1. Introduction
Basalt fiber, a natural fiber obtained from basalt rock through specialized processing techniques, exhibits several beneficial properties, including low density, high tensile strength, notable toughness, and abundant raw material availability [
1]. The production of basalt fiber is characterized by low carbon emissions and minimal environmental pollution, rendering it an environmentally sustainable material that aligns with China’s green development objectives [
2,
3]. As a result, basalt fiber has experienced increased utilization in civil engineering applications in recent years. Basalt fibers are typically classified into two categories: chopped bundle basalt fibers (CBFs) and flocculent basalt fibers (FBFs) [
4]. CBFs are manufactured by melting basalt rock at elevated temperatures, followed by fiber drawing and treatment with lipophilic impregnating agents to form fiber bundles [
5]. Conversely, FBFs are produced through high-temperature melting of basalt rock, succeeded by high-speed centrifugal spinning, purification, and treatment with a silane-coupling agent [
6]. This agent functions as a cationic impregnating substance that adsorbs onto the fibers via its cationic moiety and establishes covalent bonds with the fiber surface through hydrolysis and condensation reactions of siloxane. This mechanism effectively links the inorganic fibers with organic asphalt, thereby enhancing interfacial adhesion.
Currently, the CBFs are primarily utilized as short fibers in civil engineering materials, including asphalt mixtures and cement concrete. This approach represents the main application method of basalt fibers within the civil engineering field and has proven to be highly effective [
7,
8,
9,
10]. Nevertheless, despite the enhanced mechanical properties of CBFs relative to FBFs, their dispersion within high-viscosity asphalt mixtures remains challenging, thereby complicating construction procedures [
11]. Additionally, the bundling manufacturing process results in increased production costs, which restricts their widespread use in cost-sensitive pavement engineering applications [
12].
In contrast to engineering structures such as bridges and tunnels, which require basalt fibers with exceptionally high structural strength, asphalt pavements do not impose stringent mechanical property requirements on the fibers used. Instead, emphasis is placed on achieving uniform fiber dispersion, facilitating ease of construction, and ensuring cost-effectiveness [
13]. As a result, the FBFs are considered particularly well-suited for incorporation into asphalt mixtures, leading to extensive research aimed at exploring their potential applications. Zhao et al. [
14] examined the influence of CBF content on the performance of AC-13 asphalt mixtures, thereby confirming the feasibility of integrating basalt fibers into asphalt composites. However, their study did not address the impact of FBF on the road performance of asphalt mixtures. Wu et al. [
15] and Cai et al. [
16] modified SMA-13 asphalt mixtures using four different fiber types and demonstrated that FBF provided more comprehensive performance improvements than other fibers, particularly with respect to high-temperature stability. Nevertheless, these studies considered only a single variable—FBF content—leaving a gap in the systematic evaluation of FBF size parameters (length and diameter) and their interactive effects with fiber content. Wang et al. [
17], Yang et al. [
18], and Xing et al. [
19] investigated warm-mix recycled asphalt mixtures modified with both CBF and FBF at varying contents, focusing primarily on their cracking behavior. These studies concluded that the inclusion of FBF significantly enhanced the cracking resistance of recycled asphalt mixtures compared to those containing CBF. However, a detailed analysis of the size characteristics of FBF was not provided in these investigations.
Previous research has primarily focused on examining the influence of individual parameters, such as fiber type or fiber content, on the road performance of various asphalt mixtures [
20,
21]. Nonetheless, there remains a notable gap in comprehensive studies that analyze the combined effects of three key factors—fiber diameter, length, and content—within recycled asphalt mixtures (RAMs) containing the FBFs [
22]. This gap impedes the broader application and practical utilization of FBF in pavement engineering. Furthermore, in the context of China’s commitments to reach a peak in carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060, the engineering significance of RAMs has attracted growing academic attention. Typically, RAMs exhibit relatively inferior performance and stability compared to conventional asphalt mixtures, often necessitating reinforcement through fiber addition [
23,
24].
Accordingly, the present study aims to investigate the synergistic effects of FBF size parameters (diameter and length) and fiber content on the road performance of RAMs. Previous investigations into fiber effects have largely employed traditional multiple linear regression models, which are limited in their capacity to capture complex nonlinear interactions [
25]. In contrast, machine learning techniques have shown enhanced predictive accuracy and generalizability in modeling the behavior of construction materials [
26].
This research evaluates the impact of FBF size characteristics on the optimal asphalt–aggregate ratio (OAR), mechanical properties, high-temperature stability, low-temperature crack resistance, and water stability of SMA-13 recycled asphalt mixtures (RAM-SMA-13) across varying fiber contents. Additionally, it integrates multifactorial experimental designs with a random forest (RF) modeling approach to quantitatively assess the interactive effects of FBF size and content on RAM-SMA-13 properties. The results offer a foundational framework to inform the compositional design of FBF-reinforced recycled asphalt mixtures.
2. Materials and Test Preparation
2.1. Materials
The SBS-modified asphalt, together with the coarse aggregate, fine aggregate, and filler, were produced and supplied by Jiangxi Ganuo Expressway Co., Ltd., Nanchang, China. The technical properties of these materials are presented in
Table 1,
Table 2,
Table 3 and
Table 4, respectively, and conform to the requirements specified in the Chinese standard “Technical Specification for Construction of Highway Asphalt Pavements (JTG F40-2004).” [
27]. The SBS content in the modified asphalt is maintained at a mass fraction of 4.5%, with a number average molecular weight of 120,000 g/mol. The PG grade of the SBS-modified asphalt is classified as PG 76-22. The coarse aggregate is basalt, the fine aggregate is sourced from river sand, and the filler consists of limestone powder.
Recycled aggregates derived from the screening of milled asphalt pavement materials are produced in two distinct size fractions: 0–8 mm and 8–12 mm. The technical characteristics of these aggregates are detailed in
Table 5.
The FBFs utilized in this research were procured from Hangzhou Jialu Transportation Technology Co., Ltd., Hangzhou, China. These fibers are available in three distinct specifications, where the first parameter denotes the diameter and the second corresponds to the length: d—6 μm × L—4 mm, d—3 μm × L—4 mm, and d—6 μm × L—2 mm. The technical characteristics of the fibers are presented in
Table 6.
2.2. Instruments and Equipment
Mechanical property assessments of asphalt mixtures are conducted utilizing the LHPL-6 comprehensive performance testing apparatus. Rutting resistance is evaluated through the application of the LHCX-1 asphalt mixture rutting tester (Beijing Zhongjian Luye Instrument Equipment Co., Ltd., Beijing, China). Furthermore, low-temperature beam bending tests at −10 °C, along with freeze–thaw splitting tests, are performed using the LHZH-6 low-temperature comprehensive performance testing device for asphalt mixtures.
2.3. Sample Preparation
Table 7 presents the gradation range for RAM-SMA-13. In each aggregate specification, 60% of the total mass is replaced with recycled aggregates. Sample preparation follows the standardized protocols outlined in the Chinese standard “Standard Test Methods of Asphalt and Asphalt Mixture for Highway Engineering (JTG 3410-2025)” [
28].
Due to the high specific surface area of the FBF and its propensity to agglomerate during mixing—factors that negatively impact the road performance of the mixture—the following preparation procedure was adopted in this study:
Step 1: The FBF was dried in an oven at 60 °C for 2 h to reduce its moisture content to below 0.5%. The dried fibers were then subdivided into smaller portions to minimize agglomeration caused by batch feeding.
Step 2: The recycled and virgin aggregates were thoroughly mixed and preheated to 180 °C.
Step 3: A total of 70% of the preheated aggregates were added to the mixer and dry-mixed for 10 s to form the aggregate base mixture.
Step 4: While the mixer remained in operation, the FBF was gradually introduced into the aggregate base mixture, followed by the addition of the remaining preheated aggregates.
Step 5: The mixture was considered homogeneously combined after continuous stirring for 120 s.
2.4. Performance Testing
Mechanical properties are evaluated through compressive strength and splitting strength tests. High-temperature performance is assessed using the rutting test, while low-temperature performance is measured by the bending test conducted at −10 °C. Water stability is examined via the freeze–thaw splitting test. All experimental procedures adhere to the Chinese standard “Standard Test Methods of Asphalt and Asphalt Mixtures for Highway Engineering (JTG 3410-2025).” To ensure the reliability of the results, five samples are used for determining the optimum asphalt content (OAR), three samples for the rutting test, and six samples for the strength, bending, and freeze–thaw splitting tests.
5. Quantitative Evaluation of the Geometric Properties of Flocculent Basalt Fibers
Random Forest (RF), an ensemble learning method consisting of multiple decision trees, constitutes a robust approach for evaluating variable importance and conducting weight analysis. This technique effectively captures nonlinear relationships between fiber parameters—such as diameter, length, and content—and road performance without necessitating prior assumptions. Furthermore, RF outperforms traditional regression methods in modeling complex interactions among multiple variables, while simultaneously delivering a high predictive accuracy and interpretable assessments of feature significance. Subsequently, weight analysis is conducted to identify the principal factors influencing the OAR and the performance characteristics of RAM-SMA-13. In this study, the weight analysis derived from the RF model reflects the relative contribution of each factor to the model’s predictive capability, rather than representing the absolute magnitude of the factor weights.
5.1. Weight Analysis of the Asphalt–Aggregate Ratio
The input features selected for this study include fiber size characteristics, namely diameter and length, along with content, while the outcome variable is the OAR of RAM-SMA-13. The dataset is partitioned such that 20% constitutes the test set, with the random state fixed at 42 to ensure reproducibility and mitigate variability arising from random data splits. A 5-fold cross-validation approach is employed to stratify the dataset, facilitating multiple iterations of training and validation to reduce the impact of random small sample divisions on model performance. The number of decision trees is varied from 1 to 200 in increments of 25, with the optimal number determined once model accuracy reaches stability, accommodating the constraints of the limited sample size. Furthermore, the model’s average error is assessed, and the final ranking of the factors influencing the outcome, based on their relative importance, is depicted in
Figure 8.
Table 8 reveals several key insights:
- (1)
Fiber content emerges as the most critical factor, as indicated by its highest weighting in the analysis, whereas fiber diameter and length exhibit substantially lower significance.
- (2)
Fiber content significantly affects the OAR within the mixture. Notably, increasing fiber content more effectively enhances the specific surface area of fibers in RAM-SMA-13 compared to modifications in fiber size parameters. The addition of fibers modifies the bonding characteristics of the mixture, thereby necessitating an increase in asphalt content to optimize the OAR and achieve superior performance.
- (3)
The impact of fiber size characteristics is relatively minor, likely due to their limited influence on the overall fiber performance within the mixture and on asphalt demand. Moreover, the homogeneous dispersion of fibers throughout the mixture, combined with appropriate adjustments in fiber content, further mitigates these effects.
5.2. Weight Analysis of the Road Performance
Using fiber size characteristics and content as the primary input variables, and the road performance of RAM-SMA-13 as the response variable, a two-stage hyperparameter optimization approach is employed to enhance the model’s predictive accuracy. Initially, Randomized Search CV is utilized to explore and refine the range of decision tree numbers (from 50 to 200) and maximum depths (from 10 to 40) in a gradient fashion, aiming to identify the most effective hyperparameter combination. Subsequently, Grid Search CV conducted an exhaustive search across all parameters to determine the global optimal set. The model is then trained using these optimal parameters, and finally, an importance ranking of the influencing factors is generated.
5.2.1. Mechanical Properties
Table 9 displays the results pertaining to compressive strength (R
C) and splitting tensile strength (R
T).
As illustrated in
Table 9, the mechanical properties of RAM-SMA-13 are significantly influenced by the fiber content and diameter, while the effect of fiber length is relatively limited. The fiber content and diameter of the FBF play a crucial role in determining the dispersion and spatial distribution of fibers within the composite. These parameters are more easily regulated during processing and fabrication, enabling the achievement of a consistent fiber proportion and uniform dispersion. In contrast, increasing fiber length may introduce processing and construction challenges, such as heterogeneous mixing, which can hinder improvements in strength performance and consequently reduce its overall impact on the mechanical properties.
5.2.2. High-Temperature Stability
Table 10 presents the findings related to dynamic stability (DS).
As illustrated in
Table 10, the content and diameter of FBF exert a more significant influence on high-temperature stability than fiber length, which demonstrates a relatively minor effect. The FBF content and diameter are essential parameters for establishing an effective fiber spatial network and strengthening the interfacial bonding with the asphalt binder. While fiber length plays a role in enhancing dynamic stability mainly by controlling crack propagation and increasing toughness, the overall dynamic stability is largely determined by the fiber network configuration and the interfacial adhesion between the fibers and asphalt. Therefore, the effect of FBF length on dynamic stability is comparatively limited.
5.2.3. Low-Temperature Crack Resistance
Table 11 displays the results pertaining to flexural tensile strength (R
B), flexural modulus (S
B), and flexural tensile strain (ε
B).
As illustrated in
Table 11, the content and diameter of the FBF play a critical role in enhancing resistance to low-temperature cracking, whereas the effect of fiber length is comparatively minor. Although variations in FBF length can moderately improve the composite’s toughness, the content and diameter of the FBF have a more pronounced impact on the uniform dispersion within the asphalt binder and the development of its structural framework. The establishment of a robust three-dimensional network facilitates more efficient stress distribution, thereby enhancing both flexural tensile strength and flexural modulus. Moreover, the content and diameter of the FBF represent more readily controllable parameters that promote homogeneous fiber dispersion, in contrast to longer fibers, which may result in uneven distribution and adversely affect overall composite performance. Therefore, adjustments in FBF content and diameter yield more substantial improvements in the mechanical properties of the composite.
5.2.4. Water Stability
Table 12 presents the findings related to residual stability (MS
0) and the freeze–thaw splitting strength ratio (TSR).
As illustrated in
Table 12, both the content and diameter of the FBF exert a significant influence on water stability, whereas the length of the FBF has a comparatively minor effect. The fiber content predominantly affects the network structure and the bonding strength among fibers, thereby markedly enhancing the overall properties of RAM-SMA-13. While an increase in FBF length contributes to improved toughness, its impact on the formation of a robust fiber network is notably less substantial compared to the other two factors. Furthermore, the use of longer fibers may lead to uneven dispersion within RAM-SMA-13, potentially diminishing uniformity and consequently limiting the extent of performance enhancement.