Engineering geogrid enabled low carbon and aggregate efficient flexible pavements

Abstract

As the world emphasizes sustainable infrastructure, the pavement sector is under pressure to minimize material utilization and environmental footprint while maintaining functionality. Flexible pavements are mainly responsible for consuming virgin aggregates and related carbon emissions. Reinforcement with geosynthetics, notably geogrids for flexible pavements, provides an effective route to managing these challenges through improved structural efficiency and material savings. This study comprehensively evaluates the impact of geogrid reinforcement on the structural and environmental performance of flexible pavements constructed over relatively soft subgrades with California Bearing Ratios (CBR) of 2%, 5%, and 10%. By systematically varying the geogrid axial stiffness between 400 and 1500 kN/m, a series of advanced 3D finite element simulations were conducted using PLAXIS 3D, with validation against field plate load test data to ensure model reliability. Optimal geogrid placement depths were identified for each subgrade condition—at H/4 and H/3 (H = total thickness of granular layer) from the top of the granular layer for CBR values of 2% and 5%, respectively, and at the base–subbase interface for CBR 10%, demonstrating the significance of tailored reinforcement approaches. The findings witnessed Modulus Improvement Factors (MIF) ranging from 1.3 to 3.0, correlating directly with geogrid stiffness and contributing to substantial reductions in pavement layer thickness, 15–30% for asphalt and granular layers. These reductions enhance material efficiency and result in a 6–24% reduction in carbon footprint, highlighting the double advantage of geogrid application in structural optimisation and environmental sustainability. Field verification proved in good agreement with simulations, with the deviations in settlement being less than 10%, affirming the reliability of the numerical method. This study gives practical advice for integrating geosynthetic reinforcement in pavement design (mechanistic), encouraging resource-saving construction methods, and contributing to the worldwide move toward low-carbon infrastructure solutions.

Introduction

India’s highways are not just roads–they are the veins of a nation on the move. Spanning more than 6.671 million kilometers, India is now the world’s second-largest road network, an achievement representative of its dogged quest for connectivity, financial buoyancy, and regional integration¹. From bustling metropolises to remote villages, these roads carry over 85% of passenger traffic and 70% of freight, forming the invisible backbone of everyday life and commerce. At the heart of this expansion are flexible pavements, widely adopted for lower initial costs and quicker construction². However, their construction depends heavily on virgin aggregates, especially in base and subbase layers. The lack of quality local materials in many northern regions leads to long-distance transportation from southern quarries, driving up costs, energy use, and carbon emissions. The IRC 37:2018 notes that while 10–15% of aggregates are used in the surface course, the base layers can account for 80–90%. These figures highlight the urgent need for sustainable practices to reduce environmental impact and optimize material usage^3,4.

In this context, geosynthetics have emerged as a transformative solution in modern pavement design. Geosynthetics are a group of materials fabricated from synthetic or naturally occurring polymeric materials, such as polypropylene and polyethylene, and have been considered environmentally friendly alternatives in geotechnical engineering⁵. Geosynthetics offer a cost-effective way to enhance pavement performance by reinforcing soil, reducing settlement, and distributing loads more efficiently across the pavement structure⁶. Geogrids are primarily employed in pavement systems to enhance performance by interlocking with aggregates, distributing loads over a broader area, reducing permanent deformation, providing lateral confinement, and increasing bearing capacity and shear strength^7–9. The reinforcement mechanisms through which geogrids improve pavement behavior are broadly categorized into (a) tension membrane effect, (b) increased bearing capacity, and (c) lateral confinement^10–17. Further reinforcement is provided by mechanisms such as surface friction along the geogrid, passive resistance offered by transverse ribs, and aggregate interlocking through apertures, which vary depending on loading and environmental conditions^15,18,19. However, geogrids are considered the best for reinforcement and economic purposes, particularly in flexible pavements²⁰. They are known for lowering aggregate consumption due to the reduction in the thickness of granular base layers^5,18,21–25.

By reinforcing subgrade and base layers, geogrids distribute loads more evenly²⁶, as seen in Fig. 1, and stabilize the soil, limiting movement and preventing excessive downward displacement.

Fig. 1.

Hypothetical load distribution in flexible pavements – (a) without vs. (b) with geogrid.

This reduction in settlement is especially significant on weak subgrades with improved bearing capacity²⁷, where geogrids improve rutting resistance and minimize both immediate and long-term settlement^28–31. Existing research has primarily focused on aggregate savings, often overlooking the broader environmental implications, such as the carbon footprint associated with material sourcing, processing, and transportation.

Several experimental studies^6,21,28,32 have demonstrated that including geogrids enhances the structural response of pavement systems, particularly under repeated or static loading. One of the most direct and effective methods to assess these improvements is the plate load test (PLT), which measures the relationship between applied bearing pressure and resulting surface deformation^33,34. This study uses the bearing pressure vs. settlement behavior obtained from modeled static PLTs as per³⁵ to assess and compare the performance of reinforced and unreinforced pavements, forming a basis for quantifying stiffness improvement and long-term deformation control.

A critical factor influencing geogrid efficacy is axial stiffness, which governs the reinforcement’s ability to interact with the subgrade. This parameter is especially significant in the context of subgrade strength, typically measured by the California Bearing Ratio (CBR). The different subgrades with a CBR that is less than 3%, between 3 and 8%, and more than 8% are considered weak, moderate, and firmer, respectively³⁶. Low-CBR soils are prone to excessive deformation, while high-CBR bases require optimized reinforcement to balance performance and cost. However, the real-world variability of soil conditions often exposes gaps between theoretical predictions and practical outcomes, highlighting the necessity for robust model validation. As road construction is a resource-intensive activity, understanding greenhouse gas (GHG) emissions related to sourcing, construction, and maintenance is essential for sustainable development³⁷.

Carbon footprints, measured in CO₂-equivalents (CO₂e), account for significant GHGs like CO₂, CH₄, and N₂O³⁸, and vary with extraction, production, fuel use, and transport distances³⁹. Studies like those of Sreedhar et al.⁴⁰ using life cycle assessment (LCA) found that flexible pavements emit around 25% fewer GHGs than rigid ones. Figure 2 shows the schematic of the typical differences between conventional and geogrid-reinforced flexible pavements.

Fig. 2.

Environmental impact of conventional vs. geogrid-reinforced pavements.

Although most road-related emissions occur during vehicle operation, construction still contributes up to 10%^41,42. The inclusion of geogrids improves the structural performance and material efficiency while reducing environmental impact, making them a sustainable solution aligned with global climate goals.

Addressing these challenges, this research systematically evaluates the performance of conventional geogrids with varying axial stiffness (400, 800, 1200, and 1500 kN/m) in flexible pavements across a different range of CBR conditions (2, 5, and 10%). Using PLAXIS 3D, a state-of-the-art finite element analysis tool, the study simulates geogrid-soil interactions with high fidelity, capturing stress distribution and deformation patterns under sustained loading. Unlike previous studies that rely on idealized assumptions, this work grounds its findings in field experiments, validating numerical predictions against real-world pavement performance data. A pioneering aspect of this study is integrating carbon footprint analysis, quantifying the emissions associated with geogrid production and installation. Ultimately, the study seeks to redefine geogrid optimization in pavement engineering, offering actionable strategies for resilient, cost-effective, and environmentally responsible road infrastructure.

Materials and methods

This study adopts a combined analytical and numerical approach to assess the effectiveness of geogrid reinforcement in flexible pavements. Pavement sections are initially designed using IITPAVE software following IRC 37:2018 guidelines for varying subgrade CBR values. Geogrids with different axial stiffnesses are incorporated at optimized positions within the pavement layers. PLAXIS 3D was used to simulate the plate load test and analyze the pavement response under incremental loading. Key performance indicators such as load–settlement behavior, Modulus Improvement Factor (MIF), and carbon footprint reduction were evaluated to quantify the reinforcement benefits.

The following subsections briefly present the pavement design process, the material properties used, the PLAXIS 3D modeling methodology, and the MIF and carbon footprint evaluation.

Trial and error procedure to obtain effective pavement thicknesses

Initial pavement sections are designed using IITPAVE software for subgrade CBR values of 2%, 5%, and 10%, with an ideal traffic load of 50 MSA. These designs ensured that the vertical and tensile strains remained within the permissible limits for rutting and fatigue as per IRC 37:2018, with the results summarized in Table 1. To proceed with MIF estimation, the sections were optimized by iteratively reducing the layer thicknesses while continuously checking that the strain values stayed within the safe range. This approach helped create slightly leaner sections that still satisfied design criteria and provided a better basis for evaluating the performance improvements due to geogrid inclusion. The final optimized sections used for PLAXIS 3D simulations are presented in Table 2.

Table 1.

Final pavement thicknesses obtained and safety criteria for different subgrade CBRs.

CBR	Layer thickness (mm)			εv (10–3)		εt (10–3)		Criteria
	Asphalt	Base	Subbase	Limiting	Calculated	Limiting	Calculated	Rutting & Fatigue
2%	230	370	280	0.372	0.295	0.178	0.134	safe
5%	180	250	200		0.279		0.145
10%	145	250	200		0.244		0.142

Note, ε_t = maximum horizontal tensile strain at the bottom of the bottom bituminous layer; ε_v = vertical compressive strain at the top of the subgrade.

Table 2.

Optimized pavement thicknesses and safety criteria for different subgrade CBRs.

CBR	Layer thickness (mm)			εv (10–3)		εt (10–3)		Criteria
	Asphalt	Base	Subbase	Limiting	Calculated	Limiting	Calculated	Rutting & Fatigue
2%	210	350	220	0.372	0.367	0.178	0.160	safe
5%	150	250	200		0.326		0.175
10%	130	200	200		0.304		0.168

Note, ε_t = maximum horizontal tensile strain at the bottom of the bottom bituminous layer; ε_v = vertical compressive strain at the top of the subgrade.

Materials

Pavement layer properties for numerical modeling

According to several previous studies^8,36,43–45, the materials properties utilized in pavement construction and geogrid reinforcement are defined in this work, which are widely accepted in pavement engineering practice. To effectively mimic the behavior of the pavement layers under static loading in PLAXIS 3D, the main thing is to consider the properties of individual materials. Table 3 lists each layer’s material parameters and thicknesses for various CBR situations considered in this study. All pavement layers in this study—the base, subbase, and subgrade—are modeled using the Mohr–Coulomb constitutive model in PLAXIS 3D. This model was widely used in geotechnical analysis owing to its ease of use and representational capability of soils under load’s basic stress–strain behavior. It captures key features like elastic response at low stress, plastic deformation beyond yield, and gradual stiffness reduction near failure, typical in granular and cohesive soils. The model requires five main input parameters: cohesion, friction angle, Young’s modulus, Poisson’s ratio, and unit weight, which were obtained through laboratory testing or credible sources. Its balance of accuracy and efficiency makes it a suitable choice for pavement modeling under static loading conditions^43,46,47.

Table 3.

Material properties and layer thicknesses for pavement layers in PLAXIS 3D.

Layer	Property	Units	CBR
			2%	5%	10%
Subgrade	Thickness	mm	500	500	500
	Cohesion (c)	kPa	18	25	46
	Friction angle (ϕ)	Degrees	23	36	26
	Young’s modulus (E)	MPa	20	50	76.83
	Poisson’s ratio (ν)	—	0.4	0.35	0.35
	Unit weight (γ)	kN/m3	16.5	18.5	19.5
Subbase	Thickness (mm)	mm	220	200	200
	Cohesion (c)	kPa	1
	Friction angle (ϕ)	Degrees	40
	Young’s modulus (E)	MPa	45.31	108.51	166.73
	Poisson’s ratio (ν)	—	0.4	0.35	0.35
	Unit weight (γ)	kN/m3	22
Base	Thickness (mm)	mm	350	250	200
	Cohesion (c)	kPa	1
	Friction angle (ϕ)	Degrees	42
	Young’s modulus (E)	MPa	126.48	260.35	361.83
	Poisson’s ratio (ν)	—	0.35	0.35	0.35
	Unit weight (γ)	kN/m3	24

The CBR conditions of 2%, 5%, and 10% were selected to represent weaker, moderate, and firmer subgrades, respectively, as analyzing all possible CBR conditions would be time-consuming. Moreover, the study aims to evaluate the benefit associated with geogrids within their subgrade applicability. The study intends to produce data that offers insights applicable across a broader spectrum of subgrade conditions by concentrating on these critical ranges.

Geogrid properties for reinforced pavement modeling

In PLAXIS 3D, axial stiffness is a crucial parameter that significantly affects the performance of geogrids in reinforced pavements. It dictates how the geogrid deforms under axial loads, thereby influencing its capability to distribute stresses and provide tensile support to the surroundings. For this study, a polypropylene biaxial geogrid with varying stiffness levels has been utilized. The geogrid stiffness values were selected based on a wide commercially available range to capture the practical variability and ensure broader applicability of the findings. This range of axial stiffness from 400 to 1500 kN/m covers almost all existing geogrids, which fall within this limit. Table 4 provides a complete list of the geogrids’input attributes.

Table 4.

Geogrid properties for PLAXIS 3D.

Type of geogrid	Material type	Property	Axial stiffness (kN/m)
Polypropylene biaxial geogrid	Elastic	Isotropic	400
			800
			1200
			1500

Steel plate properties for pavement contact pressure simulation

As per⁴⁸, steel plates are used to apply contact pressure in plate load tests, and their properties are crucial for accurate simulation in PLAXIS 3D. In this study, a steel plate was modeled to replicate actual test conditions and observe settlement behavior under applied loads. Steel is chosen due to its high strength, stiffness, and durability. The material parameters used in the model follow the standard specifications and are summarized in Table 5.

Table 5.

Steel plate properties for PLAXIS 3D.

Material type	Unit weight (γ) (kN/m3)	Young’s modulus (E) (GPa)	Poisson’s ratio (ν)	Thickness (d) (mm)
Elastic	78.5	200	0.25	25

Finite element modeling (PLAXIS 3D)

The static plate load test was modeled in PLAXIS 3D to simulate the behavior of flexible pavements under varying subgrade CBR conditions (2%, 5%, and 10%). The Mohr–Coulomb material model, which is appropriate for representing the nonlinear behavior of soil layers under loading, was used in the model’s construction. Figure 3 shows how the entire soil and structural components are built up in the PLAXIS 3D. For modeling the pavement geometry, a length and width of 2 m × 2 m are used. This size was selected to minimize boundary effects and ensure realistic simulation results. The total thickness varied based on the subgrade CBR values, as detailed in the Table 3.

Fig. 3.

Model geometry in PLAXIS 3D.

Mesh generation

The mesh was generated using the"fine"element distribution setting in PLAXIS 3D to ensure high simulation accuracy in the finite element analysis. Additionally, the “Full 10-noded element model” was selected as the model type to enhance the precision of stress distribution and deformation results, especially in geotechnical applications involving complex soil-structure interaction. A finer mesh helps capture more detailed stress and deformation patterns, which are significant for pavement structures subjected to varying loads. Furthermore, Fig. 4 compares the mesh configurations for unreinforced and geogrid-reinforced cases, highlighting the consistency in mesh density to maintain uniformity in result interpretation across both scenarios.

Fig. 4.

The generated mesh variation a) without and b) with geogrid at a depth of 1/3^rd from the top of the base + subbase layer.

Boundary conditions

A set of standard fixities is automatically imposed by PLAXIS 3D on the geometry model’s borders. Denoting that vertical borders are unmovable in the expected direction, but allow for movement in the downward direction, which depicts the pavement’s realistic conditions. Boundaries are free in one direction and fixed in two when they do not align with the principal axes. No movement is permitted since the model’s bottom border is completely fixed. The top, representing the ground surface, is entirely free and can move in any direction. These boundary conditions help ensure realistic simulation results during analysis.

Loading conditions

To simulate the static plate load test in PLAXIS 3D, a point load ranging from 7.068 kN to 70.68 kN was applied on the steel plate. This corresponds to bearing pressures between 100 and 1000 kPa, encompassing the typical tire pressure of 560 kPa as specified in IRC 37:2018. The range was chosen to reflect realistic field loading conditions that pavements will likely encounter. The stepwise increase in the load enables a precise analysis of settlement response and overall performance of the pavement layers for different intensities of loading, as well as unreinforced and geogrid-reinforced ones. Such an approach guarantees an overall understanding of pavement behavior under various types of loading.

The output from PLAXIS 3D simulations primarily included load versus settlement behavior, which is further converted to bearing pressure versus settlement plots. They are first analyzed to determine the optimum placement location of the geogrid for each subgrade condition. Once the ideal position was identified, typically where settlement reduction was most effective, further simulations were conducted by varying the axial stiffness of the geogrid to study its influence on pavement performance. These results formed the basis for calculating the MIF, which quantifies the enhancement in stiffness due to reinforcement.

MIF and carbon footprint estimation

MIF is the ratio of the elastic modulus of the reinforced section to that of the unreinforced section, calculated from the initial linear portion (typically up to 3 mm) of the bearing pressure versus settlement curves. The MIF procedures pertaining to elastic solutions of granular layers can be obtained elsewhere^49,50

Once the MIF values are obtained for various subgrade conditions and pavement thicknesses, they are used to redesign the pavement sections in IITPAVE by modifying the modulus of the base layer, thereby reducing the overall thickness of pavement layers while maintaining structural adequacy as per^51,52.

The carbon footprint estimation was conducted for unreinforced and geogrid-reinforced sections to evaluate the environmental impact of different pavement configurations. The study estimated the embodied carbon emissions associated with the construction materials used in each pavement layer: the asphalt, base, and subbase courses.

The estimation was conducted on a per square meter (m²) basis, using the following general formula:

1

The embodied carbon (EC) factors, in terms of CO₂e factors, were calculated for all phases of the material life cycle (extraction, transportation, production, and construction). For each pavement material considered in the study, the factors are listed in Table 6.

Table 6.

Material-specific carbon footprint factors (kgCO₂e/kg) for pavement components.

S.no	Component	Value (kgCO2e/kg material)	References
1	Bitumen	0.48	Gupta et al.53, Hammond and Jones54, Goud and Umashankar39, Chong and Wang37
2	Coarse aggregate	0.0216	Gupta et al.53, Maini and Thautam55
3	Fine aggregate	0.002	Gupta et al.53, Maini and Thautam55, White et al.56, Sreedhar et al.40
4	Polypropylene geogrid	2.97	Raja et al.57, Goud and Umashankar39

Field procedure and setup for the static plate load test

To validate the PLAXIS 3D model results, field static plate load tests were conducted on both unreinforced and geogrid-reinforced pavement sections. These tests were carried out strictly in accordance with the ASTM D1196³⁵ guidelines. The complete test setup, procedure, and quality assurance measures are outlined below.

Test setup and components

The test setup included all components specified in the³⁵ standard, and can be seen in Fig. 5. A rigid circular steel plate of 300 mm diameter was used to apply the load to the pavement surface. A hydraulic jack and pump with a 500 kN capacity were used to apply vertical loads through a calibrated load cell, ensuring precise force application. Settlements were measured using dial gauges with a least count of 0.01 mm, mounted on magnetic stands attached to rigid datum bars for stability. The reaction system consisted of a loaded truck providing approximately 400 kN of counterweight, including its self-weight. This ensured the hydraulic load could be effectively resisted during the test.

Fig. 5.

Static plate load test setup with all components.

Testing procedure

The steel plate was carefully placed on the compacted surface, ensuring proper contact and seating. Incremental loads are applied using the hydraulic system. The maximum load applied was 100 kN, corresponding to a bearing pressure of approximately 1400 kPa on the 300 mm diameter circular steel plate. Settlement readings are recorded using the dial gauges at each load increment. The test was repeated under identical conditions for unreinforced and geogrid-reinforced sections to ensure a fair comparison of load-settlement behavior. The loading was applied gradually in controlled increments, allowing sufficient time at each step for the settlement to stabilize before taking readings. Additionally, magnetic stands and rigid datum bars were used to secure the measuring devices firmly and reduce any vibration or disturbance during the test.

Results and discussions

Optimizing geogrid location for different subgrade CBRs

PLAXIS 3D models are run for subgrade CBRs of 2%, 5%, and 10% using a consistent axial stiffness of 800 kN/m to determine the most effective geogrid placement within the granular layers. For each case, the geogrid was placed at different depths, and the resulting bearing pressure versus settlement curves were analyzed to find the position that offered the least settlement while maintaining realistic overburden. The optimal geogrid location varied with subgrade strength due to differences in layer thicknesses. In this study, geogrid locations were varied at H/4, H/3, and the interface of granular layers (Base and subbase). These optimal locations are based on minimum overburden that holds the geogrid together and on corroborative existing literature that contributes to effective interlocking, membrane support, and bearing pressure increment.

For 2% CBR, the best performance was observed when the geogrid was placed at one-fourth the total granular depth (H/4) from the top. Regarding 5% CBR, the H/3 from the top position gave the most favorable results. For 10% CBR, although H/3 showed slightly better settlement control, the interface between base and subbase was selected as the optimal location due to practical placement considerations and similar performance. These observations are consistent with studies such as those by Karami et al.⁵⁸, Abu-Farsakh and Chen²⁸, Demir et al.⁵⁹,and Al-Qadi et al.⁶⁰, which suggest that shallower geogrid positions are more effective over weaker subgrades due to the concentration of stress closer to the surface. The comparison of results across all positions is shown in Fig. 6 for different subgrade conditions.

Fig. 6.

Bearing pressure vs. settlement behavior for unreinforced and different location geogrid-reinforced pavement sections overlying different subgrade conditions with CBR equal to a) 2%, b) 5%, and c) 10%.

Influence of geogrid stiffness on load–settlement contours under varying CBR conditions

To quantify the influence of geogrid reinforcement on pavement, PLAXIS 3D simulations were conducted for various combinations of subgrade CBR values (2%, 5%, and 10%) and geogrid axial stiffnesses (ranging from 400 kN/m to 1500 kN/m). The geogrid was placed at its previously optimized positions, and the outcomes are presented in the form of both settlement contour plots and bearing pressure–settlement curves. The contour plots visually understand the deformation patterns under the static plate load, clearly showing how geogrid reinforcement reduces vertical settlement across all cases. The bearing pressure versus settlement plots (Fig. 7) clearly depicts the influence of increasing geogrid stiffness, with settlement reducing from 17 to 10 mm for 2% CBR, 8.2 mm to 4.4 mm for 5% CBR, and 5.5 mm to 3.2 mm for 10% CBR when reinforced with 1500 kN/m geogrid. Intermediate stiffness values (400, 800, and 1200 kN/m) also show progressive improvement, as seen within this range. The enhanced pavement performance noted in this study, specifically in reduced settlement and further optimized layer designs, can be credited explicitly to the established geogrid mechanisms by improved load-bearing capacity and improved lateral confinement. The comparative plots clearly show that the effectiveness of geogrid reinforcement is highly dependent on subgrade strength. While weak soils (CBR 2%) benefit significantly from increased stiffness, the advantage diminishes in stronger soils (CBR 10%). Additionally, the benefit curve flattens beyond 1200 kN/m geogrid stiffness across all cases, suggesting an optimal design threshold. These plots help quantify surface stiffness improvements and highlight the comparative performance of different reinforcement configurations. Complementing these, the load-settlement contours are presented in Fig. 8, depicting the pavement surface response under increasing loading for each reinforcement condition with a CBR of 5%. This combined representation, both visual and analytical, forms a comprehensive basis for assessing geogrid effectiveness and serves as the foundation for MIF calculations.

Fig. 7.

Bearing pressure versus settlement behavior for different reinforcement conditions having subgrade CBR of a) 2%, b) 5%, and c) 10%.

Fig. 8.

Comparison of settlement contours at 5% CBR: a) Unreinforced, b) Reinforced with 400 kN/m geogrid, c) Reinforced with 800 kN/m geogrid, d) Reinforced with 1200 kN/m geogrid, e) Reinforced with 1500 kN/m geogrid.

Quantification of MIF values for different geogrid stiffnesses

The MIF values are derived from the bearing pressure versus settlement curves obtained for reinforced and unreinforced pavement models. The results are displayed using circular ring diagrams for different CBR values (2%, 5%, and 10%). Each ring was divided into four segments, corresponding to the geogrid axial stiffness values of 400, 800, 1200, and 1500 kN/m. A dark color gradient represented the stiffness levels—lighter tones indicate lower stiffness (400 kN/m). In comparison, darker shades denote higher stiffness (1500 kN/m), as the legend shows. Figure 9shows that MIF values increase with increased geogrid stiffness and decreased subgrade strength. For example, in the CBR 2% ring, MIF rises from 2.236 at 400 kN/m to 2.955 at 1500 kN/m. Similarly, for CBR 5% and 10%, MIF values increase with geogrid stiffness but to a lesser extent, peaking at 2.267 and 1.493, respectively, for the highest stiffness. Across all CBR conditions, the MIF showed a steep rise from unreinforced to 1500 kN/m geogrid reinforcement. However, the improvement beyond 1200 kN/m became marginal, especially in 10% CBR, indicating that the soil stiffness was already sufficient to carry the applied loads efficiently. This suggests that beyond a certain stiffness, the geogrid does not actively participate in stress distribution, highlighting the principle of diminishing returns in reinforcement effectiveness. At 2% CBR, the highest MIF was observed at 1500 kN/m, showing that the geogrid contribution is more critical in weaker soils, making reinforcement highly beneficial. The influence of geogrid placement on the MIF observed in this study aligns well with previously published research. Banerjee et al.⁴³ reported an increase in MIF ranging from 1.8 to 2.3 when geogrids were placed just above the subgrade in soft soil conditions. Goud et al.⁶¹ emphasized that geogrid positioning significantly affects reinforcement efficiency, with MIF values ranging between 1.02 and 1.76 for subgrades with CBR values between 3 and 7%. Similarly, Bodhanam et al.⁶² recorded an MIF of 1.96 for a geogrid-reinforced pavement constructed on a 10% CBR subgrade. Saride et al.⁶³ further demonstrated that MIF values could range from 1.2 to 3.5 for subgrades with CBR values between 1 and 8%. These findings support the results of the current study and emphasize the importance of optimal geogrid placement in achieving maximum structural benefit.

Fig. 9.

Comparative MIF values for CBR 2%, 5%, and 10% at different axial stiffnesses (400, 800, 1200, 1500 kN/m) of geogrids.

Comparison of pavement catalogues for unreinforced and reinforced sections

Geogrid reinforcement has a noticeable influence on the overall pavement design by improving the stiffness of the base layers. This improvement makes it possible to reduce the total thickness of the pavement while still meeting performance requirements. To study the effect, flexible pavement sections were redesigned using MIF values that were improved through reinforcement following IRC 37⁵¹ guidelines. The comparison between unreinforced and reinforced designs showed a consistent reduction in the required thickness. These reductions are observed in both asphalt and granular layers. On average, asphalt layers decreased by about 15% to 30%, while granular layers reduced by around 20% to 30%, depending on the geogrid’s stiffness and the subgrade’s strength. Several studies have supported these observations. Perkins and Ismeik²⁵ reported up to a 25% reduction in base course thickness with geogrid use, while Adams et al.⁶⁴ noted a minimum 15% reduction based on traffic class and subgrade conditions. Large-scale model testing by Saride and Baadiga⁵ showed a 33% decrease in granular layer thickness. Ghafoori and Sharbaf⁶⁵ observed reductions between 11 and 44% using different geogrid types. However, Alimohammadi et al.²¹ indicated that geogrid effectiveness may decrease with increasing base thickness. These findings underline the importance of geogrid placement within the pavement structure—optimal positioning closer to the subgrade often leads to more efficient load distribution and greater layer reduction, supporting the results observed in this study. The pavement cross-section catalogues for each case are shown in Fig. 10. Visually, the differences are noticeable, particularly in softer subgrades where reinforcing had a greater effect. These cuts contribute to more environmentally friendly paving options by lowering carbon emissions associated with construction and material use.

Fig. 10.

Design catalogue for the pavement with asphalt, granular base, and subbase layers—subgrade CBR a) 2%, b) 5%, and c) 10% for 50 MSA traffic load; (* = Optimized unreinforced pavement).

Validation of PLAXIS 3D model results with field observations

Static plate load tests were conducted in the field as per ASTM D1196³⁵ to validate the PLAXIS 3D results, recording the bearing pressure versus settlement behavior at both locations in the field, and the setup can be seen in Fig. 11. Field test data were referenced from the ongoing Raipur bypass section of NH (National Highway)−53 during the study to validate the numerical modeling results. The pavement at this site was designed for a design traffic of 150 msa, with a subgrade CBR of 10%. Plate load tests are conducted at two distinct chainages: 62 + 710 m, representing the unreinforced section, and 62 + 690 m, where a geogrid-reinforced section was constructed. In the field, the geogrid was placed at the interface between the base and the subbase, following recommendations from existing literature and as supported by the findings of this study. The geogrid roll was then deployed across the full length of the pavement section. To ensure wrinkle-free installation, temporary fasteners (e.g., nails or stakes) were used to maintain tension and alignment during placement. Subsequently, the base layer was constructed and compacted in strict compliance with IRC SP59⁵², IRC 37⁵¹, and MoRTH⁶⁶ specifications. The field material composition and layer configuration were replicated in the PLAXIS 3D model to maintain consistency between field and numerical conditions.

Fig. 11.

Field setup of the static plate load test showing the loading frame, dial gauges, and test plate on compacted base layer.

The engineering parameters for the subgrade, subbase, and base layers were determined using a combination of standard laboratory and in-situ field tests. For the subgrade, laboratory investigations included CBR tests (as per⁶⁷), direct shear tests to determine cohesion and angle of internal friction (as per⁶⁸), and Modified Proctor compaction tests (as per⁶⁹). Field verification of compaction was carried out using the core cutter method (as per⁷⁰). Similar laboratory tests were conducted for the sub-base and base layers, which are more granular, and in-situ compaction was verified using the sand replacement method (as per⁷¹). Field compaction results validated over the laboratory maximum dry density, as per IRC 37⁵¹ and MoRTH⁶⁶ guidelines for the pavement layers. The parameters that were obtained were then used as inputs in the PLAXIS 3D model, and the outputs are seen in Table 7.

Table 7.

Material properties and layer thicknesses obtained from lab and field testing, used for validating the PLAXIS 3D model.

Properties	Units	Layers
		Base	Subbase	Subgrade
Thickness	mm	200	200	500
Cohesion (c)	kPa	7	13	45
Friction angle (ϕ)	Degrees	43	38	27
Young’s modulus (E)	MPa	361.83	166.73	76.91
Poisson’s ratio (ν)	—	0.35	0.35	0.35
Unit weight (γ)	kN/m3	22.8	21.5	18.9

A comparison of the PLAXIS 3D simulation vs. the field is presented in Fig. 12. There were slight differences, which can be attributed to site-specific factors like variations in testing compaction. This verifies that pavement performance is well represented in the simulation model.

Fig. 12.

Validation of PLAXIS 3D bearing pressure versus settlement results with field test data for unreinforced and geogrid-reinforced sections.

Carbon footprint estimation for pavement sections

Geogrid incorporation into flexible pavements leads to a reduction in layer thickness, which directly affects the total carbon emissions associated with material use. Embodied carbon (EC) for each material was computed using Eq. 1 based on standardized CO₂e factors and applied across all pavement layers under varying subgrade CBR conditions and reinforcement scenarios.

The total embodied carbon across all cases ranged between 24 and 47 kgCO₂e/m². Figure 13 presents a heatmap of embodied carbon values for each combination of subgrade CBR and reinforcement condition. A consistent trend of decreasing carbon footprint with increasing subgrade stiffness is observed.

Fig. 13.

Heatmap showing embodied carbon (kgCO₂e/m²) for pavement sections with varying subgrade CBRs and reinforcement conditions (* = Optimized unreinforced pavement).

Carbon footprint reductions were then quantified by comparing geogrid-reinforced sections against two baseline conditions: conventional IRC design and optimized unreinforced design. These values are illustrated in Figs. 14 and 15, representing maximum and minimum carbon reduction cases, respectively. For a subgrade with 2% CBR, the reduction ranged from 15 to 24%, depending on geogrid stiffness. For 10% CBR, the reductions varied between 5 and 20%. The findings of this study regarding carbon footprint reduction with geogrid inclusion are strongly supported by previous research. Mazurowski⁷² demonstrated that the use of hexagonal geogrids in pavement aggregate layers can reduce carbon emissions by approximately 16% when compared to conventional, unreinforced structures. Similarly, Goud and Umashankar³⁹ calculated the EC for both unreinforced and geogrid-reinforced pavements and observed a reduction in EC ranging from 7 to 24%, depending on the design configuration and subgrade conditions.

Fig. 14.

Heatmap illustrating maximum carbon footprint reduction (%) in reinforced pavements compared to conventional IRC:37 design across varying subgrade CBRs.

Fig. 15.

Heatmap showing minimum carbon footprint reduction (%) of geogrid-reinforced pavements relative to optimized unreinforced sections across varying subgrade CBR.

Conclusions

This study reports an extensive and systematic evaluation of geogrid reinforcement towards improving the performance and sustainability of flexible pavements built over different subgrade conditions and axial stiffness (400 to 1500 kN/m) of geogrid. Further, important mechanical design input parameters, such as the Modulus Improvement Factor (MIF), were evaluated for all conditions considered in the study. Finally, the field validation and carbon footprint reductions in conjunction with geogrid inclusion are reported. Based on the systematic and rigorous numerical simulations, the following important conclusions are drawn:

• The study has revealed that the required thickness of pavement sections decreases with increasing subgrade CBR. Weaker subgrades demand thicker structural layers to ensure adequate load-bearing capacity, while stronger subgrades permit thinner sections, optimizing material usage.

• The ideal location for geogrid reinforcement varies systematically with subgrade stiffness. For subgrades with CBR < 3%, the geogrid performs best when placed at one-fourth (H/4) of the total granular thickness from the top. For intermediate subgrades (3% < CBR < 8%), the optimal position is H/3 from the top, while for stiffer subgrades (CBR > 8%), it is most effective at the interface between the base and subbase layers.

• As the total thickness of the pavement increases, the optimum geogrid position shifts upward from the base-subbase interface, reflecting the depth at which reinforcement most effectively intercepts stress distribution.

• Inclusion of geogrids significantly reduces the required thickness of both asphalt and granular layers. Asphalt layer thicknesses were decreased by 15%–30%, and granular layers by 20%–30%, leading to a substantial decrease in virgin aggregate consumption, which protects the Earth from sustaining natural resources safely

• MIF, an indicator of reinforcement effectiveness, ranged from 1.3 to 3 across the range of geogrid axial stiffnesses (400 to 1500 kN/m). MIF decreased with increasing subgrade CBR, indicating that reinforcement benefits are more pronounced in weaker soils.

• While MIF increases with geogrid stiffness, the improvement shows diminishing marginal gains. Further increases beyond a specific stiffness level result in relatively minor performance enhancements for the same subgrade condition.

• Geogrid reinforcement contributes significantly to reducing the embodied carbon footprint of pavement construction. The reduction ranges between 10 and 24% for weak subgrades, 9% and 21% for intermediate subgrades, and 6% and 20% for strong subgrades. These savings are primarily due to reductions in material usage across all pavement layers. This is the more sustainable and environmentally friendly outcome.

Therefore, the incorporation of geogrids in pavement design not only enhances structural performance but also aids in sustainable infrastructure development through the mitigation of dependence on virgin aggregates, reduction of carbon emissions, and facilitating cost-efficient design optimizations.

Author contributions

PSM: Conceptualization, methodology, modeling, data analysis, investigation, and preparation of the original draft. And BR: Supervise, validate, review, and editing. All authors have read the manuscript and reviewed the final manuscript.

Data availability

The data generated in the present study are available on a reasonable request to the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.