Modeling of carbonation, de-carbonation and re-carbonation processes of structural concrete subjected to high temperature heating

Iwama, Keitai; Maekawa, Koichi

doi:10.1016/j.cemconcomp.2022.104493

Cited by 13 publications

(5 citation statements)

References 47 publications

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“…According to thermodynamics and poromechanics, the vaporization of liquid water (both free and released from bound ones) in pores will create vapor pressure that may cause volumetric tension and porous skeleton expansion. In addition, the loss of crystallized water (also noted as changing porosity) by dehydration will lead to a reduction of strength, porous skeleton stiffness, and change in thermal conductivity [24]. Thermal stress by restrained thermal expansion of both external and selfconfinement leads to meso-cracks or even spalling of concrete, which is integrated by using the smeared crack model.…”

Section: Fire Damage and Static Loadmentioning

confidence: 99%

“…In addition, a lower w/c and slower freezing speed may help Fig. 10 Materials and structural behaviors under the effect of fire damage [24,25]. The notations can be referred to [24,25].…”

Section: Frost Damage and Sustained Loadmentioning

confidence: 99%

“…10 Materials and structural behaviors under the effect of fire damage [24,25]. The notations can be referred to [24,25]. SEM: scanning electron microscope, FEM: Finite element model, and RH: relative humidity.…”

Section: Frost Damage and Sustained Loadmentioning

confidence: 99%

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Multi-scale and multi-chemo–physics lifecycle evaluation of structural concrete under environmental and mechanical impacts

Wang¹,

Gong²,

Maekawa³

2023

Journal of Intelligent Construction

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Keywordslifecycle evaluation reinforced concrete structures multi-scale modeling multi-chemo-physical analysis coupled deteriorationThis paper presents a multi-scale and multi-chemo-physics platform to evaluate the lifecycle of structural concrete under environmental and mechanical impacts. Following the scheme from nanometer scale of cement hydrated with water molecule to meter scale of reinforced concrete (RC) members, the platform covers characteristic upscaling based on causality and behavioral response toward both environmental and mechanical impacts. During the process, multi-chemo-physics is incorporated to tackle the local equilibrium and global movement, where the associating impact is also considered with mesoscale cracks. With this platform, long-term structural performances are captured while the coupled durability issue is also investigated.

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Section: Fire Damage and Static Loadmentioning

confidence: 99%

“…In addition, a lower w/c and slower freezing speed may help Fig. 10 Materials and structural behaviors under the effect of fire damage [24,25]. The notations can be referred to [24,25].…”

Section: Frost Damage and Sustained Loadmentioning

confidence: 99%

See 1 more Smart Citation

Multi-scale and multi-chemo–physics lifecycle evaluation of structural concrete under environmental and mechanical impacts

Wang¹,

Gong²,

Maekawa³

2023

Journal of Intelligent Construction

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“…Nevertheless, the model was applicable only to cement-lime mortars. A multi-scale model was developed based on the physicochemical principles, considering carbonation, decarbonation, and recarbonation processes [ 15 ]. A successful prediction was obtained for the compressive and tensile strength recovery after high-temperature loading; however, the complexity of the model was high.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete

et al. 2023

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Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors.

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“…In terms of solid hydrates, the dissolution of CO 2 during hydration causes the destabilisation of portlandite and ettringite while calcite, gypsum, and aluminum hydroxide precipitates [1]. These changes in the solid hydrates are also accompanied by changes in the pore solutions which affect the sorptivity of reducing alkalis by the C-S-H as calcium is diminished in the system due to ongoing carbonation [2][3][4][5][6]. The changes in the hydrated cement due to carbonation as reported by [7] includes the preference of monosulfate and Friedel salt to monocarbonate which, in turn, forms stratlingite, high Ca;Si C-S-H gels to low Ca:Si C-S-H gels, calcite and water as ettringite dissolves and gibbsite precipitates, leaving sulphate ions in solution.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic Modelling of Harsh Environments on the Solid Phase Assemblage of Hydrating Cements Using PHREEQC

2022

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Poor durability of reinforced concrete structures can lead to serious structural failures. An accurate model to observe the effects of aggressive agents like carbonation, sulfate ingress, and seawater solutions on the solid phase assemblage will help designers and specifiers better understand how cement behaves in these environments. This paper presents the first steps in developing such a model using the PHREEQC geochemical software by accounting for alkali binding and dissolution. It also presents the use of discrete solid phases (DSPs) to account for the solid-solution behaviour of siliceous hydrogarnet and magnesium silicate hydrate (M-S-H). A new thermodynamic description of the vaterite phase has also been developed for this work using the cemdata18 thermodynamic database. The predicted phase assemblages of cements in these environments here agree with previously published findings using a different thermodynamic model supported with experimental data.

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Modeling of carbonation, de-carbonation and re-carbonation processes of structural concrete subjected to high temperature heating

Cited by 13 publications

References 47 publications

Multi-scale and multi-chemo–physics lifecycle evaluation of structural concrete under environmental and mechanical impacts

Multi-scale and multi-chemo–physics lifecycle evaluation of structural concrete under environmental and mechanical impacts

Interpretable Machine Learning for Prediction of Post-Fire Self-Healing of Concrete

Thermodynamic Modelling of Harsh Environments on the Solid Phase Assemblage of Hydrating Cements Using PHREEQC

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