Evaluation of the Mechanical Behavior of Asphaltic Mixtures Utilizing Waste of the Processing of Iron Ore

Abstract

Mineral extraction is an important operation for the economy of different countries and generates millions of tons of mining waste. In this context, and in association with the high demand for paving aggregates and the lack of raw materials for this purpose, the feasibility of using iron ore processing waste has emerged as a promising alternative. This study evaluates the physical and mechanical behavior of asphalt mixtures incorporating waste from the company Samarco S.A., collected in Mariana-MG, to replace the fine aggregate in asphalt concrete mixtures, with a view to applications in the bearing layer of local traffic roads. Two mixtures, M2 and M3, containing 20% and 17% waste, respectively, were formulated and analyzed, compared to a reference mixture, M1. Evaluations were carried out using the Marshall method parameters, mechanical tests of resilience modulus, and fatigue life under controlled tension, as well as mechanistic analysis. Brazilian mechanistic–empirical design software (MeDiNa—v 1.5.0) contributed to this analysis. This analysis revealed that, for a traffic level of N = 5 × ${10}^6$ (average traffic) on a local road, pavements containing the M1 and M3 mixtures had the same layer thicknesses (6.9 cm), as well as the same fatigue class, equal to 1. The pavement with the M2 mixture had the thickest asphalt layer (8.2 cm) and a lower fatigue class equal to 0. But if compared in terms of the percentage of cracked area over 10 years, it still offers ideal performance conditions compared to the M1 and M3 mixes. Thus, it can be considered feasible to replace fine aggregate with iron ore waste in asphalt concrete for use on local roads in the region without altering the bearing capacity of the pavement.

1. Introduction

It is well understood that the advance of our society necessitates industries to supply the energy and goods that sustain modern lifestyles. However, industrial activities generate large quantities of a wide variety of waste materials, each with distinct characteristics, originating from various sectors such as metallurgy, chemical, petrochemical, mining, transport and food industries, among others [1,2,3,4,5].

In the mining sector, substantial volumes of waste are produced during the beneficiation process of ores [6]. While waste itself is not inherently harmful, its disposal can become an environmental liability. Consequently, finding a useful purpose for industrial waste has become a primary concern for companies engaged in these activities, together with environmentalists, regulatory bodies, and research institutions focused on environmental preservation [7]. Transforming an industrial waste into a byproduct or raw material for other production lines not only presents new market opportunities [8,9,10,11,12] but also offers a more ecologically sound and safer alternative to replace disposal, especially given the imminent risks of potential catastrophes. This concern is underscored by the tragic dam failures at the Samarco mining operation in Mariana, in 2015, and the Córrego do Feijão mine in Brumadinho, state of Minas Gerais (MG), in 2019, which stand as the most significant environmental disasters in Brazil’s history [13,14].

In this scenario, the search for new ways of tailing disposal from the iron mining process has been the focus of different research. Some studies point to the feasibility of inserting this waste into construction materials [15], such as applying iron ore tailings as a substitute for natural fine paving aggregate [16]. Iron ore tailings have also been applied as a partial substitute for cement in concrete [17,18], in colored mortars [19], in the production of pigments for paints [20], in the manufacture of bricks [21,22], in the production of microconcretes [23], and as a precursor in the synthesis of geopolymers [24,25,26,27].

Within this context, and given the high and growing volume of waste generated by iron ore production in Brazil, many mining companies are exploring more sustainable methods of landfill disposal [28,29]. Road construction and concrete structures consume millions of tons of aggregates, which is why highway engineering has become a focus of extensive research projects aimed at assessing the technical viability of using recycled aggregates and waste materials. These materials can originate from construction, steelmaking, mining, and other industries. One promising approach to mitigating the environmental impact of mining waste is its use as an alternative aggregate in asphalt paving.

For instance, Silveira et al. [9] investigated the feasibility of recycling iron ore rejects through cost-effective pavement techniques and demonstrated significant improvements in the performance of stabilized mixtures with the addition of anti-dust treatments. Similarly, Apazza et al. [30] researched the incorporation of iron ore as an aggregate in cold asphalt mixtures and found it to be a viable alternative that reduces the consumption of natural resources and offers environmental benefits. Additionally, Friber et al. [8] evaluated the technical feasibility of using calcined aggregates with mining waste in various pavement layers, including the base, subbase, and wearing course layers. Furthermore, Loures et al. [31] studied the use of steel slag in cold pre-mixed mixtures and concluded that, based on both field and laboratory analyses, the pavement could withstand stress levels consistent with medium traffic conditions. In particular, Shamsi and Zakerinejad [32] evaluated the impact of using Iron Ore Overburden (IOO) materials in comparison to virgin aggregates on the properties of Hot Mix Asphalt (HMA), specifically for Topeka and binder layers. The findings revealed that all IOO samples met the minimum technical specifications, demonstrating low chemical risks associated with large-scale use. Moraes et al. [33] investigated the influence of percentages of iron ore tailing in the contents (7.5%, 10.0%, and 12.5%) as a substitute for fine natural aggregate on hot asphalt mixtures’ mechanical and thermal performance. The use of 12.5% provided a greater circularity and a reduction in the surface temperature of the pavement by 2.9 $°$C, and the iron ore tailing was considered viable in constructing highways close to places that produce iron ore. This concern regarding the use of natural resources has been relevant in the development of new, more sustainable road paving projects since a sustainable pavement reduces the environmental impact during its construction [32,34,35,36]. However, it is also necessary for this waste to provide the pavement with adequate mechanical behavior for the traffic to which the structure will be subjected [37,38]. Therefore, analyzing the influence of asphalt mixture composition on the design of flexible pavement using mechanistic–empirical methods is crucial for the continued advancement of pavement engineering, contributing to the construction of more efficient, sustainable, and resilient road infrastructure.

The landscape of asphalt pavement in Brazil is undergoing transformation with the development of a new method to calculate the dimensions of road structures. This new approach employs a mechanistic–empirical methodology, contrasting with the current predominantly empirical method. MeDiNa (National Pavement Dimensioning Method) is a software designed to size asphalt pavements using a mechanistic–empirical approach [36,39]. It evaluates and computes pavements by analyzing the elastic properties of multiple layers, considering the resilience modulus and Poisson’s ratio to assess layer stiffness and utilizing coefficients to model deterioration parameters such as fatigue and permanent deformation.

The results of this analysis offer crucial insights for the effective selection of materials, the determination of layer thicknesses, and the forecasting of pavement mechanical behavior over time. By evaluating factors such as aggregate types, asphalt binder content and types, and the inclusion of additives, it is possible to enhance pavement performance and ensure its adequacy for specific traffic and climatic conditions. Consequently, it is essential to account for elastic stiffness and layer thickness to achieve proper stress distribution from traffic loads and to manage deformations across various asphalt mixture configurations.

Therefore, the total or partial replacement of virgin aggregates with waste is evaluated in the laboratory and in the field using experimental sections. With this in mind, the aim of this work was to evaluate the feasibility of using iron ore processing waste to replace fine aggregate in asphalt concrete mixtures, using the MeDiNa software, through mechanical laboratory tests and empirical mechanistic analysis, with a view to its use in the rolling layer of road pavement.

2. Materials and Methods

The waste sample underwent a series of evaluations to assess its physical, chemical, and mineralogical properties, as well as to verify the feasibility of its application in paving. Figure 1 presents a flowchart of the tests and characterizations performed. The characterizations and tests conducted on the tailings will be explained in the following subsections.

3. Materials

The waste used in this research comes from Samarco Mineração S.A., Mariana, MG, Brazil. This material is generated in the flotation stage of the iron ore beneficiation at the Mariana Mine and deposited at the Germano dam. Its collection was performed on 24 October 2014. The samples were removed at a distance of 50 m from the barrage crest, with spacing between collection points of 100 m and a total of three holes. Due to the proximity of the water table, a depth of 2 m was determined for sample collection. This material was packed in plastic drums and then transported and stored at the Military Institute of Engineering (IME), Rio de Janeiro, Brazil, Soil Laboratory.

To prepare the mixtures, coarse aggregates from crushed gneiss-granitic origin (gravel 1 and gravel 0) and fine aggregate (sand), both from the state of Rio de Janeiro (RJ), were used. All the materials were subjected to a complete evaluation of their physical, chemical and mechanical properties, aiming for their application as paving aggregates.

Regarding the asphalt binder, we chose to use a CAP 50/70, supplied directly by BR Distribuidora S.A, Rio de Janeiro, Brazil.

3.1. Characterization of Materials 

The tests for the characterization of the waste and aggregates were carried out according to the current DNER-ME Brazilian standard, which includes the following: (i) granulometric analysis [40] (see Figure 2), (ii) Los Angeles abrasion [41], (iii) impact loss using the Treton device [42], (iv) absorption [43] and density determination of the coarse aggregate [44], (v) fine aggregate real density [44], (vi) adhesiveness to asphalt binder, (vii) durability [45], and (viii) sand equivalent [46]. These characteristics are presented in the

Table 1. Test results for aggregates.

Tests	Gravel 1	Gravel 0	Sand	Waste	DNIT Limit
Los Angeles Abrasion Loss	27%	46%	-	-	max 50%
Impact Loss in Treton Apparatus	12%	-	-	-	-
Real Density	2.798	2.703	2.620	2.830	-
Apparent Density	2.750	2.666	-	-	-
Absorption	0.6%	0.8%	-	-	-
Sand Equivalent	-	-	64%	74%	min 55%
Adhesion to Asphalt Binder (CAP 50/70)	Not Satisfactory	-	-	-	-
Durability (Magnesium Sulfate—5 Cycles)	5.92%	2.67%	9.69%	5.56%	max 12%

.

Preliminarily, the materials were found to be of good quality for use in asphalt mixtures, and, in particular, the waste can be used as a product classified as fine aggregate or sand. The adhesion test showed the need to use 0.10% adhesion improver in the binder.

The asphalt binder was characterized according to the Brazilian standard DNIT 095 [47], as shown in

Table 2. Characterization of the 50/70 binder used in this research.

Tests	Result	Limits
Penetration—100 g, 5 s, 25 $°$C (0.1 mm)	61	50 to 70
Softening Point ($°$C)	47	min 46
Brookfield Viscosity (cP)
at 135 $°$C, SP 21, 20 rpm	465	min 274
at 150 $°$C, SP 21, 50 rpm	226	min 112
at 177 $°$C, SP 21, 100 rpm	81	57–285
Thermal Susceptibility Index	−1.5	−1.5 to +0.7
Flash Point ($°$C)	>265$°$	min 235
Solubility in Trichloroethylene	99.5%	min 99.5%
Specific Mass and Relative Density, 25 $°$C	1.055	-
Ductility, at 25 $°$C (cm)	>120	min 60
After RTFOT @ 163 $°$C, 85min
Mass Variation	0.02%	max 0.5%
Ductility, at 25 $°$C (cm)	>120	min 20
Increase in Softening Point ($°$C)	0.05	max 8
Retained Penetration	55%	min 55%

.

In regard to the organic matter content test, it was determined that the iron mining waste does not contain any organic impurities. Additionally, both the Leaching test NBR 10004 [48] and the Solubilization test NBR 10.006 [49] yielded positive results, indicating that the sandy waste from iron ore beneficiation can be classified as Class II B—Non-hazardous and Inert. Therefore, its use in pavement construction is feasible. It is important to note that the company responsible for the waste routinely conducts these verifications, ensuring compliance with the required standards.

3.2. Dosage

To investigate the incorporation of iron ore waste (IOW) in HMA, the Marshall method was employed, following the procedures outlined in the DNER 043 standard [50]. Two mixtures, M2 and M3, containing IOW were designed for comparison with an on-waste control mixture M1. The mix design was conducted to comply with the Brazilian technical standard DNIT 031 [51], with gradation falling within Range C, targeting its application in the surface layer of flexible pavement. Figure 3 shows the gradation of the aggregates, and

Table 3. Nomenclature of the asphalt mixtures used in this research.

Material	Mass Percentage
	M1	M2	M3
Gravel 1	20	15	20
Gravel 0	40	65	55
Sand	40	-	8
Waste	-	20	17

details the quantities of each material used.

For the preparation of the Marshall samples, 5 specimens were molded for each bitumen content in all three mixtures. In mixture M1, the bitumen content started at 4.0%, with increments of 0.5% up to 6.0%, resulting in five different contents. After verifying the mix design parameters, an additional content of 6.5% was prepared to confirm the design bitumen content. In mixture M2, the bitumen content ranged from 4.5% to 6.5% in 0.5% increments, and the same approach was followed for mixture M3. The optimal bitumen content was determined based on the voids in the mixture (Vv) and the voids filled with bitumen (VFB), as per the method described by Bernucci et al. [52], by using the intersection of the trend lines for both parameters and calculating the average of the two central bitumen contents.

Based on the optimum asphalt content for each mixture, obtained through the Marshall method, stability and indirect tensile strength (ITS) tests were conducted. To carry out the ITS tests, three specimens were used for each of the three mixtures prepared using the Marshall method. The samples were subjected to the diametral compressive tensile strength test, in accordance with the guidelines established in standard DNIT 136 [53]. All the specimens had a tensile strength higher than the minimum value recommended by the DNIT 031 [51] standard, which is 0.65 MPa.

Table 4. Final Marshall dosage parameters.

MIXES	% BINDER	Vv %	RBV %	STRENGTH (kgf)	RT (MPa)	VAM %
DNIT-ES 031/2006	4.5–9.0	3 to 5	75 to 82	>500	>0.65	>15
M1	5.9	3.1	81.4	1710	1.4	16.3
M2	5.6	3.9	76.7	1361	1.5	16.7
M3	5.5	3.1	80.7	1442	1.6	15.7

M1: reference mix; M2: 20% waste; M3: 17% waste and 8% sand.

presents the results of these tests, along with the optimum asphalt content for each mixture and the reference parameters specified in the DNIT 031 standard [51].

3.3. Resilient Modulus and Fatigue Life Analysis

For the resilient modulus (RM) test, the specimens were molded according to the Marshall methodology. This test was performed at the Military Institute of Engineering—IME Laboratory of Binders and Asphalt Mixtures—using an automated system (see Figure 4) that measures the elastic deformation of the specimen during cyclic loading, applied by a pneumatic loading system. The test was conducted at a temperature of 25 $°$C, with the specimens conditioned for two hours prior to testing. The RM calculation followed the guidelines of DNIT 135 [53].

For each of the three mixtures, five specimens were compacted using the Marshall method for testing. The results for each specimen were averaged across three MR values, and the overall MR for each mixture was determined from the average of the five specimens. In this research, the RM results for each specimen were grouped into a single dataset, resulting in 15 RM values per mixture, which were statistically processed to yield the mean RM.

Fatigue life tests in Brazil are typically performed under controlled stress and temperature, using the same equipment as the resilient modulus test. The tests apply cyclic loads at a frequency of 1 Hz (0.1 s of loading followed by 0.9 s of rest) in indirect tensile tests on specimens with a diameter of 100 mm and heights ranging from 35 mm to 70 mm. The vertical load amplitude is kept constant until specimen failure.

In this study, the fatigue life test was conducted following the standard DNIT-ME 183 [54]; all three mixtures, prepared using the Marshall method, were subjected to controlled stress fatigue life tests at the IME Laboratory of Binders and Asphalt Mixtures using the equipment depicted in Figure 5. This equipment applies cyclic diametral compression loads at a frequency of 1 Hz at a temperature of 25 $°$C. A pulse counter in the equipment recorded the number of load cycles until specimen failure.

To characterize the asphalt mixtures mechanically, ten specimens per mixture were tested. For fatigue life determination, five stress levels (10%, 20%, 30%, 40%, and 45% of the indirect tensile strength) were applied. These stress levels allowed the plotting of a log stress difference versus a load cycle curve. Additionally, based on the mean MR of the mixture, a graph of resilient strain versus load cycles was also plotted.

3.4. Mechanistic–Empirical Evaluation

For the analysis and comparison of the mechanical behavior of a flexible pavement structure composed of the studied mixtures, traditional mixture, and mixture containing waste, the Brazilian National Pavement Design Method—MEDINA—was utilized. MEDINA is the result of research developed between 2015 and 2018 by the Road Research Institute—IPR—and the Alberto Luiz Coimbra Institute of Postgraduate and Research in Engineering, Federal University of Rio de Janeiro—COPPE—with the collaboration of the Petrobras Research Center—CENPES—and several universities in Brazil. Additionally, it is an update of the SISPAV program developed by Franco in his doctoral thesis [55].

The software uses as input data the dimensions of the layers, the mechanical properties of the materials (resilient modulus, fatigue life model, permanent deformation model, and Poisson’s ratio) composing the layers, and the loads generated by the passage of vehicles. Once the stresses, strains, and displacements are calculated, the software verifies whether the number of load applications will lead to excessive cracking of the asphalt pavement or cemented layers, or to rutting in the wheel track exceeding the established limit. Through the use of this tool, it is possible to determine the thicknesses of the wearing course, base, sub-base, and subgrade reinforcement layers, with these layers responsible for distributing the loads imposed by vehicles. The reliability of the results regarding pavement layer thicknesses, service life, and crack area is essential. This requires accurate data on traffic and asphalt concrete properties to be entered into the software.

Fritzen et al. [56] developed a methodology to classify asphalt mixtures into four classes based on fatigue performance, where a higher class indicates better mixture behavior. This classification involves establishing the Wholer curve relating the number of cycles (Nf) to initial tensile strain (${\epsilon }_i$). By regressing this curve, the fatigue factor of the mixture (FFM) can be determined using Equation (1). Subsequently, correlations between resilient modulus and FFM are established to determine the specific class of each mixture. This methodology, developed by Fritzen et al. [56], is implemented in the MeDiNa software.

Table 5. Fatigue classes, standard repetition number intervals, and regressions.

Fatigue Class	Standard Repetition Number Interval (N)	Regression
0	$N\le 4.5\times {10}^6$	—
1	$4.5\times {10}^6<N\le 6.0\times {10}^6$	$FFM=74.58R{M}^{-0.526}$
2	$6.0\times {10}^6<N\le 7.5\times {10}^6$	$FFM=31.31R{M}^{-0.410}$
3	$7.5\times {10}^6<N\le 1.0\times {10}^7$	$FFM=74.58R{M}^{-0.316}$
4	$N>1.0\times {10}^7$	$FFM=74.58R{M}^{-0.207}$

presents the regression used by Fritzen et al. [56] in determining the classes for a standard number of repetitions with 30% cracked area (according to the criterion considered in the MeDiNa software).

$$FFM=0.2\left[log\left(N_{100}\right)+log\left(N_{250}\right)\right]$$

(1)

Moreover, based on the regression coefficients obtained from the fatigue test, the software automatically calculates an FFM for each model inserted. Comparing this value with the resilient modulus of the mixture, the fatigue class of the material is calculated. According to the Medina manual, the higher the fatigue class (ranging from 1 to 4), the better the mechanical behavior of the asphalt mixture.

The hypothetical structure of the flexible pavement was composed according to

Table 6. Characteristics of materials and traffic for road pavement design.

Layers	Type	RM (MPa)	Thickness (cm)
Base	Granular Material (Gneiss C5)	381	15
Sub-base	silty soil NS’	250	15
Subgrade	clayey soil LG’ (1)	189	-

, taking into account the characteristics of the base, sub-base, and subgrade materials of the pavement. In this stage, the subgrade and the sub-base and base layers remained unchanged, while the materials of the asphalt layers were replaced by the mixtures evaluated in this study. These characteristics were selected for a standard Brazilian single-wheel axle load and considering a design life of 10 years, based on representative medium traffic (5 × ${10}^6$), with the local road type. The type of route defines the sizing stopping criteria, as well as the degrees of reliability of the software analyses. Therefore, for a local system road, the reliability is 65%, a maximum of 30% of cracked area (CA), and a maximum of 20 mm of permanent deformation in the pavement, aiming to determine the ideal thicknesses of the asphalt layers (binder and wearing course) necessary to meet the MeDiNa criteria, and, keeping all thicknesses constant, evaluating the performance of each mixture in terms of cracks and rutting in the wheel path over time.

4. Results and Discussions

4.1. Resilient Modulus

Table 7. Statistical treatment of the RM results.

Mixture	Reliability Interval (95%) (MPa)	Standard Deviation (MPa)	Coefficient of Variation (%)	Average RM (MPa)
M1	5347 to 5883	140	2.5%	5640
M2	4927 to 5421	163	3.2%	5137
M3	5062 to 5773	127	2.4%	5244

shows the results of the RM tests and their respective statistical parameters. The average RM was obtained as the arithmetic average of all the results from the tests, after the Grubbs test was applied, which verifies the existence of abnormal values within a sample space.

From the results found on Table 7, a reduction can be noticed in both the RM and the coefficient of variation values. It can be concluded that there was a decrease of about 9% in the RM value of the mixture M1 (5640 MPa) in relation to M2 (5137 MPa), and in relation to M3 (5244 MPa), the reduction was 7%. Therefore, the M2 mixture is a little less rigid than the others.

In the literature, it is not very common to find references on the use of this specific type of waste in asphalt mixtures. However there is record of usage of other wastes that are not very common. Paulsen et al. [57] performed RM tests on asphalt concrete containing 20% of roofing waste (roofing waste), obtaining values between 2873 MPa and 3000 MPa, with binder content varying between 4% and 6%.

Oluwasola et al. [58] studied asphalt mixtures containing electric arc furnace steel slag and copper mine tailings. Four blends were prepared, where the first was with 100% granite aggregate (reference mixtures), the second with 80% granite and 20% copper mine tailings, the third with 80% slag and 20% tailings, and the fourth with 40% granite, 40% slag, and 20% copper mine tailings. Two types of binders and three aging situations were used: without any type, short-term aging, and long-term aging.

The closest situation used in this work, a similar binder without aging, presented a RM varying between 3800 MPa, M1, and 5000 MPa, M3, where these values are similar to those observed in the this study between 5137 MPa and 5640 MPa.

Shafabakhsh and Sajed [59], investigated the mechanical behavior of asphaltic mixtures containing glass cullet as a substitute for the conventional fine aggregate, showing an improvement in performance. In this case, the material used was the one that came closest in terms of granulometry of the sandy waste of this research, though there was a little less material passing through the nº 200 sieve: only 2% glass cullet and 22.7% sandy waste, both with 100% passing through the nº4 sieve. However, considering the same order of magnitude of asphaltic binder content (5.5%) adopted for mixtures containing sandy waste, the RM was observed between 300 and 900 MPa, considering a variation in the content of glass cullet between only 0 and 20%, that is, well below those obtained in the present study.

According to Medeiros et al. [33], samples containing 7.5% IoT showed RM values between 6000 MPa and 6200 MPa, while for 10% iron ore tailing (IoT), the values ranged from 6800 MPa to 7000 MPa, and for 12.5% IoT, between 6600 MPa and 7000 MPa. Souza et al. [60], using the SUPERPAVE method with 30/45 penetration asphalt binder, found that the samples with IoT reached an average RM value of 12,935 MPa, higher than the mixtures without IoT. In addition, Medina and Motta [61] pointed out that the type of asphalt binder and the grain size of the aggregates can directly influence the resilience modulus, suggesting that the grain size of the IoT used may have contributed to the stiffness gain in the mixtures. It is therefore essential to consider the granulometric composition as a whole, in addition to using mechanistic analysis as a parameter to assess the performance of the mixture. Comparing these results with those obtained in the present study, although the mixtures analyzed showed lower RM values than those recorded by Medeiros and Souza, a stiffness behavior similar to the control mixture was observed, corroborating the tendency for stiffness to increase with the addition of waste.

4.2. Fatigue Life Analysis

The fatigue test was carried out with five stress levels, set at 10%, 20%, 30%, 40% and 45% of the ITS. From these results, it was possible to draw the curves of stress difference ($\Delta$$\sigma$) x number of cycles required to rupture (Nf), as shown in Figure 6.

In Figure 7, the resilient characteristic of each mixture was taken into account through the RM, so a relationship of the specific resilient deformation ($ϵ$r) was also established with Nf.

Normally, it is not possible to make a direct comparison of the fatigue curves, but in this case, as can be seen in the

Table 8. Regression parameters of fatigue life curves for the mixtures in this study.

Mixture	Nf = a1 ($\mathit{\Delta }\mathit{\sigma }$${)}^{\mathit{b}1}$			Nf = k1 (${\mathit{\epsilon }}_{\mathit{r}}$${)}^{\mathit{k}2}$
	a1	b1	${\mathit{R}}^{\mathbf{2}}$	k1	k2	${\mathit{R}}^{\mathbf{2}}$
M1	3573	−2.676	0.9894	$8\times {10}^{-9}$	−2.676	0.9894
M2	3514	−2.909	0.9650	$1\times {10}^{-9}$	−2.909	0.9650
M3	4370	−2.802	0.9857	$3\times {10}^{-9}$	−2.802	0.9851

, the RM values are close, and it can be seen that the M3 mixture has a slightly longer fatigue life than the other two. However, a more appropriate investigation is through the analysis of a pavement structure, taking into account the traffic and the mechanical characteristics of the materials. This aspect will be evaluated in the next topic.

4.3. Mechanistic–Empirical Design

Table 9. Characteristics added to the Medina software for the analyzed mixtures.

Asphalt Concrete	M1	M2	M3
Poisson’s Ratio	0.30	0.3	0.3
Modulus (MPa)
Constituent Model	Linear Resilient	Linear Resilient	Linear Resilient
Modulus (MPa)	5640	5137	5244
Characteristics
Type of Binder (CAP)	50/70	50/70	50/70
Specific Gravity (g/cm3)	2.37	2.00	2.00
Tensile Strength (MPa)	2.08	1.16	1.09
Asphalt Content (%)	5.9	5.6	5.5
Void Volume (%)	3.1	3.9	3.1
Standard or Specification	DNIT ES 31	DNIT ES 31	DNIT ES 31
Fatigue Curve
Constituent Model	$K1$ ($ϵt{k}^2$)	$K1$ ($ϵt{k}^2$)	$K1$ ($ϵt{k}^2$)
Regression Coefficient (k1)	8.00 $\times {10}^{-9}$	1.00 $\times {10}^{-9}$	3.00 $\times {10}^{-9}$
Regression Coefficient (K2)	−2.676	−2.909	−2.802
Fatigue Class	1	0	1
FFM	0.83	0.82	0.85

shows the characterization of each asphalt mixture in the MeDiNa program database, showing a significant influence of the granulometric composition on the fatigue characteristics of the mixtures. The table was designed to reflect the interface of the MeDiNa program, making it easier for the reader to familiarize themselves with its visualization and data analysis. Notably, M3 achieved a fatigue class of 1, indicating similar to reference, M1, mixturemechanical performance. In contrast, M2 exhibited fatigue classe of 0.

In

Table 10. CA, PD, and thickness values of the pavements under analysis.

Medium Traffic (N 5 $\times {10}^6$) Layer Thickness
Mixture	% CA in 10 Years	PD in 10 Years (mm)	Surface (cm)	Base (cm)	Sub-Base (cm)	Total Thickness (cm)
M1	29.3	4.4	6.9	20	15	36.9
M2	29.8	4.1	8.2	20	15	38.4
M3	29.9	4.5	6.9	20	15	36.9

, a summary of the designed structures for light traffic is presented by considering a local system road, reliability is 65%, a maximum of 30% of CA, and a maximum of 20 mm of PD in the pavement for the various mixtures evaluated in this study.

Table 10 revealed that, for the traffic level of N = 5 $\times {10}^6$ (medium traffic) for a local road, the pavements containing the mixture M1 and M3 had the same layer thicknesses. While the pavement containing the M2 mixture had the greatest asphalt layer thickness, 8.2 cm, if compared in terms of the percentage of cracked area over 10 years, it still offers ideal performance conditions compared to the M1 and M3 mixtures. Thus, it can be considered feasible to replace sand with IOW in asphalt concrete for use on local roads in the region, without altering the bearing capacity of the pavement. Furthermore, the local reuse of this waste must be a priority, as it eliminates the environmental and economic impact of transporting this material and strengthens income generation with new products that can be consumed locally.

Furthermore, when comparing the M2 and M3 mixtures containing iron ore tailings with those studied by Barros and Silva, which used encapsulated rejuvenating agents, it can be seen that the performance in terms of the percentage of cracked area over 10 years was similar. Studies carried out at the Federal University of Campina Grande, which evaluated asphalt mixtures with asphalt binder viscosity reducers, including rejuvenating agents and recycled mixtures, also showed cracked area values of between 28.2% and 29.9%, as discussed in the study by Medeiros et al. [36].

Guabiroba et al. [62] investigated the mechanical performance of asphalt mixtures produced in Goiás, introducing mixtures identified as Ganisse Bailey and Micaxisto Bailey. For these types of mixture, the MeDiNa method generated values of 29.2% cracked area over 10 years, with wheel tracks of 6.1 and 6.2 mm and a fatigue class of 4.

When comparing these results with those obtained in the present study, it can be seen that the thicknesses of the surfacing layers were similar to those of most of the mixtures analyzed, as was the percentage of cracked area, although the fatigue class was lower. Therefore, it is correct to say that, under the conditions analyzed, iron ore tailings can partially replace fine aggregates in asphalt concrete without compromising performance, in relation to both the reference mixtures and other mixtures containing sustainable aggregates or additives.

5. Conclusions

This paper investigates the potential of reusing iron ore processing waste as a sustainable substitute for fine aggregates in asphalt mixtures, focusing on local road construction. The waste, sourced from Samarco S.A. in Mariana-MG, was integrated into two mixtures, M2 and M3, with 20% and 17% waste content, respectively. These were compared to a reference mixture, M1, to assess their physical and mechanical properties. The study employed the Marshall method, resilience modulus, fatigue life tests, and mechanistic analysis using Brazilian mechanistic–empirical design software. Results demonstrated that asphalt mixtures containing the waste exhibited performance comparable to the reference mixture, underscoring the feasibility of utilizing mining waste specifically for local road infrastructure projects.

Future research should prioritize a comprehensive evaluation of production costs and environmental impacts throughout the entire life cycle of these asphalt mixtures. This analysis should encompass stages from material extraction to maintenance and disposal. For instance, when incorporating sustainable additives such as mining waste, it is essential to assess not only the initial implementation costs but also the long-term benefits, including carbon emissions reduction and increased pavement durability. This approach will enable a more precise assessment of how the technical application of these sustainable mixtures aligns with economic and environmental goals, providing robust data for strategic decision-making in the pavement sector.