Micromechanical testing and modelling of blast furnace slag cement pastes

Šavija, Branko; Zhang, Hongzhi; Schlangen, Erik

doi:10.1016/j.conbuildmat.2019.117841

Cited by 22 publications

(15 citation statements)

References 74 publications

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“…The porosity for OPC with different w/c ratios (0.3, 0.4 and 0.5) was found to be 8.44%, 11.84% and 17.5%, respectively. For blended cement paste with slag, similar specimens using CEM III/B were also examined by XCT [45]. The porosity for different w/b ratios (0.3, 0.4 and 0.5) was found to be 5.73%, 7.89% and 9.79%, respectively.…”

Section: Methodsmentioning

confidence: 99%

Micro-cantilever testing on the short-term creep behaviour of cement paste at micro-scale

Gan

Vandamme

Zhang

et al. 2020

Cement and Concrete Research

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This study proposes an experimental method for studying the short-term creep behaviour of cement paste at micro-scale. The micro-bending tests on miniaturized cantilever beams were used to characterize the viscoelastic properties of cement paste. The effects of w/b ratio, the type of binder and the stress level on the microscopic creep behaviour were investigated. It is found that the short-term creep of cement paste at microscale can be satisfactorily described by a power-law function. A linear viscoelastic behaviour has been observed in different cementitious systems at the microscale with the stress level up to 67.9%. When compared with the creep results in microindentation tests and conventional macroscopic tests, the obtained creep compliance function in this study is found to be both qualitatively and quantitatively representative of the macroscopic results. This experimental study underlines the importance of microstructural effect on the creep behaviours of cementitious materials at microscale.

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Section: Methodsmentioning

confidence: 99%

Micro-cantilever testing on the short-term creep behaviour of cement paste at micro-scale

Gan

Vandamme

Zhang

et al. 2020

Cement and Concrete Research

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“…α N and α M represent the normal force influence factor and the bending influence factor. Their values are generally adopted as 1.0 and 0.05, respectively (Qian, 2012; Šavija, Zhang, & Schlangen, 2020).…”

Section: Methodsmentioning

confidence: 99%

Modeling of microstructural effects on the creep of hardened cement paste using an experimentally informed lattice model

Gan

Rodriguez

Zhang

et al. 2021

Computer aided Civil Eng

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This paper presents a method to numerically investigate the microstructural effect on the creep behavior of cement paste at the microscale. The lattice fracture model is extended to consider local time-dependent deformations of calcium-silicate-hydrate phases in the cement paste by imposing local forces.The term "experimentally informed model" is used herein as the heterogeneous microstructures of hardened cement pastes were obtained by using the Xray computed microtomography and directly implemented into the model. The mechanical and creep properties of different constituents at the resolution of 5 µm were inversely identified from the fracture and creep bending tests on cementitious microcantilever beams at the microscale. The model is then validated through the comparison with the testing results of cement pastes with different w/c ratios and microstructures. It is found that the developed model can successfully reproduce experimentally observed behaviors and be applied to explain the experimental results in detail. With the method presented in this paper, the relationship between the volume fractions of different components and the global creep behavior of cement paste can be established. The validation of the model performed at the microscale forms a basis for the multiscale analysis of concrete creep.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…We also indicate the composition of the system according to the data provided in ref. [14]. The reverse predictions using GP yield distributions of input variables containing the experimental composition data.…”

Section: Towards the Estimation Of The Composition From A Target Elastic Modulusmentioning

confidence: 99%

CSI (Cement Science Investigation): Using Machine Learning to Guess the OPC Pastes Composition from the Elastic Response

2021

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Precise prediction of the elastic response is crucial to model cracking at early and late ages of cement-based materials and structures. Here, we use Machine Learning (ML) techniques to predict the elastic properties of Ordinary Portland Cement (OPC) pastes. A database with 365 observations is built on experimental studies from in the literature. We show that micromechanicsbased estimations may provide missing data in databases to be interrogated by ML, increasing the accuracy of predictions. Finally, we explore the formulation space of OPC pastes using Monte Carlo computations, which enables guessing which are the compositions that can be associated with a given elastic response. Applications of our results include the development of tailored formulations for a target elastic response and also in the forensics of existing cement pastes.

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Micromechanical testing and modelling of blast furnace slag cement pastes

Cited by 22 publications

References 74 publications

Micro-cantilever testing on the short-term creep behaviour of cement paste at micro-scale

Micro-cantilever testing on the short-term creep behaviour of cement paste at micro-scale

Modeling of microstructural effects on the creep of hardened cement paste using an experimentally informed lattice model

CSI (Cement Science Investigation): Using Machine Learning to Guess the OPC Pastes Composition from the Elastic Response

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