Relationships Between SiO2, Al2O3, Fe2O3, CaO, K2O, and Expansion in the Determination of the Alkali Reactivity of Basaltic Rocks 11Communicated by M. Moranville-Regourd.

Marfil, Silvina Andrea; Maiza, Pedro; Bengochea, Amado L.; Sota, Jorge D.; Batic, Oscar R.

doi:10.1016/s0008-8846(97)00249-4

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…The potential alkali reactivity of volcanic rocks has been reported in several countries (Braga Reis et al, 1996;Deng et al, 1996;Katayama et al, 1989;Marfil et al, 1998;Shayan and Quick, 1988). The reactivity has been attributed to the presence of silica minerals (cristobalite and tridymite) and of interstitial glass of dacitic and rhyolitic compositions in fresh volcanic rocks saturated or oversaturated with silica (Katayama, 2010b;Wakizaka, 2000).…”

Section: Resultsmentioning

confidence: 94%

“…The reactivity has been attributed to the presence of chalcedony, microcrystalline and cryptocrystalline quartz (Deng et al, 1996). The study of seventeen basalts from different locations in Argentina led to the conclusion that expansion is caused especially by volcanic glass, the alteration products from devitrification, such as argillaceous minerals, and by poorly crystallised silica (Marfil et al, 1998).…”

Section: Resultsmentioning

confidence: 99%

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Identification of alkali-reactive aggregates: some examples

Fernandes¹,

Ramos²,

Noronha³

et al. 2014

Proceedings of the Institution of Civil Engineers - Constructio

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Alkali-aggregate reactions have been known for decades although the acceptance of their occurrence has been delayed in some countries until the 1990s. Potential reactivity has been identified in most of the rock types used as aggregates around the world. The most common reactive rocks are sedimentary (e.g. opaline sandstone) and metamorphic (e.g. gneiss). However, igneous rocks such as granites have also proved to be reactive in some areas and innocuous in many other regions. It has been concluded that the designation is not enough to label a rock as reactive and that microstructure is very often the determinant factor of reactivity. In the present study a summary is made on the state-of-the-art on alkaliaggregate reactions. Emphasis is given on the identification of potentially reactive aggregates based on petrographic methods. Examples from a large number of samples analysed in the scope of a research project are presented.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Identification of alkali-reactive aggregates: some examples

Fernandes¹,

Ramos²,

Noronha³

et al. 2014

Proceedings of the Institution of Civil Engineers - Constructio

View full text Add to dashboard Cite

show abstract

“…This analysis was mainly focused on the identification of the forms of silica, which could trigger the ASR [1]. In addition, other minerals that are considered to cause damage to concrete, namely clay minerals of the smectite group, were identified [2]. SEM/EDS was used for the qualitative chemical analysis of some mineral phases.…”

Section: Methodsmentioning

confidence: 99%

Potentially Reactive Forms of Silica in Volcanic Rocks Using Different Analytical Approaches

Esteves

Fernandes

Janeiro

et al. 2017

IOP Conf. Ser.: Earth Environ. Sci.

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“…21 In most literature, the oxide composition, consisting of SiO 2 , Al 2 O 3 , TiO 2 , Fe 2 O 3 , CaO, Na 2 O, MgO, FeO, K 2 O, and so forth, is used to illustrate the chemical composition of the raw basalt. 17,[22][23][24] Many studies also show that the tensile strength of BCF could be increased by regulating the raw basalt oxide composition. SiO 2 and Al 2 O 3 are the basic oxides of BCF, 5,25 and they have a significant impact on the tensile strength and heat resistance of BCF.…”

Section: Introductionmentioning

confidence: 99%

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

et al. 2023

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Tensile strength is the major mechanical property of basalt continuous fiber and is closely related to their chemical composition. This study constructed a machine learning model framework to predict the tensile strength of basalt continuous fiber. The database included the characteristic variables of oxides and their derived parameters, as well as the target variables of tensile strength. The mean squared error (MSE) were calculated to evaluate the performance of six machine learning models of decision tree, kernel ridge regression, multivariable linear regression, support vector regression, random forest, and k‐nearest neighbors. The k‐nearest neighbors model had a minimum MSE and was tuned using the grid search method with cross‐validation. The optimal hyper‐parameters of the k‐nearest neighbors model were K = 6, p = 10, and the determination coefficient of the model reached the maximum of 0.7110 on the test data. The limitations and implications of the present study were also demonstrated. We finally expected that the constructed framework could achieve higher prediction accuracy for tensile strength at a low cost in the future.Highlights A machine learning framework was built to predict the tensile strength of BCF. Oxides and their derived parameters were used for modeling. K‐nearest neighbors performed best in predicting the tensile strength. The framework was robust, cost‐saving, and efficient.

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Relationships Between SiO2, Al2O3, Fe2O3, CaO, K2O, and Expansion in the Determination of the Alkali Reactivity of Basaltic Rocks 11Communicated by M. Moranville-Regourd.

Cited by 10 publications

References 2 publications

Identification of alkali-reactive aggregates: some examples

Identification of alkali-reactive aggregates: some examples

Potentially Reactive Forms of Silica in Volcanic Rocks Using Different Analytical Approaches

Prediction of tensile strength of basalt continuous fiber from chemical composition using machine learning models

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