From Waste to Resource: Evaluation of the Technical and Environmental Performance of Concrete Blocks Made from Iron Ore Tailings

Abstract

This study investigates the use of iron ore tailings (IOTs) as recycled aggregates in segmental blocks, focusing on technical performance, CO₂ emissions, and embodied energy using the cradle-to-gate approach. IOTs replaced fine aggregates in concrete at 25%, 50%, and 75% by volume, achieving compressive strengths of 16.23 MPa, 10.02 MPa, and 3.93 MPa, respectively. Raw material production accounted for 98% of CO₂ emissions and 86% of embodied energy. Producing blocks at mining sites offered limited environmental benefits due to longer transport distances. Despite this, the results showed a 6% reduction in CO₂ emissions and a 35% improvement in mechanical–environmental performance (CO₂ emissions weighted by compressive strength) compared to traditional concrete. These findings underscore the potential of IOT-based concrete for segmental block production.

1. Introduction

The worldwide rise in Greenhouse Gas (GHG) emissions and growing energy consumption have increased the urgency of addressing climate change. The construction sector, the second largest energy consumer globally, contributes approximately 38% of all primary GHG emissions [1]. In addition, cement production is the third largest source of human-made CO₂ emissions [2].

The United Nations (UN) Sustainable Development Goal (SDG) 13 focuses on combating climate change. While it does not directly target the construction industry, its goals (13.1; 13.2; 13.3) are relevant to the sector, particularly in managing products and waste. SDG 12, which promotes “Responsible Consumption and Production”, supports efforts related to reuse, recycling, and the sustainable use of natural resources [3]. As a result, reducing the carbon footprint of construction products is key for meeting climate change targets [4], particularly in developing countries where most future construction will take place [5].

Recent studies show that the construction sector can make use of iron ore tailings (IOTs) as a way to reduce environmental impacts, due to the large amount of waste produced during mineral extraction and processing, which corresponds to approximately 35% to 65% of the extracted material [6]. The difficulties of storing these tailings, along with the high costs of monitoring and maintaining tailing dams, have led to the consideration of IOTs as a useful alternative raw material [7]. Additionally, mining operations often cause serious social and environmental problems, pushing the sector to rethink its economic and production models [8]. In this context, adopting circular models that focus on recycling and reusing waste offers a promising solution [9].

At the same time, there is increasing scientific interest in replacing natural materials with recycled materials (e.g., aggregates, fillers, and supplementary cementitious materials) derived from IOTs in concrete compositions (substitution rates vary depending on the use, reaching up to 100% of conventional aggregates) [10,11,12]. These efforts include high-volume replacements [13,14,15], specific applications [10,16], and the integration of social and environmental values, local identity, and social expression [17]. However, it is important to consider not only performance and durability but also the environmental impact of these replacements. This can be achieved using Life Cycle Assessment (LCA) [18,19]. Still, research on LCA for unconventional materials, like concrete made with IOTs, is limited.

Using IOTs in concrete enables the production of interlocking segmental blocks in various shapes, textures, and colors, typically assembled without mortar [20]. This method is particularly beneficial for slope stabilization in Brazil, which faces significant landslide risks, especially in mountainous regions during the summer. Between 1988 and June 2022, 959 landslides resulted in 4146 fatalities across 269 municipalities in 16 states [21]. Approximately 5.7% of Brazil’s land is highly vulnerable to landslides, with an additional 10.4% at moderate risk, primarily in the Southeast and South regions [22].

While segmented retaining walls reinforced with geosynthetics offer advantages, they raise sustainability concerns due to high natural resource consumption. However, research in this area is scarce, predominantly focusing on filling materials [23,24,25].

This study aims to fill these gaps by exploring the production of eco-efficient concretes using IOTs, promoting resource efficiency, reducing environmental impacts, and fostering innovative practices in construction. It investigates the mechanical performance of IOT-based concrete for segmented blocks, assessing their potential to lower GHG emissions and energy use. The study’s main contribution is the integration of results from a mix design that incorporates high amounts of IOTs into concrete blocks, specifically for segmental retaining walls. Additionally, it examines the environmental performance in two scenarios: a plant at the mining site and another facility serving the final consumer.

2. Materials and Method

2.1. Materials, Dosages, and Methods for Characterizing the Concrete

The concrete production used high-early-strength Portland cement (Sete Lagoas, Minas Gerais, Brazil) CPV-ARI, equivalent to American Type III); IOTs from the Maravilhas II dam in Itabirito, MG, Brazil; natural quartz sand (NQS) from Porto Firme, MG, Brazil; Artificial Gneiss Sand (AGS) from São Geraldo, MG, Brazil; Crushed Gneiss Aggregate (CGA) with particle sizes from 4.75 to 12.5 mm, sourced from Belo Horizonte, MG, Brazil; and a plasticizing additive, Murasan BWA 16 (MC Bauchemie), for concretes with low water/cement ratios.

Table 1. Specific mass and particle size parameters of the aggregates.

Parameters	IOT	AGS	NQS	CGA
Specific mass (g/cm3)	3.99	2.86	2.63	2.66
D10 (mm)	0.13	0.13	0.35	6.35
D50 (mm)	0.75	0.68	0.97	8.70
D90 (mm)	3.62	2.10	4.25	11.85

shows the specific mass values and particle size parameters of the materials used in the study.

Concrete mixtures with IOTs were designed to achieve high compaction levels based on the packing criterion from the Modified Andreassen model [26]. IOTs were used to replace part of the fine aggregates in these mixtures. In the first phase of the study, replacement proportions of 25% (IOT25), 50% (IOT50), and 75% (IOT75) by volume were tested to optimize IOT use for the intended application (segmental blocks for containment), ensuring good performance.

Despite the significant difference in specific masses between aggregates and IOTs, the high specific mass of concrete is still beneficial for the stability of containments made with the studied blocks. However, using high proportions of IOT distorts the particle size distribution curves of the mixtures, which negatively affects particle packing. Additionally, optimal moisture levels between 7% and 15% were identified, resulting in the highest apparent densities in the fresh state.

To evaluate the performance of mixtures with different moisture contents and IOT replacing fine aggregates, six test specimens (CPs) were made for each dosage (IOT25, IOT50, and IOT75). The water content was adjusted to match the optimal water/fines ratio for maximum particle packing. Cylindrical molds, 10 cm high and 5 cm in diameter, with a volume of 196.34 cm³, were used for the test specimens. These molds were filled with the fresh mix and compacted in two layers of 5 cm each.

The physical characterization of the concrete at 28 days followed the guidelines in NBR 9778 [27], including the specific mass, water absorption, and void index. The mechanical characterization involved testing the compressive strength of three specimens after a 7-day curing period, according to the NBR 5739 method [28]. This approach was chosen based on the intended application and the use of high-early-strength cement for producing precast blocks.

2.2. Life Cycle Assessment Method Used for the Segmental Blocks

After the laboratory tests for concrete characterization, a simulation study was carried out to apply the results to the production of segmental blocks. Two block models were used, block A (40 × 40 × 19.5 cm) and block B (40 × 30 × 19.5 cm), each with two holes, as shown in Figure 1.

Table 2. Proportions of the materials in the concretes considered in the study (kg/m³).

Concrete-Block	Cement	IOT	CGS	AGS	NQS	Water	Plasticizer
IOT25-A	283.1	546.6	728.8	587.7	540.4	86.8	1.5
IOT50-A	280.5	1085.7	722.2	388.2	357.0	95.0	1.5
IOT75-A	271.9	1578.6	700.0	188.2	173.0	122.8	1.5
REF-A	234.5	0.0	599.7	1934.4	0.0	22.5	0
IOT25-B	283.1	547.8	728.8	587.7	540.4	86.8	1.5
IOT50-B	280.5	1085.7	722.2	388.2	357.0	95.0	1.5
IOT75-B	271.9	1578.6	700.0	188.2	173.0	122.8	1.5
REF-B	427.0	0.0	546.0	1761.4	0.0	41.1	0

presents the concrete composition. Additionally, to compare them with blocks made from IOT concrete, an analysis was performed on blocks made from conventional concrete (REF), which did not include IOTs and only used NQS as fine aggregate.

2.2.1. CO₂ Emissions and Embodied Energy Assessment

To evaluate the environmental impacts comprehensively, calculations for CO₂ emissions and embodied energy were conducted at each stage of the life cycle.

Equation (1) was used to calculate CO₂ emissions from the materials ($E_{{CO2}_{mat}}$) in kgCO₂. In this equation, $F_{e_{mat}}$ is the CO₂ emission factor for the material (in kgCO₂ per unit), and $Q$ is the quantity of material (in mass).

$$E_{{CO2}_{mat}}=F_{e_{mat\times }}\times Q$$

(1)

Equation (2) was used to calculate CO₂ emissions from transportation ($E_{{CO2}_t}$) in kgCO₂. In this equation, $F_{e_t}$ is the CO₂ emission factor for transportation (in kgCO₂ per tonne.km), $D$ is the distance traveled (in km), and $M$ is the mass of the material transported (in tonnes).

$$E_{{CO2}_t}=F_{e_t}\times D\times M$$

(2)

Equation (3) was used to calculate CO₂ emissions from the production process ($E_{{CO2}_{pp}}$) in kgCO₂. In this equation, $F_{e_{pp}}$ is the CO₂ emission factor for production (in kgCO₂ per kWh), $P$ is the power demanded (in kW), and $t$ is the operating time (in hours).

$$E_{{CO2}_{pp}}=F_{e_{pp}}\times P\times t$$

(3)

Equation (4) was used to calculate the embodied energy in the materials $({EE}_{mat}$) in MJ. In this equation, ${IE}_{mat}$ is the embodied energy index (in MJ/unit), and $Q$ is the quantity of material (in mass).

$${EE}_{mat}={IE}_{mat}\times Q$$

(4)

Equation (5) was used to calculate the embodied energy from transportation (${EE}_t$), in MJ. In this equation, ${IE}_t$ is the embodied energy index for transportation (in MJ/kmt), $D$ is the distance traveled (in km), and $M$ is the mass of the material transported (in tonnes).

$${EE}_t={IE}_t\times D\times M$$

(5)

Finally, Equation (6) was used to calculate the embodied energy from the production process (${EE}_{pp}$) in MJ. In this equation, 3.6 is the conversion factor from kWh to MJ, $P$ is the power demanded (in kW), and $t$ is the operating time (in hours).

$${EE}_{pp}=3.6\times P\times t$$

(6)

2.2.2. Scope of the LCA

The scope of this study was delimited from cradle-to-gate, as shown in Figure 2, covering inputs such as raw materials, water, energy, and fuel, as well as outputs. This approach was chosen because the use and end-of-life phases are comparable among the blocks [30], making the exclusion of the “end-of-life” stage appropriate for the proposed comparison.

The impact of producing segmental blocks, with and without the addition of IOTs, was analyzed under two scenarios: (i) an industrial plant located within the mining company (Factory X) and (ii) an industrial plant situated in a metropolitan region (Factory Y). This analysis aims to guide decisions on the optimal location for the production plant to achieve better environmental performance. The authors recognize that this is not an exhaustive approach, and considering additional scenarios or other environmental impact categories into the Life Cycle Assessment would enhance its comprehensiveness.

The functional unit for this study is 1 cubic meter of segmental block used for slope facing. For the Life Cycle Inventory (LCI), all inputs and outputs from the life cycle phases were considered. Emission and embedded energy factors were determined by averaging the minimum and maximum values from the SIDAC data collection reports (2022) for cement, CGA, NQS, and AGS [31], as shown in

Table 3. Emission and embedded energy factors considered in the research.

Material	Emission Factor (kg CO2/tonne)	Embedded Energy Index (MJ/tonne)
Cement	885.35	4906.00
CGA	4.67	49.00
AGS	4.67	49.00
NQS	12.51	193.40
Plasticizer	940.0	13,000.00

.

The CO₂ emission factor and embedded energy index of IOTs were not considered. This is because the CO₂ emissions and energy use from dam de-characterization activities are already included in the life cycle of iron ore. Plasticizer factors were taken from the study by Alnahhal et al. [32].

The transportation methods and distances traveled for the raw materials used in block production were estimated using Google Maps (web version) and are outlined in

Table 4. Distance and transport modality considered in the study.

Material	Transport Modality	Distance (km)
		Factory X	Factory Y
Cement	Road	97	61
IOT		1	51
CGA		35	19
NQS		35	19
AGS		35	19
Plasticizer		670	625

.

The emission factor for road transportation was considered to be 37 gCO₂Eq/tonne.km, based on average values from Campos et al. [33], and 1.62 MJ/tonne.km, the average value from Tavares [34]. For embedded energy, average impact indices for diesel vehicles were used, as the trucks used for transportation vary in models, years of manufacture, and capacities.

During the production stage, emissions and energy consumption from the process were considered, including the following: (i) dough preparation, (ii) pressing, (iii) internal transport using conveyor belts and forklifts, and (iv) curing, packaging, and storage. Emissions from office and support activities were excluded, focusing only on the stages directly related to production.

The production stage in both factories (X and Y) was assumed to be the same, with identical equipment and operating times. To calculate the CO₂ emissions from the process, the emission factor was based on the average values over the last 5 years for electricity generation in Brazil’s National Interconnected System [35].

Table 5. Power and operating time of electrical items used in Factories X and Y to produce 1 m³ of blocks.

Item	Power (W)	Operating Time (h)
Hopper	7354.99	0.09
Scale and planetary mixer	25,742.5	0.10
Vibro press	17,468.1	0.26
Ascending conveyor	960	0.10
Lamps	1200	0.26
Descending conveyor	960	0.10

shows the power and operating time data for producing 1 cubic meter of blocks.

2.3. Eco-Efficiency Indicator for the Segmental Blocks

The eco-efficiency indicator considers the environmental impact weighted by mechanical performance [36,37]. Establishing a link between environmental and mechanical performance is essential for evaluating a material’s suitability [36], and this method is commonly used in concrete LCA. In this context, a lower indicator value means better environmental–mechanical performance of the material [37].

Equation (7) was used to calculate the eco-efficiency indicator. In this equation, ${CO}_{2}Emissions$ is expressed in kgCO₂Eq/m³, representing the environmental impact, and ${IE}_t$ is expressed in MPa, representing the mechanical performance.

$$Eco-efficiency={\displaystyle \frac{{CO}_2 Emissions}{Compressive Strength}}$$

(7)

In this study, only CO₂ emissions and compressive strength were considered in calculating the indicator, expressed in kgCO₂Eq/m³MPa. To ensure a thorough analysis of the environmental–mechanical performance of segmental blocks, alternative concretes (IOT25, IOT50, and IOT75) were compared with those made from conventional aggregates (REF concretes). These REF concretes have strengths of 6 and 18 MPa for type A and B blocks, respectively, as per the supplier’s catalog [38]. These considerations enable a more thorough assessment of the mechanical and environmental performance of the segmental blocks, providing valuable insights for material selection and sustainability decisions in construction.

3. Technical Discussion

3.1. Concrete Mixture Design

The particle size distribution of solids in the mixtures was analyzed using the Modified Andreassen curve to determine the proportions of each material in the concrete mixtures (IOT25, IOT50, and IOT75). This method was chosen for its suitability in addressing broader particle size distributions, as in the present study, which involves various aggregates with differing particle size ranges—a fact successfully demonstrated in previous studies by the research group. The good alignment of the experimental curve with the reference curve indicates a theoretical ability to achieve high packing densities. The particle size distribution curve for the IOT25 mixture was adjusted to closely match the Modified Andreassen curve, using a distribution coefficient of q = 0.3, which is within the recommended range of 0.2 to 0.37. Lower values are better for fluid mixtures, while higher values are preferred for no-slump mixtures [14]. This specific approach involves a no-slump concrete and compaction. On the other hand, the gneiss aggregates present an aspect ratio unfavorable for fluidity, making 0.3 the most adequate distribution coefficient for this study. Figure 3 shows the particle size distribution curves of the dry mixtures, along with the Modified Andreassen optimal packing curve used in this study.

The particle size distribution curve of the IOT25 mixture closely matches the Modified Andreassen curve, as it was specifically adjusted for a good fit. In contrast, the larger gap between the curves of the IOT50 and IOT75 mixtures compared to the optimal curve suggests lower packing densities. This is consistent with the higher optimal moisture contents in these mixtures [39]. Specifically, IOT25 achieved optimal moisture at 9.5%, IOT50 at 10.5%, and IOT75 at 14.0%.

However, packing density is not only influenced by water content; other factors also play a role. Compaction energy is another key factor [40]. In this study, all specimens were molded with the same compaction energy. If good packing is not achieved or if more energy is needed to compact the particles into the voids, it can lead to higher air content in the matrix. Previous studies suggest that IOT tends to trap air in composites [14,41], with the size and shape of the clay particles in IOT playing a significant role in this behavior [41].

Figure 3. Particle size distribution curves of the dry mixtures compared to the optimal particle size distribution curve (Modified Andreassen) for a q-factor equal to 0.3 [40].

Figure 3. Particle size distribution curves of the dry mixtures compared to the optimal particle size distribution curve (Modified Andreassen) for a q-factor equal to 0.3 [40].

3.2. Compressive Strength of the Concretes and Segmental Blocks

IOT25 concrete is the densest among the IOT concretes, with a density of 2310 kg/m³. In contrast, IOT50 is the lightest, with a density of 2180 kg/m³, while IOT75 has an intermediate density of 2200 kg/m³. IOT25 also recorded the highest compressive strength, which was 43.2% higher than IOT50 and 310.3% higher than IOT75. Figure 4 presents the compressive strength data for REF and IOT concretes and blocks. The compressive strengths of blocks A and B were calculated using gross area-to-net area ratios of 1.896 and 1.473, respectively.

The results for density and mechanical strength confirm that the IOT50 and IOT75 concretes were more porous. This increased porosity is a direct result of the poorer packing performance of the dry mixture, as seen in the particle size distribution curves compared to the optimal Modified Andreassen curve (Figure 3). Additionally, the higher water-to-cement ratio needed to achieve optimal moisture due to the poor packing of solid particles leads to larger interstitial volumes to hold water [39]. Other factors mentioned earlier also contribute to the low specific densities of the high-IOT concretes.

As a direct result, poorer mechanical performance was observed, as shown by the decreasing and nearly linear curve, along with lower apparent specific mass. It is important to note that the replacement is conducted in large volumes (up to 75% of the fine aggregate volume) with a much denser material (Table 2). However, the greater density of the IOT was not enough to offset the increased porosity of the matrix, which grew as the IOT content increased.

Even though IOT75 has a higher apparent density than IOT50, due to its greater volume of IOT, it still could not offset the effect of its increased porosity compared to IOT25. As a result, the hypothesis that higher levels of IOT replacement would lead to denser concrete and improve the stability of containment structures made from high-IOT-content concrete was not confirmed.

Another key point is the goal of using larger amounts of the residue to reduce the consumption of natural resources and contribute more to the environmental sustainability of the mining sector. However, increasing IOT usage reduced performance (compressive strength) and did not result in a denser material, which would have been beneficial for improving stability in structures stabilized with segmental block-based solutions.

These observations suggest that achieving denser particle packing is key to improving performance and using larger amounts of IOT. However, this depends on the particle size distribution of the residue, which limits the amount that can be used to create an optimized blend of particles with cement and aggregates. Adjusting the granulometry is not simple, as it requires additional processes for separation, leading to not only more complex reuse of the residue but also increased impacts, costs, and secondary waste. Alternatively, blending the mixture with other aggregates to achieve optimal distribution restricts the amount of IOT and requires higher quantities of other aggregates, ultimately making the concrete more expensive and less sustainable.

4. Results and Environmental Discussion

4.1. LCA of the Segmental Blocks

The potential of IOT-based concrete to reduce carbon emissions and energy use was evaluated using LCA methodology to assess its feasibility in producing segmental blocks for civil construction. Figure 5 shows the results for each type of block.

In the Factory X scenario, blocks produced with 25% fine aggregate replacement with IOT show a slight 2% increase in CO₂ emissions compared to concrete with 50% replacement, and a 4% increase compared to concrete with 75% replacement. Similarly, embodied energy increases by 4% compared to IOT50 and 8% compared to IOT75. Additionally, concretes with the same replacement proportion showed no significant differences between blocks A and B.

In the Factory Y scenario, blocks with 75% fine aggregate replacement with IOT show the highest CO₂ emissions and embodied energy, mainly due to the IOT transport stage. For type A blocks, IOT25 concrete resulted in a 77% increase in CO₂ emissions and an 86% increase in embodied energy compared to REF-A concrete. In contrast, for type B blocks, concretes with 25% waste showed similar results to conventional ones, with about a 6% reduction in CO₂ emissions and a 4% increase in embodied energy.

Figure 6 shows the CO₂ emissions and embodied energy results for each input used in block production, with cement having the greatest impact on both CO₂ emissions and embodied energy.

Blocks made with IOT75 show that cement accounts for about 95% of CO₂ emissions and 88% of embodied energy. For IOT50, these figures are 96% for CO₂ emissions and 90% for embodied energy. These results highlight a significant increase in cement’s contribution as more IOT is used in the concrete, alongside a reduction in sand consumption.

Cement consumption is the main factor in every LCA of cement-based composites, due to its major impact on CO₂ emissions and embodied energy [42]. This dominance often overshadows the role of other inputs, which can discourage efforts to promote the use of recycled aggregates for improving environmental performance.

In this context, packing performance becomes an important factor, especially when combined with Supplementary Cementing Materials (SCMs). However, it is important to consider the ongoing debate about the role of cement in performance, as well as the costs and complexities of producing multi-component mixtures. There are also concerns about the impact of processing and transporting SCMs to the production plant. These factors contribute to the broader discussion of optimizing cement use while balancing performance, cost, and environmental considerations.

When considering only the aggregates, both natural (NQS) and artificial (AGS) sand are the main contributors to CO₂ emissions and embodied energy (Figure 7). In concrete containing IOTs, these aggregates make up about 64% of CO₂ emissions and 70% of embodied energy. In contrast, in conventional concretes, AGS accounts for roughly 75% of these impacts, making it the leading contributor among the aggregates. Furthermore, exploring the conversion and utilization of CO₂ from aggregates not only has the potential to reduce the carbon emissions associated with concrete production but also fosters a more sustainable and circular approach in the construction industry.

Figure 8 shows the CO₂ emissions and embodied energy for each stage of block production. The manufacturing of materials is the stage with the largest impact, contributing 98% to CO₂ emissions and 88% to embodied energy. Additionally, block manufacturing in Factory X results in about a 66% reduction in transportation impacts compared to Factory Y for both CO₂ emissions and embodied energy.

A notable observation is the greater contribution of materials to the REF-B results produced at Factory Y, which is due to the reduced impacts associated with transportation. As a result, CO₂ emissions and embodied energy are 43% lower at Factory Y compared to those at Factory X.

On the one hand, it seems clear that transporting all the inputs to a mining plant, rather than just transporting the residue from the mining plant to a block production plant closer to the consumption market, would result in fewer transportation-related impacts. This is especially evident when considering the broader perspective, as blocks would then need to be transported again to the consumption market, potentially doubling the transportation-related impacts.

Indeed, while the observation may seem straightforward, it is important to approach it with caution. The situation is highly sensitive to various factors, including the specific configurations of consumption markets and the locations of material sources. These variables can significantly influence transportation costs and environmental impacts, making a more nuanced analysis essential.

On the other hand, in a hypothetical scenario where the mining company is strongly incentivized to promote the utilization of their residues, the production of blocks in integrated plants becomes a relevant consideration.

Managing residual materials as alternative inputs in construction material production is undeniably challenging. Substituting high-quality materials, such as natural quartz sand, with a residue requires technological approaches that may not be easily adopted or supported by conventional producers. It is important to recognize that the construction market is often conservative and gaining acceptance for new materials presents a significant challenge. This shift may require not only time but also incentives to encourage adoption, ensuring the new materials meet performance, cost, and sustainability standards.

4.2. Eco-Efficiency Indicators for the Segmental Blocks

Figure 9 presents the results of the eco-efficiency indicators for each concrete block relative to their compressive strength, manufactured in Factories X and Y. The results from both factories were similar, with the blocks made with IOT75 (IOT75-A and IOT75-B) having the lowest compressive strength and exhibiting the poorest mechanical–environmental performance indexes. These blocks showed values 155% higher than the IOT50 blocks and 309% higher than the IOT25 blocks, which demonstrated the best eco-efficiency results among the blocks with IOT incorporation.

However, despite having the highest CO₂ emission indicator, the REF-B block showcased the best performance, with an average eco-efficiency indicator of 12.72 kgCO₂Eq/m³MPa. This value is 35% lower than that of the REF-A block and 4% lower than that of the IOT25 block due to its high compressive strength. These results reinforce the importance of balancing environmental and mechanical performance to achieve optimal eco-efficiency.

The environmental–mechanical approach is indeed insightful and sheds light on the key factors discussed throughout the text. It effectively highlights the impact of incorporating large amounts of IOT in a mixture, while also neglecting crucial factors that affect performance, such as packing density. It is important to emphasize that performance is a critical aspect in an honest evaluation of eco-efficiency, as failing to meet quality or performance specifications renders a proposal unfeasible.

It is common practice in academic works to consider partial replacements of cement or aggregates in modest percentages. However, this raises questions about the effectiveness of such practices and the feasibility of incorporating low amounts of a residue into a cement matrix.

Furthermore, the academic community must expand efforts to make it feasible to incorporate larger quantities of residues while ensuring performance [14,40,43,44,45,46]. It is important to recognize that consolidating information suggesting that only small amounts of residues in cement-based composites are feasible for “promising” products could impede progress in this area and discourage industry involvement. The success stories of ground granulated blast furnace slag and fly ash, which have been consistently incorporated in substantial quantities as substitutes for clinker in Portland cement, serve as noteworthy examples.

It is worth noting that in the context of a generator interested in incorporating large quantities of a large-scale residue into a product, investing in a plant for using modest amounts may not make sense. Similarly, for a producer outside the generator, investing in processes, changing production lines, training staff, acquiring new equipment, and implementing more complex quality control methodologies to incorporate small amounts of an input, even if it is obtained at no cost, may not be economically viable. Thus, performance becomes the key determinant of eco-efficiency, as proposals that fail to meet quality or performance standards are ultimately unfeasible.

Thus, performance emerges as the primary consideration and the foremost challenge, with the environmental–mechanical eco-efficiency approach accurately highlighting this aspect. Overcoming performance obstacles associated with extensive incorporations not only facilitates effective environmental contributions but also enhances the attractiveness for the sectors involved.

5. Conclusions

The results of this study demonstrate the potential of IOT-based concretes in reducing carbon emissions and energy expenditure for use in segmental block production in civil construction. This approach not only addresses the needs of civil construction but also contributes to reducing environmental impacts. It offers practical solutions for reducing CO₂ emissions and energy consumption, indicating a promising path toward a more sustainable construction sector in line with UN sustainable development goals. Some challenges and specific considerations in this regard can be summarized as follows:

• Cement consumption significantly impacts CO₂ emissions and embodied energy in cement-based composites, often overshadowing the importance of other inputs like recycled aggregates. Therefore, reducing cement consumption is crucial for environmental performance.
• The aim to increase residue consumption is hindered by impaired concrete performance, which may necessitate costly processes or limit the amount of residue used. In this sense, factors such as compaction energy and clay content in iron ore tailings influence concrete packing density and hydration, affecting strength and durability.
• Logistics affect residue use, and despite the observations, the approaches of integrated plant production must be considered. Other scenarios should be investigated.
• Challenges in using residues in the production of cement-based composites include substituting low-cost, high-quality materials with residues and gaining acceptance in a conservative construction market.
• Efforts to incorporate greater quantities of waste, such as in construction materials or geotechnical applications, while ensuring performance are essential for progress and industry engagement.
• However, it is essential for studies to thoroughly assess the potential adverse effects of utilizing waste materials on both human health and the environment before implementing this solution in practice.