Characteristics of two types of C-S-H gel in hardened complex binder pastes blended with slag

Liu, Rengguang; Han, Fuzhu; Yan, Peiyu

doi:10.1007/s11431-013-5221-6

Cited by 41 publications

(10 citation statements)

References 21 publications

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“…For the new cement, the adhesive forces of LD C‐S‐H, Other products and HD C‐S‐H were intermediate, i.e., 30 to 50 nN (mean values). Lower adhesive forces in LD C‐S‐H was probably because the low packing density makes the microstructure of LD C‐S‐H more porous, resulting in less effective contact to steel microspheres than those of HD C‐S‐H. For the old cement, carbonation was essential to the adhesive properties of different hydrate products.…”

Section: Discussionmentioning

confidence: 99%

An experimental study on adhesion between steel and cement pastes using particle probe scanning force microscopy

Yang

Hughes

et al. 2018

J Am Ceram Soc

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This paper presents a method of using particle probe scanning force microscopy to measure the adhesion between steel microspheres and cement pastes. Particle probes were fabricated by attaching steel microspheres to the free ends of microcantilevers. Adhesive forces were collected between steel microspheres and new (4‐week old) and old (6‐month old) cement substrates in air and saturated lime water. For new cement in saturated lime water, adhesive forces between steel and low density C‐S‐H, high density C‐S‐H and other hydrated products were intermediate among all groups selected. The adhesive forces between steel and calcium hydroxide were the smallest, whereas the adhesive forces between steel and the unreacted phases were the largest. For the 6‐month old cement, the interweaving of calcium carbonate crystals and C‐S‐H during carbonation generated greater adhesive forces to steel.

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Section: Discussionmentioning

confidence: 99%

An experimental study on adhesion between steel and cement pastes using particle probe scanning force microscopy

Yang

Hughes

et al. 2018

J Am Ceram Soc

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“…Details about the instrument and the experimental setup have been presented elsewhere [16]. Maximum indentation depth was set at 200 nm in accordance to restrictions to satisfy the separability scale condition (d/10 << h max << D/10, d and D stand for the characteristic sizes of the largest heterogeneity) of cement phases [17,29,32,38]. Prior to indentation, the area function of the indenter tip was calibrated in a fused silica, a standard material for this purpose.…”

Section: Grid Nanoindentationmentioning

confidence: 99%

“…where E r is the reduced elastic modulus in GPa, p i is the peak value of probability for each individual phase, s i is the skew of the fitting curve, E m,i is the mean reduced elastic modulus value of each individual phase, and d i is the width, an input value correlated to deviation from the mean value for each phase. The phases have been identified by the use of nanoindentation to plot histograms of material parameters as for instance E r or H [3,7,15,17,29,31,[37][38][39]43] and by using the number of peaks in the density plot to manually select the number of phases to deconvolute (Figure 4a) [3,29,32,43].…”

Section: Probability Distribution Analysis-pdamentioning

confidence: 99%

“…The phases have been identified by the use of nanoindentation to plot histograms of material parameters as for instance Er or H [3,7,15,17,29,31,[37][38][39]43] and by using the number of peaks in the density plot to manually select the number of phases to deconvolute (Figure 4a) [3,29,32,43]. All parameters of bin size, volume fraction, and initial mean values are all determined by the analyst prior to using PDF for fitting the data (Figure 4a) [3,38,39].…”

Section: Probability Distribution Analysis-pdamentioning

confidence: 99%

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Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification

2020

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Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.

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“…When the SF content was 10%, more low-density C-S-H gels were formed [27], resulting in higher drying shrinkage; thus, a maximum drying shrinkage at 56 days was observed. However, when the SF content was more than 10%, some of the SF particles did not participate in the hydration reaction, thereby filling in the AAM pores and, ultimately, reducing the drying shrinkage.…”

Section: Effects Of Sf and Ns On Drying Shrinkagementioning

confidence: 99%

Mechanical Properties and Drying Shrinkage Investigation of Alkali‐Activated Mortar Using Waste Glass Powder

Gong

2020

Advances in Civil Engineering

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Alkali-activated mortar (AAM) is one of the products of waste glass recycling that exhibits promising potential for wide engineering applications such as the construction industry. In this study, recycled waste glass powder-based additives, namely, Silica Fume (SF) and Nano-SiO2 (NS), were investigated for their potential to enhance the mechanical properties (strength) and drying-shrinkage resistance of AAM. The results indicated that 5.0% and 1.5% were the optimum SF and NS dosages, respectively, for optimizing AAM performance in terms of the compressive strength, flexural strength, and drying-shrinkage resistance. A prediction model, based on backpropagation (BP) neural network analysis, was also satisfactorily formulated and preliminarily validated for predicting the drying shrinkage of AAM containing SF or NS.

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Characteristics of two types of C-S-H gel in hardened complex binder pastes blended with slag

Cited by 41 publications

References 21 publications

An experimental study on adhesion between steel and cement pastes using particle probe scanning force microscopy

An experimental study on adhesion between steel and cement pastes using particle probe scanning force microscopy

Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification

Mechanical Properties and Drying Shrinkage Investigation of Alkali‐Activated Mortar Using Waste Glass Powder

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