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Article

Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology

by
Pooja Somadas
,
Purushotham G. Sarvade
* and
Deepak Nayak
*
Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
*
Authors to whom correspondence should be addressed.
Geosciences 2024, 14(7), 182; https://doi.org/10.3390/geosciences14070182
Submission received: 22 May 2024 / Revised: 4 July 2024 / Accepted: 5 July 2024 / Published: 8 July 2024
(This article belongs to the Special Issue Soil-Structure Interactions in Underground Construction)
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Abstract

:
Stabilization using industrial by-products is presently gaining importance in the construction sector for improving the geotechnical characteristics of soft soils. The optimum dosage of stabilisers has become of great interest to experimenters in terms of improved strength, time, and economy for construction projects. This work presents the utilization of biomedical waste ash for improving the strength of soft soil. In this paper, response surface methodology (RSM) was adopted to determine the optimum combination curing period (C) and biomedical waste ash (BA) quantity for attaining the maximum unconfined compressive strength (UCS) of soft soil and to reduce the number of trial tests required. The response factors C and BA were varied from 0 to 14 days and 4% to 20%, respectively, and the experiments were conducted according to the experimental plan provided by the RSM design. Based on a Face-centred Central Composite Design (FCCCD), a mathematical equation was created for the experimental results. Analysis of variance (ANOVA) was used to determine the generated model’s significance, and the results indicated a statically significant model (p ≤ 0.05). The results revealed that the curing period imparts more influence towards strength improvement, and the optimum dosage was 19.912% BA, with curing of 14 days to yield a maximum UCS of 203.008 kPa. This optimization technique may be suggested to obtain a preliminary estimation of strength prior to stabilization.

1. Introduction

Soft soils pose undesirable engineering properties, such as low strength, high compressibility, and have low resistance to deformation under applied loads, which make them unsuitable for any construction work. The minerals present in a soil play a major role in determining the characteristics and behaviour of soft soil [1,2]. Thus, in most cases, it is preferable to use a borrowed foundation material with stable mechanical properties when building in places with such problematic soils. However, due to the unavailability of suitable soil, high costs of transportation, and limited availability of natural resources, the on-site soil itself is utilised as the base material after a suitable soil-stabilization strategy. The most common soil stabilisers are lime and Portland cement [3,4]. However, the process of producing it results in the exploitation and depletion of natural resources. It is estimated that by 2030, the global cement production will reach 4.83 billion tons, which is a 5% increment from the recorded production in 2015 [5]. Moreover, it leads to other environmental impacts such as the emission of greenhouse gases. It is estimated that about 5–8% of global anthropogenic CO2 emissions are caused due to cement manufacturing [6], and approximately 801.06 kg of CO2 is released in India per ton of cement produced as per reports by [7]. While Portland cement is widely used for soil stabilization and generally improves strength, under certain conditions, it may lead to high plastic shrinkage and, in some cases, a reduction in strength gain due to inadequate curing and incomplete hydration at early ages [8,9]. This emphasises the need to develop environmentally friendly materials that can replace the conventional binders used for soil stabilization. Using industrial and agricultural by-products has been a global environmental trend in the construction sector in recent decades, some of which include rice husk ash [10,11,12], ground granulated blast-furnace slag [13,14,15], fly ash and steel slag [16,17,18], geopolymers based on industrial wastes [19,20,21,22,23], and biopolymers [24,25,26,27,28,29,30]. This serves two purposes; it allows for safe disposal and solves the scarcity of conditioned construction materials [31]. The overall geotechnical behaviour can be effectively improved by stabilising the soil with different chemical, mineral, and polymer additions.
Biomedical waste (BMW) has been considered to be a source of emerging pollutants that is generated by healthcare facilities during medical diagnosis, treatment, and medical related research and testing. As per the World Health Organization [32], nearly 15% of the total medical waste generated is considered infectious hazardous waste, which includes hazardous organic compounds (e.g., soiled cotton and fabric waste, human organs, organic fluids), and other clinical solid waste, while the remaining 85% is general waste. There has been a significant rise in the quantity of BMW, especially after the COVID-19 pandemic. In India, it is noticed that the amount of BMW generated before the pandemic was nearly 619 tons/day, and this value had suddenly surged to 850 tons/day during the pandemic [33]. The non-uniform generation of BMW during the pandemic era has confused the waste management sector. If this hazardous waste is not properly handled and treated, not only will it lead to the spread of pathogens and other diseases, but it can also cause soil, groundwater, and surface water pollution [34,35]. There are various treatment techniques that can be applied for the effective disposal of BMW depending upon the category to which the BMW belongs. These include incineration, autoclaving, deep burial, plasma pyrolysis, UV sterilization, and chemical disinfection [36], of which incineration is the most widely opted treatment process due to its high efficiency and simple design. In accordance with the Bio-Medical Waste (Management and Handling) Rules in India, it is suggested that hazardous medical waste such as soiled wastes, discarded medicines, human and animal anatomical wastes, and cytotoxic drugs should be disposed by incineration. The wastes are incinerated at high temperatures ranging between 800 and 1200 °C, with a continuous supply of oxygen. This process ensures a 100% destruction of microorganisms and pathogens in addition to reducing the volume of the waste by 85–90% [37].
The bottom ash generated from this combustion process may contain heavy metals and are capable of leaching into the soil and nearby groundwater sources, leading to contamination [38]. An investigation carried out by [39] included the leaching behaviour of biomedical waste ash (BA). The morphological, physical–chemical properties, and mineralogical composition of the bottom ash were evaluated, and a leaching pattern for heavy metals was established through batch leaching tests. The leaching tests revealed that at a pH of 11, high concentrations of certain heavy metals such as Hg (9.3 mg/L), Se (2.4 mg/L), and As (9.7 mg/L) were leached from the BA solution; however, reduced leaching was observed for other heavy metals, such as Ni, Cu, Co, and Cd, due to the presence of calcite and ettringite minerals. Investigation carried out by [40] determined the leaching capability of trace metals from biomedical waste bottom ash samples using different leaching techniques. It was found that the highest concentrations of most trace elements (As, Cd, Co, Cu, Cr, Mo, Ni, Pb, and Zn) were released with acetic acid extraction; however, extraction using ammonium-EDTA, the concentration of the trace metals As and Mo were at maximum levels in the solution. Another similar study was performed by [41] to evaluate the leaching characteristics and levels of polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) of incinerated medical waste bottom ash obtained from various common biomedical waste treatment facilities (CBMWTFs). It was observed that Cd, Cr, Mn, Pb, and Zn were above the regulatory limits in the leachate. The samples mostly included PAHs with four rings, and the total toxic equivalents (TEQs) ranged from 0.9 to 421.9 ng TEQ/g. The concentration of Σ19 PCB congeners ranged from 420.4 to 724.3 µg kg−1. These findings indicate a need for the treatment of bottom ashes generated from BMW incinerators, as they are hazardous in nature, to prevent negative environmental impacts associated with their disposal.
Typically, biomedical waste ash is dumped in landfills to prevent exposure to humans and limit its negative effects on the environment. Conversely, a few researchers suggest that this ash may be utilised for construction purposes as a Supplementary Cementitious Material (SCMs). Anastasiadou et al. [42] studied the feasibility of incorporating incinerated medical waste ash to OPC (ordinary Portland cement) in various ratios to enhance the compressive strength of the mixture. Although the compressive strength of mixture samples decreased as compared to pure OPC, they exceeded the standard strength specified for solidified waste forms following 28 days of solidification period. TCLP (Toxicity Characteristic Leaching Procedure) tests were performed on cement–ash samples to ascertain the leachability of heavy metals from the mixture, and the results implied that the concentrations of heavy metals were well within the permissible limits. Several other investigations have shown that using BA in concrete mixtures can only be limited to lower percentages due to their low pozzolanic reactivity [43,44,45]. This decrease in compressive strength emphasises how crucial thermal treatment and grinding are prior to addition in concrete mixes [46,47]. This study focuses on the feasibility of incorporating BA as a soil stabiliser for weak soil since studies on using this material for geotechnical applications are limited.
One of the main challenges in soil stabilization is determining the amount of stabiliser to be added for effective stabilization to take place. Through design tests, many researchers have obtained optimal dosages based on performance, which is suitable for studies with sample designs of a limited number of factors or variables. However, when the study consists of many factors that affect the resultant factor, the number of trials needed to be conducted also increases drastically, posing feasibility challenges. Since it is not practical to reduce the number of factors, as it may result in erroneous test results that lead to a biased value of the optimum dosage, it is necessary to adopt a suitable method for optimization without compromising the number of variables in the study. There are several optimization techniques that can be applied, such as Taguchi method [48], response surface methodology [49,50,51], genetic algorithm [52,53,54], Scheffe’s optimization approach [55,56,57], etc. Many studies related to geotechnical research show that the application of response surface methodology (RSM) is a beneficial modelling technique, as it provides reasonable alternate resolutions for decision making in engineering projects [58,59,60,61,62].
The objective of this research work is to assess the effect of biomedical waste ash on the unconfined compressive strength of the soft soil and to optimise the dosage of biomedical waste ash and the curing period with varying quantities in order to obtain maximum unconfined compressive strength by applying RSM technique. Various combinations of BA and curing time were considered to evaluate their impact on the initial strength gain and early-stage reactions of the stabilised soil. A statistical assessment of this methodology was performed using analysis of variance (ANOVA), which allows for a correct validation of the research work.

2. Materials and Methodology

2.1. Materials

The materials used for this present work include soft soil and biomedical waste incinerated ash. The soft soil was collected at a depth of 1 m from Manipal, which is situated in Udupi district of Karnataka state, India. There are many regions of Karnataka where studies on soft soil have been performed [63]. Biomedical waste ash (BA) was collected from an incineration plant located in Dakshina Kannada district, Karnataka, India. The basic geotechnical characteristics of the soil sample were conducted as per the Bureau of Indian Standards (IS-2720) [64,65], and the results are presented in Table 1. The particle size distribution of the soft soil is shown in Figure 1. As per the Unified Soil Classification System (USCS), the soil is classified as clayey sand (SC). The soil has a very low unconfined compressive strength (UCS) of 14 kPa, exhibiting characteristics typical to that of very soft soils [66]. The biomedical waste ash was collected from an incineration facility located in Dakshina Kannada district of Karnataka and was stored in polythene bags to avoid any moisture affecting the material. The morphological characteristics and the chemical composition of the ash were determined by using analytic testing methods such as scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS), respectively (Figure 2). XRF analysis of the BA (Figure 3) revealed that CaO is the major compound (58.803%), followed by Ag2O (7.86%) and Na2O (7.78%). The concentrations of Al2O3 (1.106%) and SiO2 (0.9%) appeared to be low. Prior to stabilization experiments, the soil was oven dried at approximately 110 °C, and the BA was sieved through a 1 mm size sieve in order to obtain the fine ash portion that is suitable for soil stabilization. The chemical composition of the BA is presented in Table 2.

2.2. Sample Preparation and Experimental Procedure

Experimental investigation was conducted in order to assess the impact of biomedical waste ash (BA) on the unconfined compressive strength (UCS) of the soft soil. Various proportions of BA were added to the soil based on the experimental design proposed by the RSM technique. The various mixtures of BA and soil were tested with standard Proctor compaction test according to IS 2720 Part VII- 1980 [64] to determine the maximum dry density (MDD) and optimum moisture content (OMC) of each blend. The UCS test specimens were moulded at MDD and OMC for each respective blend [67,68,69] to test for UCS as per IS 2720 Part X- 1991 [65]. To ensure a uniform mixture, careful attention was paid during the mixing procedure. Cylindrical samples of 38 mm diameter and 76 mm length were prepared to test for UCS. In order to comprehend the effect of curing on UCS, samples were cast and placed in a vacuum desiccator to avoid any moisture loss and were tested for UCS after a period of 0 days, 7 days, and 14 days of curing. However, vacuum was not created in the desiccator; hence, the possibility of suction development was eliminated. The curing took place in a controlled environment, and the curing room was maintained at a temperature of approximately 23–25 °C and a relative humidity of 95%. In each test specimen, the load was applied so as to produce an axial strain of 1.25 mm per minute, i.e., 1.64% per minute. The load was applied onto the specimen until failure was achieved. For the unconfined compression tests, the samples were tested in triplicate.

2.3. Response Surface Methodology (RSM)

2.3.1. General

RSM is a mathematical and statistical technique that is used in experimental design and optimization processes. This technique was developed by George E. P. Box and K. B. Wilson [70] and is particularly useful to engineers and researchers to reduce the number of experimental trials and for optimising input factors or variables in various applications [58,71]. In contrast to other statistical techniques, RSM has an advantage since it structures the experimental process to allow for the analysis of relevant data. The key components of RSM include experimental design, development of a mathematical model, statistical analysis using analysis of variance (ANOVA), generation of response surface, and parameter optimization. RSM typically involves conducting a series of experiments based on a carefully designed experimental plan. ANOVA is a statistical method that can identify the significance of each independent factor on the response by calculating its percentage of contribution. Many researchers have included ANOVA in their studies to understand the influence of multiple independent parameters and their interaction effects on the response [69,72].
The RSM technique offers two types of designs: (i) Central Composite Design (CCD) (Figure 4) and (ii) Box–Behnken Design (BBD) (Figure 5). The CCD is a design that combines factorial points with centre and axial points. The additional centre points are used to replicate the centre of design space and to estimate pure error, while the axial points are used to estimate the curvature of the response surface [73]. It is often used when the factors are expected to have curvature in their effects on the response variable. The BBD is another response surface design that is particularly suitable for three or more factors that influence the response variable, but unlike in the CCD, the BBD does not include centre points; instead, it relies on replicates at the design points to estimate the lack of fit. It is to be noted that RSM is a local analysis, which means that for regions outside of the examined ranges of factors, the developed response surface would be invalid. The RSM technique tries to fit the experimental data to a second-order polynomial; however, not all systems with curvature can be effectively suited by the second-order polynomial. This problem may be overcome by applying a transformation function, such as logarithmic, exponential, etc., to the model in order to estimate the true response surface.

2.3.2. Development of Response Model

Design-Expert software (trial version) by Stat-Ease Inc. was used for the development of the quadratic model, along with response surface, and also to perform the statistical analysis (ANOVA) on the data obtained. This software provides powerful tools for the design of experiments, taking into consideration a combination of independent factors for the optimization of the response variables. For this research work, an RSM-based Face-Centred Central Composite Design (FCCCD) was used to generate an experimental design for the response surface because it has a wider scope of application and has been used by many other researchers in similar fields of study [60,74,75]. A two-factor experimental design, which consists of four axial points, four factorial points, and a centre point with five replicas, was generated. The independent variables (input parameters) were the percentage of biomedical waste ash (BA) and curing days (C), while the response variable was the UCS value. The BA was in the range of 4–20%, and the curing period was taken as 0–14 days. Table 3 displays the input parameters and their experimental levels used by the model. The RSM model was built with a 95% confidence interval for the quadratic model. A quadratic model with natural log transformation was developed from the experimental data sets. Table 4 presents the RSM FCCCD-based experimental design, model-predicted and experimentally observed UCS values, and the percentage of error obtained.

3. Results and Discussion

3.1. Statistical Assessment of Experimental Results and Model Adequacy

Based on the experimental plan, the unconfined compressive strength tests were performed, and the results were analysed using RSM FCCCD. Statistical analysis of the experimental results was executed through ANOVA to determine the significance of independent variables (BA and C) and their interaction on the response variable (UCS value). Table 5 presents the results of the ANOVA. Statistically insignificant parameters with p ˃ 0.05 were not considered to be greatly influencing the response variable. The factor is considered significant if the p-value is less than 0.05. Higher p-values indicate the insignificance of that particular factor on the response variable [72]. From the experimental data obtained by performing UCS tests, a quadratic model with natural log transformation was developed, and from the ANOVA results, the statistically insignificant terms were identified and eliminated from the mathematical equation by performing backward analysis. This ensured that only the significant terms (p ≤ 0.05) were included to represent the relationship between input parameters and output response. From the results presented in Table 5, the sum of squares represents the deviation from the mean, and the mean square for every variable was computed from the sum of squares and their degree of freedom (df).
The contribution to UCS by RSM-built parameters was obtained by dividing the F-value of individual parameters by the total F-value and expressing it as a percentage. The percentage contribution of the statistically significant parameters is shown in Figure 6. It was observed that the curing period had a more distinct effect on the gain of UCS due to the fact that with more time, more cementitious gels formed and contributed to the strength of the soil matrix. It can be noted that the addition of biomedical waste ash to the soil, with sufficient curing time, caused a significant increase in strength and hard consistency. Upon curing, hydration process takes place within the soil matrix, and during these reactions, Ca2+ ions supplied by the biomedical waste ash reacts with the Si and Al available in the matrix, leading to the formation of cementitious compounds. A similar trend of increase in UCS can be seen when compounds such as ground granulated blast-furnace slag (GGBS) and cement kiln dust (CKD) are added to soft soil for soil stabilization purposes [76]. Yadu and Tripathi [77] reported an increase in UCS by nearly 28% on addition of GGBS to soft soil.
Equation (1) represents the relationship between the UCS value with percentage of biomedical waste ash and curing period. Since the p-value of the interaction factor BA*C was much greater than 0.05, there was no significant contribution towards the gain of strength of the samples, and hence, it was eliminated from the model equation through backward analysis.
U C S = e 2.291 + 0.098 B A + 0.02 C − 0.002 B A 2 + 0.009 C 2  
The adequacy of a model can be examined by observing the coefficient of determination (R2), adjusted R2, and predicted R2 from the ANOVA results. The model generated for this study has an R2 value of 0.9961 (Table 5), which is close to 1. This indicates that the model fits well to the input factors. The predicted and experimentally observed UCS values are plotted in Figure 7. The plot demonstrates that the equation of the model closely aligns with the experimental data, thus indicating a satisfactory level of agreement and validating the model’s predictive capability. The predicted R2 value indicates 98.37% variability in the estimate of new data. Given that the difference is less than 0.2, the adjusted R2 and the predicted R2 of are in reasonable agreement. Adequate precision measures the signal-to-noise ratio. More than four is a desired ratio, and this model’s ratio shows an adequate signal. The design space can be navigated using this model. The model has a coefficient of variation (C.V.%) of less than 10%, indicating that it is potentially replicable.
Residual analyses can also be another method to validate the adequacy of the model. Figure 8 shows the normal probability of residuals. This plot can be used to check the residual of the least-squares fit, and since the plot is a straight line, the assumption of normality is satisfactory [78]. In Figure 8, it can also be observed that residuals scatter randomly across the plot, which indicates the variance of the original observations is constant for all values of the response [73].
The effect of independent variables or input factors on the output response can be visualised by a 3D surface plot, as shown in Figure 9. As illustrated in the figure, the influence of curing period on UCS is much more than the percentage of biomedical waste ash. Figure 10 is a 2D contour plot of the UCS vs the various combinations of biomedical waste ash and curing period.

3.2. Optimization of Variables

For this research, the optimal proportions for the UCS of stabilised soil were established using the desirability function method provided by Design Expert software (trial version). In this method, the dependent variable is converted into an individual desirability function (di) ranging between 0 and 1, where 1 indicates the goal (i.e., to maximise UCS value) and 0 designates the region falling outside the function. When the response variable reaches the goal, the desirability function becomes 1 (di = 1). The total desirability (D) is generally calculated as the geometrical mean of the individual desirability functions (Equation (2)).
D = d 1 w 1 d 2 w 2 d 3 w 3 … d n w n
where n represents the number of dependent variables, and w is the weight of the variable.
The optimization function for this study was performed by selecting “in range” preference for the factors, BA and C, and “maximize” for UCS. The ranges for all the variables in the optimization phase were as per the experimental data sets presented in Table 4. The results of the optimization approach are presented in Table 6.
In Table 6, it can be seen that the goals were easy to achieve since the overall desirability value (D) of the function was equal to 1, and also, better results were predicted. The optimum values for C and BA were observed to be 14 days and 19.912%, respectively, to produce a maximum UCS value of 203.008 kPa for the stabilised soil. At optimal combinations, the reactions between the ash and the soil particles create cementitious compounds, enhancing the soil’s load-bearing capacity. Higher or lower ash contents and shorter curing times can be less effective for strength gain. From these analyses, it can be observed that the strength of the soft soil can be significantly improved by the addition of biomedical waste ash.

4. Conclusions

In this study, the application of biomedical waste ash for the stabilization of soft soil through response surface modelling was addressed. The effects of ash concentration and curing time on the unconfined compressive strength of soft soil were evaluated through a series of experimental tests performed. The investigation led to the following conclusions:
  • A response surface methodology (RSM) model was developed to predict the unconfined compressive strength (UCS) of stabilised soft soil. The model consists of a quadratic equation with natural log transformation to express the nonlinear relationship between the input variables and UCS. The input variables in this model are the percentage of biomedical waste ash (BA) and the curing period (C).
  • The statistical significance of the model and input parameters were validated through analysis of variance (ANOVA), showing the contribution of each parameter to the UCS value of the stabilised soil. The results from ANOVA showed that curing time had a more significant impact on the UCS than BA. The p-values and F-values obtained from ANOVA indicate that the model and its parameters are statistically significant.
  • This study found that both the percentage of BA and curing time have an impact on the UCS of the stabilised soil. Increasing the amount of ash led to an increase in the strength of soil; however, the curing time was found to have an even more substantial impact on the UCS, as with more time, the cementitious compounds were able to form and bond the particles together. This conclusion highlights the importance of allowing for sufficient curing time in order to achieve excellent soil stabilization results.
  • Through the RSM model, an optimization analysis was conducted to determine the ideal combination of biomedical waste ash percentage and curing period that maximises the UCS. It was found that the optimal dosage for biomedical waste ash was 19.912% and the optimal curing period was 14 days, as predicted by the model, to produce a maximum UCS value of 203.008 kPa. This process ensures effective and efficient use of materials for soil stabilization.

Author Contributions

Conceptualization, P.S.; Methodology, P.S. and P.G.S.; Validation, P.S., P.G.S., and D.N.; Formal Analysis, P.S.; Investigation, P.S. and P.G.S.; Writing—Original Draft Preparation, P.S.; Writing—Review and Editing, P.S., P.G.S., and D.N.; Visualization, P.S., P.G.S., and D.N.; Supervision, P.G.S.; Project Administration, P.S. and P.G.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data analysed in this study are included in this published article.

Acknowledgments

We extend our gratitude to Manipal Academy of Higher Education, Manipal, Karnataka, India, for their invaluable support.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships, which could appear to have influenced the work reported in this paper. The authors declare no conflicts of interest.

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Figure 1. Particle size distribution of soft soil.
Figure 1. Particle size distribution of soft soil.
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Figure 2. SEM and EDS image of biomedical waste ash.
Figure 2. SEM and EDS image of biomedical waste ash.
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Figure 3. Results of XRF analysis.
Figure 3. Results of XRF analysis.
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Figure 4. Central Composite Design (CCD).
Figure 4. Central Composite Design (CCD).
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Figure 5. Box–Behnken Design (BBD).
Figure 5. Box–Behnken Design (BBD).
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Figure 6. Contribution of parameters influencing the UCS of stabilised soil.
Figure 6. Contribution of parameters influencing the UCS of stabilised soil.
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Figure 7. Comparison of measured UCS with model-predicted UCS values.
Figure 7. Comparison of measured UCS with model-predicted UCS values.
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Figure 8. Normal probability plot and residuals against predicted values.
Figure 8. Normal probability plot and residuals against predicted values.
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Figure 9. Software-generated 3D response surface plot.
Figure 9. Software-generated 3D response surface plot.
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Figure 10. Response surface contour plot.
Figure 10. Response surface contour plot.
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Table 1. Characteristics of unstabilised soil.
Table 1. Characteristics of unstabilised soil.
Soil CharacteristicValue
Specific Gravity2.23
Liquid Limit (%)44.40
Plastic Limit (%)25.14
Shrinkage Limit (%)16.43
Maximum Unit Weight (kN/m3)15.99
Optimum Moisture Content (%)17
Unconfined Compressive Strength (kPa)14
Table 2. EDS analysis of BA.
Table 2. EDS analysis of BA.
ElementWeight%Atomic%
C0.280.56
O41.5563.44
Mg0.710.72
Al0.190.17
Si0.520.45
Cl1.010.70
K0.740.46
Ca54.8433.42
Ti0.160.08
Totals100.00
Table 3. Experimental level of input variables.
Table 3. Experimental level of input variables.
Input VariablesLevel
−101
BA41220
C0714
Table 4. FCCCD-based experimental design and UCS results.
Table 4. FCCCD-based experimental design and UCS results.
Run OrderExperimental DesignLaboratory
Experiments		Compaction
Characteristics		UCS (kPa)Error (%)
BACBACMDD (g/cc)OMC (%)RSM-PredictedExperimentally Observed
1001271.45021.040.4540.2−0.62
2001271.45021.040.4540.2−0.62
3−114141.64817.6107.77111+2.91
4102071.39427.246.9952.2+9.98
5001271.45021.040.4540.2−0.62
60−11201.45021.022.6522.4−1.12
7−1−1401.64817.614.1515+5.67
80112141.45021.0172.43176.8+2.47
91−12001.39427.226.3125−5.24
10001271.45021.040.4540.2−0.62
11−10471.64817.625.0323−8.83
12001271.45021.040.4540.2−0.62
131120141.39427.2202.35193.4−4.63
---16 *141.36427.0195.25190−2.76
* without RSM design.
Table 5. Results of statistical analysis using ANOVA.
Table 5. Results of statistical analysis using ANOVA.
SourceSum of SquaresdfMean SquareF-Valuep-Value
Model7.3541.84512.75<0.0001
BA0.592610.5926165.25<0.0001
C6.2316.231736.95<0.0001
BA20.068410.068419.070.0024
C20.533310.5333148.71<0.0001
Residual0.028780.0036
Lack of fit0.028740.0072
Pure Error0.000040.0000
Cor Total7.3812
R20.9961
Adjusted R20.9942 Std. Dev.0.0599
Predicted R20.9837 Mean3.83
Adeq Precision71.7946 C.V%1.56
Table 6. Optimization results.
Table 6. Optimization results.
NumberBACUCSDesirability
119.91214.000203.0081
219.79714.000202.9991
319.71714.000202.9811
419.96913.999202.9721
519.65014.000202.9711
618.85214.000202.3920.999
718.79114.000202.3210.999
818.47014.000201.8880.998
918.28714.000201.5950.997
1018.02614.000201.1230.997
1116.72814.000197.7950.990
1216.66314.000197.5860.990
1314.03214.000186.0710.967
1413.12514.000180.8250.957
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Somadas, P.; Sarvade, P.G.; Nayak, D. Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology. Geosciences 2024, 14, 182. https://doi.org/10.3390/geosciences14070182

AMA Style

Somadas P, Sarvade PG, Nayak D. Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology. Geosciences. 2024; 14(7):182. https://doi.org/10.3390/geosciences14070182

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Somadas, Pooja, Purushotham G. Sarvade, and Deepak Nayak. 2024. "Advancements in Soft Soil Stabilization by Employing Novel Materials through Response Surface Methodology" Geosciences 14, no. 7: 182. https://doi.org/10.3390/geosciences14070182

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