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

Haoran Liu, Eryu Zhu, Min Ji, Zhengwei Bai, Teng Li

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Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Haoran LiuSchool of Civil Engineering, Beijing Jiao Tong University, Beijing 100044, China.

Eryu ZhuSchool of Civil Engineering, Beijing Jiao Tong University, Beijing 100044, China.

Min JiSchool of Civil Engineering, Beijing Jiao Tong University, Beijing 100044, China.

Zhengwei BaiSchool of Civil Engineering, Beijing Jiao Tong University, Beijing 100044, China.ORCID 0000-0003-1766-1369

Teng LiChina Academy of Transportation Sciences, Beijing 100029, China.

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No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

cement pastemicrostructurepowers’ hydration modeltotal porosityX-ray computed tomography

Identifiers

PMID41753407

DOI10.3390/ma19040686

PMCPMC12941572

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.