance highlights the strong influence of early-age behavior on the long-term shrinkage response of concrete. The present results reflect the composition range represented in the curated database, and can be applied primarily to mixes without explicit slag/silica-fume/fly-ash tagging.