Experimental study on gas permeability of cement-based materials

Shi, Jiashun; Qian, Rusheng; Wang, Dafu; Li, Yong; Chun, Qing; Zhang, Yunsheng

doi:10.1016/j.cemconcomp.2022.104491

Cited by 17 publications

(4 citation statements)

References 77 publications

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“…Changes of the pore structure significantly influence the mechanical properties such as hardness [9], Young's modulus [10,11] and compressive strength [12,13]. Literature also shows that transport properties, including diffusion [10,14,15] and permeation [16][17][18], are related to porosity, pore size, connectivity, and tortuosity of cement pastes. Because of the significant impacts on the properties, pores are usually an important factor for understanding metallic corrosion in cement pastes.…”

Section: Introductionmentioning

confidence: 99%

In situ determination of cement paste porosity in parallel with corrosion study of aluminium alloy 1100 in Portland cement pastes

Li,

Caes,

Pardoen

et al. 2024

Preprint

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In the context of the corrosion study of aluminium alloy 1100 in contact with Portland cement pastes by electrochemical impedance spectroscopy (EIS), a quantitative characterization of the capillary porosity of cement pastes was performed by combining the fitting parameter of the electrical equivalent circuit model constructed to analyse the EIS response with a two-phase model derived from the general effective media theory (GEM). The effective resistance of the cement paste entering the GEM is approximated by the resistance of connected capillary pores as provided by the EIS analysis to determine the evolving porosity of cement pastes. The mercury intrusion porosimetry data agree with the porosity results obtained from EIS. The influence of the curing conditions is also studied to extract the impact on the porosity.

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Section: Introductionmentioning

confidence: 99%

In situ determination of cement paste porosity in parallel with corrosion study of aluminium alloy 1100 in Portland cement pastes

Li,

Caes,

Pardoen

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Laboratory testing is also a practical method to evaluate the permeability of concrete [15][16][17]. The American Association of State Highway and Transportation Officials (AASHTO) provides various testing methods for measuring the permeability of different concrete materials.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Microstructure-Based Model for Evaluating the Permeability Coefficient of Heterogeneous Construction Materials

Chen,

Yu,

Huang

et al. 2023

Materials

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The permeability coefficient of construction materials plays a crucial role in engineering quality and durability. In this study, a microstructure model based on real aggregate shape and digital image technology is proposed to predict the permeability coefficient of concrete. A two-dimensional, three-component finite element model of cement concrete was established considering the interfacial transition zone (ITZ) between aggregate and mortar. The permeability coefficient prediction model was developed by the finite element method. The accuracy of the model was verified by experimental data, and the influence of the water−cement ratio on the permeability coefficient of concrete was analyzed. The results show that this method has good prediction accuracy with a relative error of 1.73%. According to the verified model, the influences of aggregate content, aggregate characteristics, aggregate location, ITZ thickness, and other factors on the permeability of concrete were explored. The higher the water−cement ratio, the higher the permeability coefficient. With the increase in aggregate content, the permeability coefficient decreases. Aggregate permeability has a significant influence on the effective permeability coefficient of concrete within a certain range. The greater the roundness of aggregate, the greater the permeability of concrete. On the contrary, the larger aggregate size causes lower permeability. The permeability coefficient of concrete with segregation is lower than that with uniform distribution. At the same time, the permeability increases with the increase of ITZ thickness.

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“…The quantification of gas permeability in sustainable cement mortar can be achieved through the application of physical experimentation [11][12][13]. In this regard, extant experimental inquiry points towards the decisive role played by the internal crack dimension and stress state in shaping the gas permeability dynamics of sustainable cement mortar featuring such cracks [14][15][16][17]. However, physical test research requires the possession of experimental sites, apparatus and staff, which is labour and funding-consuming.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Gas Permeability of Sustainable Cement Mortar Containing Internal Cracks by Combining Physical Experiments and Hybrid Ensemble Artificial Intelligence Algorithms

Chao,

Yang,

Zhang

et al. 2023

Materials

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The presence of internal fissures holds immense sway over the gas permeability of sustainable cement mortar, which in turn dictates the longevity and steadfastness of associated edifices. Nevertheless, predicting the gas permeability of sustainable cement mortar that contains internal cracks poses a significant challenge due to the presence of numerous influential variables and intricate interdependent mechanisms. To solve the deficiency, this research establishes an innovative machine learning algorithm via the integration of the Mind Evolutionary Algorithm (MEA) with the Adaptive Boosting Algorithm-Back Propagation Artificial Neural Network (ABA-BPANN) ensemble algorithm to predict the gas permeability of sustainable cement mortar that contains internal cracks, based on the results of 1452 gas permeability tests. Firstly, the present study employs the MEA-tuned ABA-BPANN model as the primary tool for gas permeability prediction in cement mortar, a comparative analysis is conducted with conventional machine learning models such as Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) optimised ABA-BPANN, MEA optimised Extreme Learning Machine (ELM), and BPANN. The efficacy of the MEA-tuned ABA-BPANN model is verified, thereby demonstrating its proficiency. In addition, the sensitivity analysis conducted with the aid of the innovative model has revealed that the gas permeability of durable cement mortar incorporating internal cracks is more profoundly affected by the dimensions and quantities of such cracks than by the stress conditions to which the mortar is subjected. Thirdly, puts forth a novel machine-learning model, which enables the establishment of an analytical formula for the precise prediction of gas permeability. This formula can be employed by individuals who lack familiarity with machine learning skills. The proposed model, namely the MEA-optimised ABA-BPANN algorithm, exhibits significant potential in accurately estimating the gas permeability of sustainable cement mortar that contains internal cracks in varying stress environments. The study highlights the algorithm’s ability to offer essential insights for designing related structures.

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Experimental study on gas permeability of cement-based materials

Cited by 17 publications

References 77 publications

In situ determination of cement paste porosity in parallel with corrosion study of aluminium alloy 1100 in Portland cement pastes

In situ determination of cement paste porosity in parallel with corrosion study of aluminium alloy 1100 in Portland cement pastes

Two-Dimensional Microstructure-Based Model for Evaluating the Permeability Coefficient of Heterogeneous Construction Materials

Predicting the Gas Permeability of Sustainable Cement Mortar Containing Internal Cracks by Combining Physical Experiments and Hybrid Ensemble Artificial Intelligence Algorithms

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