Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks

Inthata, Somchai; Kowtanapanich, Wichuda; Cheerarot, Raungrut

doi:10.1617/s11527-012-0009-x

Cited by 36 publications

(18 citation statements)

References 28 publications

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“…Besides natural aggregate replacement, waste and supplementary cementing materials such as fly ash, blast furnace slag, silica fume, rice husk ash and metakaolin can also be used as partial replacement for Portland cement. These materials can improve concrete durability, reduce the risk of thermal cracking in mass concrete, and are less energy and CO 2 intensive than cement [6][7][8][9]. Generally, the use of mineral additions was found to have a positive effect on the resistance to chloride ion penetration.…”

Section: Introductionmentioning

confidence: 99%

Influence of fly ash and decreasing water-powder ratio on performance of recycled aggregate concrete

Malešev¹,

Радоњанин²,

Draganić³

et al. 2017

JCE

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Influence of fly ash and decreasing water-powder ratio on performance of recycled aggregate concrete Investigation results of the combined effect of partial cement replacement with fly ash, and the use of recycled concrete aggregate to improve the sustainability of concrete, are presented in this paper. The compressive strength, capillary water absorption, water permeability, and chloride ion penetration, are analysed by testing nine concrete mixtures, in which the fly ash content and water to powder ratio were varied. The results show that compressive strength slightly decreases as the fly ash content increases, but that this can be compensated by reducing the water to powder ratio. Utjecaj letećeg pepela i smanjenja vodovezivnog omjera na svojstva betona s recikliranim agregatom U radu su prikazani rezultati ispitivanja zajedničkog učinka djelomične zamjene cementa s letećim pepelom i upotrebe recikliranog agregata s ciljem poboljšanja održivosti betona. Ispitivano je devet betonskih mješavina, u kojima su varirani količina letećeg pepela i vodovezivni omjer, analizirani su: tlačna čvrstoća, kapilarno upijanje, vodopropusnost i otpornost na prodor klorida. Dobiveno je da se povećanjem količine letećeg pepela neznatno smanjuje tlačna čvrstoća, ali da se smanjenjem vodovezivnog omjera to može nadomjestiti.

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Section: Introductionmentioning

confidence: 99%

Influence of fly ash and decreasing water-powder ratio on performance of recycled aggregate concrete

Malešev¹,

Радоњанин²,

Draganić³

et al. 2017

JCE

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“…Even if employing intelligent data analytics is already becoming a common practice in different disciplines, since recent times, its use in the field of concrete durability is at its infancy. For example to detect rebar corrosion [79,81,113], to characterize carbonation resistance [114][115][116][117][118][119][120], Cl − permeability [121][122][123][124][125][126], hygrothermal performance in concrete [127,128], Cl − diffusion coefficient [129][130][131][132], chloride penetration and carbonation predictors [116,133], and. Indeed, these works largely employ short-term data generated either from accelerated laboratory tests or field tests, but not real-time data from embedded sensors.…”

Section: Evolution Of Data-driven Corrosion Assessmentmentioning

confidence: 99%

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Taffese

Nigussie

2020

Sensors

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In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl−) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl− sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl− wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures.

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“…In the field of concrete engineering, for the experimental datasets of concrete properties, an ANN model was proposed to predict the compressive strength of concrete containing pozzolanic materials such as fly ash, silica fume, metakaolin, and ground granulated blast furnace slag [1][2][3]. In addition, the ANN model has been extended for predicting the properties of concrete, for instance, workability, corrosion currents, split tensile strength, water permeability, and chloride permeability [4]. Many studies have reported that ANN models lead to a more accurate and precise prediction than that obtained using linear and nonlinear regression techniques, and it can be easily expanded to new additional databases, enabling the retraining of the network [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the ANN model has been extended for predicting the properties of concrete, for instance, workability, corrosion currents, split tensile strength, water permeability, and chloride permeability [4]. Many studies have reported that ANN models lead to a more accurate and precise prediction than that obtained using linear and nonlinear regression techniques, and it can be easily expanded to new additional databases, enabling the retraining of the network [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

Tuntisukrarom

Cheerarot

2020

Advances in Materials Science and Engineering

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The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

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Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks

Cited by 36 publications

References 28 publications

Influence of fly ash and decreasing water-powder ratio on performance of recycled aggregate concrete

Influence of fly ash and decreasing water-powder ratio on performance of recycled aggregate concrete

Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

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