Surface Chloride Concentration Research Articles

An experimental and numerical study on chloride transport in concrete under single bending load and coastal soft soil environment

This paper presents an experimental and numerical simulation study on the corrosion of chloride ions in concrete components, using a single pre-applied bending load to represent an additional bending moment caused by an external one-off disturbance. Besides, a novel correlation testing method, the Pearson-Mutual Information (PMI) method, is proposed based on Pearson correlation coefficients and mutual information theory. The findings indicate that a significant effect on chloride ion transport is only observed when the ratio of the applied bending load to the ultimate bending load (λ) exceeds 0.3. There is a distinctly positive and nonlinear relationship between λ and free chloride concentration (CCl), the apparent chloride diffusion coefficient (Da), as well as apparent surface chloride concentration (Cs). The bivariate time-varying model developed in this study demonstrates high fitting accuracy, with simulation results that closely correspond to actual measured data, achieving a coefficient of determination (R²) of 0.98 or higher. Furthermore, a sensitivity analysis of the model input parameters indicates that Cs is more sensitive than Da, and λ is identified as the key parameter. The impacts of the interfacial transition zone (ITZ) and its diffusion coefficient (Ditz) were found to be minimal on the output of the model.

Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm

Chloride ions infiltrate concrete structures, causing corrosion of the steel reinforcement, which creates concrete spalling and decreases load-bearing capacity, ultimately leading to structural failure. Thus, it is essential to comprehend the diffusion process of chloride ions in concrete. Current models used to predict the chloride ion diffusion in concrete are hindered by poor accuracy due to their simplistic consideration of variables and the complex interplay of environmental and material factors affecting key parameters such as surface chloride concentration (Cs) and the chloride diffusion coefficient (D). These influences are challenging to capture with simple linear or non-linear relationships, and traditional machine learning models often overfit training data and underperform with new data. To address these challenges, this study presents physical model experiments conducted to explore the diffusion process of chloride ions under multi-field coupling conditions, examining chloride ion concentrations across different concrete layers under varying environmental temperatures (T), humidities (h), erosion times (t), water-cement ratios (W/C), and volumes of coarse aggregates (v). This study reveals the distribution patterns of chloride ions within concrete layers. An XGBoost machine learning predictive model was developed using environmental temperature, humidity, erosion time, water-cement ratio, and coarse aggregate volume as input variables, with Cs and D as output variables. The results show that when the water-cement ratio reaches 0.5 in high humidity, the SHAP value rises to 0.015, enhancing chloride diffusion. As erosion time exceeds 200 days and temperature surpasses 38 °C, the SHAP value peaks at 0.02. Additionally, when coarse aggregate volume is between 0.45 and 0.60, temperature changes have little effect on Cs. Additionally, a graphical user interface was developed for modeling Cs and D to enhance practical usability.

Experimental investigation and numerical simulation of chloride diffusion in rubber concrete under dry-wet cycles

Chloride erosion caused by the dry-wet cycles is one of the most significant factors leading to durability problems in concrete structures. In this paper, the diffusion behavior of chloride in rubber concrete with varying rubber content is studied by experimental investigation and numerical simulation based on setting up artificial dry-wet cycles exposure conditions. The determination of chloride concentration in rubber concrete is achieved through chloride concentration titration, and as a result, the surface chloride concentration and the apparent chloride diffusion coefficient are expressed as a function of time, thus providing a theoretical guide for Comsol Multiphysics to perform numerical simulations. The findings indicate that the chloride diffusion behavior in rubber concrete complies with Fick's second law and that rubber can effectively reduce the diffusion rate of chloride in rubber concrete. Furthermore, the surface chloride concentration and apparent chloride diffusion coefficient are fitted with high accuracy according to the experimental data, which ensures the precision of the numerical simulation. In addition, numerical simulation results indicate that the diffusion of chloride in rubber concrete is impeded by the aggregate, that is, chloride diffusion is faster in areas where the local aggregate volume fraction is small. Also, it is evident that the presence of interfacial transition zone (ITZ) negatively affects the resistance of rubber concrete to chloride diffusion.

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

Life-cycle performance prediction and interpretation for coastal and marine RC structures: An ensemble learning framework

Long-term exposure to coastal and marine environments accelerates the aging of reinforced concrete (RC) structures, impacting their structural safety and society impact. Traditional assessments of long-term performance deterioration in RC structures involve complex, nonlinear, and time-intensive studies of physical mechanisms. While existing machine learning (ML) methods can assess the lifetime of these structures, they often prioritize data regression over mechanistic interpretation. To enhance the efficiency and interpretability of predicting the life-cycle performance of RC structures, this study introduces a generic framework based on interpretable ensemble learning (EL) methods. The framework predicts life-cycle performance efficiently and accurately, with optimal hyperparameters automatically tuned through Bayesian optimization. Interpretability algorithms clarify the influence of environmental, durability, and mechanical parameters on structural durability and mechanical predictions. Validation employs real-world cases of RC hollow beams in the coastal area of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The comprehensive model for RC structures integrates actual data on temperature, humidity, and surface chloride content in the GBA, considering diffusion, convection, and binding effects of chloride ions, corrosion non-uniformity, and crack impact on durability estimation. Comparative analysis with existing ML methods underscores the effectiveness of the framework. The findings highlight the dynamic evolution of feature importance rankings throughout the service life, shedding light on the continuous changes in the significance of different factors when predicting mechanical resistance.

A comparative study on chloride diffusion in concrete exposed to different marine environment conditions

Chloride penetration in concrete exposed to marine environment is a complicated process. In this paper, we present an experimental study on the chloride diffusion in concrete when it is subjected to marine stationary solution, splash, submerged, and pressured environments. The tests were carried out by using a “wash machine” type device to simulate various marine environment conditions. The specimens tested involve both noncarbonated and carbonated concrete specimens to examine the effect of carbonation on chloride penetration in concretes under these exposures. The experimentally obtained data indicates that the exposure condition has a great influence on the surface chloride concentration, chloride diffusion coefficient, and chloride binding capacity of concrete, which have a large impact on the chloride penetration in concretes. The study also found that carbonation can affect the influence of exposure conditions on chloride diffusion in concrete.

Quantifying the protective efficacy of GN anti-chloride admixture on durable performance of concrete in chloride ion erosion environments

This study evaluates the effectiveness of GN (Graphene Nanoplatelets) salt inhibitor-treated concrete in inhibiting chloride ion diffusion and corrosion compared to untreated concrete under various erosion conditions. Through an accelerated indoor chloride erosion test lasting 180 days, we examined the temporal changes in internal chloride ion concentration with and without the GN salt inhibitor. Quantitative assessment of the inhibitor's impact was achieved by introducing reduction factors for surface chloride concentration and diffusion coefficient. Our findings reveal a significant reduction of 31.3 % in chloride ion diffusion coefficient and 22.67 % in surface chloride concentration in concrete treated with GN salt inhibitor. This indicates the inhibitor's potential to extend the service life of gearbox and substation foundations exposed to chloride ion erosion environments.

Language model enhanced surface chloride concentration determination for concrete within splash environment based on limited field records

Chloride ion is severely harmful to reinforced concrete (RC) structures in marine environments. For maintaining the durability and safety of the designed RC structures, the determination of chloride ion concentration on concrete surfaces is critical. Currently, surface chloride ion concentration can be determined using empirical formulas and machine learning (ML) approaches. However, these approaches only rely on the numerical information within field records, disregarding valuable semantic and background information in the records, leading to low accuracy. Meanwhile, in splash environments, it presents a significant challenge to obtain chloride ion concentration records due to the complex environment and high costs involved. Therefore, based on limited field records of surface ion concentrations in splash environments, and utilizing a state-of-the-art language model (LM), this study proposes an LM-based information generation (LMIG) model to improve the accuracy of determination of surface chloride concentrations on RC structures. This paper utilizes the numerical and semantic information in 70 sets of field records to fine-tune the LMIG model and generates 200 sets of high-quality records. These records are then used to train ML algorithms for predicting chloride ion concentrations on concrete surfaces. After conducting comparative research, it was found that incorporating records generated by the LMIG model significantly enhances the accuracy of the ML algorithm. Specifically, the predictive accuracy using the random forest algorithm increased by 33.1%. Furthermore, this paper also conducts a comparative study on the LMIG model and the generative adversarial network (GAN)-assisted data-driven method. The results demonstrate that integrating semantic and numerical information into the LMIG model shows significant advantages in enhancing ML algorithms’ accuracy.

Research on chloride ion permeability resistance of concretes bonded with CFRP sheets under dry-wet cycles

Considering the widespread application of carbon fibre-reinforced polymer (CFRP) as a protective material for reinforced concrete (RC) structures, this study focuses on the ability of CFRP laminates with different numbers of layers bonded to the concrete surface to reduce the ingress of chloride ions. Variations in the free chloride concentration distribution in concrete before and after CFRP bonding were explored systematically. Nonlinear fitting was applied to the experimental data to establish a calculation model for the diffusion of free chloride ions in concrete with CFRP bonded to the surface, which was subsequently validated. Based on the proposed model, the reduction in the surface chloride concentration of concrete after bonding different numbers of CFRP layers was quantified as the reduction value. Moreover, recommendations for the durable design of RC structures bonded with CFRP in wet-dry cyclic environments are provided. The results indicate that the surface chloride concentration in concrete increases continuously with increasing wet-dry cycles, eventually reaching a steady state. Compared with the concrete specimens without CFRP bonding, the bonding of one, two, or three layers of CFRP led to reductions in the stable surface chloride concentration of concrete by 70.8%, 82.6%, and 84.9%, respectively. And no obvious convection zone was observed in the concrete after CFRP bonding. The validation results of the proposed calculation model demonstrated its high accuracy, making it suitable for durable design and service life prediction.

An Analytical Solution of Chloride Transfer into RC Pipe Pile by Convection–Diffusion Effect Considering Time-Dependent Surface Chloride Concentration

An Analytical Solution of Chloride Transfer into RC Pipe Pile by Convection–Diffusion Effect Considering Time-Dependent Surface Chloride Concentration

Advancing service life estimation of reinforced concrete considering the coupling effects of multiple factors: Hybridized physical testing and machine learning approach

Chloride-induced corrosion is the primary factor that significantly impacts the service life of reinforced concrete structures. Therefore, it is crucial to investigate the transportation behavior of chloride ions within concrete structures to accurately estimate their service life. Previous studies have mostly examined the diffusion of chloride ions through physical tests on concrete specimens. Some data-driven models have also been developed to predict the chloride ion concentration at different depths in concrete structures. However, these prediction models often considered random factors in materials and the environment separately, neglecting their coupled effects. As a result, there can be a significant disparity between the predicted results and the actual test outcomes. To achieve a more precise evaluation of the service life, this study conducted a physical test to investigate the diffusion behavior of chloride ions in plain concrete specimens. Factors such as water-cement ratio (W/C), coarse aggregate volume fraction (v) (ratios of the mass of coarse aggregates to the total mass of concrete specimen), ratios of environmental temperature to maintenance standard temperature (T/T0), ratios of environmental humidity to maintenance humidity (h/h0), and ratios of exposure time to maintenance time (t/t0) were considered in the test. Based on the test results, machine learning approaches were employed to determine the influence of each factor on the chloride diffusion coefficient (D) and surface chloride concentration (Cs). Subsequently, a prediction model was developed using the second Fick's law, with the chloride ion concentration at each depth as the output and the aforementioned factors as the inputs. Furthermore, by analyzing the above results, the study examined the impacts of various factors on the service life of reinforced concrete structures. The findings indicate that W/C and v are the most significant factors affecting D and Cs. As W/C and T/T0 increase, both D and Cs also increase. Conversely, the volume fraction of coarse aggregate has a hindering effect on the diffusion of chloride ions in concrete structures. Additionally, the study explored the effect of each input factor on the service life of reinforced concrete structures considering the effect of the above factors. Overall, these findings provide valuable insights into improving the accuracy of estimating the service life of reinforced concrete structures by considering the coupled effects of materials and environmental factors.

Reverse-seepage and saturation action anti-corrosion tech (RS-AAT) for corrosion resistance and self-healing of cracked concrete in seawater immersion

Reverse-seepage and Saturation based Action Anti-corrosion Tech (RS-AAT) is a cutting-edge technology that saturates concrete materials and provides anti-hydraulic pressure to offer robust protection against chloride salt-induced corrosion. This study examined the crack repair capability and anti-chloride invasion effect of RS-AAT-treated concrete by experimental and numerical analyses. The results indicated that, under the effect of RS-AAT, the crack healing rate achieved the highest value of 12 % and the total chloride concentration dropped by the largest amount of 51.41 % after 120 days. Under 0.4 MPa water pressure, the surface chloride concentration decreased by the most of 69.42 % and the depth concentration curves of different crack widths approached a variance of 1. Moreover, the crack repair coefficients were introduced to improve the accuracy of numerically simulating the chloride ion migration process in cracked concrete. The findings unequivocally demonstrate that the use of RS-AAT could significantly inhibit chloride ion intrusion into the concrete's protective layer, of which effect was intensified with the increase of reverse osmosis water pressure. Such results provided invaluable insights into enhancing the durability and chloride ion resistance of marine-immersed concrete structures.

Randomness and time-varying characteristics of chloride ion transport in existing harbor concrete structures

This paper presents a study on the durability testing of a concrete facility located within the North Bay Port region, which has been in service for 200 months. Based on the results of 20 sets of concrete samples, an improved Kolmogorov-Smirnov (K-S) test method and gross error discrimination method are proposed to analyze the random distribution functions of the apparent chloride diffusion coefficient (Dapp) and the apparent surface chloride concentration (Cs), and further explore the time-varying characteristics of chloride ion transmission in concrete. The research shows that the improved K-S method presented in this paper can effectively reduce the probability of type II errors by selecting a rational sample size, which can also improve detection efficiency. When the sample size does not exceed 10, the 2σ criterion proposed in this paper can effectively identify and eliminate gross errors. In general, Dapp and Cs follow the log-normal distribution, while the time factor (m) follows a normal distribution parameter. The value of Cs basically stabilized after exposure to 80 months, whereas Dapp tended to stabilize after exposure to 100 months. The value of m obtained through fitting short-term experimental data is typically overestimated, and its value decreases as the exposure time prolongs. Therefore, m should also be considered a time-varying parameter that decreases with time. The tested RC structure will begin to experience rebar corrosion after 48.5-60 years of exposure to the marine environment based on the probabilistic assessment.

Coupled model on the variation of diffusion coefficient and surface chloride concentration in reinforced concrete structures over time

Durability analyses and service life estimates of structures in a marine environment can be developed through the modeling of chloride concentration profiles. However, as surface chloride concentration and diffusion coefficient vary over time, especially due to the cement hydration process, modeling chloride profiles from structures with recent degradation ages requires an adjustment for the temporal dependence of these two physical parameters. The variation of the surface chloride concentration (Cs) as a function of time tends to show an increasing asymptotic behavior, while the diffusion coefficient (D) has a decreasing asymptotic tendency. For this reason, the power function, usually used to describe the diffusion coefficient, has problems for advanced ages, while the exponential function has difficulties at recent ages. Therefore, the key issue in this problem is the rate of change of Cs and D over time. This article presents the development of a single time-dependence model to simultaneously describe the decrease in D and the increase in Cs over time. The coupled model is a piecewise-defined function, with continuity of the function and its first derivative in the transition time between recent ages and more advanced ages, defined as 10 years. To validate the new model, data from structures under natural degradation were used. The results demonstrated the applicability of the coupled model with individual adjustments of the exponent α, which implicitly represents material properties and environmental parameters. However, in the absence of sufficient information, a standard α exponent of 0.25 per year can be adopted, demonstrating a moderate to strong adherence of the coupled model to the Cs and D data over time. Three stages of temporal dependence were identified: I – up to 10 years: marked increase in Cs and decrease in D over time; II – between 10 and 25 years: decrease in the variation rate of Cs and D; and III – from 25 years: stabilization of Cs and D.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Modeling of chloride-induced corrosion in concrete bridge using the simplified and full probabilistic methods

Concrete and reinforced concrete structures are subjected to chloride corrosion, which can shorten their service life and safety of use. Reliability and safety prediction of concrete structures is a crucial task for optimizing their life cycle design and maintenance and for minimizing their life cycle costs. In the paper a probabilistic analysis of concrete durability in structural members is presented. Two methods: simplified-probabilistic and full-probabilistic have been applied for modelling chloride-induced corrosion in concrete structures. The uncertainty of the key parameters including surface chloride concentration, chloride threshold, cover depth and diffusion coefficient, which govern the chloride ingress into concrete and corrosion of reinforcing steel have been analyzed. A case study of a reinforced concrete bridge has been used to illustrate the capability and efficiency of these probabilistic methods in modeling the uncertainty and predicting the time-dependent probability of corrosion. FREeT-D and ProCAAT software have been used for the analysis.

Influence of the synthesis method of Cu/Y zeolite catalysts for the gas phase oxidative carbonylation of methanol to dimethyl carbonate

Dimethyl carbonate is an environmentally friendly molecule with increasing applications as reactant, solvent, and fuel additive. Oxidative carbonylation of methanol, with reactants in the gas phase and catalyzed by copper-exchanged Y zeolites, is a promising alternative route for dimethyl carbonate production. This work is focused on the improvement of the catalyst formulation and preparation methods. Catalysts were prepared using two methodologies: solid-state ion exchange and liquid-ethanol ion exchange. Five different copper salts were used as precursors and the NH4+ and Na+ forms of the Y zeolite as supports. The Na+ form of the Y zeolite removed the Brønsted acidity of the support and, therefore, the reaction rate of acid-catalyzed undesired dehydration and decomposition reactions was reduced. The use of copper chloride salts as precursors was crucial to obtain suitable catalysts for dimethyl carbonate formation. Among all the tested catalysts, those prepared using CuCl2 precursor and the Na-Y zeolite by the liquid-ethanol ion exchange method showed the best performance (0.72 molDMC/molCu h). This catalyst exhibited the highest surface chloride content and Lewis acidity.

Measurement plan targeting the accuracy of calibrated chloride ingress model for concrete structures in marine environment

Surface chloride concentration (CS) and chloride diffusion coefficient (DCl) are key parameters for durability assessment of concrete structures in marine environment; they are time-varying and highly dependent on the exposure condition. To reasonably model their behaviors at a specific location, durability measurement data are often needed to calibrate the apparent chloride ingress model based on Fick’s second law. In view of the significant variability of measurements and the bias of chloride ingress model, it remains unaddressed how to formulate a measurement plan to make the calibrated model achieve the required accuracy. This paper first establishes the probabilistic time-dependent models of CS and DCl with both sample variance and model bias considered, and then introduces the Bayesian method to update the two models using measurement data. By assuming realistic models of CS and DCl and comparing them with updated ones, the effectiveness of Bayesian updating method is demonstrated, and the key factors affecting the updated model accuracy are discussed, including prior estimate of parameters, model bias and measuring times. On this basis, a determination method of measurement plan targeting the calibrated model accuracy is proposed, which works for both Bayesian updating and linear fitting for model calibration. And finally numerical examples are presented to show the validity of the proposed method. The sample size obtained by the proposed method is exact for linear fitting and slightly more than required for Bayesian updating.

Modeling of bidirectional chloride convection-diffusion for corrosion initiation life prediction of RC square piles under drying-wetting cycle

Modeling chloride penetration mechanism in unsaturated concrete is essential for corrosion initiation life prediction of marine reinforced concrete (RC) structures. This paper presents a bidirectional chloride convection-diffusion model for the chloride concentration distribution of RC square piles in the tidal and splash zone. The finite difference numerical method is used to solve the convection-diffusion governing equations with the drying-wetting cycle. The numerical model is validated by two experimental results and the analytical solution with constant surface chloride conditions. The peak value and convection depth in the chloride distribution curve of RC square piles are investigated. The results show that the periodic drying-wetting chloride environment is the dominant factor of the convection effect formed in the surface layer of the square piles. Moreover, a coupling effect is observed in the diagonal areas of square piles. The corrosion initiation life under a periodic drying-wetting chloride environment is shorter than that with constant surface chloride concentration. Furthermore, the sensitivity analysis results show that the diffusion coefficient, the concrete cover depth, and the variable rate of drying-wetting cycles are the critical parameters for predicting the corrosion initiation life of RC square piles under the drying-wetting cycle.

Effects of steel bar and bi-directional erosion on chloride diffusion in reinforced concrete: A 3D mesoscale study

This work proposes a novel numerical framework to investigate 3D chloride diffusion behavior in reinforced concrete (RC). An efficient dynamic modeling approach is developed to generate the RC models with real-shaped aggregates at mesoscale. A semi-closed form solution of Fick’s second law is established to account for the time-varying chloride diffusion coefficient and surface chloride concentration through a genetic algorithm. Moreover, the effects of aggregate distribution and morphology on chloride diffusion are examined. The validated results demonstrate that the random distribution of aggregates leads to varied chloride concentration, and anisotropic aggregate morphology increases hindrance to chloride diffusion. Steel bar also affects chloride diffusion, causing an increase in chloride concentration on the steel surface. This hinder effect intensifies as the steel diameter increases and the protective layer thickness decreases. Additionally, bi-directional diffusion elevates chloride concentration on the corner steel surface, while smaller steel diameter and thicker protective layer render the corner steel bar more susceptible to bi-directional diffusion. The proposed framework has incorporated various influencing factors with comprehensive parametric analyses and holds potential for engineering design of RC structures considering durability, safety, and sustainability.