Prediction of corrosion product thickness by DOProC method
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F23%3A10253716" target="_blank" >RIV/61989100:27120/23:10253716 - isvavai.cz</a>
Výsledek na webu
—
DOI - Digital Object Identifier
—
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Prediction of corrosion product thickness by DOProC method
Popis výsledku v původním jazyce
Corrosion coupon analysis is a crucial factor in the proper design of a steel structure's service life. Corrosion coupons should be exposed for a longer amount of time (such as 10 years or more) if a long-term estimation of corrosion losses is needed. Therefore, when designing steel structures exposed to corrosion damage, designers usually use corrosion prediction models or derived corrosion maps. Average annual temperature, average annual relative humidity, average yearly deposition of chloride ions and sulfur dioxide, or average annual concentration of dust particles are usually the main input parameters for these models. This paper presents a stochastic approach to corrosion prediction models. The thickness of the corrosion products after one year of exposure is then determined by processing the input parameters using stochastic methods. The comparison with in-situ measurement data at sites near roadways is also included in the article.
Název v anglickém jazyce
Prediction of corrosion product thickness by DOProC method
Popis výsledku anglicky
Corrosion coupon analysis is a crucial factor in the proper design of a steel structure's service life. Corrosion coupons should be exposed for a longer amount of time (such as 10 years or more) if a long-term estimation of corrosion losses is needed. Therefore, when designing steel structures exposed to corrosion damage, designers usually use corrosion prediction models or derived corrosion maps. Average annual temperature, average annual relative humidity, average yearly deposition of chloride ions and sulfur dioxide, or average annual concentration of dust particles are usually the main input parameters for these models. This paper presents a stochastic approach to corrosion prediction models. The thickness of the corrosion products after one year of exposure is then determined by processing the input parameters using stochastic methods. The comparison with in-situ measurement data at sites near roadways is also included in the article.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
—
OECD FORD obor
20100 - Civil engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/GA22-19812S" target="_blank" >GA22-19812S: Vliv plynného a dopravou vyvolaného znečištění na trvanlivost železobetonových konstrukcí</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů