1. Introduction
In recent years, winter maintenance strategies in the US have undergone a significant shift from passive deicing to proactive anti-icing practices [
1,
2]. When applied in a timely manner, anti-icing prevents the formation of bonded snow and ice, yielding superior results compared to deicing while also reducing costs [
3,
4,
5]. Salt brine, often combined with calcium chloride (CaCl
2) or magnesium chloride (MgCl
2), is the most favored by many state departments of transportation (DOTs), due to its effectiveness in ice control. The inclusion of CaCl
2 in NaCl brine enhances its performance at temperatures below 15°F by attracting moisture and maintaining surface adherence [
6]. Similarly, MgCl
2-NaCl solutions exhibit comparable efficacy, though their use is more restricted due to their potential to create dangerously slippery conditions [
7]. While non-chloride deicers have attracted attention for their reduced corrosiveness and superior performance, their limited availability in large volumes and the need for specialized storage systems render them impractical and cost prohibitive. On average, non-chloride deicers cost approximately USD 2.50 per gallon, whereas conventional salt brine solutions containing CaCl
2 or MgCl
2 range from USD 0.40 to USD 0.50 per gallon [
8]. As a result, chloride-based deicers remain the preferred choice, particularly as modern anti-icing strategies continue to improve service levels, reduce overall deicer usage, and offer significant cost savings, safety enhancements, and mobility benefits [
9].
Despite their benefits, chloride-based deicers pose substantial challenges to the durability of concrete pavements [
10,
11]. The detrimental effects of these deicers, including cracking, scaling, and reinforcement corrosion, have been reported globally and extensively studied [
12,
13,
14]. Research has identified multiple mechanisms underlying these forms of deterioration. The crystallization of salts within concrete pores disrupts pore structures, leading to scaling and spalling [
15]. Hydraulic pressure from freeze–thaw (F-T) cycles exacerbates cracking, unless it is mitigated by well-connected pores [
16]. Also, prolonged exposure to salt brine dissolves calcium hydroxide in cement paste, increasing porosity and compromising F-T durability through the formation of chloroaluminate crystals [
17]. Furthermore, CaCl
2 and MgCl
2 initiate chemical reactions that weaken concrete, with CaCl
2 forming expansive calcium oxychloride and MgCl
2 inducing porosity through the formation of brucite and magnesium–silicate–hydrate (M-S-H) compounds [
18,
19,
20,
21]. Chloride ions further elevate corrosion risks in steel-reinforced concrete when specific temperature and humidity conditions are met [
22].
The potential for chloride penetration into concrete pavement is highly influenced by the characteristics of concrete pores, including pore size distribution, interconnectivity, and the chemical composition of the pore solution [
23]. These properties are largely governed by the water-to-cement (w/c) ratio, cement type, and the inclusion of supplementary cementitious materials (SCMs) and chemical admixtures. Combined with curing conditions, these parameters influence the permeability of concrete to chloride ions. While concrete pores play a crucial role in mitigating F-T damage, their complex interactions with chloride penetration remain poorly understood. Nili et al. provided insights into the effects of entrained air on concrete resistivity, but its interactions with chloride diffusion mechanisms require further investigation [
24]. SCMs—particularly fly ash—are widely recognized for their role in reducing chloride ingress. Class C fly ash, rich in calcium oxide (CaO), enhances early-stage strength development, while Class F fly ash, with higher silica (SiO
2) content, promotes long-term pore refinement through pozzolanic reactions. The concentration and type of cations present in brine solutions, such as Na
+ and Ca
2+, can significantly affect chloride binding capacity and transport mechanisms, introducing further complexity in predicting chloride ingress [
25].
Various models have been proposed to predict chloride ingress potential, ranging from empirical diffusion models to sophisticated multi-phase transport models, in cement mortars or concrete mixes with SCMs [
26,
27,
28,
29,
30,
31]. A pivotal advancement in this area was Andrade’s introduction of the reaction factor in electrical resistivity-based chloride ingress models, which has been widely applied as an effective tool in characterizing microstructural variations in cementitious systems [
32,
33]. A recent study by Pablo-Calderón et al. showed a relationship between electrical resistivity and chloride ingress in cement-based materials [
34]. Similar research works by Geng et al. and Kang et al. demonstrated the influence of fly ash on electrical resistivity in concrete, further reinforcing the role of concrete resistivity as a key durability indicator [
35,
36]. Raczkiewicz et al. reported that blast-furnace slag cement and an air-entraining agent provided the best protection against corrosion in reinforced concrete [
37]. Chloride ingress appeared to be influenced by various mix variables, such as recycled aggregates, expanded clay, lightweight mortars, and sand content [
38,
39,
40,
41,
42]. Despite these developments, the combined effects of key mix variables on chloride ingress remain underexplored, especially in chloride-rich environments. Furthermore, little research has investigated the long-term behavior of chloride ingress in the absence of F-T cycles, a critical condition for regions where chloride-based deicers are used under milder winter conditions without frequent freezing. Consequently, many winter roadway maintenance practices still rely on empirical observations, leading to the overuse of deicers and premature concrete deterioration.
To address these gaps, this study explores the synergistic impact of entrained air and fly ash on chloride ingress in concrete pavements. Three-year ponding tests were conducted on laboratory specimens in both indoor and outdoor settings. Surface resistivity measurements were utilized to refine the reaction and aging factors in Andrade’s resistivity model and to develop an alternative predictive tool for corrosion initiation in concrete pavements. The findings provide actionable insights for both state and local road agencies, enabling optimized deicer selection and application strategies to mitigate premature pavement deterioration. This research lays the foundation for developing performance-based approaches to winter roadway maintenance, offering critical infrastructure management challenges.
3. Results and Discussion
3.1. Concrete Resistivity of Water-Saturated Concrete Samples
The evolution of concrete resistivity is crucial for understanding pore refinement and its impact on chloride ingress. Previous studies have indicated that SCMs, particularly fly ash, can significantly alter the pore structure of concrete, influencing its electrical resistivity and durability [
35,
36].
Figure 5 shows the trends of conductivity (i.e., inverse of resistivity) in water-submerged concrete samples. Each data point represents the average value of six measures from six samples per batch. The standard deviations ranged from 0.1 kΩ-cm to 10 kΩ-cm. Concrete resistivity increased over time, reflecting continuous pore refinements in the concrete. Overall, the rate of resistivity increase depended on the volume of entrained air and the fly ash type. Class F fly ash samples exhibited a moderate resistivity increase, which is consistent with the delayed pozzolanic activity that gradually refines pore structures. The maximum percentage increase in this group was approximately 1064% (9.8 kΩ-cm to 114.8 kΩ-cm) at 0% entrained air (sample ID: 0.0%-F). In the samples with Class C fly ash, lower entrained air content led to faster resistivity gain, likely due to denser hydration products and reduced pore connectivity. The maximum percentage increase in resistivity was about 1608% (11.9 kΩ-cm to 203 kΩ-cm) at 2.7% entrained air (sample ID: 2.7%-C), indicating a resistivity growth rate about 1.5 times that of the Class F fly ash samples.
A previous study by Andrade and D’Andrea proposed an aging factor (q) in the concrete resistivity model to characterize the complexity of pore refinement [
46]. With a q value of 0.57 suggested for Type II/A-V cement (12% Ordinary Portland Cement + 8% fly ash), the time-corrected resistivity (ρ
t) computed at t and t
0 for each sample was compared to the measured effective resistivity in the figure. This comparison revealed that this empirical q value does not fully account for the observed trends and variability of effective conductivity (ρ
ef).
This study developed a simple procedure for defining the relationship between the aging factor and the volume of entrained air for each class of fly ash. First, individual aging factors were derived from the power terms of regression lines fitted to the data. These individual regression lines were not included in the figure for simplicity. Second, the obtained aging factors were correlated with the corresponding entrained air contents using a linear function for each class of fly ash. The resulting coefficients of determination (r-squared values) for two linear functions were 0.833 for Class C fly ash and 0.880 for Class F fly ash. With the newly proposed aging factors, the resistivity ρ
t in Equation (6) was calculated (i.e., calculated resistivity) and then compared again to the measured effective resistivity (ρ
ef).
Figure 6 shows the measured and calculated conductivity (inverse of resistivity). Notably, a robust strong correlation was observed between the calculated and measured resistivity values for the samples containing Class C fly ash (r
2 of 0.9623) and those containing Class F fly ash (r
2 of 0.9832) across the entrained air contents. These findings underscore the necessity of incorporating entrained air volume into resistivity-based chloride ingress models, extending Andrade’s approach to accommodate pore refinement unique to air-entrained concrete [
32,
33].
3.2. Chloride Ingress in Concrete—Phenomenological Observation
The outdoor ponding test provided insights into the progression of chloride ingress in concrete. Apparent resistivity, representing the electrical resistivity of the concrete samples soaked in brine, emerged as a key indicator of chloride ingress.
Figure 7 shows the average apparent resistivity that varies with the chloride concentration of the brine solution the concrete samples were submerged in. The number of sample replicates was six for each of the entrained air–fly ash groups. To account for the temperature dependency of resistivity, the resistivity readings were adjusted to a reference temperature of 25 °C [
60].
During the initial exposure period (approximately 45 days), the concrete resistivity increased as the chloride concentration rose from the pure salt solution (3.99 mol/L) to a blend of CaCl2 and NaCl. This trend suggests that chloride transport dominates chloride ingress in the early stages.
A shift in trend was observed at around 200 days, as resistivity began to decrease with the increasing chloride concentration. This phenomenon likely resulted from active chloride binding. The reduced resistivity coincided with the presence of highly conductive chloride ions in the concrete pores, potentially due to the accumulation of chlorides within the cement hydrates. Chloride binding appeared more pronounced in the NaCl-CaCl
2 blends than in the pure salt solutions, confirming higher chloride binding propensity for bivalent cation (Ca++), as reported in a previous study [
25]. As chloride binding progressed, fewer free chloride ions were available for ingress, slowing the overall rate of chloride ingress.
By the end of the ponding tests (around 890 days), the apparent resistivity stabilized at levels below the high-risk chloride ingress threshold of 12 kΩ-cm (see
Table 5) in the CaCl
2 and NaCl blends. This illustrates that the chloride binding had reached an equilibrium. At this stage, the risk of reinforcement corrosion and concrete erosion was at its peak due to the saturation of binding sites.
These observations highlight the utility of apparent resistivity in assessing the degree and rate of chloride ingress at different chloride concentrations. However, apparent resistivity alone may not fully capture the impact of entrained air and fly ash class on chloride ingress, nor does it account for pore refinement in concrete. Furthermore, the degree and rate of chloride ingress are affected by chloride binding with the cement matrix and the transport of unbound chloride ions through concrete pores. While chloride binding is crucial in characterizing chloride ingress, direct atomic level measurements lie beyond the scope of this study. Instead, a novel approach was proposed to evaluate its contribution to chloride ingress using a chloride reaction factor, which connects apparent and effective resistivity.
3.3. Impact of Entrained Air and Fly Ash on Chloride Ingress—Chloride Reaction Factor Approach
The reaction factors (r
Cl) were calculated by the ratio of ρ
ap to ρ
ef (r
Cl = ρ
ap/ρ
ef), where ρ
ap was measured at six to seven designated times for up to 1200 days, while ρ
ef represents the porosity of each sample at 28 days. As long as concrete is fully saturated in either brine or water, this method of obtaining r
Cl is theoretically valid and offers practical benefits, as it does not require the performance of multi-regime tests for diffusion coefficients (D
ap and D
ef), which often yield a wide data scatter [
61].
Figure 8 shows the variation in the reaction factors that were temperature corrected and then averaged over the duration of the ponding tests. Like the snapshot of apparent resistivity ρ
ap observed in
Section 3.2, reaction factors exhibit an exponential decay across the range of chloride concentrations. At low chloride concentrations, the reaction factors are relatively high, as chloride ions are effectively bound or immobilized within the concrete matrix through mechanisms such as physical adsorption on the C-S-H gel, chemical reactions with the cementitious phases, or incorporation into hydration products [
25]. However, as the chloride concentration increases, the availability of binding sites within the concrete matrix becomes scarcer, leading to a reduction in chloride binding efficiency. Once all available binding sites are fully occupied, further increases in chloride concentration rarely impact chloride binding.
Both entrained air and fly ash play a crucial role in this process. The reaction factors decrease as entrained air increases. Specifically, at each chloride concentration, the reaction factors are up to 67% higher in the concrete samples with 3.5% entrained air compared to those with 10.0% entrained air. This suggests that lower entrained air contents enhance chloride binding efficiency in concrete. Across all chloride concentrations, the reaction factors decreased by 57% in the Class F fly ash samples and 41% in the Class C fly ash samples across all chloride concentrations. However, in the pure NaCl solutions, Class F fly ash exhibited a stronger impact on reaction factors compared to Class C fly ash. This is attributed to its higher silica content in Class F fly ash, which enhances the formation of calcium–silicate–hydrate (C-S-H) gel through extended pozzolanic reactions with calcium hydroxide (Ca(OH)
2) in the concrete matrix [
62]. The C-S-H gel improves pore refinement and increases chloride binding capacity by trapping chloride ions, thereby reducing their mobility and ingress. In addition, the finer particle size and the higher pozzolanic reactivity of Class F fly ash contribute to a denser microstructure, limiting chloride permeability by reducing capillary pore connectivity, particularly in environments dominated by NaCl solutions. In contrast, Class C fly ash, with a higher calcium oxide content, promotes rapid pozzolanic reactions that lead to the early densification of the concrete matrix. However, this may result in a coarser pore structure over time, which can allow for greater chloride ingress compared to Class F fly ash.
It is worth noting that the mitigating effect of Class F fly ash on chloride ingress diminishes when exposed to NaCl-CaCl
2 blends. This reduction is likely due to increased competition for chloride binding sites within the C-S-H gel, as calcium ions (Ca
2+) have a stronger binding affinity than sodium ions (Na
+). The presence of Ca
2+ can alter the chloride binding capacity of the C-S-H gel, favoring the formation of calcium-rich phases, which may reduce the gel’s ability to sequester chloride ions effectively. These findings indicate that the role of fly ash in reducing chloride ingress is influenced not only by its pozzolanic properties but also by the ionic composition of the deicing environment. Further investigation at the atomic level, using techniques such as X-ray diffraction, spectroscopy, and nuclear magnetic resonance, could provide deeper insights into the microstructural changes and chloride binding mechanisms [
25].
The pronounced effects of entrained air on the reaction factors are illustrated in
Figure 9. At each chloride concentration, a good correlation was achieved between the reaction factor and the inverse of entrained air, with r-squared values ranging from 0.67 to 0.83. As already noted in
Figure 8, samples with Class F fly ash followed the overall trend quite well across the chloride concentrations, except for the pure salt solution.
These findings confirm that the reaction factor of concrete was affected by more than the cement type; variables like entrained air and chloride concentration also play a role. By performing a multiple regression analysis on the data presented in
Figure 9, an alternative model for the reaction factor can be derived. For the tested ranges of entrained air and chloride concentration, the proposed model is as follows:
where V
e = entrained air in percent and C
c = chloride concentration in M.
In comparison, the independent data published by Andrade et al. were predicted using the model reflected in Equation (7). These reaction factors were determined based on the D
ef and D
ap (Equation (2)) obtained from a multi-regime test involving 150 mm diameter, 20 mm thick concrete samples conditioned in 1 M of NaCl, and distilled water as the anolyte [
33].
Figure 10 presents the prediction results for the reaction factors. Overall, the model demonstrates reasonably good predictive performance, with the exception of a sample mixed with ASTM Type II cement and 50% slag (w/c = 0.34), which exhibited some deviations. These discrepancies may stem from differences in cement composition, pore structure, chloride concentration, temperature, aging, and other influencing factors.
The model coefficients were estimated based on experimental data under controlled conditions. Therefore, uncertainties may arise when applying the model to broader datasets due to variations in concrete mix proportions, field exposure conditions, and long-term aging effects. The model’s predictive accuracy can be improved by incorporating an extensive dataset that captures diverse environmental conditions, as well as by performing sensitivity analyses to assess the impact of individual parameters on chloride ingress.
3.4. Prediction of Corrosion Initiation in Concrete Pavement
Combining Equations (5) and (6) leads to an expression for the initiation period of reinforcement corrosion, t
i, in years. Theoretically, given the three key factors of the electrical resistivity model, t
i can be calculated, but environmental factor, k
CI, is not easily determined, as it is affected by so many experimental conditions such as humidity, temperature, species of chloride ions, and so on. Thus far, very few values and associated empirical models have been proposed for a handful of illustrative examples [
32,
46].
In this section, a novel approach is proposed to back-calculate kCI that is expected to induce corrosion in concrete during the target design period. Here, kCI, is designated as a maximum allowable environmental factor that is known to be associated with the design parameters, including design period, cover thickness, chloride concentration, and entrained air. Incorporated into the expression for kCI are the models developed for determining the aging and reaction factors, as presented in the previous sections. Models for class F fly ash were not considered for predictions due to the lack of data.
Table 7 presents the maximum allowable environmental factors forecast for a 10-year corrosion initiation period. As noted, the effect of entrained air was conspicuous at the selected chloride concentrations. As the volume of entrained decreased, concrete was better protected from corrosion, suggesting a prolonged protection period. A similar observation was made for a range of cover thicknesses (3 cm, 4 cm, 5 cm, and 6 cm).
Figure 11 shows the prediction of k
CI levels matched with the corresponding chloride concentrations (C
c) for four corrosion initiation periods—10, 15, 20, and 50 years. The cover thickness was set at 3 cm for all the k
CI predictions that were compared with the exposure designations shown in
Table 1. It demonstrates that, even at the same level of chloride concentration, k
CI could differ greatly depending on the volume of entrained air, along with other design parameters.
The model’s back-calculation approach allows for the estimation of critical environmental factors to predict corrosion initiation for long-term exposure periods. With proper calibrations, this approach can offer an alternative solution to traditional tests on diffusion coefficients for the determination of kCI, which is notorious for being quite time-consuming and destructive, with poorly replicable outputs. Moreover, an advanced design process can be achieved for a more durable and corrosion-resistant concrete pavement, where deicing with salt brine is a common practice. However, these predictions are still based on controlled laboratory conditions and may not fully capture the complexities of long-term field exposure. Therefore, future research should focus on extending the experimental duration and incorporating variable environmental conditions to enhance the model’s reliability for long-term applications.