Under the dual pressures of global extreme climates and the demand for long service lives in major engineering projects, conventional concrete materials increasingly struggle to meet performance requirements for critical infrastructures like cross-sea bridges and nuclear facilities, highlighting a urgent need for advanced construction materials. Ultra-high performance concrete (UHPC) emerges as a transformative solution for modern engineering challenges due to its exceptional compressive strength (>120 MPa), durability, and lightweight design advantages. However, UHPC development faces critical barriers, i.e., the intricate nonlinear relationships among its composition, microstructure, and properties undermine conventional modeling approaches, while the scarcity of high-quality datasets (typically <200 samples) with inconsistent experimental protocols severely limits the reliability of data-driven models. This “algorithm-data mismatch” dilemma necessitates innovative database enhancement strategies.

The existing efforts in transfer learning and data augmentation, including generative adversarial networks (GANs) and Kriging interpolation, show a promise but encounter three fundamental limitations, i.e., physical inconsistency in synthetic data (e.g., illogical porosity-strength correlations), high-dimensionality failures in interpolation methods, and domain-shift distortions during cross-material knowledge transfer. To address these challenges, this study was to propose a dual-driven data enhancement framework integrating conditional GAN (CGAN) and Markov Chain Monte Carlo (MCMC) techniques. The methodology systematically resolved dimensionality issues through preprocessing, generated physically constrained virtual samples via CGAN under constitutive material relationships, and ensures topological consistency using MCMC statistical correction. A Bayesian-optimized XGBoost model could validate the enhanced dataset, with experimental verification from marine floating platform UHPC applications, demonstrating the framework efficacy in overcoming multi-dimensional data generation constraints. This approach was to establish a robust paradigm for intelligent performance prediction under small-sample conditions, advancing digital design capabilities for concrete structures in complex marine environments.

This study developed a CGAN-MCMC model to address small database augmentation and intelligent prediction for UHPC. The process commenced with standardized preprocessing of the original UHPC database, where missing values and outliers were systematically handled through the multiple imputation by chained equation (MICE) method and isolation forest algorithm, respectively, and followed by K-fold cross-validation partitioning. The CGAN-MCMC model was then employed to enhance the UHPC database as distribution comparisons and Kolmogorov-Smirnov (KS) tests conducted to verify the homogeneity between the original and augmented databases. Subsequently, a Bayesian optimization (BO)-tuned XGBoost model was implemented to evaluate performance prediction capabilities by both pre-and post-augmentation datasets. The model validity was further confirmed through validation against an actual UHPC engineering dataset from a floating platform project in Guangdong Province, China, demonstrating its practical applicability and robustness in engineering scenarios.

The data standardization effectively eliminates feature scale discrepancies, mitigating overfitting risks caused by parameter weight bias. The MICE method significantly enhances model noise resistance and prediction stability, establishing a high-reliability data foundation for UHPC performance prediction models. The isolation forest (IF) algorithm effectively removes outliers induced by instrument drift or mixing errors, while improving dataset robustness. The K-fold cross-validation further strengthenes model generalization via comprehensively evaluating performance across diverse data subsets. The optimized dataset demonstrates marked improvements in prediction accuracy and consistency across all performance metrics.

The CGAN-MCMC model is applied to augment the preprocessed UHPC database (N=145), generating an enhanced dataset (N=1,500). A statistical homogeneity between original and augmented data is confirmed through comparative analysis of distribution patterns, Pearson correlation matrices, and Kolmogorov-Smirnov (KS) tests (p-values >0.05). A Bayesian optimization (BO)-tuned XGBoost model reveals substantial improvements in test-set R² values for 3-day hydration heat (62%), median pore size (55%), compressive strength (18%), flexural strength (46%) and workability (35%) when using augmented data, quantitatively validating the CGAN-MCMC enhancement methodology.

The validation with engineering data from a floating platform project in Guangdong Province, China, demonstrates an enhanced model performance (i.e., test-set R² increases from 0.65 to 0.92, with mean absolute error (MAE) reductions of 35% (training) and 50% (testing), alongside root mean squared error (RMSE) decreases of 39% (training) and 48% (testing)). These results empirically confirm an engineering feasibility of the proposed framework. The proposed methodology advances UHPC intelligent design through improved data quality and model generalization, providing an innovative solution for performance optimization.

This study demonstrated a transformative potential of machine learning in overcoming data limitations and enhancing UHPC performance prediction. The study established a paradigm-shifting approach for UHPC design optimization via integrating advanced data augmentation with optimized ML modeling. The CGAN-MCMC framework could resolve critical challenges in material informatics and provide actionable insights for developing sustainable, high-performance concrete systems, accelerating the digital evolution of construction materials engineering.

Ultra-high performance concrete (UHPC) has garnered widespread attention in the field of civil engineering due to its exceptional mechanical properties, durability, and workability. These superior properties are primarily attributed to its dense microstructure and unique material composition, which typically includes cement, silica fume, quartz powder, and superplasticizers, along with reinforcing materials like steel fibers. The remarkable performance of UHPC not only meets the demands for high-performance materials in modern engineering but also contributes to reducing the life-cycle costs of structures by minimizing section sizes and extending service life.

However, the development and application of UHPC face several challenges. The complexity of the preparation process, stringent requirements for raw materials, and the high sensitivity of UHPC properties to mix design variations increase both production costs and technical difficulties. Traditional experimental design methods struggle to efficiently optimize UHPC mixtures, and issues such as shrinkage and autogenous cracking require precise design solutions. These challenges make the composition design and performance prediction of UHPC a focal point in the field. With advancements in computer technology and data science, machine learning (ML) offers new tools and approaches for UHPC research and development. Therefore, this study aims to promote the application of ML technology in the UHPC field, thereby advancing the intelligent development of advanced construction materials.

The study introduced a comprehensive ML-based framework for UHPC performance prediction and precise design. The framework began with data cleaning, where the multiple imputation by chained equation (MICE) method with a predictive mean matching (PMM) kernel was employed to impute missing data in the UHPC database. Subsequently, the isolation forest algorithm was applied to identify and eliminate outlier data, thereby improving the quality of the dataset. The cleaned and optimized dataset was then used to train an XGBoost model, optimized via Bayesian hyperparameter tuning, to accurately predict various UHPC properties. Two AI-driven approaches for UHPC mix design were also proposed: one that combined the MAA model with ML predictions for multi-performance optimization, and another that integrated the ML model with genetic algorithm (GA) for multi-objective optimization.

The application of MICE combined with PMM resulted in a substantial improvement in the accuracy of the imputed data, as evidenced by the reduced average deviation between imputed and original data values. This enhanced imputation process allowed for a more reliable dataset, which directly contributed to the performance of the XGBoost prediction models. Outlier detection via the isolation forest algorithm effectively removed data points that exhibited significant deviation from the norm, particularly in compressive strength and density measurements. The refined dataset led to more accurate and consistent predictions across all performance metrics. After hyperparameter optimization, the XGBoost model demonstrated exceptional predictive capabilities, with notable improvements in performance metrics. The predictive accuracy of the model was further validated against experimental data, confirming its effectiveness in real-world applications. These results highlight the potential of ML techniques in advancing the field of UHPC research. The integration of MICE and PMM into the data preprocessing pipeline ensured more accurate and reliable datasets, which were crucial for developing robust predictive models. The success of the XGBoost model, particularly after Bayesian optimization, underscores the importance of hyperparameter tuning in enhancing model performance.

Furthermore, the study proposes two ML-assisted design strategies for UHPC: 1) a combined physical packing theory and ML prediction model for initial mixture design, and 2) a multi-objective optimization approach using a metaheuristic algorithm for fine-tuning UHPC compositions. These strategies provide a framework for the intelligent and efficient design of UHPC materials, aligning with the growing demand for high-performance, sustainable construction materials.

This research demonstrates the significant potential of ML in predicting and optimizing the performance of UHPC. By addressing data challenges and enhancing prediction models, the study provides valuable insights into the application of ML in UHPC design. The proposed ML-assisted design strategies offer a practical approach to developing UHPC with tailored properties, paving the way for more sustainable and efficient construction practices.

Large amounts of CO₂ emissions have caused escalating challenges of global warming risk. The construction industry significantly contributes to global CO₂ emissions, prompting the need for sustainable building materials. Carbon capture, utilization, and storage (CCUS) technologies provide a promising solution by reducing emissions and integrating carbon storage capabilities into materials. To this end, foam concrete, a lightweight porous material (normally 500–1600 kg/m³), has gained increasing attention for its significant advantages in energy-efficient and low emission buildings. Traditional foam concrete is lightweight with good insulation properties but has low strength and limited environmental benefits. In contrast, CO₂ foam concrete offers the potential of reducing emissions during production and CO₂ sequestration, making it a more environmentally friendly alternative.

The objective of this research was to develop a high-strength CO₂ foam concrete (HSCFC) that incorporates carbon capture and storage (CCS). By enhancing foam stability and optimizing the concrete mixture, this study firstly developed a new HSCFC product with assessing the CO₂ sequestration efficiency and micro/macro performance of the devloped material, thereby verifying its potential as both a sustainable building material and a solution for carbon reduction.

The design concept of HSCFC was based on integration of a CO₂ foam precursor into a high-strength cement-based paste, utilizing particle packing theory to optimize strength and density. The CO₂ foam was produced using a amphiphilic nano-silica modified sodium dodecyl sulfate foaming agent, enhancing the stability of the foam and preventing collapse of foam concrete. The HSCFC was prepared by mixing the CO₂ foam with a high-strength cement slurry, followed by curing and a series of micro and macro testing to evaluate HSCFC performance, including compressive strength, concrete density, pore structure and microstructures.

1) The CO₂ foam exhibited a well-distributed pore size of 50–100 μm, which grew over time due to natural drainage and coalescence processes. The stability of the foam was attributed to the surfactant and nanoparticle interactions, which prevent CO₂ diffusion and enhance liquid film strength. The overal performance of CO₂ foam met the requirments of the Chinese standard for the concrete foam, which thus could be used to fabricate the foam concrete products. 2) The rheological behavior of HSCFC followed the Bingham model, with an increase in yield stress as CO₂ foam volume increases, up to 110% compared to the control group. However, excessive foam content led to a reduction in yield stress due to the weakening of the matrix from enlarged pores. The plastic viscosity was inversely proportional to foam content, enhancing workability by reducing internal friction. 3) The increase in CO₂ foam content led to the decrease in compressive strength of HSCFC. The compressive strength of HSCFC was found to be significantly higher than conventional foamed concretes, which is more than double that of typical foam concretes of similar density. Microhardness tests revealed values above 90 HV, close to ultra-high-performance concrete, despite the presence of foam. 4) X-ray computed tomography (XCT) analyses indicated that the pore structure of HSCFC became more complex as foam content increased, with the peak value of the pore size enlarging from 162.8 μm to over 1100 μm. This indicated the importance of balancing the compressive strength and density for the HSCFC. Additionally, using CO₂ foam improved the cement hydration degree and led to the in-situ growth of calcium carbonate (CaCO₃) on pore walls, enhancing pore wall strength. However, excessive foam amounts resulted in non-uniform pore distribution, leading to reduced material strength. 5) The thermal conductivity of HSCFC was lower than conventional concretes due to its porous structures. This property made the material suitable for energy-efficient buildings, where insulation is critical for reducing energy consumption. Therefore, the use of CO₂ foam could realize the synergy of active and passive carbon reductions by carbon mineralization and decreasing engergy uses respectively.

This research demonstrated the successful design and development of a high-strength CO₂ foam concrete system with superior mechanical properties, enhanced carbonation, and thermal insulation capabilities. The use of CO₂ foam not only provided an effective CCUS solution but also significantly improved the performance. The synergistic effects of foam stability, internal carbonation, and matrix densification showed a promising pathway for the sustainable development of carbon-neutral building materials. Future research could focus on improving the overall performance of HSCFC and investigating its long-term service values. This material presents a novel approach for CO₂ capture and utilization, alongside advancements in foam concrete technology. The developed materials can be further optimized to improve carbon sequestration efficiency, facilitating their use in innovative structures like energy-efficient and floating buildings.