Chloride Diffusion Coefficient Of Concrete Research Articles

Effect of Biochar Dosage and Fineness on the Mechanical Properties and Durability of Concrete

Biochar (BC), a byproduct of agricultural waste pyrolysis, shows potential as a sustainable substitute material for ordinary silicate cement (OPC) in concrete production, providing opportunities for environmental sustainability and resource conservation in the construction industry. However, the optimal biochar dosage and fineness for enhancing concrete performance are still unclear. This study investigated the impact of these two factors on the mechanical and durability properties of biochar concrete. Compressive and flexural strength, carbonation resistance, and chloride ion penetration resistance were evaluated by varying biochar dosages (0%, 1%, 3%, 5%, 10%) and fineness dimensions (44.70, 73.28, 750, 1020 μm), with the 0% dosage serving as the control group (CK). The results showed that the addition of 1-3 wt% of biochar could effectively reduce the rapid carbonation depth and chloride diffusion coefficient of concrete. The compressive and flexural strength of BC concrete initially increased and then decreased with the increase in biocarbon content, BC with a fineness of 73.28 μm having the most significant effect on the mechanical strength of concrete. At the dosage of 3 wt%, BC was found to promote the hydration degree of cement, improving the formation of cement hydration products. These findings provide valuable insights for the development of sustainable and high-performance cement-based materials with the appropriate use of biochar as an additive.

Design Method for Concrete Mix Proportion Based on Indices of Strength and Durability of Concrete Exposed to Chloride Attack

In the current design code, concrete mix proportions are not based on durability parameters that can quantitatively design the requirement of service life for chloride-exposed structures. Multifactor calculation models of durability index are developed, and the replacement of mineral admixtures in concrete is determined according to predetermined concrete strength and durability requirements. The chloride diffusion coefficient and aging factor of concrete are chosen as index parameters for the resistance of concrete to chloride salts. A multifactor model of the chloride diffusion coefficient of concrete is then established by regression analysis based on the experimental data, and explicit expressions for the water-to-binder ratio and replacement of admixture were determined by solving the multivariate nonlinear equation system. By calculating the average compressive strength and chloride diffusion coefficient from their specified values, the mix proportion of concrete is determined. The superiority of the proposed method is demonstrated by the comparison with the current code. Lastly, an orthogonal experiment was conducted to demonstrate the validity of the proposed method for designing concrete mix proportions by referencing engineering practices. The results show that major parameters of concrete mix proportion can be determined quantitatively from structural design parameters such as compressive strength, chloride diffusion coefficient, and aging factor, satisfying the requirements of ultimate bearing capacity and service life for concrete structures in chloride environments, as demonstrated in this paper.

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms.

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

Seismic Fragility Analysis of the Aging RC Columns under the Combined Action of Freeze–Thaw Cycles and Chloride-Induced Corrosion

The combined action of freeze–thaw cycles and chloride-induced corrosion are generally recognized as one of the main causes of the degradation of the mechanical properties and seismic performance of reinforced concrete (RC) structures in the northern frozen coastal regions. To investigate the degradation mechanisms of the seismic performance of RC columns subjected to the combined action of freeze–thaw cycles and chloride-induced corrosion, the impact of freeze–thaw cycles on the chloride diffusion coefficient of concrete was studied through concrete deterioration tests and theoretical analysis. This paper proposed a time-dependent deterioration model for RC columns, which is suitable to consider the combined action of freeze–thaw cycles and chloride-induced deterioration. The proposed deterioration model could be applied to the investigations of time-dependent seismic performance and the seismic fragility of RC columns. Based on the established deterioration model, this paper proposed a time-dependent seismic fragility analysis framework for the aging RC columns, considering the combined action of freeze–thaw cycles and chloride-induced corrosion. In addition, a representative three-span RC continuous T-shaped girder bridge that is located in the high-latitude northern frozen coastal regions of China was taken as the case study, and the time-dependent seismic fragility analysis of RC columns was conducted considering the involved uncertainties in geometric parameters, the deterioration mechanisms of the materials, and ground motions. The time-dependent seismic fragility curves of RC columns were obtained at different service time points. The results indicated that the combined action of freeze–thaw cycles and chloride-induced deterioration had a significant influence on the time-dependent seismic responses of the deteriorating RC columns. Under the combined action of freeze–thaw cycles and chloride-induced corrosion, when the RC bridge was in service for 75 years, the stirrup strength decreased by 3.88% and the cross-sectional area decreased by 30.03%. The peak stress of the confined concrete decreased by 52.1% and its peak strain increased by 12.2 times, respectively. Moreover, the time-dependent seismic fragilities of the aging RC columns under different damage states exhibited a nonlinear increase as the service life increased.

Estimation of chloride diffusion coefficient from water permeability test of cementitious materials

Chloride diffusion coefficient of concrete is a critical parameter indicating a durability property of steel-reinforced concrete elements in chloride-laden environments. However, chloride diffusion tests of concrete could take months or even years to finish in order to obtain the steady state chloride diffusion coefficient (Dss). Most engineers have to take a rapid chloride migration test to get a chloride migration coefficient, which could still take one or a few days to finish. In contrast, a water permeability test could be finished within only a few hours, and a steady state water permeability coefficient (k) can be obtained. This study aims to build a theoretical model between Dss and k, and the model has been validated by experimental work. The obtained model shows that relationship between Dss (in m2/s) and k (in m2) can be simply described using a formula Dss/k = 32Daq/d2pore, where Daq (in m2/s) is the chloride diffusion coefficient in free solution, and dpore (in m) is the equivalent pore diameter. The experimental validation work showed that the model is in good agreement with the measured data, and dpore in the formula can be replaced with the average pore diameter da. Applying this model in engineering projects could aid to predict the difficult-to-get Dss of cementitious materials through the rapid measurements of k and da.

Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials

Chloride diffusion coefficient is an important durability indicator in durability design of concrete structure according to performance-based approach. However, this indicator is difficult to anticipate and design due to a variety of factors such as mix design of concrete, the replacement of supplemental cementitious materials (SCMs), and binder selection. This study proposes an extensive number of machine learning algorithms to predict the chloride diffusion coefficient of concrete containing Supplementary Cementitious Materials (SCMs) such as silica fume, ground granulated blast furnace slag, and fly ash. A database containing nine input variables is created, eight machine learning models consisting of Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbors (KNN), Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGB), Random Forest (RF), Gradient Boosting (GB), AdaBoost (AdB) are evaluated via performance criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), coefficient of correlation ® and Root Mean Square Error (RMSE). Gradient Boosting model has highest performance in prediction of chloride diffusion coefficient. SHapley Additive exPlanations (SHAP), Individual Conditional Expectation ICE and Partial Dependence Plot PDP 2D allow the most influential inputs to be identified, quantify the influence of input variables on chloride diffusion of concrete. Selection of the best ML algorithm Gradient Boosting is useful to develop a dependable soft computing tool in durability design of concrete structure including the mix design optimization and binder selection.

Relationship between chloride diffusion coefficient and ASTM C1202 electric flux under different environments and durability evaluation

The relationship of the diffusion coefficient and charge passed is developed employing the analysis of the traditional model and 244 groups of experimental data. The differences in the types of concrete were considered to determine the variation coefficient of the diffusion coefficient in the models. Results revealed that the different environmental conditions of the concrete chloride diffusion coefficient cannot be described solely by the existing maturity method. Reasonably good agreement is observed between the experimental and computational model of this paper, thus indicating that the proposed prediction model can be successfully used to estimate the chloride diffusion coefficient. An example of a durability assessment is performed to verify the applicability of the concrete durability assessment method. The concrete durability assessment method can control the durability quality and direction of the production of concrete.

Experimental assessment of accelerated test methods for determining chloride diffusion coefficient in concrete

Abstract The performance of accelerated test methods to determine the chloride diffusion coefficient in concrete is an important tool in the evaluation of the durability-related properties of the material and in the service life prediction of structures. In this way, this paper presents an experimental assessment of three standardized test methods for determining chloride diffusion coefficient. The migration-based tests presented by NT Build 492 and UNE 83987 and the diffusion-based test presented by ASTM C1556 were carried out. Six concrete mixes were produced: three with different water/binder ratios (0.45, 0.55, and 0.65) and three with replacement of cement by silica fume in the levels of 5, 10, and 20%. The results indicate that the method standardized by NT Build 492 has the best correlation with the diffusion test, in addition to being the one that requires the shortest execution time and has the lowest coefficient of variation of the results.

Correlation of chloride diffusion coefficient and microstructure parameters in concrete: A comparative analysis using NMR, MIP, and X-CT

Permeability is a major indicator of concrete durability, and depends primarily on the microstructure characteristics of concrete, including its porosity and pore size distribution. In this study, a variety of concrete samples were prepared to investigate their microstructure characteristics via nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and X-ray computed tomography (X-CT). Furthermore, the chloride diffusion coefficient of concrete was measured to explore its correlation with the microstructure of the concrete samples. Results show that the proportion of pores with diameters 50 µm, X-CT obtains the maximal contributive porosity, followed by MIP and NMR.

Differences between Time-Dependent Instantaneous and Apparent Chloride Diffusion Coefficients of Concrete in Tidal Environment

AbstractTime-dependent chloride diffusivity can reflect the chloride transport in concrete, which is greatly affected by different factors such as material composition and environment condition. In...

Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation

Chloride ingression is the main reason for causing durability degradation of reinforced concrete (RC) structures. In this study, the distinguishing features of artificial neural network (ANN) technique are utilized to develop a rational and effective predictive model for chloride diffusion coefficient of concrete. An extensive and reliable database comprising of 653 distinctive diffusion coefficient results, from literature, was utilized for establishing the network model. The developed ANN models used 13 most influential parameters, varying from concrete constituents, mechanical property and experimental process, as input to incorporate complex underlying physical phenomena for prediction of diffusion coefficient. The significance of normalization of the variables is highlighted through a comparative study. The performance of the developed model is assessed by conducting several in-depth statistical error analysis as per the recommendations in literature. The results of the study reveals that the models are robust and possess a strong prediction potential. The findings revealed that ANN can be an effective tool to identify the discrepancies in the experimental findings, and would be particularly useful for evaluating the chloride resistance of RC structures serving in complex or harsh environment.

Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates.

Parent concrete coming from a wide range of sources can result in considerable differences in the properties of recycled coarse aggregate (RCA). In this study, the RCAs were obtained by crushing the parent concrete with water-to-cement ratios (W/Cparent) of 0.4, 0.5 and 0.6, respectively, and were strengthened by carbonation and nano-silica slurry wrapping methods. It was found that when W/Cparen was 0.3, 0.4 and 0.5, respectively, compared with the mortar in the untreated RCA, the capillary porosity of the mortar in the carbonated RCA decreased by 19%, 16% and 30%, respectively; the compressive strength of concrete containing the carbonated RCA increased by 13%, 11% and 13%, respectively; the chloride diffusion coefficient of RAC (DRAC) containing the nano-SiO2 slurry-treated RCA decreased by 17%, 16% and 11%; and that of RAC containing the carbonated RCA decreased by 21%, 25% and 26%, respectively. Regardless of being strengthened or not, both DRAC and porosity of old mortar in RCAs increased with increasing W/Cparent. For different types of RCAs, DRAC increased obviously with increasing water absorption of RCA. Finally, a theoretical model of DRAC considering the water absorption of RCA was established and verified by experiments, which can be used to predict the DRAC under the influence of different factors, especially the water absorption of RCA.

A multi-phase mesoscopic simulation model for the diffusion of chloride in concrete under freeze–thaw cycles

Reinforced concrete (RC) structures in cold regions are subjected to double attacks of chloride erosion and freeze–thaw cycles (FTCs), accelerating the deterioration of concrete performance. Here, a multi-phase mesoscopic numerical model is proposed to systematic study the diffusion mechanism of chloride under the FTCs. Unlike most of existing models, the interface transition zone (ITZ) with random thickness is taken into account in the proposed concrete meso-structure. Moreover, the relationship between FTCs damage and chloride diffusion coefficient of concrete is deduced from the classical Aas-Jakobsen S-N equation. The reliability of the proposed model is further verified by compared with the third-party experiments data. It is evident from the proposed model that FTCs can promote the chloride diffusion. More importantly, this promoting effect is obvious when the number of FTCs is close to the limit number of FTCs. Specially, the mechanism of ITZ promoting the chloride diffusion is revealed by simulating the chloride diffusion trajectories in concrete meso-structure. Interestingly, despite the existence of FTCs in cold regions, chloride is diffused more rapidly in the tropical regions. Our findings reveal previously ignored fundamental aspects of the chloride diffusion mechanism under FTCs, and provide insights for the durability prediction of RC structures.

Electrical resistivity model for quantifying concrete chloride diffusion coefficient

The effects of chloride salts on concrete durability have been financially and logistically overwhelming. Current test methods for quantifying diffusivity of chlorides in concrete are time consuming and demanding. A comprehensive electrical resistivity model has been developed to quantify the chloride diffusion coefficient of concrete. The model employs the modified Nernst-Einstein equation along with an ionic concentration sub-model, a pore solution conductivity sub-model, and a binder hydration sub-model. The sub-models, which account for the concentrations of Na+, K+, Ca2+, and OH− ions in the pore solution derived from the binder degree of hydration, binder chemical composition, and alkalis binding to the cement hydration products, are included to accurately quantify the conductivity of the pore solution. Moreover, an experimental program was developed to verify and validate the accuracy and completeness of the electrical resistivity model. The experimental variables are volume fraction of coarse aggregate, water to binder ratio, total binder content, and silica fume (SF) and ground granulated blast furnace slag (GGBFS) as supplementary cementing materials (SCMs). The model results are found statistically equal to experimentally quantified chloride diffusion coefficient values and possess high accuracy and precision for a wide range of concrete mixture, binder composition and age.

A holistic model for cement paste and concrete chloride diffusion coefficient

Strength and durability are pivotal for designing sustainable concrete structures. For reinforced concrete, chloride diffusion coefficient is a measure for durability. Phenomenological multi-scale models are proposed for quantifying chloride effective diffusion coefficient of bulk cement paste (DClbcp) and concrete (DClc). DClbcp model accounts for w/cm, and cement composition and degree of hydration. DClc model comprises of DClbcp, tortuosity factor and ITZ, interfacial transition zone, factor sub-models. DClbcp and DClc models results are statistically equal with 95% confidence to the experimental values and possess high accuracy and precision for a wide range of w/cm, cement content and composition, aggregate content and age. DClbcp and DClc models' results have an overall error of 12% and 11%, respectively. The errors in DClc model results are less than the standard test method repeatability and the coefficient of variation of the reproducibility. DClc model standard error, COV and R2 values are 0.36 × 10−12 m2/s, 26% and 0.94.

Sustainability of Reinforced Concrete Beams with/without BF Influenced by Cracking Capacity and Chloride Diffusion

Concrete’s production causes pronounced environmental impacts. It is confirmed that adding basalt fiber (BF) into concrete can improve the mechanical properties and reduce the chloride diffusion coefficient of concrete. Moreover, research on the environmental impact of BF and its application in concrete has gradually emerged in recent years. However, there is little research on the chloride diffusivity of concrete structures with BF under the coupling interaction of external loads and chloride action. Therefore, at first, six beams were cast to obtain the depth-dependent chloride diffusivity of concrete under the coupling interaction of chloride penetration and 50% and 80% of the cracking capacity. Then, a functional unit (FU) combining durability, cracking capacity and volume was proposed to evaluate the sustainability of the concrete structure. In addition, three extra FUs (volume, considering volume and cracking capacity simultaneously and considering volume, cracking capacity and durability simultaneously) were also proposed and compared with the first FU. Results indicate that, regardless of the applied load level, the average chloride diffusion coefficient of a reinforced concrete (RC) beam with BF is larger than that of an ordinary RC beam. Moreover, the sorting of life cycle assessment (LCA) results will vary significantly with the different preset functional units. When taking the cracking capacity into consideration, adding BF into concrete is a suitable solution to improve the sustainability of RC beams.

Time-varying relationship between pore structures and chloride diffusivity of concrete under the simulated tidal environment

Based on an exposure test of concrete with admixtures under a simulated tidal environment, the free chloride ion concentrations at different exposure times were measured, the apparent chloride diffusion coefficients of test concrete were fitted with Fick’s second law, and the pore structural parameters of concrete at the same time were measured by mercury intrusion porosimetry (MIP). Based on the measured results, the time-varying characteristics of porosity and pore size distribution of test concrete have been analyzed, and the time-varying relationship between the proportion of different pore diameters as well as porosity and apparent chloride diffusion coefficients of concrete have been investigated. Results indicate that, under the simulated tidal environment, the total porosity and apparent chloride diffusion coefficient of test concrete have obvious time-variation, an obvious correlation between porosity and apparent chloride diffusion coefficient can be observed, i.e. both of them decrease with time. It has been found that the time-varying proportion of various pore diameters in concrete is different. Moreover, the correlation coefficients between contribution porosities of different pore diameters and apparent chloride diffusion coefficients show an increasing trend with exposure time.

Phenomenological model for quantifying concrete chloride diffusion coefficient

Compressive strength and the chloride diffusion coefficient are the common and principal performance measures for designing durable concrete structures. Hence, a comprehensive and consistent phenomenological model is put forward that quantifies a concrete’s effective chloride diffusion coefficient based on the tortuosity factor, aggregate volume fraction, porosity, cement paste chloride diffusion coefficient, compressive strength, cement and cementitious material content and composition. The phenomenological model results are first shown to depict chloride ion diffusion phenomena in porous media. Experimental data reported in the scientific literature are then used to evaluate visually and statistically the accuracy and precision of the phenomenological model. The model results are found statistically equal to experimentally quantified chloride diffusion coefficient values to a 95% confidence level and possess high accuracy and precision for a wide range of concrete mixture proportions, composition and age. The model average percent error is less than the standard test method repeatability and the COV of the reproducibility. The standard error, samples mean, and R2 are found to be 0.41 × 10−12 m2/s, 1.46 × 10−12 m2/s and 0.94, respectively.

Probabilistic prediction model for chloride diffusion coefficient of concrete in terms of material parameters

Abstract In order to provide an efficient way to describe the probabilistic characteristics of chloride diffusion coefficient, a probabilistic prediction model which involves both aleatory and epistemic uncertainties was developed for chloride diffusion coefficient of concrete in terms of material parameters. The influences of important material parameters including water-to-binder ratio as well as contents of fly ash and slag on chloride diffusion coefficient of concrete were investigated based on 295 sets of experimental data measured by the rapid chloride migration method. Then a deterministic prediction model for chloride diffusion coefficient of concrete was developed based on the stepwise regression method by taking into account the influences of the above material parameters. Subsequently, a probabilistic prediction model which involves both aleatory and epistemic uncertainties was developed for chloride diffusion coefficient of concrete based on the Bayesian theory and the Markov Chain Monte Carlo method (MCMC) method. Finally, the accuracy of the proposed probabilistic prediction model was validated by comparing with experimental data and traditional deterministic prediction models. Analysis results show that the proposed probabilistic prediction model not only provides a rational approach to describe the probabilistic characteristics of chloride diffusion coefficient by means of the probability density function and the cumulative distribution function, but also provides an efficient way to calibrate the accuracy of traditional deterministic prediction models based on the confidence interval and the confidence level.

Quantification of concrete chloride diffusion coefficient – A critical review

Most concrete deterioration mechanisms are driven by the transport properties, in particular the chloride diffusion coefficient. As such, experimental and analytical tools have been developed to quantify the chloride diffusion coefficient. This study employs the experimental data reported in the literature to critically assess the accuracy and precision of the proposed analytical models for quantifying the concrete chloride diffusion coefficient. The models assessed range in scope from statistical models to hybrid models with the former employing statistical tools to fit the response function and the latter combining material science, physical science and mathematical tools to characterize the property. The test data include two standard test methods, i.e. bulk diffusion and migration test, and consider the following mixture design variables: water to cement ratio, cement type and content, supplementary cementing material, aggregate volume fraction, and air content. The results revealed that hybrid models are more accurate and precise in comparison to the statistical model. The “best” model is found to account for the capillary porosity, pore structure parameters, solid phase diffusivity, cement paste diffusivity, aggregate volume fraction, and interfacial transition zone properties yielding a ±95% confidence predictability of 0.86 and 0.97 of the quantified diffusion coefficients. Cement paste thickness which controls the ratios of the various diffusion coefficients is found missing in all the models.