Evaluation of high-strength cementitious composite with iron ore tailing and gneiss waste as mineral admixture

Avaliação de compósito cimentício de alta resistência com rejeito de minério de ferro e resíduo de gnaisse como adições minerais

Abstract

This study aimed to evaluate the physical-mechanical properties, electrical resistivity and microstructural characteristics of high-strength cementitious composites (HSCC) incorporating iron ore tailings (IOT) and quarry residue (gneiss powder - GN) at 28 and 180 days. The results showed that IOT and GN samples exhibited high compressive strength (> 90 MPa) and flexural strength (> 6 MPa). Little difference was identified regarding the hydrated phases. In the interfacial transition zone, the IOT and GN samples presented low pore content and reduced extension. It was concluded that high-strength cementitious composites with excellent physical and mechanical and durability performance could be developed using a mixture design based on particle packing and the incorporation of IOT and GN.

Key words
Iron Ore Tailings (IOT); Gneiss waste; Mineral admixture; High-strength cementitious composite; Microstructure

Resumo

Objetiva-se avaliar as propriedades físico-mecânicas, resistividade elétrica e características microestruturais de compósitos cimentícios de alta resistência (HSCC) com adição de rejeito de Mineração de Ferro (IOT) e resíduo de pedreiras (pó de gnaisse - GN) aos 28 e 180 dias de idade. Os resultados mostraram que as argamassas IOT e GN apresentam alta resistência à compressão (> 90 MPa) e à flexão (> 6 MPa). Pouca diferença foi identificada em relação às fases hidratadas. Na zona de transição interfacial, as amostras IOT e GN apresentaram baixo teor de poros e extensão reduzida. Conclui-se que, empregando um planejamento de mistura baseado em empacotamento de partículas, foi possível desenvolver compósitos cimentícios de alta resistência com excelente desempenho físico-mecânico e durabilidade com a adição de IOT e GN.

Palavras-chave
Rejeito de mineração de ferro; Resíduos de gnaisse; Adição mineral; Compósito cimentício de alta resistência; Microestrutura

Introduction

The construction industry consumes approximately 30% of the world’s raw materials and 25% of its available freshwater (Giannetti et al., 2018). However, this sector has great potential to incorporate alternative materials, from industrial and mining waste, contributing to the reduction of the social, economic, and environmental impacts of its production (Almada et al., 2022; Leão et al., 2024; Torres et al., 2024).

In 2022, Brazil produced approximately 640 million tons of coarse and fine aggregates by processing granitoid rocks, such as granite and gneiss (ANEPAC, 2024). The final waste product from rock crushing, a powdery material known as gneiss powder (GN) (Schankoski et al., 2019), is often improperly stored outdoors, leading to environmental degradation and inefficient use of mineral resources (Chen et al., 2015).

Several studies have investigated the use of gneiss powder in both conventional and self-compacting concrete production, demonstrating significant improvements in mechanical properties and durability (Chen et al., 2015; Schankoski et al., 2019; Yannick et al., 2024). Gneiss can also be used in asphalt mixtures, where it has been shown to increase tensile strength by 22% (Chen et al., 2015). Additionally, it has the potential to be used in “eco-friendly cement mortar”, where replacing 50% of cement with GN resulted in a 20% reduction in compressive strength (Yannick et al., 2024).

Iron ore holds significant importance for Brazil, which ranks as the second-largest producer in the world, surpassed only by Australia. In 2020, Brazil produced approximately 400 million tons of iron ore, accounting for 66.36% of the country’s total mineral production (USGS, 2021; BMI, 2021). Of this amount, 370.9 million tons were exported, generating around 36.6 billion dollars in revenue. According to the National Mining Agency (NMA, 2021), the states of Minas Gerais and Pará were responsible for 89.4% of the country’s iron ore production. Iron ore tailings (IOT) are by-products of mining operations, and their disposal poses significant risks to surrounding communities and the environment (Pires et al., 2019). Large volumes of IOT are stored in dams or piles, which leads to leaching processes that contaminate surface water, groundwater, and soil with heavy metals (Luo et al., 2020; Cancio et al., 2018). Brazil has recently faced two major dam collapse tragedies: one in Mariana (2015) and one in Brumadinho (2019). Both occurred in the state of Minas Gerais and resulted in significant loss of life and widespread environmental devastation (Cancio et al., 2018; Chácara; Oliveira Filho, 2021).

The use of IOT as a fine aggregate in cementitious composites has already been well-documented in the literature (Carrasco et al., 2017; Ling et al., 2021; Lv et al., 2021). However, the finest portion (< 75 µm) cannot be used as fine aggregate (sand). Instead, it can be used as an alternative mineral admixture. Studies have shown that pozzolanic activation of IOT is difficult, even after mineral admixture grinding and magnetic separation processes (Almada et al., 2022). Several studies have reported a reduction in mechanical properties when IOT was used in cementitious binders (Cancio et al., 2018; Gu et al., 2022; Magalhães et al., 2018; Morais et al., 2021; Ling et al., 2021). However, there is limited research on its use as a mineral admixture to improve the performance of conventional mortars and concrete (Almada et al., 2022, 2023; Castro et al., 2021; Carvalho et al., 2022). These latter authors observed increases of over 20% in mechanical and physical properties when 40% of IOT was added, compared to the reference mix. However, none of these studies have compared the results of IOT composites with those of other conventional mineral admixtures, such as GN. Moreover, there is a lack of research focusing on the finest fraction of IOT powder, especially regarding its impact on physical and mechanical properties, durability, and microstructural features.

The objective of this study is to evaluate the physical and mechanical properties, electrical resistivity, and microstructural characteristics of high-strength cementitious composites (HSCC) containing the fine portion (smaller than 75 μm) of iron ore tailings (IOT) and quarry waste (gneiss powder - GN) at 28 and 180 days of age.

Materials and methods

Materials

The materials used were: ordinary Portland cement (with up to 5% limestone filler – CPV-ARI, similar to Type III cement according to C150 (ASTM, 2024); quartz sand; silica fume (SiO2 > 90%, specific surface: 20,000 m²/kg, specific gravity: 2,220 kg/m³ and particle diameter: 200 nm); polycarboxylate superplasticizer with normal setting time (density 1,120 kg/m³); iron ore tailings (IOT); and gneiss powder (GN), with 90% passing through a 150 µm sieve (Figure 1).

Figure 1
Illustrative images of the materials used: (a) iron ore tailings (IOT) and (b) gneiss powder (GN)

The IOT was extracted from a mine located in the Iron Quadrangle, in the state of Minas Gerais, Brazil. The silica fume, collected in bag filters, originated from the manufacturing process of metallic silicon and ferrosilicon. It was stored in suitable silos and packaged in kraft paper bags. The gneiss filler was supplied by Mineração Santiago, a company located in the Céu Azul neighborhood of Belo Horizonte, in the state of Minas Gerais. For GN, only the fraction passing through the 150 µm sieve was used. Both IOT and GN were dried at 105 °C for 24 hours

Materials characterization

Portland cement, silica fume, IOT and gneiss powder were characterized in terms of their physical, chemical and mineralogical properties, according to the procedures described below:

1. the chemical composition was determined by X-ray fluorescence (XRF) analysis, using lithium tetraborate fusion. Loss on ignition (LOI) was assessed by calcining the sample at 1000 °C;

2. the mineralogical composition was identified using X-ray diffraction (XRD), with a Philips PW 1710 instrument and CuKα radiation (λ = 1.54 Å, V = 50 kV), at a step size of 0.02°/s. Phase identification was performed using the Crystallography Open Database (COD) in the HighScore Plus software;

3. particle size distribution was obtained using a laser diffraction granulometer, Sympatec brand model Helos 12LA, computerized with a 50 mm lens. A 0.05% sodium hexametaphosphate solution was used as dispersant. For cement analysis, isopropyl alcohol was used as a dispersant in a CILAS 1064 granulometer. The smallest measured particle diameter was 0.03 μm, and the sample was agitated in an ultrasonic bath for 60 seconds before and during the analysis. Quartz sand was analyzed by sieving (ABNT, 2015);

4. specific surface area (SSA) and porosity: The Brunauer, Emmett, and Teller (BET) model was used to calculate the SSA, and the Non-Local Density Functional Theory (NLDFT) model was applied for pore size distribution. The Quantachrome Nova 1200e equipment and the Quantachrome NovaWin software version 11.02 were used to determine the SSA and pore distribution of the IOT, gneiss filler and cement. The samples were previously dried under vacuum at 150 °C for 48 hours to clean the surface. Nitrogen (N2) was used as the adsorbate, and to calculate the complete isotherm, the analysis time for each sample was 24 hours. The specific surface area was calculated using the multipoint BET adsorption isotherm, and porosity was determined using the NLDFT model applied to the desorption isotherm;

5. morphology and microstructure features: Scanning electron microscopy (SEM) was used to obtain micrographs of the particle shape and surface texture. The dust materials were deposited onto self-adhesive carbon tapes and gold-coated using the Quorum Q150R ES equipment. The images were acquired using a Tescan's MEV-VEGA3 SEM, which operates by thermionic emission with a tungsten filament. The samples were vacuumed for 15 minutes prior to imaging, with an accelerating voltage of 20 kV. INCA X-act energy dispersive spectrometer (EDS) from Oxford instruments, coupled to the SEM, was used to perform elemental mapping and identify the chemical elements present in the particles of the materials; and

6. specific gravity: The helium gas pycnometer technique was used to determine the true specific gravity of the materials. The pycnometer model used was the Quantachrome Multi Pycnometer MVP-1. The samples were kept in the oven at 70 °C for 12 hours before the test.

Mix design

Cementitious composite mixes were designed using the particle packing method with the Elkem Materials Mixture Analyzer (EMMA) software, based on the modified Andreasen and Andersen model. The input values were the largest and smallest particle sizes: 2.4 mm (sand) and 0.1µm (IOT and cement), respectively. A distribution modulus of q = 0.30 was adopted, which indicates satisfactory flow capacity (Almada et al., 2022; ABNT, 2019). The 1:3 cement-to-sand mixture showed the best packing performance when combined with 25% silica fume and 40% IOT or gneiss powder (by cement mass). The water-to-cement ratio (w/c) was set at 0.375, and the amount of superplasticizer was adjusted to achieve a target consistency of 230 ± 10 mm on the flow table (Figure 2).

Figure 2
Illustrative images showing the flow of mortars containing (a) IOT and (b) GN

The amount of raw materials used in the composites is shown in Table 1. The raw materials were mixed in an inclined shaft concrete mixer, following this sequence: sand, silica fume, cement, IOT and GN; followed by half of the mixing water; and then the remaining water along with the superplasticizer. The fresh mixtures were cast into metallic molds (cubic, prismatic and cylindrical) and compacted on a vibrating table for one minute. After 24 hours, the specimens were demolded and cured in water until the testing ages of 28 and 180 days.

Table 1
Quantity of raw materials for the production of mixtures

Sample	Cement (kg/m3)	Sand (kg/m3)	Silica Fume (kg/m3)	IOT (kg/m3)	GN (kg/m3)	Water (kg/m3)	Superplasticizer (kg/m3)
Ref.	507	1520	127	0	0	190	10
IOT	479	1437	120	192	0	180	14
GN	469	1407	117	0	188	176	14

Characterization of high-strength cementitious composite

Compressive strength was determined based on the average of five cubic specimens (5x5x5 cm³) (ABNT, 2019), using an EMIC Universal Testing Machine (model DL2000) with a loading speed of (0.45 ± 0.15) MPa/s. Flexural strength was determined using the average of three prismatic specimens (4x4x16 cm³) (ABNT, 2005a), with the same EMIC DL2000 equipament and a loading speed at (1.00 ± 0.15) MPa/s. Water absorption by capillary rise (ABNT, 2012), water absorption by total immersion, open porosity and specific gravity (ABNT, 2005b) were determined based on the average results of four cylindrical specimens (ɸ5x10 cm²), measured with a precision scale with 0.01g accuracy.

Volumetric and surface electrical resistivity tests were performed on cylindrical samples (Ø10x20 cm²), using the PROCEQ Resipod. The equipment features four pins that make contact with the sample surface. A current is applied through the two outer pins, while the resulting potential difference is measured across the two inner pins (ASTM, 2019). Prior the test, the specimens were kept immersed in water for 72 hours.

Microstructural analysis was performed using SEM-EDS to quantify hydrated and unhydrated compounds, as well as to evaluate the width of the Interfacial Transition Zone (ITZ). Samples were coated with a 15 nm layer of carbon and subjected to analysis using a FEI Quanta 3D scanning electron microscope. Images were obtained in backscattered electron mode, with an acceleration voltage of 20 kV and a working distance of 10 mm. ImageJ software was used for binary segmentation and phase analysis (CH, anhydrous cement, C-S-H), and their distribution on the micrographs. Three fragments per sample were tested, and at least 20 micrographs of each fragment were analyzed to determine the number of pores and anhydrous phases.

Thermogravimetric analysis was carried out using a Shimadzu thermal analyzer (model TGA-51) under a nitrogen atmosphere, with a flow rate of 50 mL/min. The temperature ranged from 25 °C to 1000 °C, with a heating rate of 10 ºC/min. This test aimed to quantify the contents of calcium hydroxide and calcium carbonate.

Results and discussion

Materials characterization

The particle size distribution of the raw materials is shown in Figure 3 and Table 2. The average particle size of gneiss powder (GN) was 39.90 µm, which is larger than that of the iron ore tailing (IOT), with an average of 4.70 µm. The IOT result is similar to those reported in the literature: Chinese studies: 5.00 µm – 10.00µm (Liu et al., 2019), 5.00 µm (Huang; Ranade; Li, 2013) and 2.76 µm (Guang; Deng; Wang, 2014); and Brazilian authors - 0.61 µm (Cancio et al., 2018), 0.86 µm (Carvalho et al., 2022) and 0.86 µm (Magalhães et al., 2018).

Figure 3
Accumulated particle size distribution of cement, silica fume, IOT, GN and sand

Table 2
Physical properties of the materials

Physical properties	Diameter	Silica fume	IOT	GN	Cement
Particle size (µm)	D10	3.76	0.43	3.28	7.30
	D50	13.74	3.63	32.62	28.00
	D90	24.41	12.90	87.79	47.00
Specific gravity (g/cm³)		2.20	3.84	2.70	3.12
Specific surface area (m²/g)		22,010	35,113	2,240	1,743
Pore Volume (10-3 cm³/g)		1.901	0.5712	6.725	5.896

The IOT showed the highest specific surface area value (35,113 m²/g), followed by the silica fume (22,010 m²/g), the gneiss powder (2,240 m²/g) and the Portland cement (1,743 m²/g). The SSA value of the IOT is significantly higher than those reported other studies: 29,547 m²/g (Carvalho et al., 2022), 10,107 m²/g (Almada et al., 2023), 6,020 m²/g (Castro et al., 2021), 2,030 m²/g (Cheng et al., 2016), 1,185 m²/g (Zhang et al., 2020) and 1,735 m²/g (Bezerra et al., 2021). This indicates that the IOT used in the present study is considerably finer than the tailings used by other authors. Furthermore, the IOT had the lowest total pore volume (0.571x10^-3 cm³/g) among the raw materials analyzed, followed by the silica fume (1.901x10^-3 cm³/g), the Portland cement (5.896x10-3 cm³/g) and the gneiss powder (6.725x10^-3 cm³/g) (Table 1). This may be due to the presence of finer particles in the IOT and the silica fume that were not detected by the particle size analyzer, thus contributing to the higher SSA values observed.

The GN exhibited a density 30.59% lower than that of the IOT waste, due to differences in their chemical compositions, as shown in Table 3. The IOT is primarily composed of iron (48.00%) and silicon (21.20%) oxides. On the other hand, GN mainly consists of silicon (70.50%) and aluminum (14.30%) oxides. The silica fume is predominantly composed of silicon (61.00%) and iron (13.60%) oxides, while the cement contains mostly calcium (60.90%) and silicon (18.20%) oxides. These results are corroborated by the chemical mapping of the raw materials obtained through EDS analysis (Figure 4).

Figure 4
Chemical mapping (by EDS) of the materials: (a) IOT, (b) Ref., and (c) GN

In the IOT samples, iron (Fe) is the predominant element, followed by silicon (Si) and aluminum (Al). In the reference sample, calcium (Ca) is present in the highest quantity, followed by silicon (Si) and aluminum (Al). For the GN samples, silicon (Si) is the dominant element, with smaller amounts of aluminum (Al) and iron (Fe).

Crystalline phases of IOT and GN are shown in Figure 5. IOT contains kaolinite, quartz, goethite, hematite and magnetite, whereas GN is composed of kaolinite, albite, biotite and quartz.

Figure 5
X-ray diffraction (XRD) results of the studied waste materials: (a) IOT, (b) GN

Mix design by the particle packing method

The optimal cement-to-sand ratios (1:1; 1:2; 1:3 and 1:4) were determined using the least squares method (LSM), as proposed by Garcia et al. (2022). Among the tested ratios, the 1:3 mixture exhibited the best performance compared to the theoretical values (Figure 6a), as indicated by the lowest LSM value. Based on this result, the 1:3 proportion (cement to sand) was adopted for subsequent tests. Silica fume contents of 25%, 30%, and 35% were evaluated to determine the ideal contents of the IOT and the GN, which were varied from 0% to 50% (Figure 6b). The silica fume range was selected due to its impact on workability and tendency to increase water demand. The convergence of the LSM curves for silica fume at 25%, 30%, and 35% indicated an optimal IOT content of 40%. This finding is consistent with the results reported by other authors (Almada et al., 2022, 2023; Castro et al., 2021). Despite the differences in particle size distribution between the IOT and the GN, no significant variation was observed in LSM values. Thus, the same IOT content (40%) was adopted for the GN samples.

Table 3
Chemical composition of materials by FRX

Oxide	Silica fume	IOT	GN	Cement
Fe2O3	13.60	69.90	3.72	3.08
SiO2	61.00	19.20	70.50	18.20
Al2O3	11.30	3.46	14.30	4.37
CaO	3.44	0.22	3.18	60.90
MgO	1.86	0.10	1.60	0.60
TiO2	0.91	0.10	0.39	0.24
P2O5	0.36	0.10	0.14	0.25
Na2O	0.70	0.10	3.85	0.15
K2O	2.31	---	2.85	0.68
MnO	0.26	0.15	0.05	0.07
SO3	---	---	---	3.96
LOI	5.27	5.75	0.89	6.62

Figure 6
Least Squares Method (LSM) results of the (a) cement to sand ratios with varied silica fume contents, and (b) silica fume contents with varied IOT contents

The EMMA software was used to define the theoretical particle-packing curve of the cementitious composite and to estimate the quantity of each raw material. After that, the mix design was adjusted experimentally to determine the water-to-cement ratio (w/c) and the superplasticizer content. The w/c was set at 0.375, and the superplasticizer dosage was adjusted to achieve a consistency of (230 ± 10) mm, measured in the flow table. The final proportions of the raw materials obtained from the experimental tests are presented in Table 4. Although the GN and the IOT have different particle size distribution, the optimal GN dosage was also approximately 40%, indicating that the relationship between density and particle size led to a convergence in the required amounts of these materials within the studied mixtures.

Table 4
Proportion of the raw materials of mixtures

Sample	Cement	Sand	Silica Fume	IOT	GN	Water	Superplasticizer
IOT	1.000	3.000	0.250	0.400	0.000	0.375	0.030
GN	1.000	3.000	0.250	0.000	0.400	0.375	0.030
Ref.	1.000	3.000	0.250	0.000	0.000	0.375	0.020

Physical and durability parameters

Figure 7 shows the open porosity results, all of which were below 4%, significantly lower than the typical range for conventional concrete (10 - 15%) (Marvila et al., 2017). These results suggest the formation of a composite material with high-strength potential. Since the values are low, small variations can converge to large proportional variations - IOT (16.85% compared to Ref.) and GN (14.40% compared to Ref.). However, great similarities between the samples studied can be seen. Particularly at 180 days, only IOT specimen showed a significant increase (58%) in porosity, which may be associated with experimental errors or any unknown deleterious reaction or shrinkage of the sample. The low values of porosity and minimal results variability can be attributed to an effective particle packing effect (item 3.2), which distinguishes this study from others in the literature. Almada et al. (2022) showed the lowest value of open porosity was 13.0% for IOT samples (compared to 2.33% at 28 days in this research). The theoretical porosity also presented low values (0.09%) when compared to those reported by of other authors (Almada et al., 2022; Castro et al., 2021), who found values close to 25%.

Figure 7
Open porosity and density results of the samples

This observation (Figure 7) is further supported by a slight increase in the density of the samples, 9.87% for IOT and 7.29% for GN, when compared to the reference mixture. Although this increase could potentially lead to a higher self-weight in structural applications, the relatively small variation suggests the overall impact may be minimal. The higher density observed in IOT and GN composites may be attributed to the efficient particle packaging promoted by the dosing method.

When analyzing water absorption by immersion (Figure 8), the few existing pores (Figure 7) appear to be poorly interconnected (Figure 8). The packing effect led to a reduction in the quantity and size of pores (Figure 6). The GN mineral admixture resulted in maximum packing, with a 24.56% reduction in water absorption, while IOT resulted in reduction of only 11.50% in this property. All samples exhibited satisfactory results, mostly lower than those found in the literature: 24% (Almada et al., 2022), 13% (Castro et al., 2021) and 17% (Balestra et al., 2020). Regarding the age of the tests, the results were generally consistent, with a notable exception in the IOT samples, which showed a 59% increase in water absorption. This suggests the presence of interconnected surface pores that contributed to increased porosity and water absorption. These pores appear to be relatively large, as they did not significantly influence the capillary absorption results.

Figure 8
Absorption by immersion and capillarity coefficient results

The capillarity coefficient can be an indicator for understanding and predicting the durability of mortars (Magalhães et al., 2020). All results exhibited low values, indicating low pore connectivity, which may suggest an enhanced durability. The GN reduced capillary absorption by 20%, which may be associated with improved silica fume reactivity due to increased nucleation sites and the clogging open pores at advanced ages.

Figure 9a shows the results of volumetric electrical resistivity. This property is of great importance for evaluating durability (ASTM, 2019), as it can also be correlated with the probability of chloride penetration (Medeiros; Lima, 2016). Chloride penetration is considered high for resistivity values lower than 120 Ω.m; moderate between 120 Ω.m and 210 Ω.m; low between 210 Ω.m and 370 Ω.m; very low between 370 Ω.m and 2,540 Ω.m; and insignificant for resistivity values greater than 2,540 Ω.m. The resistivity values obtained in the present study were 1,077 Ω.m and 1,024 Ω.m for IOT and GN samples, respectively, which are considered too low for chloride penetration (Medeiros; Lima, 2016). However, other authors (Almada et al., 2022) reported much lower values for IOT composites (around 36 Ω.m), which may suggest a high probability to chloride penetration.

Figure 9
Volumetric and surface electrical resistivity results

Results of surface electrical resistivity (Figure 9b) were satisfactory for all samples. At 28 days, both IOT and GN samples had similar values, around 207 kΩ.cm, suggesting a very low corrosion potential (Hornbostel; Larsen; Geiker, 2013) According to Balestra et al. (2020), corrosion potential in concrete is very high for resistivity values lower than 5 kΩ.cm; high between 5 kΩ.cm and 9 kΩ.cm; moderate between 9 kΩ.cm and 17 kΩ.cm; low between 17 kΩ.cm and 41 kΩ.cm; very low 41 kΩ.cm and 220 kΩ.cm; and insignificant for values greater than 220 kΩ.cm. However, compared to the reference samples, a decrease of approximately 30% was observed in samples with the IOT and the GN. This reduction may be associated with changes in water interaction at the surface of the samples, with increased water affinity in the IOT and GN mixtures. A significant drop was also observed between 28 and 180 days, with a 45% reduction for Ref. 40% for IOT and 33% for GN. Notably, the mineral admixture of IOT and GN resulted in a smaller reduction, highlighting their positive effect on the performance of these materials.

The adopted w/c ratio (0.375) may have also contributed to the satisfactory electrical resistivity result, as it is lower than the ratios used in other studies, such as a w/c of 0.50 (Almada et al., 2022). Therefore, the lower the amount of water, the lower the porosity and its interconnections, resulting in a denser composite and greater electrical resistivity (Medeiros; Lima, 2016). Another reason for high electrical resistivity may be attributed to the mineral admixture of 25% of silica fume, which leads to pores refinement and reduced permeability.

Therefore, the results of surface and volumetric electrical resistivity indicate that both the IOT and GN composites exhibit adequate durability, evidenced by their very low susceptibility to chloride penetration and corrosion initiation. These findings are also supported by other durability indicators, such as open porosity, water absorption by immersion and density, which also demonstrated satisfactory performance.

Mechanical parameters

Results of compressive and flexural strengths are shown in Figure 10. They can be classified as high-strength materials, with compressive strength values of approximately 94.2 MPa for IOT samples and 98.0 MPa for GN samples. At 28 days, the compressive strength results for all three conditions are similar, indicating that the incorporation of IOT and GN did not significantly change the mechanical behavior. The high compressive strength in IOT and GN composites is associated with efficient particle packing and fine particle sizes. The results indicate that all composites fall within the classification of high-strength cementitious materials. This is particularly evident when compared to other studies involving IOT (Almada et al., 2022, 2023), which reported compressive strength results of 48.30 MPa and 44.00 MPa, respectively. Regarding the age of the test, the Ref. sample showed the greatest mechanical gain with an increase of 14%, while IOT and GN showed slight increases of 3% and 7%, respectively. At 180 days, GN exceeded 100 MPa compressive strength.

Figure 10
Compressive and flexural strength results

There is a slight variation in the flexural strength by the mineral admixture of IOT and GN, resulting in increases of 2.69% and 10.32% compared to the Ref., respectively. This is due to improved bonding of waste particles within the cement matrix, where they act as nucleation sites and enhance the pozzolanic activity of silica fume. This contributes to better hydration in cementitious composites with a low w/c ratio (0.375). However, when analyzing strength development over time (from 28 to 180 days), the Ref. mix showed an increase of 12.73%, while IOT and GN composites exhibited slight reductions of 1.37% and 5.14%, respectively. This suggests that strength gains in IOT and GN composites primarily occur within the first 28 days, with minimal variation thereafter. This demonstrates that nucleation points accelerate the reactions of silica fume (Fajardo; Souza; Santos, 2025).

Microstructure parameters

The quantity of pores of the composites at 28 days was determined by image analysis (Figure 11). It was obtained by the area of pores in relation to the total area of the sample (2D images). All samples had similar percentage of pores: Ref. (1.46%), IOT (1.49%) and GN (1.70%). The data obtained by image analysis followed the same trend as the values obtained by open porosity.

Figure 11
Quantity of pores and anhydrous phase by SEM and calcium carbonate and hydroxide mass

Ref. and IOT samples exhibited similar quantity of anhydrous phases, with 2.97% and 3.10%, respectively, while the GN sample presented a slightly higher value of 5.22%. However, it is important to note that, despite this small variation, all samples showed a high degree of hydration and a well-densified cement paste.

Figure 11 shows the thermogravimetric analysis results. The mass loss related to both calcium hydroxide (CH) and calcium carbonate was low in all samples. Although a difference is observed between the Ref. and GN samples, these variations in mass loss are not significant in the TGA analysis. Figure 12 shows BSE-SEM micrographs used to assess porosity of the interfacial transition zone (ITZ), particularly in the size range of 5 µm and 50 µm.

Figure 12
Segmentation of Ref. sample to porosity analysis and development of porosity of samples throughout the ITZ

All composites presented the highest pore content closer to the aggregate (within the 5 µm range) and with a tendency to reduce up to the 50 µm range. The Ref. sample had the highest pore content, 2.86%, followed by GN (1.71%) and IOT (1.29) samples. Therefore, both the IOT and GN mineral admixture resulted in a denser ITZ microstructure, supporting the pore count results (Figure 12) and open porosity data (Figures 6 and 7). This effect may be attributed to enhanced particle packing, where the fine particles were also able to lodge close to ITZ, densifying this region. Their presence, associated with the nucleation effect, did not reduce the mechanical properties, as shown in Figure 8. In samples with IOT and GN, porosity is more evenly distributed within 5-10 µm range, while in the Ref. sample, this distribution occurs primarily in the 20-25 µm band. It can be seen that both mineral admixtures reduce the size of the interfacial transition zone, resulting in a denser, stronger, and more durable cementitious matrix (Almada et al., 2022, 2023).

Conclusions

This paper evaluated high-strength cementitious composite with iron ore tailings (IOT) and gneiss powder (GN) as mineral admixture. Based on the experimental study, the following conclusions can be drawn:

1. the physical characteristics (particle size of 4.7 µm, surface area of 35,133 m²/g, and density of 3.85 g/cm³) and chemical compositions (48% Fe₂O₃ and 21.20% SiO₂) of IOT and GN influence the performance of high-strength cementitious composites;

2. the fine particles of IOT and GN, along with the effective packaging effect, resulted in highly densified composites with reduced porosity (less than 2.6% at 28 days);

3. water absorption was reduced by the mineral admixture of IOT and GN due to the filler effect and decreased pore interconnectivity, as evidenced by low water absorption by immersion, low theoretical porosity, and low capillarity coefficient;

4. both mineral admixtures improved the mechanical properties, with compressive strength exceeding 90 MPa, flexural tensile strength above 6 MPa, and increased durability. Surface and volumetric electrical resistivity values were higher than 47 kΩ.cm and 370 Ω.m, respectively. These results are attributed to improved particle anchoring within the cement paste, enhanced pozzolanic activity of the silica fume due to nucleation sites provided by the IOT and GN, effective particle packing, and low w/c ratio (0.375); and

5. microstructural analysis of the interfacial transition zone revealed low pore contents in the IOT and GN samples (1.29% and 1.71%, respectively) within the 5 µm range near the aggregate. Additionally, both wastes effectively reduced the thickness of this weaker zone zone by approximately half, concentrating porosity within the 5 to 10 µm range.

Therefore, by employing a mix design approach based on the particle packing method, it was possible to develop high-strength cementitious composites with excellent physical, mechanical and durability performance. Furthermore, the study demonstrated the feasibility of producing high-strength composites through incorporation of IOT and GN waste materials.

Acknowledgments

The authors acknowledge National Council for Scientific and Technological Development (CNPq - grants 304596/2022-1 and 409493/2023-6), Coordination for the Improvement of Higher Education Personnel (CAPES) and Minas Gerais Research Support Foundation (FAPEMIG - grants APQ-00584-21, APQ-00062-22, APQ-06746-24, APQ-04155-23) for their financial support.

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Edited by

• Editor-chefe:
  Enedir Ghisi

• Editora convidada:
  Luciani Somensi Lorenzi

Publication Dates

• Publication in this collection
  12 Sept 2025
• Date of issue
  2025

History

• Received
  27 Jan 2025
• Accepted
  27 May 2025