Accelerated Simulation of Chloride Ingress Into Concrete Under Dryingв

Construction and Building Materials 93 (2015) 205–213

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Construction and Building Materials

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Accelerated simulation of chloride ingress into concrete under drying–wetting alternation condition chloride environment ⇑ Zhiwu Yu a,b, Ying Chen a,b, Peng Liu a,b,c, , Weilun Wang c a School of Civil Engineering, Central South University, 22 Shaoshan Road, Changsha 410075, China b National Engineering Laboratory for High Speed Railway Construction, Central South University, Changsha 410075, China c School of Civil Engineering, Shenzhen University, 3688 Nanhai Road, Shenzhen 518060, China highlights

We investigate the chloride ingress into concrete under various environments. Acceleration factor is deﬁned to represent environmental effect on chloride ingress. We propose the chloride diffusion coefﬁcient model coupled with environment factors. Environmental conditions and concrete properties affect the acceleration factor. Numerical simulations and tests of chloride ingress are conducted. article info abstract

Article history: In this study, the chloride diffusion coefficient model coupled with environmental factors is proposed to Received 6 April 2015 describe chloride ingress into concrete. The S curve model is also presented to fit the error function. Received in revised form 3 May 2015 Simultaneously, field test and artificial simulated environment experiments are conducted. Moreover, Accepted 11 May 2015 some typical RC structures are used as examples to investigate the application of the aforementioned models in forecasting service life. The results show that the chloride diffusion in concrete can be described by an equivalent chloride diffusion coefficient model proposed in this study, and the measured Keywords: data of chloride content in concrete correspond with the fitted profiles. Under natural and artificial sim- Concrete ulated environments, the differences of chloride ingress into concrete are in terms of surface chloride Chloride Diffusion coefficient content, convective zone depth, and diffusion coefficient. Numerical simulation indicates that the accel- Acceleration factor eration factor can be considered as a key parameter to determine the correlation of chloride ingress into Accelerated simulation concrete under various environments. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction modeling of chloride transport or diffusion coefficient coupled with enhanced moisture conductivity, exposure time, and environ- Chloride attack is considered one of the most important factors mental factors in concrete exposed to marine environment. In that affect the service life of reinforced concrete (RC) structures addition, numerical modeling to predict the service life of RC struc- [1,2], and the degradation of RC because of chloride penetration tures exposed to chloride environments has been conducted has been a serious problem in civil engineering for many years [3,10,11]. Subsequently, a series of studies has been conducted to [3]. Substantial research has been conducted by experts all over address the influence of cracking on the penetration resistance or the world [4], such as Saetta and Nielsen et al. [5,6] who investi- permeability of concrete [12–14]. Although existing research has gated chloride diffusion into partially saturated concrete and pro- made astounding advances in the development of civil engineer- posed the chloride diffusion coefficient model. Song et al. [7] ing, deficiencies and divergence exist in test procedures, corrosion predicted the time to corrosion of steel in a reinforced concrete mechanics, and theoretical models, particularly with regard to the tunnel box exposed to seawater. Some studies [3,8,9] noted the interaction between the natural and accelerated tests. The accurate and short-term forecast of the service life of RC becomes a significant problem. ⇑ Corresponding author at: School of Civil Engineering, Central South University, Two approaches, namely, real test (or natural environment test) 22 Shaoshan Road, Changsha 410075, China. Tel.: +86 15116277646. and accelerated experiment, have been employed to investigate E-mail address: [email protected] (P. Liu). http://dx.doi.org/10.1016/j.conbuildmat.2015.05.090 0950-0618/Ó 2015 Elsevier Ltd. All rights reserved. 206 Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213 the influence of chloride on RC degradation. However, these two 2.2. Equivalent chloride diffusion coefficient in concrete coupled with approaches have limitations that are difficult to overcome; the environmental factors accelerated experiment frequently adopts electric current to achieve acceleration, which results in inconsistency of RC degrada- Chloride diffusion coefficient in concrete is a key parameter for tion with real cases [15–17], and the real test often consumes sig- the durability of marine RC structures, which characterizes the nificant amounts of time, manpower, and physical resource. To velocity of chloride penetrating into concrete. Environmental fac- overcome these deficiencies, the accelerated simulation test [or tors such as temperature (T) and relative humidity (RH) can make artificial simulated environment (ASE)] with advantages of reliable the chloride diffusion in concrete dominant; thus, these factors are results, good correlation, and obvious acceleration effect, has been significant in investigating their influence on the diffusion coeffi- improved. For example, the similarity of the chloride ingress in cient of chloride in concrete. In this study, the equivalent chloride concrete under natural and artificial simulated environments was diffusion coefficient De is assumed as a function of temperature, determined [18], and a statistical treatment was proposed to relative humidity, and exposure time. The coefficient is formulated establish a deterministic service life model of concrete structures as: in marine environments [19]. However, the researchers have not D ¼ D f f f ; ð4Þ reached a consensus on the major issues in the criteria for test pro- e app t T RH cedures and experimental parameters. where fT, fRH and ft indicate the influence coefficient of temperature, The goal of this study is to propose the accelerated simulation of RH and exposure time on the chloride diffusion coefficient, chloride ingress into a concrete under chloride environment. Based respectively. on theoretical analysis, the chloride diffusion coefficient coupled Existing research [21] proposes that the influence of RH on the with environmental factors is modeled. Moreover, the acceleration chloride diffusion coefficient can be written as: factor model between the natural and artificial simulated environ- "# 1 ments is established, thereby providing a novel approach to evalu- 1 h 4 f RH ¼ 1 þ ; ð5Þ ate the durability and forecast the service life of RC structures 1 hc under natural and artificial simulated environments.

where h represents relative humidity in concrete, and hc represents the critical relative humidity with a recommended value of 0.75. 2. Theoretical analysis Temperature has a double effect on chloride diffusion [22]. Based on Nernst–Einstein equation [5], the influence of tem- 2.1. Chloride diffusion in concrete perature on the chloride diffusion coefficient can be written as: Chloride transport can occur in concrete through several mech- q 1 1 T0 T1 anisms, including diffusion, absorption, migration, f T ¼ðT1=T0Þe ; ð6Þ pressure-induced flow, and wick action. Diffusion is the primary mechanism of chloride transport in a concrete under chloride envi- where T0 and T1 are reference temperature and service environmen- ronment with no applied electric field and stable moisture condi- tal temperature, respectively; q represents activation energy of tion of pore structure in concrete [17]. For a one-dimensional to hydration divided by gas constant, which relates to semi-infinite medium, Fick’s second law of diffusion is widely used water-to-cement ratio (W/C) such that if the W/C is 0.4, 0.5, and to evaluate the behavior of chloride transport in concrete [1,5,20], 0.6, then the value of q is equal to 6000, 5450, and 3850 K, respec- as written in Eq. (1). Given a constant diffusion coefficient, an ana- tively. The linear interpolation and extrapolation methods can be lytical solution of Eq. (1) can be expressed by Eq. (2) with proper used to determine the q value for different W/C. The diffusion coefficient is time-dependent because the process initial and boundary conditions (i.e., C(0,t) = Cs, C(1,t) = C0, C(x,0) = C0): of cement hydration results in connection and condensation of concrete pore structures [1]. The influence factor of time can be @C @ @C ¼ Dapp ; ð1Þ expressed as a decay function of the following equation: @t @x @x ( m ðtR=tÞ ; t 6 30 years "# ! f ¼ ; ð7Þ t ðt =t Þm: t > 30 years x Dx R lim Cðx; tÞ¼C0 þðCs C0Þ 1 erf pffiffiffiffiffiffiffiffiffiffiffi ; ð2Þ 2 Dappt where the limit time of the equation, tlim, is 30 years. tR and t are the reference time and actual exposure time, respectively. m represents where Dapp is the apparent chloride diffusion coefficient in concrete; the rate of diffusion coefficient decay and depends on the content of C(x, t) represents the chloride content as a function of position x and fly ash and slag, whose value is 0.25 for ordinary concrete and time t; Cs and C0 are the surface and initial chloride contents, reaches 0.6 for concrete with admixture. respectively; t is the exposure time; x is the depth from the concrete surface to the test position; and erf is the error function. The S curve model is derived to overcome the insufficient data 2.3. Acceleration factor of chloride ingress into concrete between and characterize the error function as follows: natural and artificial accelerated simulated environments

The accelerated simulation test can increase the chloride diffu- 1:6145 erf ðzÞ¼1:00958 ÀÁ; ð3Þ sion coefficient in concrete; thus, setting appropriate test parame- 1 þ exp z0:18526 0:37611 ters can reduce the chloride ingress time. Based on Eq. (2), if the ingress time for the chloride to reach the same content level at where z stands for the variable. the same depth can be determined, then the ingress time for the As mentioned, if the exposure time for the chloride content to natural and artificial simulated environments can be defined. reach a critical level near the reinforcement can be deduced, then Thus, the corresponding acceleration factor of chloride ingress into the service life of RC structures can be roughly forecasted. concrete can be written as: Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213 207 2 32 1 CC0 0.20 erf 1 Measured data t1 De2 4 Cs2C0 5 k ¼ ¼ ; ð8Þ Fitted curve 2 2 t D 1 CC0 C = 0.016, C = 0.21, D = 3.65E-6 mm /s, R =0.99 2 e1 erf 1 0 s Cs1C0 0.15 where k represents the acceleration factor of chloride ingress into concrete; t1 and t2 are the corrosion time of the chloride to reach 0.10 the same content at the same depth for the natural and artificial simulated environments, respectively. De1 and De2 are the equiva- 0.05 lent diffusion coefficients of chloride in concrete under the natural Chloride content /% and artificial simulated environments, respectively. Cs1 and Cs2 are the surface chloride contents of concrete under the natural and arti- 0.00 ficial simulated environments, respectively. 0 5 10 15 20 Depth /mm

3. Experimental procedure Fig. 1. Measured data and ﬁtting curve of chloride content in concrete.

3.1. Raw materials and mix proportions of concrete and equipments

Raw materials including local ordinary Portland cement with grade of PO 42.5, water reducing agent of polycarboxylic series, I type fly ash, S95 grade slag, local be determined by fitting the measured data with the least regres- 2 river sand, and water are used throughout the work. Local limestone as a coarse sion coefficient R of the plot in Fig. 1. aggregate with sizes ranging from 5 mm to 20 mm is also considered. Table 1 shows the mix proportion of concrete. 4.2. Distribution of chloride content in concrete under artificial simulated environment 3.2. Specimen casting and experimental procedures

The specimen production of concrete is in accordance with the Chinese stan- Based on the test procedure in Section 3.2, the distribution of dards of JTGE30-2005. The concrete specimens dimensions are the chloride content in concrete under an artificial simulated envi- 150 mm 150 mm 150 mm and 100 mm 100 mm 400 mm, and the speci- ronment is discussed, as illustrated in Fig. 2. mens are demolded after 24 h. Each sample is placed in saturated limewater to cure Fig. 2 depicts that the chloride content and ingress depth in con- at room temperature (i.e., 20 °C) for 28 days. The specimen’s surfaces for the tests are sealed except on one side. crete increase with time, and that the maximum value of chloride The temperature for a cycle of artificial simulation test is divided into three content and a convection zone on the concrete surface exist. The stages: at 40 °C for 30 h, 50 °C for 12 h, and 60 °C for 30 h. Simultaneously, accord- similarities and differences of various classes of concrete are ing to spraying water whether or not the cycle of test consists of wetting and drying observed in terms of chloride content extremum, convective zone process: the wetting process is carried out at 40 C for 50 min, and the last test time ° depth, and chloride content gradient, which are due to the chloride is drying process. Sodium chloride solution of the test is of 5%, and the corresponding test wind speed is 3 m/s. The samples are periodically taken out to measure the content gradient and diffusion coefficient resulting in chloride chloride content at different depth of concrete. transport and accumulation in concrete with time. The extremum Prior to the measurement, the specimen is initially milled by profile grinding of chloride content on the concrete surface is a result of the machine of PF1100 type to pass through 75 lm sieve. Then, the total and dynamic equilibrium between the chloride content transport from water-soluble chloride content is measured in accordance with the Chinese stan- dards of JTJ 270-1998. the external environment to the concrete surface and from the concrete surface to the inner concrete. Furthermore, the larger compressive strength of concrete indicates higher chloride content 4. Result and discussion in concrete surface because of the characteristics of concrete (i.e., microstructure and porosity that affect the diffusion coefficient), 4.1. Rationality of the S curve model to describe the chloride ingress and the initial saturation that influences the concentration gradi- into concrete ent of chloride. In other words, concrete generates a corresponding response to meet the artificial simulation condition. In the present To investigate the rationality of the S curve model and describe study, the artificial simulated environment test parameters are set the chloride ingress into concrete, we immerse the concrete sam- to meet the class C50 concrete; thus, the result of chloride ingress ples of class C50 into saturated limestone solution with 5% sodium into concrete is more reasonable for C50. chloride at 20 °C for 28 days and measure the chloride content in In the following section, the C50 concrete is used as an example concrete at different depths. Fig. 1 presents the measured data of to investigate the distribution of chloride content in concrete chloride content in concrete and the corresponding fitting curve. under the artificial simulated environment, as shown in Fig. 3a. Fig. 1 indicates that the chloride content in concrete decreases To simplify the solution, the chloride content in the convective with increasing depth, and the fitting curve of the chloride content zone is neglected. The fitted curves of the chloride content in con- accords well with the measured data, which implies that the S crete are plotted based on the initial chloride content C , which is curve model is suitable to characterize the error function of 0 approximately 0.016%, and the concrete surface chloride content C Fick’s second diffusion law. Based on the discussion in s is set as the maximum value of the measured data. Ref. [18] pro- Section 2.1, the chloride diffusion coefficient in concrete can also posed that a new equilibrium state of saturation in concrete can be generated to respond to the artificial simulated environment; Table 1 thus, the fitted curves of the chloride content of different concrete Mix proportion of concrete/(kg m3). conducted in the artificial simulated environment for 8 months are

Concrete Cement Slag Fly Sand Coarse Water Water also plotted, as shown in Fig. 3b. grade ash aggregate reducing As shown in Fig. 3a, the ﬁtted curves are consistent with the agent measured data of chloride content in concrete, and the chloride C20 220 65 60 780 1030 176 3.9 content in concrete increases with time, which implies that Fick’s C30 290 50 60 730 1050 164 4.2 second diffusion law is suitable to describe the change and distri- C40 355 70 45 710 1060 155 4.5 bution of the chloride content with time. Compared with Fig. 3a, C50 375 85 35 720 1085 152 5.0 Fig. 3b indicates that the measured data of the chloride content 208 Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213

0.8 initial saturation, the convective zone depth for various classes of Measured data for 3 months concrete also differs. Measured data for 4 months To investigate the difference of the chloride diffusion coefficient 0.6 Measured data for 8 months in the artificial simulated environment with time, we discuss the change of the chloride diffusion coefficient with time based on 0.4 Fick’s second diffusion law and the fitted results, as plotted in Fig. 4. Fig. 4 illustrates that the chloride diffusion coefficient decreases 0.2 with time and tends to be constant when the time exceeds a cer- Chloride content /% tain period (i.e., 4 months), which may be due to the artificial sim- 0.0 ulated environment providing a high temperature and relative 0 5 10 15 20 humidity condition that increases the hydration of cementitious Depth /mm and concrete density. Moreover, the chloride diffusion coefficient (a) C50 varies for different classes of concrete. For class C30 concrete, the change of the chloride diffusion coefficient differs from that of 0.8 the others because it is caused by the saturation change in con- Measured data for 3 months crete. More water transports into concrete with time and changes Measured data for 4 months Measured data for 8 months the initial saturation of concrete; thus, a new equilibrium state 0.6 appears and the chloride diffusion coefficient changes.

0.4 4.3. Distribution of chloride content in concrete under natural environment 0.2 Chloride content /% To establish the correlation of chloride content in concrete between the natural and artiﬁcial simulated environments, we 0.0 measure the change of chloride content in concrete of a trestle 051015202530 under a natural marine environment, as shown in Fig. 5. The eleva- Depth /mm tion is converted from near zero at Zhujiang Harbor, and the ﬁtted (b) C40 curves of the chloride content in concrete are based on the measured data in the diffusion zone. Fig. 6 shows the distribution of 0.8 the chloride content in concrete for different RC structures. Measured data for 3 months Measured data for 4 months 0.6 Measured data for 8 months 0.8 Measured data for 3 months Measured data for 4 months 0.4 Measured data for 8 months Fitted curve of measured data for 3 months 0.6 Fitted curve of measured data for 4 months Fitted curve of measured data for 8 months 0.2 Chloride content /% 0.4 0.0

0 5 10 15 20 25 0.2

Depth /mm /% Chloride content (c) C30 0.0 0.8 0 5 10 15 20 Measured data for 3 months Depth /mm Measured data for 4 months Measured data for 8 months (a) C50 0.6

0.75 Measured data of C40 Fitted curve of measured data of C40 0.4 Measured data of C30 Fitted curve of measured data of C30 Measured data of C20 Fitted curve of measured data of C20

0.2 0.50 Chloride content /%

0.0 0 5 10 15 20 25 0.25 Depth /mm Chloride content /% content Chloride (d) C20

0.00 Fig. 2. Distribution of chloride content in concrete under artificial simulated 0 5 10 15 20 25 environment. Depth /mm (b) C20, C30 and C40 in concrete corresponds with the fitted profiles when the test is conducted for more than a certain period (i.e., 8 months). Fig. 3. Fitted curves and measured data of chloride content in concrete under Furthermore, because of the different concrete properties and artificial simulated environment. Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213 209

2.0 0.18 Fitted curve of measured data of upper bridge road

-1 Measured data of C20 -8 2 2 s

· C D x t s =0.114, = 6.25 10 mm /s, = 12a, R = 0.94. 2 Measured data of C30 Measured data of C40 Fitted curve of measured data of small bridge road C =0.127, D = 1.35x 10-8mm2/s, t = 18a, R2= 0.97. /mm Measured data of C50 s -6 1.5 Measured data of upper bridge road 0.12 Measured data of small bridge road

1.0 0.06 Chloride content /% 0.5

Diffusion coefficient 10 0.00 345678 0 5 10 15 20 25 30 Time /month Depth /mm (a) Upper Bridge Road and Small Bridge Road Fig. 4. Curves of chloride diffusion coefﬁcient in concrete under artiﬁcial simulated environment. 0.18 Fitted curve of measured data of Nanshui Bridge C = 0.12, D = 1.47x 10-8mm2/s, t = 27 a, R2=0.91. s Fitted curve of measured data of Jiti Gate Bridge 0.90 C = 0.137, D = 2.17x 10-8mm2/s, t = 21 a, R2=0.93. Measured data at 1.1 m and fitted curve s Measured data at 1.6 m and fitted curve 0.12 Measured data of Nanshui Bridge Measured data of Jiti Gate Bridge Measured data at 2.1 m and fitted curve 0.75 Measured data at 2.6 m and fitted curve Measured data at 3.1 m and fitted curve 0.60 0.06

0.45 /% content Chloride

0.00 Chloride content /% 0.30 0 5 10 15 20 Depth /mm 0.15 (b) Nanshui Bridge and Jiti Gate Bridge 0 5 10 15 20 25 Depth /mm

0.04 Fitted curve of measured data of First Road Fig. 5. Curves of chloride content in concrete of a trestle. C D x -8 2 t 2 S = 0.016, = 8.88 10 mm /s, = 6 a, R =0.95. Fitted curve of measured data of Second Road C = 0.018, D = 9.09x 10-8 mm2/s, t = 11 a,R2=0.95. 0.03 S Fitted curve of measured data of Third Road Fig. 5 indicates that the chlorine content of a trestle concrete -8 2 2 C D x t S= 0.025, = 4.56 10 mm /s, = 15 a, R =0.99. signiﬁcantly varies with height. When the concrete is in a tidal Measured data of First Road 0.02 Measured data of Second Road or shallow zone near sea level (i.e., 1.1 m), the chlorine content Measured data of Third Road within a certain depth from the concrete surface is generally stable and has a maximum value. When the concrete structure is in a

Chloride content /% 0.01 tidal or splash zone (i.e., 1.6 m), the distribution of the chlorine content in concrete follows a trend similar to the aforementioned except that the chloride convection zone and the chlorine content 0.00 increase, which can be explained by the saturation of the concrete 05101520 surface by capillary action during the wetting process. At the orig- Depth /mm inal drying period of ebb tide, the chloride solution in concrete can (c) First Road, Second Road, and Third Road be transported toward the surface, and the water in pores can evaporate because of concentration, hysteresis, and crystallization Fig. 6. Curve of chloride content in concrete under natural environment. effects, which leads to a bilateral diffusion of chloride in concrete. With the drying and wetting cycles, the convection depth and sea fog and wind decreases with an increase in distance. chloride content increase. These situations only occur in concrete Furthermore, Fig. 6 shows that when the depth is more than a cer- surface because of the blocking effects of the concrete pores and tain level from the concrete surface, the fitted curves of the chlo- the hysteresis effects of chloride diffusion. Therefore, Fick’s diffu- ride content correspond well with the measured data, which sion law remains suitable to characterize regularity. When the con- implies that Fick’s second diffusion law is suitable to represent crete locates in the atmosphere zone (i.e., 2.1, 2.6, and 3.1 m), the the chloride content distribution in concrete under a natural chloride content in a larger zone of the concrete surface is gener- environment. ally constant because of the chloride in the atmosphere zone, which mainly comes from ocean fog, rain, and atmosphere. Fig. 6 implies that all of the RC structures have the convective 4.4. Correlation analysis of chloride ingress into concrete between zone and maximum value of chloride, and the corresponding val- natural and artificial simulated environments ues relate to the build time and the distance from the seaside; the chloride content in concrete surface increases with build time Although the rationality of the chloride diffusion coefficient because of increased chloride transport and accumulation in con- model has been verified, establishing the correlation analysis of crete over time [23]. Simultaneously, the corresponding chloride chloride ingress into concrete between natural and artificial simu- content in concrete surface increases with the distance from the lated environments is necessary. Thus, the following section uses seaside because the chloride resource of the atmosphere from Changsha as an example to discuss the correlation. The average 210 Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213

monthly temperature and relative humidity rates based on the 1.20 In artificial simulation environment for 0.5 a meteorological data of Changsha from 2000 to 2010 are listed in In artificial simulation environment for 1 a In artificial simulation environment for 5 a Table 2. 0.96 In artificial simulation environment for 10 a In natural environment for 10 a In Table 2, this study divides the months into the following In natural environment for 20 a three stages based on temperature: low temperature stage (i.e., In natural environment for 50 a 0.72 In natural environment for 100 a the average temperature in January, February, March, November, and December is approximately 10 °C), normal temperature stage 0.48 (i.e., the average temperature in April and October is approximately 20 °C), and high temperature stage (i.e., the average tem- Chloride content /% 0.24 perature in May, June, July, August, and September is approximately 30 °C). Thus, the time ratio for the entire year based 0.00 on the average monthly temperature can be set to 5:2:5. As known, 20 40 60 the chloride activity is enhanced with an increase of temperature, Depth /mm which results in an increase of the chloride diffusion coefficient. To (a) Depth-dependent curves achieve high acceleration, the artificial simulated environment test should choose the high temperature. However, an extremely high 1.44 Cover of 10 mm in ASE Cover of 10 mm in NE temperature can induce the decomposition of hydration and lead Cover of 20 mm in ASE Cover of 20 mm in NE Cover of 30 mm in ASE Cover of 30 mm in NE to failure of the experiment. Therefore, the experimental tempera- Cover of 40 mm in ASE Cover of 40 mm in NE 1.20 Cover of 50 mm in ASE Cover of 50 mm in NE ture should be controlled below 60 °C. Based on Eqs. (4–6), the fea- sible experimental temperature is set, as listed in Table 2, which 0.96 can ensure that the chloride diffusion coefficient is enhanced 9 times in the artificial simulated environment. 0.72 In the following section, C50 is used as an example to discuss 0.48 the numerical simulation to demonstrate the application of the Chloride content /% aforementioned result. The measured density and porosity of con- 0.24 crete is approximately 2.4 g/cm3 and 10%, respectively. The critical chloride content in concrete pore is assumed to be 0.00 [Cl]/[OH] = 0.8, and the pH value of the solution in the concrete 0306090120 Time /a pore is 13 [24]. Thus, the corresponding critical chloride content in (b) Time-dependent curves concrete is approximately 0.12% by mass of concrete, which is within the recommended range in Ref. [25]. Fig. 7. Curves of chloride content with depth and time in concrete at splash zone. Owing to the 5% sodium chloride solution used throughout the test, the corresponding conversion concrete surface chloride content is about 1.25% by mass of concrete. The measured salt content Fig. 8 shows that the chloride content reaches a maximum of seawater is approximately 2.9%, which can be considered as the value at approximately 5–18 mm inside the concrete. Beyond this sodium chloride solution content. Thus, the chloride content in depth, the chloride content decreases with an increase in the depth concrete is about 0.862% by mass of concrete. Fig. 7 shows the from the exposed surface, and the change trend and distribution of curves of chloride content with depth and time in concrete at the chloride content in concrete can be determined based on the splash zone. measured data. Moreover, Fig. 8 indicates that the chloride content Numerical simulation results in Fig. 7 show that the chloride in concrete decreases with altitude, but its value tends to be con- content in concrete decreases with depth and increases with time. stant when the altitude is more than a certain value, i.e., 3.1 m. Moreover, because of the differences in chloride diffusion coeffi- This study also investigates the chloride content change in con- cient, the change trend and ratio of various classes of concrete crete for other typical RC structures, as shown in Fig. 9. appear different. Fig. 7 also indicates that the chloride content at Fig. 9 indicates that the chloride content in concrete of the typ- different depths and time can be accurately determined by numerical RC structures in the atmospheric zone follows a similar trend ical simulation. as the trestle: its value decreases with depth and increases with The typical RC structures in the natural environment are used as time. A convective zone and maximum chloride content in the con- examples to simulate the chloride content change in concrete and crete cover exist because the chloride source is mainly sea fog, verify the rationality of the results in actual structures. All the wind, and water. Moreover, the surface chloride content in con- parameters of numerical simulation (i.e., convection zone depth, crete reaches an equilibrium state when the exposure time exceeds initial and surface chloride content in concrete, and diffusion coef- a certain period. For different RC structures, the critical chloride ficient) are determined from Section 4.3. Fig. 8 shows the curves of content values and diffusion coefficients are different, which can chloride content with depth in concrete of a trestle. relate to the microstructure and porosity of concrete [26]. Fig. 10

Table 2 Meteorological data of Changsha and the simulated temperature for test.

Stages Low temperature Normal High temperature temperature Nov. Dec. Jan. Feb. Mar. Apr. Oct. May. Jun. Jul. Aug. Sep. Items Temperature/°C 13.2 7.4 5.2 7.9 12.9 18.2 19.1 23 26.7 29.8 28.3 24.6 RH/% 72.3 72.5 74.9 75.5 72.6 73.1 73.4 73.1 75.7 72.1 74.5 72.8 Average temperature/°C 9.3 18.6 26.5 Temperature for acceleration of 9 times/°C 39.1 50.45 60.15 To set temperature/°C405060

Noted: Data is from the meteorological data sharing service network of China: http://cdc.cma.gov.cn/shuju. Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213 211

0.8 shows the curves of porosity and pore distribution for typical RC 1.1m structures. Measured data Simulaiton curve for 12 a Fig. 10 indicates that the curves of the pore structure for typical Simulaiton curve for 20 a RC structures are different in porosity, distribution, and curve 0.6 Simulaiton curve for 50 a Simulaiton curve for 100 a shape. The porosity of the trestle is the maximum, whereas that of Nanshui Bridge is the minimum. Simultaneously, two peak 0.4 zones divided by pore distribution exist, i.e., the macro pore ranges from 100 lm to 200 lm, and the micro pore ranges from 70 nm to 100 nm. Moreover, Fig. 10 implies that the ratio of the macro pore

Chloride content /% content Chloride of Nanshui Bridge is the least and the pore type is nearly micro 0.2 pore, which indicates a lower chloride diffusion coefﬁcient.

0204060 Depth /mm 4.5. Application of accelerated simulation test in forecasting the service life of RC structures in marine environment (a) 1.1 m Based on the preceding discussion, two typical RC structures in 0.50 marine environment are used as examples to investigate the appli- Measured data Simulation curve for 12 a cation of accelerated simulation test in forecasting service life. Simulation curve for 20 a The first example is Jiti Gate Bridge in Zhuhai, China. Based on Simulation curve for 50 a Simulation curve for 100 a the data processing method in Table 2, the meteorological data of Zhuhai from 2000 to 2010 and the corresponding simulated temperature for the test are listed in Table 3. This study divides the months into three stages based on tem- 0.25 perature, as listed in Table 3. The sodium chloride solution is set to 5% and the measured chloride diffusion coefficient of the con- 13 2 Chloride content /% content Chloride crete is 2 10 m /s. The surface chloride content, initial chloride content, and critical chloride content values are 0.2%, 0.006%, and 0.06%, respectively. The numerical simulation is used to deter- 0204060 Depth /mm mine the chloride content in concrete with time under natural and artificial simulated environments, as shown in Fig. 11. Table 4 pre- (b) 3.1 m sents the service life parameters of Jiti Gate Bridge under natural

Fig. 8. Curves of chloride content with depth in concrete of a trestle. and artiﬁcial simulated environments.

0.08 0.16 Trestle Measured data -1 Second road Simulation curve for 12 a . Nanshui bridge Simulation curve for 20 a 0.06 0.12 Simulation curve for 50 a /mL g Simulation curve for 100 a

0.04 0.08

0.02 0.04 Chloride /% content

Cumulative pore volume pore Cumulative 0.00 0.00 10 100 1000 10000 100000 0 204060 Depth /mm Pore diameter /nm (a) Upper Bridge Road (a) Curves of porosity

0.16 0.008 0.003 Measured data Trestle Simulation curve for 21 a Second road Simulation curve for 50 a Nanshui bridge Simulation curve for 100 a Trestle 0.12 1 0.006 - Simulation curve for 120 a Second road g dV/dD /mL . Nanshui bridge 0.002

0.08 0.004 .

0.001 -1 . 0.04 0.002 nm -1 Pore volume /mL volume Pore g Chloride content /% content Chloride

0.00 0.000 0.000 0204060 10 100 1000 10000 100000 Depth /mm Pore diameter /nm (b) Jiti Gate Bridge (b) Curves of pore distribution

Fig. 9. Curves of chloride content with depth in typical RC structures. Fig. 10. Curves of porosity and pore distribution for typical RC structures. 212 Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213

Table 3 Meteorological data of Zhuhai and simulated temperature for test.

Stages Low temperature Normal High temperature temperature Nov. Dec. Jan. Feb. Mar. Apr. Oct. May. Jun. Jul. Aug. Sep. Items Temperature/°C 21 17.5 15.6 16 19.2 23.3 24.8 26 28 28.3 28.3 27.1 RH/% 68 67 73 79 85 85 72 85 85 85 84 79 Average temperature/°C 17.86 24.05 27.54 Temperature for acceleration of 6 times/°C 46.8 54.3 58.5

Noted: Data is from the meteorological data sharing service network of China: http://cdc.cma.gov.cn/shuju.

Table 6 0.18 Cover of 30 mm in ASE Parameters of service life for Hangzhou Bay Bridge. Cover of 40 mm in ASE Cover of 50 mm in ASE Item Corrosion time Corrosion time for k Cover of 30 mm in NE for natural accelerated Cover of 40 mm in NE environment/a simulation test/a 0.12 Cover of 50 mm in NE Component Box girder 275 7.96 34.5 Pier 165 11.5 14.3 Platform 379 26.4 14.3 0.06 Pile foundation 145 10.1 14.3 Chloride content /% content Chloride

0.00 show that the acceleration factor can be accelerated to 22 times by 0 2 4 6 8 25 50 75 100 125 150 the artiﬁcial simulated environment test. Time /a The second example is Hangzhou Bay Bridge in Hangzhou,

Fig. 11. Chloride content in concrete with time under natural and artiﬁcial China. Ref. [27] proposed the parameters of concrete for this simulated environment. bridge. In conservative terms, the average seawater salinity of 10.79 g/L is considered as sodium chloride solution, i.e., 0.66% by mass of concrete. Thus, the surface and critical chloride content

Table 4 values of concrete in the splash zone are set to 0.66% and 0.12%, Parameters of service life of Jiti Gate Bridge. respectively. Based on the data processing method in Table 2, the meteoro- Item Corrosion time for natural Corrosion time for accelerated k environment/a simulation test/a logical data of Hangzhou and the corresponding simulated temperature for test are listed in Table 5. Cover/mm 30 43 1.95 22.05 Based on Eq. (6), the diffusion coefficient can be accelerated to 40 84 3.8 22.11 9 times by increasing the temperature. Under the artificial simu- 50 139 6.3 22.06 lated environment, if the sodium chloride solution is set to 5%, then the measured chloride diffusion coefficient is determined by Eq. (6) and Table 5. Simultaneously, the critical chloride contents for dif- The numerical simulation curves in Fig. 11 indicate that the ferent positions are set to 0.06% and 0.12%, respectively. The chloride content in concrete increases with time, and the time numerical simulation method is used to determine the chloride for the chloride content to reach the critical level varies for differ- content in concrete with time under natural and artificial simu- ent concrete covers. Table 4 indicates that the corrosion time of lated environments, and the results are listed in Table 6. chloride ingress into the concrete cover under the artificial simu- Table 6 indicates that the acceleration factor of different com- lated environment test differs from that of the natural environ- ponents between the natural and artificial simulated environments ment. Service life predictions can be obtained by assuming that varies: for the components in the atmospheric region, i.e., the box the chloride content of an RC structure exposed to chloride envi- girder, the acceleration factor k is 34.5, and for the other compo- ronment corresponds to the period until the critical value is nents, this factor is about 14.3. The difference in the acceleration reached. Based on the comparison of the chloride ingress into con- factor may be due to the environmental condition and concrete crete under natural environment, the numerical simulation results properties that result in different chloride diffusion coefficients.

Table 5 Meteorological data of Hangzhou and simulated temperature for test.

Stages Low temperature Normal High temperature temperature Nov. Dec. Jan. Feb. Mar. Apr. Oct. May. Jun. Jul. Aug. Sep. Items Temperature/°C 12.4 6.8 4.3 5.7 9.6 15.8 18.3 20.7 24.4 28.4 27.9 23.4 RH/% 74 72 76 76 78 76 78 76 82 78 79 81 Average temperature/°C 7.76 17.05 24.96 Temperature for acceleration of 9 times/°C 38.3 49.7 59.5

Noted: Data is from the meteorological data sharing service network of China: http://cdc.cma.gov.cn/shuju. Z. Yu et al. / Construction and Building Materials 93 (2015) 205–213 213

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