Documents disponibles écrits par cet auteur

Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling / Seung-Jun Kwon in Cement and concrete research, Vol. 40 N° 1 (Janvier 2010) 

Public ISBD [article]  in Cement and concrete research > Vol. 40 N° 1 (Janvier 2010) . - pp. 119-127 Titre : Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling Type de document : texte imprimé Auteurs : Seung-Jun Kwon, Auteur ; Ha-Won Song, Auteur Article en page(s) : pp. 119-127 Note générale : Génie Civil Langues : Anglais (eng) Mots-clés : Carbonation  Modeling  Diffusion  coefficient  Neural  network  Porosity Index. décimale : 691 Matériaux de construction. Pièces et parties composantes Résumé : Carbonation on concrete structures in underground sites or metropolitan cities is one of the major causes of steel corrosion in RC (Reinforced Concrete) structures. For quantitative evaluation of carbonation, physico-chemo modeling for reaction with dissolved CO2 and hydrates is necessary. Amount of hydrates and CO2 diffusion coefficient play an important role in evaluation of carbonation behavior, however, it is difficult to obtain a various CO2 diffusion coefficient from experiments due to limited time and cost. In this paper, a numerical technique for carbonation behavior using neural network algorithm and carbonation modeling is developed. To obtain the comparable data set of CO2 diffusion coefficient, experimental results which were performed previously are analyzed. Mix design components such as cement content, water to cement ratio, and volume of aggregate including exposure condition of relative humidity are selected as neurons. Training of learning for neural network is carried out using back propagation algorithm. The diffusion coefficient of CO2 from neural network are in good agreement with experimental data considering various conditions such as water to cement ratios (w/c: 0.42, 0.50, and 0.58) and relative humidities (R.H.: 10%, 45%, 75%, and 90%). Furthermore, mercury intrusion porosimetry (MIP) test is also performed to evaluate the change in porosity under carbonation. Finally, the numerical technique which is based on behavior in early-aged concrete such as hydration and pore structure is developed considering CO2 diffusion coefficient from neural network and changing effect on porosity under carbonation. DEWEY : 620.13 ISSN : 0008-8846 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235562%23 [...] [article] Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling [texte imprimé] / Seung-Jun Kwon, Auteur ; Ha-Won Song, Auteur . - pp. 119-127. Génie Civil Langues : Anglais (eng) in Cement and concrete research > Vol. 40 N° 1 (Janvier 2010) . - pp. 119-127 Mots-clés : Carbonation  Modeling  Diffusion  coefficient  Neural  network  Porosity Index. décimale : 691 Matériaux de construction. Pièces et parties composantes Résumé : Carbonation on concrete structures in underground sites or metropolitan cities is one of the major causes of steel corrosion in RC (Reinforced Concrete) structures. For quantitative evaluation of carbonation, physico-chemo modeling for reaction with dissolved CO2 and hydrates is necessary. Amount of hydrates and CO2 diffusion coefficient play an important role in evaluation of carbonation behavior, however, it is difficult to obtain a various CO2 diffusion coefficient from experiments due to limited time and cost. In this paper, a numerical technique for carbonation behavior using neural network algorithm and carbonation modeling is developed. To obtain the comparable data set of CO2 diffusion coefficient, experimental results which were performed previously are analyzed. Mix design components such as cement content, water to cement ratio, and volume of aggregate including exposure condition of relative humidity are selected as neurons. Training of learning for neural network is carried out using back propagation algorithm. The diffusion coefficient of CO2 from neural network are in good agreement with experimental data considering various conditions such as water to cement ratios (w/c: 0.42, 0.50, and 0.58) and relative humidities (R.H.: 10%, 45%, 75%, and 90%). Furthermore, mercury intrusion porosimetry (MIP) test is also performed to evaluate the change in porosity under carbonation. Finally, the numerical technique which is based on behavior in early-aged concrete such as hydration and pore structure is developed considering CO2 diffusion coefficient from neural network and changing effect on porosity under carbonation. DEWEY : 620.13 ISSN : 0008-8846 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235562%23 [...]

Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure / Ha-Won Song in Cement and concrete research, Vol. 39 N° 9 (Septembre 2009) 

Public ISBD [article]  in Cement and concrete research > Vol. 39 N° 9 (Septembre 2009) . - pp. 814–824 Titre : Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure Type de document : texte imprimé Auteurs : Ha-Won Song, Auteur ; Seung-Jun Kwon, Auteur Année de publication : 2009 Article en page(s) : pp. 814–824 Note générale : Génie Civil Langues : Anglais (eng) Résumé : Chloride attack is one of the major causes of deterioration of reinforced concrete structures. In order to evaluate the chloride behavior in concrete, a reasonable prediction for the diffusion coefficient of chloride ion, which governs mechanism of chloride diffusion inside concrete, is basically required. However, it is difficult to obtain chloride diffusion coefficients from experiments due to time and cost limitations. In this study, a numerical technique for chloride diffusion in high performance concrete (HPC) using a neural network algorithm is proposed. In order to collect comparative data on diffusion coefficients in concrete with various mineral admixtures such as ground granulated blast-furnace slag (GGBFS), fly ash (FA), and silica fume (SF), a series of electrically driven chloride penetration tests was performed. Seven material components in various mix designs and duration time are selected as neurons in a back-propagation algorithm, and associated learning of the neural network is carried out. An evaluation technique for chloride behavior in HPC using the obtained diffusion coefficients from the neural network algorithm is developed based on, so-called, Multi-Component Hydration Heat Model (MCHHM) and Micro Pore Structure Formation Model (MPSFM). The applicability of the developed technique is verified by comparing the analytical simulation results and the experimental results obtained in this study. Furthermore, this proposed technique using the neural network algorithm and micro modeling is applied to available experimental data for verification of its applicability. ISSN : 0008-8846 En ligne : http://www.sciencedirect.com/science/article/pii/S0008884609001288 [article] Evaluation of chloride penetration in high performance concrete using neural network algorithm and micro pore structure [texte imprimé] / Ha-Won Song, Auteur ; Seung-Jun Kwon, Auteur . - 2009 . - pp. 814–824. Génie Civil Langues : Anglais (eng) in Cement and concrete research > Vol. 39 N° 9 (Septembre 2009) . - pp. 814–824 Résumé : Chloride attack is one of the major causes of deterioration of reinforced concrete structures. In order to evaluate the chloride behavior in concrete, a reasonable prediction for the diffusion coefficient of chloride ion, which governs mechanism of chloride diffusion inside concrete, is basically required. However, it is difficult to obtain chloride diffusion coefficients from experiments due to time and cost limitations. In this study, a numerical technique for chloride diffusion in high performance concrete (HPC) using a neural network algorithm is proposed. In order to collect comparative data on diffusion coefficients in concrete with various mineral admixtures such as ground granulated blast-furnace slag (GGBFS), fly ash (FA), and silica fume (SF), a series of electrically driven chloride penetration tests was performed. Seven material components in various mix designs and duration time are selected as neurons in a back-propagation algorithm, and associated learning of the neural network is carried out. An evaluation technique for chloride behavior in HPC using the obtained diffusion coefficients from the neural network algorithm is developed based on, so-called, Multi-Component Hydration Heat Model (MCHHM) and Micro Pore Structure Formation Model (MPSFM). The applicability of the developed technique is verified by comparing the analytical simulation results and the experimental results obtained in this study. Furthermore, this proposed technique using the neural network algorithm and micro modeling is applied to available experimental data for verification of its applicability. ISSN : 0008-8846 En ligne : http://www.sciencedirect.com/science/article/pii/S0008884609001288