Quantification of Total Porosity from CT Images by Segmenting Unhydrated Cement: A Model-Informed Framework Integrating POWERS' Volume Model

通过分割未水合水泥从CT图像中量化总孔隙率:一种基于模型的框架,整合了POWERS体积模型

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Abstract

Quantification of total porosity, including the nano-scale fraction, is critical for predicting the performance of cement-based materials but remains a significant challenge. While X-ray computed tomography (CT) is a powerful non-destructive tool, a fundamental trade-off between resolution and representative sample volume prevents the direct segmentation of nano-scale pores in macroscopically relevant specimens. Herein, we propose and validate a novel model-informed framework that overcomes this limitation by integrating the classical Powers' hydration model with micro-CT analysis. The method circumvents the need for nano-scale resolution by deriving the total porosity from the volume fraction of the easily segmentable, micron-scale unhydrated cement phase. The framework's validity was demonstrated by showing a strong correlation between the CT-derived total porosity and established porosity-strength relationships. Quantitative analysis indicated that the total porosity of the cement pastes ranged from 36.5% to 60.3% as the w/c ratio increased from 0.4 to 0.7. Laboratory strength data show good correlation (R(2) > 0.98) with four porosity-strength prediction models, demonstrating the feasibility of applying the Powers' volume model in CT-based analyses of cement pastes. This work transforms micro-CT from a qualitative imaging tool into a comprehensive technique for the quantitative microstructural characterization of cementitious materials.

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