Abstract

The paper presents a possible statistical evaluation of the climate data, namely temperature and relative humidity, with respect to the rapid evaluation of the risk of reinforced concrete corrosion in the laboratory conditions. Data on temperature and humidity from Leoš Janáček Ostrava Airport over the last ten years are analysed. The processed data will be used as the set up for the climate chamber where the concrete samples with steel rods will be placed.

Highlights

  • Reinforced concrete bridges in Central Europe, are typically exposed to the combined effect of carbonation, chlorides ingress, and mechanical load

  • One possible way to evaluate this durability related issue is the connection with corrosion initiation modelling [2] and internal forces computation for the analysis of minimal required steel reinforcement cross-sectional area via the bending limit equilibrium equation concerning the effect of corrosion initiation and progress

  • With such a simple analysis, we find that the data are not suitable as inputs for climate chambers because we have to find the most common combination of high temperature with high humidity and low temperature with low humidity

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Summary

Introduction

Reinforced concrete bridges in Central Europe, are typically exposed to the combined effect of carbonation, chlorides ingress, and mechanical load. It is well known that these reinforced concrete structures are susceptible to corrosion that can have an influence on load-carrying capacity decay and threaten safety. In-situ measurements of corrosion rate of reinforcement in concrete structures involve more advanced electrochemical methods, from Linear Polarization Resistance LPR [7], to advance measurements by means of Electrochemical Impedance Spectroscopy EIS [8, 9]. For these tests, it is very important to have climate data related to the expected location of the structure. The tests will be performed in a climatic and corrosion chamber under predefined temperature and humidity conditions that are location-dependent as mentioned above

Temperature and humidity data set
Processing and retrieving the input data
Analysis by years
Analysis by months
Daily analysis and deeper sorting
Conclusions
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