STRUCTURAL CONCRETE(2022 - 2022)
Spatial characteristics of stray current corrosion of reinforcing bars in pseudo concrete
Peng Y., Wang Z., Gong F., Zhao Y., Meng T., Jin W., Maekawa K.
STRUCTURAL CONCRETE, The International Federation for Structural Concrete (fib), Vol., 2022, .
(https://doi.org/10.1002/suco.202200164)
Abstract
This paper focuses on the spatial characteristics of reinforcing-bars corrosion under stray current. In the study, a corrosion experiment using a specimen with three steel bars embedded in pseudo concrete was carried out, the distribution of corrosion was obtained by three-dimensional scanning. Further, a numerical model with the same dimension of the specimen was simulated to explain experimental results. The distribution of electric field driven by stray current was discussed in detail. Results show that by observation through the transparent matrix and by three-dimensional scanning, the distribution of corrosion could be estimated qualitatively and quantitatively. Additionally, the spatial characteristics of corrosion state under stray current could be realized by the numerical simulation which combines polarization reaction with an extra stray current field. The stray current field around reinforcing bars was uneven in the cross-sectional plane, leading to the difference of corrosion rate, which reflects the strong positive correlation between the field and corrosion rate. c 2022 The Authors. Structural Concrete published by John Wiley & Sons Ltd on behalf of International Federation for Structural Concrete.
Machine learning approach in investigating carbonation depth of concrete containing Fly ash
Tran V.Q., Mai H.-V.T., To Q.T., Nguyen M.H.
STRUCTURAL CONCRETE, The International Federation for Structural Concrete (fib), Vol., 2022, .
(https://doi.org/10.1002/suco.202200269)
Abstract
Understanding and predicting concrete carbonation are significant in designing durability and maintaining the service life of reinforced concrete structures. However, this purpose can hardly be reached because of complex carbonation mechanisms depending on various variables such as cement content (C), fly ash content (FA), water content (W), concentration of CO2, relative humidity (RH), temperature (TC), and exposition time (Time). This investigation proposes a machine learning (ML) approach including eight ML algorithms such as four single ML models: XGB, GB, RF, and SVM, and four hybrid ML models: XGB_RRHC, GB_RRHC, RF_RRHC, and SVM_RRHC for investigating and predicting concrete carbonation depth containing fly ash. To achieve this purpose, a dedicated database consisting of 688 samples and seven input variables is built, and the performance of eight machine learning models is compared. Single ML model Extreme Gradient Boosting (XGB) using the default hyperparameters exhibited the highest performance with R2?=?0.9770, RMSE?=?2.2725 and MAE?=?1.5218. Shapley Additive exPlanations (SHAP) identifies the most influential feature and order of feature effect on concrete carbonation depth. The first four important features can be sorted in order: time of exposition > cement content > water content > CO2 concentration. Moreover, at a higher value of exposition time, water content, CO2 concentration, fly ash content, temperature and relative humidity, the carbonation depth of concrete increases. Using high content of cement can reduce the carbonation depth of concrete. c 2022 fib. International Federation for Structural Concrete.