The experimental program was conducted on twenty-six soil samples collected from various locations throughout Trinidad. The sampling sites were selected to ensure a broad spatial distribution and to capture a wide range of soil types, covering clays of low, medium, and high plasticity. At each location, undisturbed soil samples were retrieved from a depth of 1 meter using Shelby tubes. The samples were then transported to the University of the West Indies Soils Laboratory for extrusion and preparation for laboratory testing. The corresponding coordinates are provided in Table 1 and the geographic locations of the sampling sites are shown in Figure 1 .

Upon arrival at the laboratory, the soil samples were prepared for a series of standard geotechnical tests. The preparation procedure was similar to that used for the Atterberg limits test, involving oven-drying, pulverizing the soil clods, and sieving the material through a 425-µm (No. 40) sieve. A comprehensive laboratory testing program was then carried out to determine the index properties and engineering characteristics of each soil sample. The tests were performed according to ASTM International standards to ensure consistency and repeatability. The specific tests included Sieve Analysis (ASTM C136/C136M-19), Hydrometer Analysis (ASTM D7928-17), Atterberg Limits (ASTM D4318-17e1), Specific Gravity (ASTM D854-14), and Hydraulic Conductivity (ASTM D5856-15).

This study presents a new composite parameter, the weighted plasticity index (wPI), aimed at offering a more comprehensive measure of a soil’s swelling potential than the Plasticity Index (PI) alone. The wPI is explicitly defined as the product of the PI and the percentage of fines (i.e., the material passing the 0.075 mm or No. 200 sieve), obtained from a combined sieve and hydrometer analysis.

The theoretical foundation for the wPI is that a soil’s overall swelling behavior depends on both the mineralogical characteristics of its clay particles and the total amount of these particles. The PI acts as an indicator of the fines’ quality, reflecting the type and activity of clay minerals; a higher PI typically indicates more expansive minerals like montmorillonite ( Holtz & Kovacs, 1981 ). The percentage of fines indicates the amount of material (silt and clay) actively involved in soil-water interactions. By multiplying these two factors, the wPI creates a single index that combines both the quality and quantity of the fine-grained fraction, offering a more complete indicator of the soil’s innate capacity for volume change.

The laboratory testing program yielded a comprehensive dataset characterizing the index properties and engineering classifications of the twenty-six soil samples. The results from the grain size analysis, Atterberg limits tests, and hydraulic conductivity measurements are summarized in Table 2 . This table also includes the calculated value of the weighted plasticity index (wPI) for each sample, derived from the measured PI and percentage of fines. The soils were classified according to the Unified Soil Classification System (USCS) (ASTM D2487-17), revealing a range from lean clays (CL) and silts (ML) to fat clays (CH), as well as their expansive potential shown in Table 3 .

A linear regression analysis was conducted to examine the relationship between the calculated wPI and the measured Liquid Limit (LL) for the twenty-six soil samples. The analysis showed a very strong positive correlation between the two parameters, as shown in Figure 2 . The relationship is described by the following linear Equation 2.

$\text{wPI}=0.7451\times \left(\text{LL}\right)-14.198$ (2)

The coefficient of determination (R²) for this regression was found to be 0.94, indicating that 94% of the variance in the wPI can be predicted from the LL. This high R² value signifies a strong and dependable linear relationship between the two parameters for the soils investigated.

To relate the empirical wPI parameter to the more fundamental soil property of Cation Exchange Capacity (CEC), a two-step approach was taken, utilizing the strong exponential relationship established by Yilmaz (2004) between CEC and Liquid Limit (LL). The correlation established by Yilmaz (2004) is based on 139 fine-grained alluvial soil samples sourced from various locations across Türkiye, providing a generalized foundation for the LL-CEC relationship. These alluvial soils are characterized by a mixed mineralogy, with dominant clay fractions consisting of expansive smectite and non-expansive kaolinite, alongside lesser amounts of illite and chlorite. This mineral blend provides the justification for the wide range of swelling behavior and physicochemical properties studied. The highly successful exponential relationship derived from these data is given previously in Equation (1), which showed a very strong fit with a correlation coefficient R² of 0.97 ( Yilmaz, 2004 ). Applying this correlation to Trinidadian soils is strengthened by knowing the Turkish dataset represents a diverse, mixed-mineralogy alluvial origin.

A new equation linking CEC directly to wPI was derived through a two-step mathematical substitution. First, the empirical relationship between LL and wPI given in Equation (2) was rearranged to express LL as a function of wPI.

$\text{LL}=\left(\text{wPI}+14.198\right)/0.7451$ (3)

Next, this expression for LL (Equation 3) was substituted into Equation (1). This substitution yields Equation 4, a direct relationship between CEC and wPI:

$\text{CEC}={\text{e}}^{2.63+\left[0.02\times \left(\left(wPI+14.198\right)/0.7451\right)\right]}$ (4)

Simplifying the exponent gives:

$\text{CEC}={\text{e}}^{3.011+\left(0.0268\times \text{wPI}\right)}$

Which can be expressed in the final form:

$\text{CEC}=20.31\times {\text{e}}^{0.0268\times \text{wPI}}$

This derived equation provides a novel method to estimate the Cation Exchange Capacity of a soil directly from its calculated weighted plasticity index. Additionally, we can solve for wPI with respect to CEC, giving Equation 5.

$\text{wPI}=\left(37.31\times ln\left(\text{CEC}\right)\right)-112.35$ (5)

Using the derived relationship between CEC and wPI (Equation 4), a new swelling potential classification system was developed. The CEC threshold values for Low, Medium, High, and Very High swelling potential, as defined by Yilmaz (2004) , were used as the benchmark. The corresponding wPI values for each threshold were calculated using Equation (5). The resulting classification system, which allows for the direct assessment of swelling potential using the wPI, is presented in Table 4 and shown in Figure 3 .

The results show that the weighted plasticity index (wPI) is a highly effective parameter for characterizing the swelling potential of fine-grained soils. The strong coefficient of determination (R² = 0.94) in the linear relationship between wPI and Liquid Limit (LL) provides solid empirical support for the new parameter. This means that the wPI is not just an arbitrary index but a dependable indicator of the key soil properties that influence water retention and plasticity. The strength of this correlation can be linked to the composite nature of the wPI itself. Unlike classifications that depend only on PI or LL, the wPI combines both the quality of clay minerals (shown by PI) and the amount of fine particles (indicated by % fines). This dual approach offers a more comprehensive physical basis for evaluating a soil’s overall ability to interact with water and change volume, which likely explains its strong predictive ability.

Furthermore, the usefulness of the wPI goes beyond predicting swelling potential; it also applies to unsaturated soil mechanics. Estimating the Soil-Water Characteristic Curve (SWCC), which is key for understanding unsaturated soil behavior, can be simplified using soil index properties. Researchers have successfully developed methods to estimate the SWCC from properties like grain-size distribution and the plasticity index, showing how a soil’s physical traits relate to its water retention abilities ( Perera, Zapata, Houston, & Houston, 2005 ; Chai & Khaimook, 2020 ). This method is especially useful because direct measurement of the SWCC is time-consuming and costly. Foundational studies have demonstrated that basic soil properties can reliably predict unsaturated soil behavior, opening the door for more practical applications in routine engineering ( Zapata, 1999 ). Providing a dependable proxy, the wPI can greatly improve efficiency in critical tasks like forensic investigations of landslides, where rainfall-driven changes in suction are a main trigger ( Rahardjo, Satyanaga, & Leong, 2019 ). Using a strong index parameter like the wPI, which combines both plasticity (PI) and fines content (% fines) into a single metric, provides a more robust input for empirical Soil-Water Characteristic Curve (SWCC) models than using either parameter alone. Estimating the SWCC this way allows for faster and more affordable modeling of slope stability under different moisture conditions ( Alexander, Park, & Gay, 2020 ).

When applying the proposed wPI classification system to the 26 soil samples and comparing it with established methods, its usefulness becomes clearer. For example, many soils labeled as “Very High” potential by Chen (1988) are similarly classified by the new wPI method (e.g., soils 1, 3, 4, 5, 6, 10). This alignment with past frameworks builds confidence in the new system. However, the wPI method also offers a more detailed evaluation in some cases. Take Soil #7 (PI = 18, %Fines = 95.97%), which is rated as “High” potential by Chen (1988) but “Medium” by Seed et al. (1962) . The wPI for this soil is 17.65, placing it in the “Medium” swelling potential category under the proposed system. This indicates that although the soil’s plasticity is relatively high, the overall amount or nature of its fines may reduce its swelling capacity, illustrating the benefit of the composite index over methods based solely on plasticity. By linking mathematically to the fundamental parameter of CEC, the wPI classification can offer a more scientifically based assessment than methods relying solely on PI, potentially avoiding over-conservative estimates in some cases and correctly identifying risks in others.

The main contribution of this research is its practical application. Determining CEC is a specialized and costly lab process, often outside the scope of initial geotechnical studies, especially for smaller projects or in developing regions. The wPI method offers a scientifically supported yet affordable alternative. The parameters needed to calculate wPI Plasticity Index and percent of fines come from two of the most common and inexpensive tests in geotechnical engineering: Atterberg limits and sieve analysis. This enables engineers to quickly evaluate the swelling potential of soils using readily available data, saving time and money during the crucial site investigation stage.

This practical advantage has direct implications for environmental protection and sustainable development. Early and accurate identification of expansive soil hazards allows for better land-use planning and the implementation of suitable engineering solutions from the beginning. This proactive approach helps prevent premature infrastructure failure, thereby reducing significant material waste, resource use, and the carbon footprint linked to costly repairs and rebuilds. By enabling the design of more resilient and durable structures, the wPI method supports more sustainable construction practices on challenging soils.

While the proposed methodology offers a significant practical advance, its limitations must be acknowledged to guide future research. The main limitation of this study is that the correlation between wPI and CEC is established indirectly. The derivation depends on the LL-CEC relationship developed by Yilmaz (2004) from a different soil dataset. The current study did not include direct laboratory measurements of CEC on the 26 Trinidadian soil samples to validate the final derived equation (Equation 4). Therefore, although the framework is logical and the initial wPI-LL correlation is strong, the final wPI-CEC relationship remains an estimate that needs direct verification. This limitation clearly defines a path for future research. The most important next step is to perform direct CEC testing on the Trinidadian soils analyzed in this study or on a similar representative set. This would enable a direct correlation between wPI and measured CEC, which could confirm or refine the proposed classification thresholds. Further research could also expand the soil database to include a wider range of parent materials and geological formations to test the global applicability of the wPI-CEC relationship.