Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment

Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment Soewignjo Agus Nugroho, Gunawan Wibisono, Syawal Satibi, Muhammad Hafizh Anwar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7457421/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to develop an empirical equation for predicting the California Bearing Ratio (CBR) value based on the results of the TVA Penetrometer (Hand Cone Penetrometer) test on soft soils, particularly in coastal areas. The soil samples were prepared by mixing bentonite clay, kaolin, and sand, then compacted and tested in the laboratory to obtain both CBR and QcTVA data. A stepwise regression analysis was conducted on four parameters: CBR, QcTVA, moisture content, and dry unit weight. The results indicated that only QcTVA was statistically significant as a predictor of CBR. The resulting regression model showed a coefficient of determination (R²) of 0.70 and a p-value of 1.90×10⁻¹⁷, with good accuracy based on the MSE value of 3.214, RMSE of 1.793, and MAE of 1.295. These findings suggest that QcTVA can be used as a practical method to estimate CBR values without requiring field tests involving heavy equipment. However, the model has limitations when applied to soil types with high CBR values. Therefore, its use is recommended only for soft soils or similar ground conditions, such as those commonly found in coastal regions CBR TVA Penetrometer QcTVA Coastal Soil Stepwise Regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Soils in coastal areas generally have soft and less stable physical characteristics, so they are not able to optimally support construction loads. This is due to the dominance of the constituent materials in the form of fine sand and clay with a low density and high plasticity. These properties cause the bearing capacity of the soil to be low and tend to undergo significant deformation, especially when receiving structural loads. Therefore, planning and evaluating the carrying capacity of the land in coastal areas requires special attention, both for the development of light and heavy infrastructure. Field test methods commonly used in soil carrying capacity evaluation, such as California Bearing Ratio (CBR) testing, often face obstacles, especially in soft soils and hard-to-access locations (Waruwu et al., 2021 ). Field CBR test equipment requires supporting infrastructure such as heavy equipment that can interfere with the natural structure of the soil, so that it can reduce the accuracy of test results. This situation encourages the need to use alternative methods that are more practical, efficient, and adaptive to field conditions. One potential alternative is the use of a Hand Cone Penetrometer (HCP) tool, specifically the TVA Penetrometer type which has several cone sizes and is easy to use in the field. The tool is able to measure soil penetration resistance directly, which can then be correlated with CBR values through an empirical approach. In addition to being time- and cost-effective, this method is also safer to apply to soft soils because it does not significantly damage the soil structure. Several previous studies have examined the CBR value prediction approach based on laboratory test results and field data. Nugroho et al. ( 2019 ) developed a CBR estimation model based on HCP data in the Pekanbaru area, but limited to certain types of soil. Other research by Handayani et al. (2019) evaluate the relationship between the plasticity index and the CBR value in clay soils, while Ahmed et al. ( 2018 ) utilizing a combination of laboratory parameters such as plasticity index, dry volume weight, and unsoaked CBR value. Relationship between cone resistance (CPT test) and soil consistency of cohesive soil can be seen in Table 1 Table 1 Soil Consistency Value Based on Qonus Resistance Value (Qc) Cone Resistance, Qc soil consistency/density (kg/cm2) Cohesive Non-Cohesive less than 6 Very Soft 12 Soft 24 Medium stiff 45 Stiff 74 Very Stiff more than 75 Hard Source: (Bria et al., 2020 ) Some examples of previous research on the relationship between CBR values and qonus tip resistance can be seen in Table 2 . Table 2 Correlation of CBR with Geotechnical Parameters of previous research Researchers Soil Type Correlation (Erny, 2022 ) Sand CBR = 0.26 qc Clay Soil CBR = 0.48 qc (Kuttah, 2019 ) Sandy Soil CBRLD = 0.3546 ω + 118.3 γd – 178.886 In addition research (Cahyadi, 2019 ) regarding the correlation of the cone end resistance (Qc) with the CBR value showed a significant positive relationship between the two parameters. The higher the Qc value, the CBR value of the soil tends to increase, so penetrometer testing can be used as a quick and practical method to estimate the CBR value. . However, studies on the use of TVA Penetrometer as the main instrument in predicting CBR values, especially in soft clayey sand soils that are widely found in coastal areas, are still limited. This study aims to develop a CBR value prediction model based on TVA Penetrometer test data supported by laboratory parameters. The soil used is an engineered mixture of sand, bentonite clay, and kaolin, to resemble the characteristics of soft soils typical of coastal areas. This approach is expected to contribute to providing a more efficient, practical, and applicable method of soil carrying capacity evaluation, especially for soft soil conditions in the field Methodology The empirical relationship between the California Bearing Ratio (CBR) value, physical and mechanical properties of the soil, and the data from the TVA Hand Cone Penetrometer (TVA) test was examined in this study. The main objective of the study is to develop a regression model that is able to estimate CBR values practically based on test data that is easier and faster to perform in the field. The research is focused on a soil mixture consisting of clay and sand, which is specifically designed to represent soft soil conditions, especially those found in coastal areas. To represent the conditions of coastal soft soils in a controlled manner in the laboratory, a combination of the main materials in the form of clay and sand was used (Fig. 1). The plasticity and expansive properties of clay are highly dependent on the type of clay minerals contained in it (Das & Sobhan, 2017 ). The mineral montmorillonite in clay has the ability to absorb large amounts of water and expand significantly when wet. This property leads to a high level of plasticity, and a considerable expansive value. Meanwhile, kaolin mineral is a type of clay mineral with a stable crystal structure and non-expansive properties, in contrast to montmorillonite minerals which are more active and expansive (Utami, 2018 ). Kaolin has relatively low plasticity and minimal expansion rate, so its mechanical behavior tends to be more stable to changes in moisture content. These materials/minerals were chosen because they have different characteristics but complement each other in forming soft soil behavior (Table 3 ). Bentonite (montmorillonite) acts as an expansive clay with high plasticity, kaolin as a more stable structured clay, and sand as a granular material. Table 3 Atterberg Limit Test Results for Bentonite and Kaolin Mixtures Soil Minerals (%) Plasticity Index (%) Sand Kaolin Montmorillonite Liquid Limit (LL) Plastic limit (PL) Shrinkage Limit (SL) - - 100 100–900 50–100 8.5–15* - 100 - 30–110 25–40 25–29* - 0 100 410.50 70.82 - 20 80 338.32 36.52 - 25 75 302.42 51.58 - 30 70 308.13 44.84 - 35 65 256.52 25.72 - 40 60 291.71 62.79 - 45 55 249.46 29.58 - 50 50 246.72 63.02 - 55 45 212.65 63.36 - 60 40 195.44 38.98 - 65 35 188.10 55.33 - 70 30 132.76 42.01 - 75 25 124.47 38.06 - 80 20 102.56 33.45 - 100 0 51.63 33.85 *Source: (Mitchell & Soga, 2005 ) Sand is a soil that is mostly in the form of quartz minerals. Technically, it is classified as a non-cohesive material. Sand has no cohesion and can hardly become denser, which results in a higher bearing capacity. To solve this problem, some binding materials such as cohesive soil (clay) must be added to the sand (Wibisono et al., 2018 ). Coastal soft soil modeling in this study aims to engineer the characteristics of mixed soils that meet the criteria as soils with high plasticity, but are still dominated by sand fractions. In other words, the expected sample is SC type high plasticity soil. Therefore, mixing variations were carried out to obtain the values of the liquid limit, the plastic limit, and the plasticity index that entered the high plasticity zone on the plasticity chart (Table 4 ). The composition and variation are designed in such a way as to produce soil samples (re) that correspond to the physical characteristics of the soft soils in the field, so that the regression model built later can better represent real conditions in the coastal area. Table 4 Laboratory Result of Atterberg Limit for Clayey-Sand Mixture Sand Fine Grain/Clay fraction Atterberg Limits Bentonite (%) Kaolin (%) Liq. Limit (%) Plast. Lim. (%) Plast. Ind. (%) 80 70 30 52.94 20.55 32.39 70 70 30 76.27 23.79 52.48 65 70 30 89.62 25.08 64.54 80 50 50 51.01 20.23 30.78 70 50 50 71.11 22.34 48.77 65 50 50 84.87 23.12 61.75 According to Das, (1995) The USCS system determines that the liquid limit value with the low plasticity category has a liquid limit value of < 50%, so that in this study three variations of sand were used, namely 80%, 70%, and 65%. The rest is a mixture of clay in the form of bentonite and kaolin in a certain ratio. The selection of bentonite in higher proportions is intended to maintain the expansive characteristics and increase the plasticity of the mixture because the properties and characteristics of the soil in coastal areas also have high plasticity in fine grains (Zaika et al., 2019 ). Compacting Tool, Dynamic Modification for printing test soil samples, specially designed for this study (Fig. 2). The compaction process aims to increase soil density/density so that higher shear strength and bearing capacity are obtained(Das, 2016 ). Soil density is a process of increasing soil density by reducing the distance between particles so that there is a reduction in air volume but no change in water volume (Asnur & Yunita, 2023 ). The mold used in this tool has a diameter of 20.38 cm and a height of 15.05 cm. The impact energy is generated from a weight weighing 8 kg dropped from a height equivalent to the Dynamic Cone Penetrometer (DCP), thus providing high and uniform energy to each layer of soil. Factors that affect the compaction results include the type of soil, moisture content, and compaction energy used (Holtz & Kovacs, 1981 ). In addition to the standard number of strokes of 40 strokes (a density equivalent to 25 Proctor strokes), variations of 32 and 16 strokes are also used. The moisture content used in the test is set at 15%, 20%, and 25%, with the aim of keeping the soil in low density conditions. Research conducted Nur et al. ( 2023 ) Obtaining the results of coastal areas shows that the soft soil layer has a very low cone tip resistance (QC) value, ranging from 0–5 kg/cm² (0–0.5 MPa) Follow-up testing was carried out to determine the bearing capacity and strength characteristics of the modeled soil. The two types of tests used in this study are laboratory California Bearing Ratio (CBR) test and penetration test using TVA Penetrometer (Fig. 3). CBR is a commonly used test method to measure the strength and bearing capacity of the ground surface or road base material against the penetration of a standard piston and is generally expressed in percentages. The CBR test aims to evaluate the bearing capacity of the soil, Nugroho et al. ( 2019 ) stated that the amount of carrying capacity is mainly influenced by the soil type, moisture content, and density (content weight) of the soil. Meanwhile, the TVA Penetrometer test is used to obtain penetration values as a representation of soil resistance to static penetration loads. TVA penetrometer is a test that aims to obtain information about the strength of shallow soil, especially in the top soil layer which greatly affects the stability of the building structure on it. This HCP tool is easy to use for soil research up to a depth of 1 meter below the ground (Yusa & Nugroho, 2008 ). This test is particularly suitable for use in early geotechnical survey work, especially in hard-to-reach terrain conditions or in areas where direct laboratory testing is not possible. From this TVA Test, we can determine the type of soil consistency. Soil consistency is the resistance of soil to pressure, gravitational and pulling forces and the tendency of soil masses to adhere to each other or to other bodies (Mursyid & Anwar, 2023 ). After all the test data is obtained, the analysis begins with the process of eliminating outliers using the Robust Mahala Nobis Distance method. This method is used to detect and output data that has significant deviations from the general distribution, so that the constructed prediction model becomes more accurate and not affected by extreme values. This process is carried out on all variables to be analyzed, including CBR values, penetration resistance (QcTVA), dry content weight (γdry), and moisture content (w). After the data were cleaned from the outliers, a stepwise regression analysis was performed to build a CBR value prediction model based on the most significant combination of independent variables. The analysis was conducted using the MATLAB software, with an approach that gradually selects variables based on their contribution to improving model accuracy. Model evaluation is carried out using statistical parameters such as significance values and determination coefficients (R²) to ensure that the model can optimally represent the relationships between variables. Results The analysis of laboratory test results began by looking at the relationship between moisture content and dry unit weight (γdry) in various soil mixture variations (Fig. 4). The relationship between moisture content and dry content weight (γdry) in mixed soils shows several important tendencies. First, an increase in the number of impacts (N) results in a consistent increase in γdry values, which indicates that the compaction energy is effectively distributed in the soil mass, increasing the density of the particle array. Secondly, in mixtures with high proportion of bentonite and moisture content, the γdry pattern tends to be inconsistent. This irregularity is caused by the predominance of the high plastic and cohesive properties of bentonite, thus affecting the compaction results significantly and inuniformly. Third, the influence of kaolin as a bentonite mixture is evident in the variation with a composition of 50% bentonite and 50% kaolin. In these proportions, kaolin, which has different physical properties from bentonite, contributes greatly to the increase in γdry value, suggesting that the combination of the two types of clay produces a synergistic effect in the compaction process. Furthermore, an analysis of the linear relationship between the CBR value and the parameters of the follow-up test results, namely the QcTVA value and the weight of the dry content (γ-dry) was carried out. This analysis aims to identify the relationship between soil bearing strength (CBR) and penetration resistance value (QcTVA) as well as soil dry density obtained through laboratory testing. The QcTVA value is an indicator of penetration resistance that is influenced by material characteristics and density levels, while γ-dry reflects optimal density conditions at a given moisture content. By analyzing this relationship, it is hoped that a significant linear pattern will be obtained as a basis for the preparation of a predictive model of CBR values based on these parameters. The relationship between the CBR value and the QcTVA value shows a linear trend pattern that reflects a positive correlation between the two parameters (Fig. 5). This means that the greater the bearing capacity of the soil indicated by the CBR value, the value of the resistance of the cone tip measured through the TVA Penetrometer test also increases. These findings are in line with basic principles in geotechnics, where soil mechanics parameters that describe soil strength and carrying capacity, such as CBR and Qc, are generally mutually reinforcing. Thus, the QcTVA value has the potential to be used as an indicator or initial predictor of the CBR value in the type of mixed soil tested. The relationship between CBR values and dry content (γdry) weight at different moisture content shows a pattern that varies depending on soil moisture levels (Fig. 6). To understand the characteristics of this relationship, an analysis was carried out on three different water contents, namely 15%, 20%, and 25%. At 15% moisture content, the relationship between CBR and γdry shows a strong positive linear tendency, where an increase in soil density is always followed by an increase in CBR value. This is in accordance with the basic geotechnical principle that denser soil has a higher carrying capacity. At 20% moisture, the relationship pattern still shows a positive trend, although deviations are starting to appear at some data points, which indicates the beginning of the emergence of non-linear behavior due to the sensitivity of materials such as bentonite to moisture. At 25% humidity, the relationship pattern becomes fluctuating. This is likely due to the formation of cavities in the soil mold due to unevenly dispersed clumping of bentonite particles, so that although the weight of the dry content appears to increase, the CBR value actually decreases. In addition, inhomogeneous water distribution also has the potential to cause inaccuracies in the actual moisture content measurement, so that the results of γdry at high moisture content are less representative than lower water content. These findings suggest that at high moisture content, the technical parameters of the soil can be influenced by other physical factors such as mixing homogeneity and soil structure stability during the compaction and testing process. After looking at the relationship of various parameters to the CBR value, the analysis proceeded to the predictor modeling stage to obtain a more accurate estimate of the CBR value. This process begins with the elimination of outlier data using the Robust Mahala Nobis Distance method, which was chosen because of its ability to identify deviant data more stably than the usual Mahala Nobis Distance method, especially on data that is not normally distributed. After the anomalous data were set aside, a stepwise regression analysis was carried out with independent parameters in the form of QcTVA values, dry soil content weight (γdry), and moisture content. From a total of 81 initial data, the Robust Mahala Nobis Distance method succeeded in filtering the data by eliminating 19 data that were indicated to be deviant, so that 62 data were obtained that were considered valid and representative for the modeling process (Fig. 7). This elimination process is carried out based on the cut-off value of the Mahala Nobis distance which is calculated robustly, where data that has a distance beyond the threshold is considered a multivariant outlier. Thus, the remaining data have a more homogeneous distribution and are able to reflect the true relationship between the input parameters (QcTVA, γdry, and moisture content) to the CBR values, thereby improving the reliability and accuracy of the regression model constructed. After obtaining the dataset that has been cleaned of outliers, the next stage is to build a CBR value prediction model using a multiple linear regression approach. The regression method used is stepwise regression, which was chosen for its ability to automatically select independent variables based on significant contributions to the model. In this modeling process, several model alternatives are constructed to evaluate the most optimal combination of independent variables. The first model uses QcTVA values as the only predictor. The second model combines QcTVA with the weight of the dry content of the soil (γdry). In the third model, the QcTVA is paired with the actual moisture content (w). Meanwhile, the fourth model is the most complex, involving all three parameters simultaneously, namely QcTVA, γdry, and ω actual. Testing of these four models was carried out to determine the extent to which the addition of independent variables can improve the accuracy of the prediction, as well as to assess the relevance of each parameter in influencing the CBR value. The results of stepwise regression analysis of the four planned models showed that the QcTVA variable had the most dominant influence in predicting CBR values. Although some models combine QcTVA with other variables such as dry soil content weight (γdry) and actual moisture content (ω), stepwise selection filters out these variables and produces the most optimal final model with only one predictor, QcTVA. Thus, the best regression equation is obtained as follows: $$\:\text{C}\text{B}\text{R}=1.0137+0.4355\times\:\:{\text{Q}\text{c}}_{\text{T}\text{V}\text{A}}$$ 1 Statistical evaluation of the resulting regression model showed a fairly good predictor performance. A determination coefficient value (R²) of 0.70 indicates that 70% variation in the bound variable (CBR) can be explained by the predictive variable QcTVA. This reflects the strength of the fairly high linear relationship between the two, so that the model is able to capture most of the patterns from the observed data. In addition, a very small p-value (1.90055 × 10⁻¹⁷) indicates that the QcTVA regression coefficient is statistically significant, even at the most stringent levels of significance. This means that there is very strong evidence to reject the zero hypothesis, which states that the influence of QcTVA on CBR is zero. The Variance Inflation Factor (VIF) value of 1 further strengthens the quality of the model, because it shows the absence of multicollinearities, so that the QcTVA variable stands as a predictor that is free from redundancy with other variables. Discussion To assess the accuracy and reliability of model predictions, three error metrics were used, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). As a result, an MSE of 3.214 represents the mean square of the difference between the observation and prediction values, while the RMSE of 1.793 represents the average predicted deviation from the actual CBR value on the same scale. Meanwhile, the MAE of 1.295 reflects the average absolute error of the model without being affected by the penciled. These three values together confirm that the model has a fairly good accuracy, with average prediction errors ranging from only 1.3 to 1.8 CBR units. Therefore, this regression model is considered suitable for use as a practical estimation tool to estimate CBR values based on QcTVA test results. Residual verification is carried out to assess the quality and reliability of the model in predicting CBR values. A comparison between the actual value and the prediction shows that the model produces estimates that are close to observational data. Further residual analysis is displayed through various visualizations, such as Quantile-Quantile (Q-Q) Plots to assess distributions, residual histograms to observe error spreads, and residual versus fitted plots to detect systematic patterns or Indications of heteroskedasticities. The visualization results show that the residual is randomly dispersed, does not form a specific pattern, and is close to the normal distribution. This indicates that the model not only has a good match to the data, but also meets the classical assumptions of linear regression, making it reliable for estimating CBR values based on QcTVA. Verify the model by comparing the actual CBR value to the predicted CBR value. An error tolerance of 3% is applied as a reasonable limit of deviation between the predicted and observational values. The results of the evaluation showed that most of the data was within that tolerance range, which indicated that the model had an acceptable level of accuracy. A complete visualization and results of the residual analysis are presented in Fig. 8 , which reinforces the reliability of the model in representing the relationship between the independent variable and the CBR value. The residual histogram shows that the residual interval in the range [–1, 0] has the highest frequency, with the number of observations ranging from 20 to 24 data shown in Fig. 9. This pattern indicates that most predicted values are slightly larger than actual values, which means that models tend to over-predict systematically on a relatively small scale. This distribution remains within the predetermined error tolerance limit, so the model can still be considered valid for use in predicting CBR values. The results of visualization through the Quantile-Quantile (Q-Q) plot show that the residual points follow the theoretical normal distribution line quite well, especially in the middle (the quantile between − 1 to + 1). This indicates that the residual distribution tends to be normal around the average value. However, in the tail part of the distribution, i.e. the quantile is less than − 2 and more than + 1.5, there is a build-up of points that deviate from the theoretical line. This phenomenon reflects the presence of a slight characteristic of heavy tails, which suggests that there are some residual with extreme values, both positive and negative, that are potentially outliers. However, these deviations are still within acceptable limits for the purposes of the model's predictors. Overall, the Q-Q plot gives an indication that the residual normalization assumption has been fulfilled quite well, so that the regression model developed can be said to be valid and reliable to predict the CBR value in the analyzed data range. Visualization through the Residual vs Fitted plot shows that there are no obvious curved patterns or systematic trends, such as residual tendencies increasing or decreasing with fitted values. This indicates that the assumptions of the linear regression model have been fulfilled quite well. In addition, the residual spread appears to be relatively even along the range of fitted values, reflecting the characteristics of homogenedasticity or constant residual variance. However, at the highest range of fitted values (about 8 to 11), there are some significantly deviating residual points (more than ± 3) shown in Fig. 10 . These findings are in line with indications of heavy tails previously identified on the Q-Q plot, leading to the possible presence of several residual outliers. However, in general, the residual distribution pattern in this plot supports the validity of the regression model used. To test the stability of the model and its ability to generalize to new data that has never been used in the training process, verification was carried out using the k-fold cross-validation method. The test results showed that the value of the determination coefficient (R²) in the test data was 0.703, which means that the model was able to explain almost 70% of the variation in CBR values. This is a good indication of the model's generalization capabilities. In addition, the Root Mean Square Error (RMSE) value in the test data of 1.782 is still within the acceptable margin of error, and only slightly smaller compared to the RMSE value in the training data of 1.855. This small difference indicates that the model does not experience significant overfitting symptoms. Overall, the results of the validation through k-fold cross-validation confirm that the model built has a stable and accurate predictive performance, and is feasible to predict the CBR value based on the available geotechnical parameters. Conclusion The results of the analysis showed that the moisture content (ω) had a very low influence on the CBR value, shown by the elimination of this variable by the stepwise regression method. Similarly, the dry content weight variable (γdry) is not suitable for use as a predictor due to the fluctuating data especially at moisture content above 20%. From the regression results, the QcTVA parameter is the most significant variable in predicting the CBR value, as reflected in the model that only uses QcTVA as a predictor in the equation. This model can be recommended as an empirical approach in estimating CBR values based on TVA Penetrometer testing, while keeping in mind certain geotechnical limitations. The use of the model allows for quick estimates without heavy equipment, and although at the CBR value of >7% there is a residual average increase of ±1–1.5, this is still within the tolerance limits for initial planning. If needed, additional field validation can be focused on critical points to ensure safety factors. Declarations Author contributions SAN, GW, MHA collected and analysis data; SAN, GW, SS, MHA and KY wrote the main manuscript; GW, SS, KY translated the manuscript; MHA prepared all figure and run MATLAB; KY prepared discussions; All authors reviewed the manuscript Acknowledgements We would like to express our sincere gratitude to LPPM of Universitas Riau for providing partial financial support for this research through its DIPA 2025 fund. Funding This Research was partially funded by DIPA grant from The Institute for research and Community Service Universitas Riau (LPPM UNRI) for year 2025 under contract number: 29080/UN19.5.1.3/AL.04/2025. Data availability All data generated or analyzed during this study are included in this published manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Originality statement The work titled “ Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment ” has not been published elsewhere, in part, or any other form Conflicts of Interest The authors declare that they have no conflict of interest, either financial or non-financial, in the relation to the work submitted. All author has read and approved the final manuscript and declare that the work is an honest and accurate account of the research performed. References Ahmed, S. S., Hossain, N., Khan, A. J., & Islam, M. S. (2018). Prediction of Soaked Cbr Using Index Properties, Dry Density and Unsoaked Cbr of Lean Clay. Malaysian Journal of Civil Engineering , 28 (2), 270–283. https://doi.org/10.11113/mjce.v28.15975 Asnur, H., & Yunita, R. (2023). Comparison of Soil Density Levels in Five Districts of Payakumbuh City with the Standard Proctor Method. 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IOP Conference Series: Materials Science and Engineering , 316 (1). https://doi.org/10.1088/1757-899X/316/1/012038 Yusa, M., & Nugroho, S. A. (2008). Correlation of field density testing and static hand penetrometer to laboratory cbr results on several soil types. Media Teknik Sipil , 8 (1), 25–32. Zaika, Y., Rachmansyah, A., & Harimurti. (2019). Geotechnical Behaviour of Soft Soil in East Java, Indonesia. IOP Conference Series: Materials Science and Engineering , 615 (1). https://doi.org/10.1088/1757-899X/615/1/012043 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":284196,"visible":true,"origin":"","legend":"\u003cp\u003esoil materials\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/eb8a59c6c4b07e4476ec0072.png"},{"id":94118189,"identity":"cb908222-17d4-479e-b33e-7b70a5d90c26","added_by":"auto","created_at":"2025-10-22 14:44:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101632,"visible":true,"origin":"","legend":"\u003cp\u003ecompacting aparatus and Mold\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/30d2265998f481fd28cbded8.png"},{"id":94118192,"identity":"78fb30cd-afcd-4619-b1d0-dbaef6b16383","added_by":"auto","created_at":"2025-10-22 14:44:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80523,"visible":true,"origin":"","legend":"\u003cp\u003eTesting Apparatus\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/3b0f0607d829cfeafa0a3519.png"},{"id":94118197,"identity":"4a5359d4-b7ed-48d4-8e1c-c26b0797cc9e","added_by":"auto","created_at":"2025-10-22 14:44:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172910,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between actual water content and dry density\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/0c2f279d0e517e397e554ab7.png"},{"id":94121397,"identity":"1d438d46-e47a-49a5-bf2c-d2c328a830ce","added_by":"auto","created_at":"2025-10-22 15:08:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45756,"visible":true,"origin":"","legend":"\u003cp\u003eCBR and QcTVA Value Relationship\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/e98c22fc87847b15f12984b8.png"},{"id":94118195,"identity":"8510ff30-443c-4eba-b22d-57b0a0867a83","added_by":"auto","created_at":"2025-10-22 14:44:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54185,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of CBR and γdry Values\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/57fec0c1442d3b82b5366824.png"},{"id":94119344,"identity":"dfd12ab4-5b99-492e-bacd-5d20f0cff472","added_by":"auto","created_at":"2025-10-22 14:52:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":96846,"visible":true,"origin":"","legend":"\u003cp\u003eInlier vs Outlier Robust Mahala Nobis Distance\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/303d84106d0946400b49d860.png"},{"id":94121400,"identity":"a7ad01ce-4467-4183-8507-95c5e3406ef4","added_by":"auto","created_at":"2025-10-22 15:08:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":196281,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Actual CBR Values and Predicted CBR\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/679a6321e4bd414cdfe35402.png"},{"id":94120530,"identity":"1c9d3124-ff8a-493f-b115-c187af3ce4ba","added_by":"auto","created_at":"2025-10-22 15:00:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":34177,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram Residual and Fitted\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/ddd1dba6a42d5ddcb7dcf636.png"},{"id":94118204,"identity":"f201b30b-a76d-48e2-a334-786c07f580b2","added_by":"auto","created_at":"2025-10-22 14:44:34","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":125249,"visible":true,"origin":"","legend":"\u003cp\u003eVerify Quantile-Quantile Plot\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/def19ee4b08354f3518196eb.png"},{"id":102419716,"identity":"003c652a-2798-4e1a-9b72-0128faf4cde9","added_by":"auto","created_at":"2026-02-11 13:27:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7457421/v1/855b4356-8dd1-4b2f-a25c-be1aab7be3ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoils in coastal areas generally have soft and less stable physical characteristics, so they are not able to optimally support construction loads. This is due to the dominance of the constituent materials in the form of fine sand and clay with a low density and high plasticity. These properties cause the bearing capacity of the soil to be low and tend to undergo significant deformation, especially when receiving structural loads. Therefore, planning and evaluating the carrying capacity of the land in coastal areas requires special attention, both for the development of light and heavy infrastructure.\u003c/p\u003e\u003cp\u003eField test methods commonly used in soil carrying capacity evaluation, such as California Bearing Ratio (CBR) testing, often face obstacles, especially in soft soils and hard-to-access locations (Waruwu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Field CBR test equipment requires supporting infrastructure such as heavy equipment that can interfere with the natural structure of the soil, so that it can reduce the accuracy of test results. This situation encourages the need to use alternative methods that are more practical, efficient, and adaptive to field conditions.\u003c/p\u003e\u003cp\u003eOne potential alternative is the use of a Hand Cone Penetrometer (HCP) tool, specifically the TVA Penetrometer type which has several cone sizes and is easy to use in the field. The tool is able to measure soil penetration resistance directly, which can then be correlated with CBR values through an empirical approach. In addition to being time- and cost-effective, this method is also safer to apply to soft soils because it does not significantly damage the soil structure.\u003c/p\u003e\u003cp\u003eSeveral previous studies have examined the CBR value prediction approach based on laboratory test results and field data. Nugroho et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) developed a CBR estimation model based on HCP data in the Pekanbaru area, but limited to certain types of soil. Other research by Handayani et al. (2019) evaluate the relationship between the plasticity index and the CBR value in clay soils, while Ahmed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) utilizing a combination of laboratory parameters such as plasticity index, dry volume weight, and unsoaked CBR value. Relationship between cone resistance (CPT test) and soil consistency of cohesive soil can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil Consistency Value Based on Qonus Resistance Value (Qc)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCone Resistance, Qc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003esoil consistency/density\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(kg/cm2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohesive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Cohesive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eless than 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Soft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium stiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Stiff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emore than 75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: (Bria et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSome examples of previous research on the relationship between CBR values and qonus tip resistance can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation of CBR with Geotechnical Parameters of previous research\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearchers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCorrelation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Erny, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCBR\u0026thinsp;=\u0026thinsp;0.26 qc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClay Soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCBR\u0026thinsp;=\u0026thinsp;0.48 qc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Kuttah, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandy Soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCBRLD\u0026thinsp;=\u0026thinsp;0.3546 ω\u0026thinsp;+\u0026thinsp;118.3 γd \u0026ndash; 178.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition research (Cahyadi, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) regarding the correlation of the cone end resistance (Qc) with the CBR value showed a significant positive relationship between the two parameters. The higher the Qc value, the CBR value of the soil tends to increase, so penetrometer testing can be used as a quick and practical method to estimate the CBR value.\u003c/p\u003e\u003cp\u003e. However, studies on the use of TVA Penetrometer as the main instrument in predicting CBR values, especially in soft clayey sand soils that are widely found in coastal areas, are still limited. This study aims to develop a CBR value prediction model based on TVA Penetrometer test data supported by laboratory parameters. The soil used is an engineered mixture of sand, bentonite clay, and kaolin, to resemble the characteristics of soft soils typical of coastal areas. This approach is expected to contribute to providing a more efficient, practical, and applicable method of soil carrying capacity evaluation, especially for soft soil conditions in the field\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe empirical relationship between the California Bearing Ratio (CBR) value, physical and mechanical properties of the soil, and the data from the TVA Hand Cone Penetrometer (TVA) test was examined in this study. The main objective of the study is to develop a regression model that is able to estimate CBR values practically based on test data that is easier and faster to perform in the field. The research is focused on a soil mixture consisting of clay and sand, which is specifically designed to represent soft soil conditions, especially those found in coastal areas.\u003c/p\u003e\n\u003cp\u003eTo represent the conditions of coastal soft soils in a controlled manner in the laboratory, a combination of the main materials in the form of clay and sand was used (Fig. 1). The plasticity and expansive properties of clay are highly dependent on the type of clay minerals contained in it (Das \u0026amp; Sobhan, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The mineral montmorillonite in clay has the ability to absorb large amounts of water and expand significantly when wet. This property leads to a high level of plasticity, and a considerable expansive value. Meanwhile, kaolin mineral is a type of clay mineral with a stable crystal structure and non-expansive properties, in contrast to montmorillonite minerals which are more active and expansive (Utami, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Kaolin has relatively low plasticity and minimal expansion rate, so its mechanical behavior tends to be more stable to changes in moisture content. These materials/minerals were chosen because they have different characteristics but complement each other in forming soft soil behavior (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Bentonite (montmorillonite) acts as an expansive clay with high plasticity, kaolin as a more stable structured clay, and sand as a granular material.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAtterberg Limit Test Results for Bentonite and Kaolin Mixtures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSoil Minerals (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePlasticity Index (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMontmorillonite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiquid Limit (LL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlastic limit (PL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShrinkage Limit (SL)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026ndash;900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u0026ndash;15*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;29*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*Source: (Mitchell \u0026amp; Soga, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSand is a soil that is mostly in the form of quartz minerals. Technically, it is classified as a non-cohesive material. Sand has no cohesion and can hardly become denser, which results in a higher bearing capacity. To solve this problem, some binding materials such as cohesive soil (clay) must be added to the sand (Wibisono et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Coastal soft soil modeling in this study aims to engineer the characteristics of mixed soils that meet the criteria as soils with high plasticity, but are still dominated by sand fractions. In other words, the expected sample is SC type high plasticity soil. Therefore, mixing variations were carried out to obtain the values of the liquid limit, the plastic limit, and the plasticity index that entered the high plasticity zone on the plasticity chart (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The composition and variation are designed in such a way as to produce soil samples (re) that correspond to the physical characteristics of the soft soils in the field, so that the regression model built later can better represent real conditions in the coastal area.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLaboratory Result of Atterberg Limit for Clayey-Sand Mixture\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFine Grain/Clay fraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAtterberg Limits\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBentonite (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaolin (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiq. Limit (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlast. Lim. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlast. Ind. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to Das, (1995) The USCS system determines that the liquid limit value with the low plasticity category has a liquid limit value of \u0026lt;\u0026thinsp;50%, so that in this study three variations of sand were used, namely 80%, 70%, and 65%. The rest is a mixture of clay in the form of bentonite and kaolin in a certain ratio. The selection of bentonite in higher proportions is intended to maintain the expansive characteristics and increase the plasticity of the mixture because the properties and characteristics of the soil in coastal areas also have high plasticity in fine grains (Zaika et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCompacting Tool, Dynamic Modification for printing test soil samples, specially designed for this study (Fig. 2). The compaction process aims to increase soil density/density so that higher shear strength and bearing capacity are obtained(Das, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil density is a process of increasing soil density by reducing the distance between particles so that there is a reduction in air volume but no change in water volume (Asnur \u0026amp; Yunita, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mold used in this tool has a diameter of 20.38 cm and a height of 15.05 cm. The impact energy is generated from a weight weighing 8 kg dropped from a height equivalent to the Dynamic Cone Penetrometer (DCP), thus providing high and uniform energy to each layer of soil. Factors that affect the compaction results include the type of soil, moisture content, and compaction energy used (Holtz \u0026amp; Kovacs, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). In addition to the standard number of strokes of 40 strokes (a density equivalent to 25 Proctor strokes), variations of 32 and 16 strokes are also used. The moisture content used in the test is set at 15%, 20%, and 25%, with the aim of keeping the soil in low density conditions. Research conducted Nur et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) Obtaining the results of coastal areas shows that the soft soil layer has a very low cone tip resistance (QC) value, ranging from 0\u0026ndash;5 kg/cm\u0026sup2; (0\u0026ndash;0.5 MPa)\u003c/p\u003e\n\u003cp\u003eFollow-up testing was carried out to determine the bearing capacity and strength characteristics of the modeled soil. The two types of tests used in this study are laboratory California Bearing Ratio (CBR) test and penetration test using TVA Penetrometer (Fig.\u0026nbsp;3). CBR is a commonly used test method to measure the strength and bearing capacity of the ground surface or road base material against the penetration of a standard piston and is generally expressed in percentages. The CBR test aims to evaluate the bearing capacity of the soil, Nugroho et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) stated that the amount of carrying capacity is mainly influenced by the soil type, moisture content, and density (content weight) of the soil. Meanwhile, the TVA Penetrometer test is used to obtain penetration values as a representation of soil resistance to static penetration loads. TVA penetrometer is a test that aims to obtain information about the strength of shallow soil, especially in the top soil layer which greatly affects the stability of the building structure on it. This HCP tool is easy to use for soil research up to a depth of 1 meter below the ground (Yusa \u0026amp; Nugroho, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). This test is particularly suitable for use in early geotechnical survey work, especially in hard-to-reach terrain conditions or in areas where direct laboratory testing is not possible. From this TVA Test, we can determine the type of soil consistency. Soil consistency is the resistance of soil to pressure, gravitational and pulling forces and the tendency of soil masses to adhere to each other or to other bodies (Mursyid \u0026amp; Anwar, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAfter all the test data is obtained, the analysis begins with the process of eliminating outliers using the Robust Mahala Nobis Distance method. This method is used to detect and output data that has significant deviations from the general distribution, so that the constructed prediction model becomes more accurate and not affected by extreme values. This process is carried out on all variables to be analyzed, including CBR values, penetration resistance (QcTVA), dry content weight (\u0026gamma;dry), and moisture content (w).\u003c/p\u003e\n\u003cp\u003eAfter the data were cleaned from the outliers, a stepwise regression analysis was performed to build a CBR value prediction model based on the most significant combination of independent variables. The analysis was conducted using the MATLAB software, with an approach that gradually selects variables based on their contribution to improving model accuracy. Model evaluation is carried out using statistical parameters such as significance values and determination coefficients (R\u0026sup2;) to ensure that the model can optimally represent the relationships between variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis of laboratory test results began by looking at the relationship between moisture content and dry unit weight (\u0026gamma;dry) in various soil mixture variations (Fig. 4). The relationship between moisture content and dry content weight (\u0026gamma;dry) in mixed soils shows several important tendencies. First, an increase in the number of impacts (N) results in a consistent increase in \u0026gamma;dry values, which indicates that the compaction energy is effectively distributed in the soil mass, increasing the density of the particle array. Secondly, in mixtures with high proportion of bentonite and moisture content, the \u0026gamma;dry pattern tends to be inconsistent. This irregularity is caused by the predominance of the high plastic and cohesive properties of bentonite, thus affecting the compaction results significantly and inuniformly. Third, the influence of kaolin as a bentonite mixture is evident in the variation with a composition of 50% bentonite and 50% kaolin. In these proportions, kaolin, which has different physical properties from bentonite, contributes greatly to the increase in \u0026gamma;dry value, suggesting that the combination of the two types of clay produces a synergistic effect in the compaction process.\u003c/p\u003e\n\u003cp\u003eFurthermore, an analysis of the linear relationship between the CBR value and the parameters of the follow-up test results, namely the QcTVA value and the weight of the dry content (\u0026gamma;-dry) was carried out. This analysis aims to identify the relationship between soil bearing strength (CBR) and penetration resistance value (QcTVA) as well as soil dry density obtained through laboratory testing. The QcTVA value is an indicator of penetration resistance that is influenced by material characteristics and density levels, while \u0026gamma;-dry reflects optimal density conditions at a given moisture content. By analyzing this relationship, it is hoped that a significant linear pattern will be obtained as a basis for the preparation of a predictive model of CBR values based on these parameters.\u003c/p\u003e\n\u003cp\u003eThe relationship between the CBR value and the QcTVA value shows a linear trend pattern that reflects a positive correlation between the two parameters (Fig. 5). This means that the greater the bearing capacity of the soil indicated by the CBR value, the value of the resistance of the cone tip measured through the TVA Penetrometer test also increases. These findings are in line with basic principles in geotechnics, where soil mechanics parameters that describe soil strength and carrying capacity, such as CBR and Qc, are generally mutually reinforcing. Thus, the QcTVA value has the potential to be used as an indicator or initial predictor of the CBR value in the type of mixed soil tested.\u003c/p\u003e\n\u003cp\u003eThe relationship between CBR values and dry content (\u0026gamma;dry) weight at different moisture content shows a pattern that varies depending on soil moisture levels (Fig. 6). To understand the characteristics of this relationship, an analysis was carried out on three different water contents, namely 15%, 20%, and 25%. At 15% moisture content, the relationship between CBR and \u0026gamma;dry shows a strong positive linear tendency, where an increase in soil density is always followed by an increase in CBR value. This is in accordance with the basic geotechnical principle that denser soil has a higher carrying capacity. At 20% moisture, the relationship pattern still shows a positive trend, although deviations are starting to appear at some data points, which indicates the beginning of the emergence of non-linear behavior due to the sensitivity of materials such as bentonite to moisture.\u003c/p\u003e\n\u003cp\u003eAt 25% humidity, the relationship pattern becomes fluctuating. This is likely due to the formation of cavities in the soil mold due to unevenly dispersed clumping of bentonite particles, so that although the weight of the dry content appears to increase, the CBR value actually decreases. In addition, inhomogeneous water distribution also has the potential to cause inaccuracies in the actual moisture content measurement, so that the results of \u0026gamma;dry at high moisture content are less representative than lower water content. These findings suggest that at high moisture content, the technical parameters of the soil can be influenced by other physical factors such as mixing homogeneity and soil structure stability during the compaction and testing process.\u003c/p\u003e\n\u003cp\u003eAfter looking at the relationship of various parameters to the CBR value, the analysis proceeded to the predictor modeling stage to obtain a more accurate estimate of the CBR value. This process begins with the elimination of outlier data using the Robust Mahala Nobis Distance method, which was chosen because of its ability to identify deviant data more stably than the usual Mahala Nobis Distance method, especially on data that is not normally distributed. After the anomalous data were set aside, a stepwise regression analysis was carried out with independent parameters in the form of QcTVA values, dry soil content weight (\u0026gamma;dry), and moisture content.\u003c/p\u003e\n\u003cp\u003eFrom a total of 81 initial data, the Robust Mahala Nobis Distance method succeeded in filtering the data by eliminating 19 data that were indicated to be deviant, so that 62 data were obtained that were considered valid and representative for the modeling process (Fig. 7). This elimination process is carried out based on the cut-off value of the Mahala Nobis distance which is calculated robustly, where data that has a distance beyond the threshold is considered a multivariant outlier. Thus, the remaining data have a more homogeneous distribution and are able to reflect the true relationship between the input parameters (QcTVA, \u0026gamma;dry, and moisture content) to the CBR values, thereby improving the reliability and accuracy of the regression model constructed.\u003c/p\u003e\n\u003cp\u003eAfter obtaining the dataset that has been cleaned of outliers, the next stage is to build a CBR value prediction model using a multiple linear regression approach. The regression method used is stepwise regression, which was chosen for its ability to automatically select independent variables based on significant contributions to the model.\u003c/p\u003e\n\u003cp\u003eIn this modeling process, several model alternatives are constructed to evaluate the most optimal combination of independent variables. The first model uses QcTVA values as the only predictor. The second model combines QcTVA with the weight of the dry content of the soil (\u0026gamma;dry). In the third model, the QcTVA is paired with the actual moisture content (w). Meanwhile, the fourth model is the most complex, involving all three parameters simultaneously, namely QcTVA, \u0026gamma;dry, and \u0026omega; actual. Testing of these four models was carried out to determine the extent to which the addition of independent variables can improve the accuracy of the prediction, as well as to assess the relevance of each parameter in influencing the CBR value.\u003c/p\u003e\n\u003cp\u003eThe results of stepwise regression analysis of the four planned models showed that the QcTVA variable had the most dominant influence in predicting CBR values. Although some models combine QcTVA with other variables such as dry soil content weight (\u0026gamma;dry) and actual moisture content (\u0026omega;), stepwise selection filters out these variables and produces the most optimal final model with only one predictor, QcTVA. Thus, the best regression equation is obtained as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:\\text{C}\\text{B}\\text{R}=1.0137+0.4355\\times\\:\\:{\\text{Q}\\text{c}}_{\\text{T}\\text{V}\\text{A}}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eStatistical evaluation of the resulting regression model showed a fairly good predictor performance. A determination coefficient value (R\u0026sup2;) of 0.70 indicates that 70% variation in the bound variable (CBR) can be explained by the predictive variable QcTVA. This reflects the strength of the fairly high linear relationship between the two, so that the model is able to capture most of the patterns from the observed data. In addition, a very small p-value (1.90055 \u0026times; 10⁻\u0026sup1;⁷) indicates that the QcTVA regression coefficient is statistically significant, even at the most stringent levels of significance. This means that there is very strong evidence to reject the zero hypothesis, which states that the influence of QcTVA on CBR is zero. The Variance Inflation Factor (VIF) value of 1 further strengthens the quality of the model, because it shows the absence of multicollinearities, so that the QcTVA variable stands as a predictor that is free from redundancy with other variables.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo assess the accuracy and reliability of model predictions, three error metrics were used, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). As a result, an MSE of 3.214 represents the mean square of the difference between the observation and prediction values, while the RMSE of 1.793 represents the average predicted deviation from the actual CBR value on the same scale. Meanwhile, the MAE of 1.295 reflects the average absolute error of the model without being affected by the penciled. These three values together confirm that the model has a fairly good accuracy, with average prediction errors ranging from only 1.3 to 1.8 CBR units. Therefore, this regression model is considered suitable for use as a practical estimation tool to estimate CBR values based on QcTVA test results.\u003c/p\u003e\n\u003cp\u003eResidual verification is carried out to assess the quality and reliability of the model in predicting CBR values. A comparison between the actual value and the prediction shows that the model produces estimates that are close to observational data. Further residual analysis is displayed through various visualizations, such as Quantile-Quantile (Q-Q) Plots to assess distributions, residual histograms to observe error spreads, and residual versus fitted plots to detect systematic patterns or Indications of heteroskedasticities. The visualization results show that the residual is randomly dispersed, does not form a specific pattern, and is close to the normal distribution. This indicates that the model not only has a good match to the data, but also meets the classical assumptions of linear regression, making it reliable for estimating CBR values based on QcTVA.\u003c/p\u003e\n\u003cp\u003eVerify the model by comparing the actual CBR value to the predicted CBR value. An error tolerance of 3% is applied as a reasonable limit of deviation between the predicted and observational values. The results of the evaluation showed that most of the data was within that tolerance range, which indicated that the model had an acceptable level of accuracy. A complete visualization and results of the residual analysis are presented in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, which reinforces the reliability of the model in representing the relationship between the independent variable and the CBR value.\u003c/p\u003e\n\u003cp\u003eThe residual histogram shows that the residual interval in the range [\u0026ndash;1, 0] has the highest frequency, with the number of observations ranging from 20 to 24 data shown in Fig. 9. This pattern indicates that most predicted values are slightly larger than actual values, which means that models tend to over-predict systematically on a relatively small scale. This distribution remains within the predetermined error tolerance limit, so the model can still be considered valid for use in predicting CBR values.\u003c/p\u003e\n\u003cp\u003eThe results of visualization through the Quantile-Quantile (Q-Q) plot show that the residual points follow the theoretical normal distribution line quite well, especially in the middle (the quantile between \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1). This indicates that the residual distribution tends to be normal around the average value. However, in the tail part of the distribution, i.e. the quantile is less than \u0026minus;\u0026thinsp;2 and more than +\u0026thinsp;1.5, there is a build-up of points that deviate from the theoretical line. This phenomenon reflects the presence of a slight characteristic of heavy tails, which suggests that there are some residual with extreme values, both positive and negative, that are potentially outliers. However, these deviations are still within acceptable limits for the purposes of the model\u0026apos;s predictors. Overall, the Q-Q plot gives an indication that the residual normalization assumption has been fulfilled quite well, so that the regression model developed can be said to be valid and reliable to predict the CBR value in the analyzed data range.\u003c/p\u003e\n\u003cp\u003eVisualization through the Residual vs Fitted plot shows that there are no obvious curved patterns or systematic trends, such as residual tendencies increasing or decreasing with fitted values. This indicates that the assumptions of the linear regression model have been fulfilled quite well. In addition, the residual spread appears to be relatively even along the range of fitted values, reflecting the characteristics of homogenedasticity or constant residual variance. However, at the highest range of fitted values (about 8 to 11), there are some significantly deviating residual points (more than \u0026plusmn;\u0026thinsp;3) shown in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. These findings are in line with indications of heavy tails previously identified on the Q-Q plot, leading to the possible presence of several residual outliers. However, in general, the residual distribution pattern in this plot supports the validity of the regression model used.\u003c/p\u003e\n\u003cp\u003eTo test the stability of the model and its ability to generalize to new data that has never been used in the training process, verification was carried out using the k-fold cross-validation method. The test results showed that the value of the determination coefficient (R\u0026sup2;) in the test data was 0.703, which means that the model was able to explain almost 70% of the variation in CBR values. This is a good indication of the model\u0026apos;s generalization capabilities. In addition, the Root Mean Square Error (RMSE) value in the test data of 1.782 is still within the acceptable margin of error, and only slightly smaller compared to the RMSE value in the training data of 1.855. This small difference indicates that the model does not experience significant overfitting symptoms. Overall, the results of the validation through k-fold cross-validation confirm that the model built has a stable and accurate predictive performance, and is feasible to predict the CBR value based on the available geotechnical parameters.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the analysis showed that the moisture content (ω) had a very low influence on the CBR value, shown by the elimination of this variable by the stepwise regression method. Similarly, the dry content weight variable (γdry) is not suitable for use as a predictor due to the fluctuating data especially at moisture content above 20%. From the regression results, the QcTVA parameter is the most significant variable in predicting the CBR value, as reflected in the model that only uses QcTVA as a predictor in the equation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis model can be recommended as an empirical approach in estimating CBR values based on TVA Penetrometer testing, while keeping in mind certain geotechnical limitations. The use of the model allows for quick estimates without heavy equipment, and although at the CBR value of \u0026gt;7% there is a residual average increase of ±1–1.5, this is still within the tolerance limits for initial planning. If needed, additional field validation can be focused on critical points to ensure safety factors.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSAN, GW, MHA collected and analysis data; SAN, GW, SS, MHA and KY wrote the main manuscript; GW, SS, KY translated the manuscript; MHA prepared all figure and run MATLAB; KY prepared discussions; All authors reviewed the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to LPPM of Universitas Riau for providing partial financial support for this research through its DIPA 2025 fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis Research was partially funded by DIPA grant from The Institute for research and Community Service Universitas Riau (LPPM UNRI) for year 2025 under contract number: 29080/UN19.5.1.3/AL.04/2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eOriginality statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work titled\u0026nbsp;\u0026ldquo;\u003cstrong\u003eEmpirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment\u003c/strong\u003e\u0026rdquo;\u0026nbsp;has not been published elsewhere, in part, or any other form\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest, either financial or non-financial, in the relation to the work submitted. All author has read and approved the final manuscript and declare that the work is an honest and accurate account of the research performed.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, S. S., Hossain, N., Khan, A. J., \u0026amp; Islam, M. S. (2018). Prediction of Soaked Cbr Using Index Properties, Dry Density and Unsoaked Cbr of Lean Clay. \u003cem\u003eMalaysian Journal of Civil Engineering\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 270\u0026ndash;283. https://doi.org/10.11113/mjce.v28.15975\u003c/li\u003e\n\u003cli\u003eAsnur, H., \u0026amp; Yunita, R. (2023). Comparison of Soil Density Levels in Five Districts of Payakumbuh City with the Standard Proctor Method. \u003cem\u003eSAINTEKES: Jurnal Sains, Teknologi Dan Kesehatan\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 54\u0026ndash;61. https://doi.org/10.55681/saintekes.v2i1.21\u003c/li\u003e\n\u003cli\u003eBria, Y., Alwi, A., \u0026amp; Aprianto, A. (2020). 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A., \u0026amp; Umam, K. (2018). The Influence Of Sand\u0026rsquo;s Gradation And Clay Content Of Direct Sheart Test On Clayey Sand. \u003cem\u003eIOP Conference Series: Materials Science and Engineering\u003c/em\u003e, \u003cem\u003e316\u003c/em\u003e(1). https://doi.org/10.1088/1757-899X/316/1/012038\u003c/li\u003e\n\u003cli\u003eYusa, M., \u0026amp; Nugroho, S. A. (2008). Correlation of field density testing and static hand penetrometer to laboratory cbr results on several soil types. \u003cem\u003eMedia Teknik Sipil\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 25\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eZaika, Y., Rachmansyah, A., \u0026amp; Harimurti. (2019). Geotechnical Behaviour of Soft Soil in East Java, Indonesia. \u003cem\u003eIOP Conference Series: Materials Science and Engineering\u003c/em\u003e, \u003cem\u003e615\u003c/em\u003e(1). https://doi.org/10.1088/1757-899X/615/1/012043\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CBR, TVA Penetrometer, QcTVA, Coastal Soil, Stepwise Regression","lastPublishedDoi":"10.21203/rs.3.rs-7457421/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7457421/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to develop an empirical equation for predicting the California Bearing Ratio (CBR) value based on the results of the TVA Penetrometer (Hand Cone Penetrometer) test on soft soils, particularly in coastal areas. The soil samples were prepared by mixing bentonite clay, kaolin, and sand, then compacted and tested in the laboratory to obtain both CBR and QcTVA data. A stepwise regression analysis was conducted on four parameters: CBR, QcTVA, moisture content, and dry unit weight. The results indicated that only QcTVA was statistically significant as a predictor of CBR. The resulting regression model showed a coefficient of determination (R\u0026sup2;) of 0.70 and a p-value of 1.90\u0026times;10⁻\u0026sup1;⁷, with good accuracy based on the MSE value of 3.214, RMSE of 1.793, and MAE of 1.295. These findings suggest that QcTVA can be used as a practical method to estimate CBR values without requiring field tests involving heavy equipment. However, the model has limitations when applied to soil types with high CBR values. Therefore, its use is recommended only for soft soils or similar ground conditions, such as those commonly found in coastal regions\u003c/p\u003e","manuscriptTitle":"Empirical Correlation of CBR and TVA Penetrometer Resistance for Coastal Soft Soil Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 14:44:28","doi":"10.21203/rs.3.rs-7457421/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f52f6eac-51fd-47a0-952a-279e2073396e","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T13:26:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 14:44:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7457421","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7457421","identity":"rs-7457421","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}