CO2 Impact on Steel Slag Materials | #CarbonCapture

Introduction

Steel slag, a by-product generated during steel manufacturing, poses significant environmental challenges due to its disposal issues. However, it also presents an opportunity for carbon utilization through carbonation curing. By exposing steel slag to carbon dioxide, its properties can be enhanced while simultaneously reducing CO2 emissions. This study explores how varying CO2 concentrations influence the curing process, focusing on the carbon uptake, mechanical strength, and microstructural transformations of compact steel slag. The investigation aims to optimize the use of industrial flue gas as a sustainable means of CO2 sequestration and material strengthening.

Experimental Design and CO2 Concentration Range

The research examined a broad spectrum of CO2 concentrations, ranging from 0.04% (representing ambient atmospheric levels) to 27% (simulating industrial flue gas conditions). Compact steel slag samples were subjected to these environments for carbonation curing. The experimental setup allowed observation of how differing CO2 levels influence carbonation kinetics and hydration processes. Through controlled exposure and precise measurement, the study assessed both chemical and physical responses within the material. This range provided a realistic perspective on the potential use of actual industrial emissions for efficient steel slag treatment and carbon capture.

CO2 Uptake and Carbon Sequestration Efficiency

Results revealed that even at a relatively low CO2 concentration of 4%, steel slag demonstrated substantial carbon absorption, achieving a CO2 uptake of 7.3%. This outcome highlights the material’s inherent ability to act as an effective carbon sink. As CO2 concentration increased, the rate and extent of carbon sequestration also improved proportionally. The study confirms that carbonation curing is not only an environmentally beneficial process but also a practical approach for industrial-scale carbon capture, utilizing waste by-products from steel production to mitigate greenhouse gas emissions effectively.

Mechanical Strength Development

Mechanical testing showed a significant improvement in the compressive strength of compact steel slag following carbonation curing. Under 4% CO2 concentration, the material achieved a strength of 42.03 MPa after 72 hours of curing. Higher CO2 concentrations further enhanced this strength, suggesting a direct relationship between carbon uptake and material densification. The increased formation of calcium carbonate compounds within the matrix contributed to pore refinement and improved bonding between particles. This enhancement demonstrates that carbonation not only supports environmental sustainability but also leads to the production of durable and high-performance construction materials.

Microstructural Transformations

Microstructure analysis revealed distinct crystal morphology changes as CO2 concentration increased. Initially, the calcium carbonate formed primarily as aragonite, but at higher CO2 levels, it transitioned into the thermodynamically stable calcite form. This transformation indicated improved material stability and densification due to enhanced carbonation. The change from aragonite to calcite crystal structures provided evidence of effective CO2 interaction with steel slag components. These microstructural modifications directly influenced mechanical performance and durability, proving that higher CO2 environments facilitate the development of stronger and more compact materials.

Role of Carbonation and Hydration Mechanisms

Both carbonation and hydration reactions contributed to the overall consolidation of steel slag during curing. At lower CO2 concentrations, hydration dominated, leading to the formation of calcium silicate hydrates that bind particles together. As CO2 levels increased, carbonation became the primary mechanism, forming dense calcium carbonate matrices that improved strength and stability. This interaction between hydration and carbonation created a synergistic effect, optimizing the material’s performance. Understanding the balance between these two processes is essential for tailoring curing conditions to maximize both carbon capture and mechanical enhancement.

Conclusion

The study demonstrates that carbonation curing of steel slag under varying CO2 concentrations offers a dual advantage—effective carbon sequestration and improved material properties. Even low CO2 environments significantly enhance performance, while higher concentrations promote greater strength and structural stability. The transition from aragonite to calcite and the balance between hydration and carbonation highlight the process’s scientific and industrial potential. Utilizing industrial flue gas directly for curing opens promising avenues for sustainable construction materials and carbon management, transforming steelmaking waste into a valuable resource for environmental and engineering applications.

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#CO2,#SteelSlag,#CarbonCapture,#Sustainability,#GreenBuilding,#MaterialsScience,#ClimateAction,#Carbonation,#EcoMaterials,#FlueGas,

Bridge Health Monitoring #SHM

 

Introduction
Bridges play a pivotal role in ensuring national mobility and supporting economic activity, making their structural health a critical concern. Structural health monitoring (SHM) systems are essential for maintaining bridge safety and durability. However, sensor faults, environmental noise, and transmission issues can compromise the quality of SHM data. To mitigate these challenges, anomaly detection methods are widely employed. Despite their popularity, there is no comprehensive evaluation comparing these techniques. This review addresses that gap by systematically analyzing recent research in SHM anomaly detection.

Motivation and Research Gap
Existing reviews on bridge SHM anomaly detection are limited in scope and often fail to synthesize comparative insights. Most studies overlook real-time performance and multivariate data analysis, which are crucial for practical deployment. Moreover, previous work rarely provides a structured framework to classify detection methods comprehensively. These limitations highlight the need for a systematic review that evaluates different approaches across multiple dimensions. Understanding these gaps motivates the development of a more thorough taxonomy and evaluation methodology.

Methodology
This systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, selected from eight reputable databases. Studies were evaluated using a four-dimensional taxonomy, including real-time capability, multivariate support, analysis domain, and detection method. Detection methods were further categorized into distance-based, predictive, and image-processing approaches. The review also assessed five key performance dimensions: robustness, scalability, real-world feasibility, interpretability, and data dependency. This methodology ensures a holistic comparison of contemporary anomaly detection techniques.

Taxonomy of Detection Methods
The review introduces a novel four-dimensional taxonomy to organize anomaly detection methods. Distance-based methods rely on similarity measures but are sensitive to environmental and dimensional variations. Predictive models balance performance with interpretability, making them practical in certain contexts. Image-processing techniques dominate the field, achieving high accuracy but requiring significant computational resources. Each category is evaluated in terms of real-time application and support for multivariate analysis. This taxonomy helps researchers identify suitable approaches for different SHM scenarios.

Comparative Evaluation
A detailed comparative evaluation revealed distinct strengths and weaknesses among methods. Image-processing methods are most frequently applied (22 studies) but face scalability challenges. Predictive models provide a balanced trade-off between interpretability and accuracy. Distance-based methods are less common due to sensitivity issues. Only 11 studies demonstrate real-time anomaly detection capabilities, while multivariate analysis remains underutilized. Time-domain signal processing dominates, whereas frequency and time-frequency methods are rarely applied, despite their potential advantages.

Challenges and Future Directions
Current SHM anomaly detection approaches face challenges in scalability, robustness, interpretability, and practical deployment. Existing models often lack adaptability and fail to handle multi-modal or uncertain data efficiently. Future research should focus on developing adaptive, interpretable frameworks suitable for real-world monitoring. Standardized evaluation protocols and cross-environment testing are necessary to validate performance. Combining predictive, distance-based, and image-processing strategies may enhance accuracy and reliability. The field needs innovation to ensure SHM systems remain practical and effective.

Conclusion
This review systematically examined recent studies in bridge SHM anomaly detection, highlighting trends, strengths, and gaps. Image-processing methods dominate but require computational optimization. Predictive and distance-based approaches offer trade-offs between accuracy and interpretability. Real-time and multivariate analysis remain underexplored. Future work should focus on adaptive, scalable, and interpretable models with multi-modal capabilities. Implementing such frameworks will enhance bridge safety and ensure reliable monitoring across diverse environments.

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#BridgeMonitoring, #StructuralHealth, #SHM, #CivilEngineering, #AnomalyDetection, #SmartInfrastructure, #BridgeSafety, #StructuralIntegrity, #EngineeringResearch, #InfrastructureHealth,