# Assessment of Mechanical Properties of Soil-Lime-Crude Oil-Contaminated Soil Blend Using Regression Model for Sustainable Pavement Foundation Construction

Authors: Gregory C. Ezeokpube; Isiguzo Edwin Ahaneku; George Uwadiegwu Alaneme; Imoh Christopher Attah; Roland Kufre Etim; Bamidele Charles Olaiya; Iberedem Monday Udousoro
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

Oil pollution causes deterioration of the physical, chemical, mechanical, and geotechnical characteristics of affected soil leading to loss of soil productivity for engineering purposes. Different stabilization methods serve as a remedy for such soil to regain its loss engineering properties. This study was concerned with the utilization of lime to stabilize crude oil contaminated soil and to investigate its efficacy for soil stabilization. The study also focused on determining the geotechnical properties of crude oil contamination and matching the result with standard specifications established for engineering works. Hydrated lime, expansive clayey soil, contaminated soil, and potable water were the materials used for the experimental investigation. The contaminated soil was treated with 6.5% lime and 0–20% crude oil contaminated materials obtained from oil exploration sites in North-Eastern Nigeria and per standard test method for laboratory evaluation of consistency limits, compaction properties, California bearing ratio (CBR), and microstructural and mineralogical assessments. The experimental results obtained were further tested statistically through one-way ANOVA and F-statistics to establish the source of variation for the geotechnical properties, while multiple linear regression and correlation statistics helped draw the connection between the consistency limits, compaction, and CBR properties of the soil-lime-COCM blend. Results indicated a coefficient of determination of 99.86. The contaminated soil materials were found to show optimal performance at a 5% ratio and 6.5% of lime for civil construction purposes.


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## Body

## 1. Introduction

Crude oil is a dark liquid that exists naturally beneath the Earth’s surface and has an oily texture (Anel et al. 2020). Natural deposits can be found in numerous places across the world, including the United States, Russia, Romania, Iran, Mexico, Iraq, Saudi Arabia, Kuwait, Libya, and Nigeria [1]. In the petroleum industries that deal in crude oil exploration and refining every year, a billion tons of crude oil, natural gas, and derivatives are produced. All of them are subsequently refined into goods such as diesel, gasoline, petrol, and lubricants [2]. According to the International Energy Agency, global oil demand in 2015 was 97 million barrels per day, with a forecast of 100 million barrels per day by 2021 [3]. Crude oil principally contains multifarious carbohydrates or a combination of it of different molecular mass and some other chemical compounds. The substances tend to have black color; however, they can also have brown or brownish-black and very rarely yellowish or greenish tint (Britannica, 2018). Crude oil is made up of various molecular weights and structure volatile liquid hydrocarbons. It is made up of about 17,000 hydrocarbons and is categorized according to the most common component found inside it [4]. Crude oil plays a very significant role in economic growth throughout the world. It serves as a major source of energy to most developed countries in the world and provides materials for single-use disposable items, which is important for healthcare, food, and pharmaceutical industries. It is the chief contributor to many developing countries’ gross domestic product [5].In recent times, the demand for energy all over the world has been on the increase despite the much research into alternative energy sources. The rapid increase in demand has led to a corresponding increase in oil drilling activities. Oil drilling no longer takes place only on land but in oceans too. The quest to exploit and use offshore oil has led to oil spills accident [6]. Crude oil is a complicated chemical mixture, as are its refined products. These chemical constituents of crude oil cause great damage and deterioration of affected soils whenever there is an oil spillage. With studies, it was discovered that oil spills cause deterioration/degradation of the different properties of soil (physical, chemical, mechanical, and geotechnical). This makes affected soils to be unsuitable for civil engineering works. Crude oil contamination, on the other hand, is a huge environmental issue that affects both aquatic and terrestrial areas. About 80% of areas are currently damaged by petroleum-derived products, that is, hydrocarbons [7]. Crude oil coats the surface of the soil, causing carbon dioxide produced by soil organisms to be trapped. It also causes soil porosity to be reduced by gluing soil particles together. The level of damage is determined by the volume and kind of oil spilled [8]. The United States Environmental Protection Agency declared polycyclic aromatic hydrocarbons (PAH) in crude oil to be a major environmental contaminant because they are mutagenic and carcinogenic [9]. Longer contact times of stable PAH with soil promote the phenomena of soil aging, which makes the soil more resistant to treatment. The contamination of groundwater by these toxins poses a threat to human health, vegetation, and the biological environment [10].The foregoing has informed many studies that have been carried out to ascertain how these polluted soils can be made suitable for civil engineering works. In other to reuse these soils for civil engineering works, researchers have discovered through research that the soils will have to be effectively remedied or the contaminants in the soil will have to be contained. Effective reuse of these contaminated soils has been discovered to be one of the alternative ways to effectively dispose of them [11, 12]. Oluremi and Osuolale [13] researched oil contaminated soil as possible relevant materials in civil engineering construction. They reviewed the mechanical and geotechnical behavior of polluted soils to ascertain their potential reuse as civil engineering materials. They discovered that changing the contaminants to a less harmful form by burning thoroughly during firing through the incineration process improves it for civil works. It is therefore seen that these contaminated soils can be used for civil engineering works when properly treated. Also, Steliga and Kluk [14] studied the level and rate of oil contamination of soils and how they affect soil properties. They worked on how these contaminated soils can be treated. They used physicochemical cleanup and thermal cleanup biological treatment methods. They discovered that crude oil presence in the soil can be reduced through these methods. Oyediran and Durojaiye [15] also conducted experiments to investigate differences in geotechnical features of crude oil-polluted soils. As a contamination simulation, 2–10% by weight of crude oil was added to soils. They saw that the hydraulic conductivity plus the geotechnical belongingness of the soil decreased with crude oil contamination but increased when the soil was cured. This shows that crude oil causes variation in the geotechnical properties of soil. More so, Osuji et al. [16] studied oil spill remediated soils to evaluate the impact of the general remediation carried out over years and to ascertain observable variation in environmental quality. They collected and analyzed a sample of remediated soils to determine important physicochemical parameters of environmental concern in soil. It was discovered that the remediation applied over the years had variable impacts on some physicochemical parameters and environmental quality. Essien and Stoeckert [17] researched crude oil spillage pollution and chemical remediation. They determined that the loss of soil permeability by up to 51% in crude oil polluted plots (0.006 cm/s) compared to the unpolluted plot was a significant difference (P = 0.01, 0.05) after running an experiment to see how it impacts the hydraulic conductivity of the soil.Exploration and transportation of crude oil have led to oil spill incidents at places where there are oil deposits. As crude oil is a complex mixture of chemicals, the spillage during the exploitation of crude oil has led to the deterioration of affected soils. The physical, chemical, mechanical, and geotechnical possessions of the soil are being impacted by the oil leak. The quality of oil-polluted soil being reduced makes it unsuitable for civil engineering works [18, 19]. To reuse these polluted soils for civil engineering works, there is a need for the treatment of these soils to reduce the effect of these contaminants on them [20]. Therefore, the major purpose of this work is to evaluate how pollution by crude oil affects the physical, chemical, mechanical, and geotechnical possessions of soil and how these soils can be treated using engineering methods to make them suitable for civil engineering works. Crude oil contaminated soils are found not to be suitable for construction works due to the deteriorated geotechnical and mechanical properties of the soil. The chemicals present in the crude oil alter and cause variations in the different properties of the soil that makes it essential for experimental assessment of the polluted soil residue from crude oil exploration sites to ascertain how useful it could be for civil engineering works [21]. This study stands to experiment on these crude oil polluted soils to locate the affected soil characteristics and then use appropriate measures to treat these soils to remove at least up to 90% of the crude oil contamination present in them. This will in turn change the soils initially seen as wastes due to crude oil pollution to suitable soils for civil engineering works [22].Multiple linear regression is a statistical method deployed to estimate or predict criterion variables response from a collection of explanatory or multiple predictor parameters and is also used to generate a set of mathematical equations or relations used to predict or evaluate dependent or target parameters from a set of predictor variables [23]. This statistical method is adopted to efficiently identify or determine the strength of the effect that the independent or predictor parameters have on the outcome variables to forecast the effects of changes impact, which implies evaluating how much the response or dependent variables changes when the regressor parameters vary. MLR is further used to predict the likely outcome of several variables and to plot the relationship between these variables. It is based on a mathematical assumption that a linear relationship exists between dependent or criterion variables and independent factors. MLR helps examine the overall fit and variance analysis of the model and the relative contribution of the predictors to the total variance explained [24]. The multiple linear regression equation is presented as follows:(1)y=β0+β1x1+⋯+βnxn+ε,where y is the predicted result of the target response parameter, β0 is a constant or the intercept term or the value of y when all the regressor parameters are set to zero, n is the number of independent variables, β1x1 is the regression coefficient of the first predictor variables, βnxn is the regression coefficient of the last predictor variables, and ε is the model error. The application of the multiple linear regression technique has been a subject of an investigation by researches previously and recently to evaluate the behavior of blended composite construction materials to phase of the empirical approach to the design of mixture experiments [25, 26]. Reference [27] conducted research on the compressive strength evaluation of concrete using evolutionary artificial neural networks that combines neural network models and genetic algorithms (GA). One hundred and seventy-three total number of data sets derived from experimental set up of cylindrical concrete specimens were used to construct the model in the study with maximum fine aggregates size, cement, water-cement ratio, coefficient of soft sand and amount of coarse aggregates taken as the input variables to predict the compressive strength response. The developed optimized model was further compared with the multiple linear regression model to validate the prediction performance, and the generated statistical results indicated a more flexible and accurate estimation of the optimized ANN model.Also, Ahangari and Ahangari [28] carried out research on the investigation of concrete curing temperature using artificial neural network (ANN) and multivariable regression techniques. Ten experimental cylindrical samples were produced under controlled conditions, whereby the concrete specimens’ temperatures were examined using thermistors through vibrating wire strain gauges. Water-cement ratio, time (hour), aggregate content, temperature, specimen diameter, and height were taken as the input variables, while the measured temperature for the ten different specimens were the output or dependent parameter. Deployment of ANN for the modeling produced a coefficient of determination (R2) of 0.999 that was observed to be higher than the result obtained for the multivariate regression model with R2 of 0.873. The derived results showed the efficient application of regression and ANN model for the evaluation of complex and multiple variables to predict concrete temperature.Soil stabilization entails the use of stabilizing agents (binder materials) in poor soils to increase geotechnical attributes such as compressibility, strength, permeability, and durability to meet design requirements. Soil stabilization involves gluing soil particles together, waterproofing the particles or a combination of the two to improve soil strength and resistance to water softening [29–32]. This research study aims to assess the effectiveness of crude oil contaminated soil materials derived from exploration sites when combined with lime to stabilize plastic clayey soil with high swelling potentials using the multiple linear regression (MLR) statistics method. When lime is applied to a reactive soil to achieve long-term strength gain through a pozzolanic reaction, soil-lime stabilization occurs. As the calcium oxides from the lime combine with the aluminates and silicates solubilized from the clay, stable calcium silicate hydrates and calcium aluminate hydrates are formed. As long as there is enough lime available and the pH remains high, the full-term pozzolanic reaction can last for years. As a result, lime treatment can lead to significant and long-term strength improvements in weak or unsuitable soil materials [33, 34]. A reactive soil, a good mix of design techniques, and trustworthy construction practices are the keys to pozzolanic reactivity and stabilization. To increase the workability and load-bearing qualities of soils, lime can be used to treat them [35–37].


## 2. Materials and Methods

### 2.1. Materials

When lime reacts with a substance that contains soluble silicates and aluminates (such as clay and silt), it generates hydrated calcium aluminates and calcium silicates, which increase the mechanical characteristics significantly. Expansive clay materials, contaminated soil, lime, and potable water are the test materials for the experimental investigation.


#### 2.1.1. Lateritic Soil

The lateritic soil sample for this investigation was taken from a borrow hole at Olokoro, between the latitudes of 05°28′36.700″ north and 07°32′23.170″ east, at a depth of 2 m, 5 km along Ubakala road from Ishi Court, Umuahia, Abia state. The sample was solid and reddish-brown in hue when it was taken. The dirt was collected from this area and air-dried in trays for 6 days before being crushed. The dry soil was pulverized in a tray with a rubber-covered pestle, and sieve characterization was performed following an ordered British standard (1S:2720-part xvi, 1999) [38].


#### 2.1.2. Lime

As a stabilizing binder, hydrated lime (Ca(OH)2) was acquired from a retail source. The reactions of soil to lime treatment are complicated and often dramatic. A number of explanations have been proposed to account for these unusual responses, including: cation exchange, i.e., replacement of exchangeable sodium, magnesium, or other cations previously retained by the soil clay with calcium cations received from the lime; flocculation of the clay, and concomitant increase in effective grain size; carbonation, i.e., the reaction of lime with carbon dioxide from the environment to produce calcium carbonate, which is claimed to have a cementing effect; and pozzolanic reactions with soil elements to produce new cementitious minerals [36, 39].


#### 2.1.3. Crude Oil Contaminated Materials (COCMs)

Samples of contaminated sands were obtained from crude oil polluted sites at Kolomani oil-well-3 in Bauchi State. The samples were collected in two distinct forms: the first part (A) was treated using thermal desorption (TDU) to separate the hydrocarbon content from the materials, while the second part was an unadulterated sample (B). This was intended to determine the grading of the soil of the area and the percentage of pollution of the soil with crude oil. The samples were obtained at about a mean depth of 3,500 m from ground level during crude oil drilling and exploration.


### 2.2. Study Area

The crude oil contaminated soil was gathered in Bauchi State at the Kolmani River −1 well, drilled in northeastern Nigeria’s Gongola Basin at 10°07′03.9″ N and 10°42′43.8″E, as shown in Figure1.Figure 1
Study area showing geological details.


### 2.3. Methods

The test materials were examined in the laboratory to derive their general engineering properties and classification. In-place mixing is commonly used to add the right amount of lime for soil and things contaminated with crude oil (COCM), blended to an acceptable depth, after a thorough mixture component design and testing have been completed. To properly blend the materials, pulverization and mixing are needed. Varying ratios of contaminated soil were used to treat the problematic soil from 0% (control) to 20% and with 6.5% of lime. The proportion of mixture ingredients selected for this experimental to estimate the role of lime and COCM in admixture stabilization of the problematic soil is based on preliminary tests to obtain the optimal result of stabilizing agents required to potentially reinforce the soil in line with the research findings of Etim et al. [35] and Fattah et al. (2014). The blended mixture response will help us envisage the impact of crude contamination in the stabilized soil samples. To evaluate the index properties of natural soil, laboratory studies were carried out following BS 1377 (1990). At the Centre for Energy Research and Training (CERT), A. B. U., Zaria, the oxide content of lime was determined using the energy-dispersive X-ray fluorescence technique (EDXRF). Also, morphological analysis of the soil-lime-COCM blend is carried out using spectrum electron microscopy (SEM) [40, 41].


#### 2.3.1. Compaction

Mechanical compaction is a frequent and cost-effective method of soil stabilization. The performance and analysis of field control tests to ensure that compacted fills match the stipulated design parameters are a very significant responsibility for geotechnical engineers. The desired density (as a percentage of the “maximum” density measured in a standard laboratory test) and the water content are commonly specified in design requirements. The compaction test enables the engineering classification and evaluates the moisture content-density relationship of the soil-lime-COCM blend [13]. Most engineering qualities of soil, such as strength, stiffness, shrinkage resistance, and imperviousness, will improve as soil density is increased. The water content that produces the maximum density for a given compaction effort is known as the optimum water content. When compacting at greater water contents than the optimum water content, the outcome is a dispersed compact. The experimental apparatus is as given in Figure 2 [42, 43].Figure 2
Particle size distribution plot.


#### 2.3.2. California Bearing Ratio (CBR)

The test was conducted on a soil-COCM-lime mixture, as specified in BS 1377 and BS 1924. The mixed samples were compacted using BSL compaction effort and their respective optimum moisture content. The BSL entails the compaction of materials in three layers, each of which receives 62 2.5 kg rammer blows. The compacted specimens were cured for 6 days before being immersed for 48 hours before being tested according to Nigerian General Specifications.(1) Calculation of CBR from Load Penetration Curve. On a natural scale, plot the load penetration curve with the load on the Y-axis and the penetration on the X-axis. Correct by drawing a tangent to the upper curve at the point of contraflexure if the curve is consistently convex upwards, even if the starting segment of the curve may be concave upwards due to surface defects. The following steps are followed:(I)
Assume the origin is the place where the tangent and theX-axis coincide(II)
Calculate the CBR values for 2.50 mm and 5.00 mm penetrationTake the adjusted load values from the load penetration curve and compute the CBR as stated in equation (2) for the penetration value at which CBR is desired.(2)CBR=Pt∗CfPs∗100,where PT denotes the corrected unit test load corresponding to the chosen penetration from the load penetration curve, PS denotes the total standard load for the same penetration depth, and Cf denotes the proving ring correction factor.


#### 2.3.3. Microanalysis

To gain a better understanding of the microstructural behavior of the soil, scanning electron microscopy (SEM) tests were performed on both natural and heated samples. In this study, a Phenom ProX Desktop SEM was used, which was set to detect particles smaller than 100 m. A beam of high-energy electrons was focused on the specimen at a voltage of 15 kV, generating a variety of signals that were communicated or presented as an SEM picture with a resolution of 536 µm and a magnification of 500×.(1) X-Ray Fluorescence (XRF). The elemental composition of materials can be determined using X-ray fluorescence (XRF), a nondestructive analytical technique. It studies a sample’s chemistry by detecting the fluorescence X-ray released by a sample when it is excited by the main X-ray source. XRF spectroscopy is an ideal technology for qualitative and quantitative examination of material composition since each element present in a sample creates a set of characteristic fluorescent X-rays that is unique to that element [44].


### 2.4. Analysis of Data

A statistical approach is adopted for the analysis of experimental results obtained for proper investigation of the soil-COCM-lime engineering behavior. To account for the cradle of disparity in the investigated geotechnical parameters, the findings were subjected to a one-way ANOVA test and an F-statistic. Also, this study deployed multiple linear regression (MLR) and correlation to determine the relationship between mixture ratios and geotechnical parameters using statistics (target response). This analytical approach would enable optimization of COCM-lime utilization for stabilization purposes saving time and resources spent on trial tests [45].


### 2.5. Determination of Mixture Blend Fraction for the Mixture Experiment

This experimental investigation involves the treatment of weak clayey soil with a constant lime ratio of 6.5% and varying percentages of crude oil contaminated soil (COCM) from 0 to 20% in a fraction quantity increment of 5% to assess the effects of COCM and lime as a stabilizing agent. Preliminary tests were carried out on the test ingredients to classify and derive the required fractions to efficiently combine these mixture ingredients to derive optimal responses. The mixture experimental runs for the combinations of the components are specified as it is in Table1.Table 1
Mixture ratios formulation of ingredients.
s/nExperimental runDescription1SoilControl2Soil + 6.5% limeSoil + lime3Soil + 6.5% lime + 5% COCMSoil + lime + COCM4Soil + 6.5% lime + 10% COCMSoil + lime + COCM5Soil + 6.5% lime + 15% COCMSoil + lime + COCM6Soil + 6.5% lime + 20% COCMSoil + lime + COCM


## 2.1. Materials

When lime reacts with a substance that contains soluble silicates and aluminates (such as clay and silt), it generates hydrated calcium aluminates and calcium silicates, which increase the mechanical characteristics significantly. Expansive clay materials, contaminated soil, lime, and potable water are the test materials for the experimental investigation.


### 2.1.1. Lateritic Soil

The lateritic soil sample for this investigation was taken from a borrow hole at Olokoro, between the latitudes of 05°28′36.700″ north and 07°32′23.170″ east, at a depth of 2 m, 5 km along Ubakala road from Ishi Court, Umuahia, Abia state. The sample was solid and reddish-brown in hue when it was taken. The dirt was collected from this area and air-dried in trays for 6 days before being crushed. The dry soil was pulverized in a tray with a rubber-covered pestle, and sieve characterization was performed following an ordered British standard (1S:2720-part xvi, 1999) [38].


### 2.1.2. Lime

As a stabilizing binder, hydrated lime (Ca(OH)2) was acquired from a retail source. The reactions of soil to lime treatment are complicated and often dramatic. A number of explanations have been proposed to account for these unusual responses, including: cation exchange, i.e., replacement of exchangeable sodium, magnesium, or other cations previously retained by the soil clay with calcium cations received from the lime; flocculation of the clay, and concomitant increase in effective grain size; carbonation, i.e., the reaction of lime with carbon dioxide from the environment to produce calcium carbonate, which is claimed to have a cementing effect; and pozzolanic reactions with soil elements to produce new cementitious minerals [36, 39].


### 2.1.3. Crude Oil Contaminated Materials (COCMs)

Samples of contaminated sands were obtained from crude oil polluted sites at Kolomani oil-well-3 in Bauchi State. The samples were collected in two distinct forms: the first part (A) was treated using thermal desorption (TDU) to separate the hydrocarbon content from the materials, while the second part was an unadulterated sample (B). This was intended to determine the grading of the soil of the area and the percentage of pollution of the soil with crude oil. The samples were obtained at about a mean depth of 3,500 m from ground level during crude oil drilling and exploration.


## 2.1.1. Lateritic Soil

The lateritic soil sample for this investigation was taken from a borrow hole at Olokoro, between the latitudes of 05°28′36.700″ north and 07°32′23.170″ east, at a depth of 2 m, 5 km along Ubakala road from Ishi Court, Umuahia, Abia state. The sample was solid and reddish-brown in hue when it was taken. The dirt was collected from this area and air-dried in trays for 6 days before being crushed. The dry soil was pulverized in a tray with a rubber-covered pestle, and sieve characterization was performed following an ordered British standard (1S:2720-part xvi, 1999) [38].


## 2.1.2. Lime

As a stabilizing binder, hydrated lime (Ca(OH)2) was acquired from a retail source. The reactions of soil to lime treatment are complicated and often dramatic. A number of explanations have been proposed to account for these unusual responses, including: cation exchange, i.e., replacement of exchangeable sodium, magnesium, or other cations previously retained by the soil clay with calcium cations received from the lime; flocculation of the clay, and concomitant increase in effective grain size; carbonation, i.e., the reaction of lime with carbon dioxide from the environment to produce calcium carbonate, which is claimed to have a cementing effect; and pozzolanic reactions with soil elements to produce new cementitious minerals [36, 39].


## 2.1.3. Crude Oil Contaminated Materials (COCMs)

Samples of contaminated sands were obtained from crude oil polluted sites at Kolomani oil-well-3 in Bauchi State. The samples were collected in two distinct forms: the first part (A) was treated using thermal desorption (TDU) to separate the hydrocarbon content from the materials, while the second part was an unadulterated sample (B). This was intended to determine the grading of the soil of the area and the percentage of pollution of the soil with crude oil. The samples were obtained at about a mean depth of 3,500 m from ground level during crude oil drilling and exploration.


## 2.2. Study Area

The crude oil contaminated soil was gathered in Bauchi State at the Kolmani River −1 well, drilled in northeastern Nigeria’s Gongola Basin at 10°07′03.9″ N and 10°42′43.8″E, as shown in Figure1.Figure 1
Study area showing geological details.


## 2.3. Methods

The test materials were examined in the laboratory to derive their general engineering properties and classification. In-place mixing is commonly used to add the right amount of lime for soil and things contaminated with crude oil (COCM), blended to an acceptable depth, after a thorough mixture component design and testing have been completed. To properly blend the materials, pulverization and mixing are needed. Varying ratios of contaminated soil were used to treat the problematic soil from 0% (control) to 20% and with 6.5% of lime. The proportion of mixture ingredients selected for this experimental to estimate the role of lime and COCM in admixture stabilization of the problematic soil is based on preliminary tests to obtain the optimal result of stabilizing agents required to potentially reinforce the soil in line with the research findings of Etim et al. [35] and Fattah et al. (2014). The blended mixture response will help us envisage the impact of crude contamination in the stabilized soil samples. To evaluate the index properties of natural soil, laboratory studies were carried out following BS 1377 (1990). At the Centre for Energy Research and Training (CERT), A. B. U., Zaria, the oxide content of lime was determined using the energy-dispersive X-ray fluorescence technique (EDXRF). Also, morphological analysis of the soil-lime-COCM blend is carried out using spectrum electron microscopy (SEM) [40, 41].


### 2.3.1. Compaction

Mechanical compaction is a frequent and cost-effective method of soil stabilization. The performance and analysis of field control tests to ensure that compacted fills match the stipulated design parameters are a very significant responsibility for geotechnical engineers. The desired density (as a percentage of the “maximum” density measured in a standard laboratory test) and the water content are commonly specified in design requirements. The compaction test enables the engineering classification and evaluates the moisture content-density relationship of the soil-lime-COCM blend [13]. Most engineering qualities of soil, such as strength, stiffness, shrinkage resistance, and imperviousness, will improve as soil density is increased. The water content that produces the maximum density for a given compaction effort is known as the optimum water content. When compacting at greater water contents than the optimum water content, the outcome is a dispersed compact. The experimental apparatus is as given in Figure 2 [42, 43].Figure 2
Particle size distribution plot.


### 2.3.2. California Bearing Ratio (CBR)

The test was conducted on a soil-COCM-lime mixture, as specified in BS 1377 and BS 1924. The mixed samples were compacted using BSL compaction effort and their respective optimum moisture content. The BSL entails the compaction of materials in three layers, each of which receives 62 2.5 kg rammer blows. The compacted specimens were cured for 6 days before being immersed for 48 hours before being tested according to Nigerian General Specifications.(1) Calculation of CBR from Load Penetration Curve. On a natural scale, plot the load penetration curve with the load on the Y-axis and the penetration on the X-axis. Correct by drawing a tangent to the upper curve at the point of contraflexure if the curve is consistently convex upwards, even if the starting segment of the curve may be concave upwards due to surface defects. The following steps are followed:(I)
Assume the origin is the place where the tangent and theX-axis coincide(II)
Calculate the CBR values for 2.50 mm and 5.00 mm penetrationTake the adjusted load values from the load penetration curve and compute the CBR as stated in equation (2) for the penetration value at which CBR is desired.(2)CBR=Pt∗CfPs∗100,where PT denotes the corrected unit test load corresponding to the chosen penetration from the load penetration curve, PS denotes the total standard load for the same penetration depth, and Cf denotes the proving ring correction factor.


### 2.3.3. Microanalysis

To gain a better understanding of the microstructural behavior of the soil, scanning electron microscopy (SEM) tests were performed on both natural and heated samples. In this study, a Phenom ProX Desktop SEM was used, which was set to detect particles smaller than 100 m. A beam of high-energy electrons was focused on the specimen at a voltage of 15 kV, generating a variety of signals that were communicated or presented as an SEM picture with a resolution of 536 µm and a magnification of 500×.(1) X-Ray Fluorescence (XRF). The elemental composition of materials can be determined using X-ray fluorescence (XRF), a nondestructive analytical technique. It studies a sample’s chemistry by detecting the fluorescence X-ray released by a sample when it is excited by the main X-ray source. XRF spectroscopy is an ideal technology for qualitative and quantitative examination of material composition since each element present in a sample creates a set of characteristic fluorescent X-rays that is unique to that element [44].


## 2.3.1. Compaction

Mechanical compaction is a frequent and cost-effective method of soil stabilization. The performance and analysis of field control tests to ensure that compacted fills match the stipulated design parameters are a very significant responsibility for geotechnical engineers. The desired density (as a percentage of the “maximum” density measured in a standard laboratory test) and the water content are commonly specified in design requirements. The compaction test enables the engineering classification and evaluates the moisture content-density relationship of the soil-lime-COCM blend [13]. Most engineering qualities of soil, such as strength, stiffness, shrinkage resistance, and imperviousness, will improve as soil density is increased. The water content that produces the maximum density for a given compaction effort is known as the optimum water content. When compacting at greater water contents than the optimum water content, the outcome is a dispersed compact. The experimental apparatus is as given in Figure 2 [42, 43].Figure 2
Particle size distribution plot.


## 2.3.2. California Bearing Ratio (CBR)

The test was conducted on a soil-COCM-lime mixture, as specified in BS 1377 and BS 1924. The mixed samples were compacted using BSL compaction effort and their respective optimum moisture content. The BSL entails the compaction of materials in three layers, each of which receives 62 2.5 kg rammer blows. The compacted specimens were cured for 6 days before being immersed for 48 hours before being tested according to Nigerian General Specifications.(1) Calculation of CBR from Load Penetration Curve. On a natural scale, plot the load penetration curve with the load on the Y-axis and the penetration on the X-axis. Correct by drawing a tangent to the upper curve at the point of contraflexure if the curve is consistently convex upwards, even if the starting segment of the curve may be concave upwards due to surface defects. The following steps are followed:(I)
Assume the origin is the place where the tangent and theX-axis coincide(II)
Calculate the CBR values for 2.50 mm and 5.00 mm penetrationTake the adjusted load values from the load penetration curve and compute the CBR as stated in equation (2) for the penetration value at which CBR is desired.(2)CBR=Pt∗CfPs∗100,where PT denotes the corrected unit test load corresponding to the chosen penetration from the load penetration curve, PS denotes the total standard load for the same penetration depth, and Cf denotes the proving ring correction factor.


## 2.3.3. Microanalysis

To gain a better understanding of the microstructural behavior of the soil, scanning electron microscopy (SEM) tests were performed on both natural and heated samples. In this study, a Phenom ProX Desktop SEM was used, which was set to detect particles smaller than 100 m. A beam of high-energy electrons was focused on the specimen at a voltage of 15 kV, generating a variety of signals that were communicated or presented as an SEM picture with a resolution of 536 µm and a magnification of 500×.(1) X-Ray Fluorescence (XRF). The elemental composition of materials can be determined using X-ray fluorescence (XRF), a nondestructive analytical technique. It studies a sample’s chemistry by detecting the fluorescence X-ray released by a sample when it is excited by the main X-ray source. XRF spectroscopy is an ideal technology for qualitative and quantitative examination of material composition since each element present in a sample creates a set of characteristic fluorescent X-rays that is unique to that element [44].


## 2.4. Analysis of Data

A statistical approach is adopted for the analysis of experimental results obtained for proper investigation of the soil-COCM-lime engineering behavior. To account for the cradle of disparity in the investigated geotechnical parameters, the findings were subjected to a one-way ANOVA test and an F-statistic. Also, this study deployed multiple linear regression (MLR) and correlation to determine the relationship between mixture ratios and geotechnical parameters using statistics (target response). This analytical approach would enable optimization of COCM-lime utilization for stabilization purposes saving time and resources spent on trial tests [45].


## 2.5. Determination of Mixture Blend Fraction for the Mixture Experiment

This experimental investigation involves the treatment of weak clayey soil with a constant lime ratio of 6.5% and varying percentages of crude oil contaminated soil (COCM) from 0 to 20% in a fraction quantity increment of 5% to assess the effects of COCM and lime as a stabilizing agent. Preliminary tests were carried out on the test ingredients to classify and derive the required fractions to efficiently combine these mixture ingredients to derive optimal responses. The mixture experimental runs for the combinations of the components are specified as it is in Table1.Table 1
Mixture ratios formulation of ingredients.
s/nExperimental runDescription1SoilControl2Soil + 6.5% limeSoil + lime3Soil + 6.5% lime + 5% COCMSoil + lime + COCM4Soil + 6.5% lime + 10% COCMSoil + lime + COCM5Soil + 6.5% lime + 15% COCMSoil + lime + COCM6Soil + 6.5% lime + 20% COCMSoil + lime + COCM


## 3. Results Discussion and Analysis

### 3.1. Material Characterization and Classification

For natural expansive soil geotechnical characterization, lime and contaminated soil samples were carried out here. The results obtained from the laboratory exercise are detailed in Tables2–7 using the Gaussian intensity method. The obtained experimental results as shown in Tables 2 and 3 for the lime samples indicates 2.750%, 2.439%, 0.083%, 0.353%, 0.651%, 5.833%, and 87.157% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, MgO, and CaO, respectively (Table 2). The element table showed 30.550, 1.354, 2.308, 0.691, 2.484, and 61.257 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), magnesium (Mg), and calcium (Ca), respectively [46, 47].Table 2
XRF result for lime characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO22.8970.331wt.%2.7500.314V2O50.0390.020wt.%0.0120.006Cr2O30.0000.000wt.%0.0000.000MnO0.0100.009wt.%0.0080.007Fe2O30.9870.034wt.%0.3530.012Co3O40.0630.012wt.%0.0150.003NiO0.0250.006wt.%0.0190.005CuO0.0460.007wt.%0.0330.005Nb2O30.0090.020wt.%0.0020.005MoO30.0150.025wt.%0.0060.010WO30.0430.032wt.%0.0110.008P2O50.0000.000wt.%0.0000.000SO30.5670.069wt.%0.4040.049CaO85.7100.469wt.%87.1570.477MgO4.1237.423wt.%5.83310.503K2O0.0000.000wt.%0.0000.000BaO0.0780.078wt.%0.0290.029Al2O34.3621.468wt.%2.4390.821Ta2O50.0240.028wt.%0.0030.004TiO20.1160.028wt.%0.0830.020ZnO0.0260.007wt.%0.0180.005Ag2O0.0000.000wt.%0.0000.000Cl0.4050.035wt.%0.6510.056ZrO20.0200.017wt.%0.0090.008SnO20.4351.151wt.%0.1650.435Table 3
XRF element result for lime characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000030.550MgKa0.9411.69362.486AlKa10.1203.40652.308SiKa35.3014.03731.354PKa0.0004.0050.000SKa40.6464.96880.227ClKa96.3928.28480.405KKa0.00010.62170.000CaKa20019.690109.627261.257TiKa15.4143.77930.069VKa6.6273.31440.022CrKa0.0003.104880.000MnKa3.7893.41140.008FeKa398.98113.91680.691CoKa30.8965.81890.046NiKa14.9503.51140.020CuKa31.2134.87260.037ZnKa19.2435.17190.021ZrKa5.6404.84640.015NbKa2.0014.37040.007MoKa2.4674.04640.010AgKa0.0002.02480.000SnLa25.48567.41820.343BaLa3.8243.80590.070TaLa4.4035.10300.020WLa7.9345.90990.034Table 4
XRF result for contaminated soil characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO230.6611.061wt.%28.6800.992V2O50.0950.032wt.%0.0290.01Cr2O30.1160.017wt.%0.0430.006MnO0.0420.011wt.%0.0330.009Fe2O35.9450.061wt.%2.0920.021Co3O40.0430.019wt.%0.0100.004NiO0.0000.000wt.%0.0000.000CuO0.0960.009wt.%0.0680.006Nb2O30.0300.012wt.%0.0070.003MoO30.0110.016wt.%0.0040.006WO30.0030.033wt.%0.0010.008P2O50.0000.000wt.%0.0000.000SO31.2300.155wt.%0.8640.109CaO30.2650.297wt.%30.3320.297MgO2.6169.239wt.%3.64812.883K2O1.2170.084wt.%0.7260.050BaO1.5610.140wt.%0.5720.051Al2O36.2412.334wt.%3.4401.286Ta2O50.0750.033wt.%0.0100.004TiO21.6070.053wt.%1.1310.037ZnO0.0140.007wt.%0.0100.005Ag2O0.0850.091wt.%0.0210.022Cl17.7540.235wt.%28.1450.373ZrO20.2950.016wt.%0.1350.007SnO20.0000.000wt.%0.0000.000Table 5
XRF element result for contaminated soil characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000032.704MgKa0.5471.93271.578AlKa13.0334.87403.303SiKa310.54910.741514.332PKa0.0005.49590.000SKa54.1676.81530.493ClKa2301.45530.503217.754KKa141.2069.72381.010CaKa4183.82041.028221.630TiKa278.2149.20690.963VKa20.9467.08880.053CrKa39.2555.87260.079MnKa19.7075.22010.032FeKa3041.86031.10074.158CoKa27.48111.94850.032NiKa0.0006.42840.000CuKa80.4697.40930.077ZnKa12.9846.4950.011ZrKa219.29811.61890.218NbKa21.2048.58890.024MoKa6.5948.93380.008AgKa6.5697.08330.079SnLa0.00026.54520.000BaLa99.5798.90331.398TaLa17.7037.71580.061WLa0.6547.92400.002Table 6
XRF result for natural soil characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO256.1721.679wt.%67.2932.011V2O50.2570.026wt.%0.1020.010Cr2O30.0680.012wt.%0.0320.005MnO0.0790.010wt.%0.0810.01Fe2O39.4500.064wt.%4.2600.029Co3O40.0460.019wt.%0.0140.006NiO0.0100.009wt.%0.0100.009CuO0.0710.007wt.%0.0640.007Nb2O30.0600.010wt.%0.0180.003MoO30.0050.014wt.%0.0020.007WO30.0000.000wt.%0.0000.000P2O50.0990.368wt.%0.0500.187SO30.3440.109wt.%0.3090.098CaO2.1540.077wt.%2.7640.099MgO0.0000.000wt.%0.0000.000K2O0.6430.063wt.%0.4920.048BaO0.1470.205wt.%0.0690.096Al2O321.8483.364wt.%15.4232.375Ta2O50.0440.027wt.%0.0070.004TiO25.8560.082wt.%5.2780.074ZnO0.0140.006wt.%0.0130.005Ag2O0.0510.067wt.%0.0160.021Cl1.5180.084wt.%3.0820.170ZrO21.0650.021wt.%0.6220.012SnO20.0000.000wt.%0.0000.000Table 7
XRF element result for natural soil characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000046.871MgKa0.0002.24300.000AlKa40.8266.285411.563SiKa416.41412.446926.257PKa1.5655.84960.043SKa9.6743.06250.138ClKa141.6637.83491.518KKa76.5257.52650.534CaKa336.10111.97651.539TiKa1624.34522.73803.511VKa91.0499.14660.144CrKa33.9795.80630.046MnKa54.1526.74700.061FeKa6911.67446.60926.610CoKa41.82917.23110.034NiKa8.9348.26560.008CuKa73.4337.74680.057ZnKa16.6906.93360.012ZrKa975.67518.94320.789NbKa51.8168.54590.047MoKa3.42110.22130.003AgKa4.6226.07460.048SnLa0.0009.99600.000BaLa15.08721.12190.131TaLa12.9398.02060.036WLa0.0008.20080.000The obtained experimental results presented in Tables4 and 5 indicate 28.680%, 3.440%, 1.131%, 2.092%, 28.145%, and 30.332% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, and CaO, respectively. The element table showed 32.704, 14.322, 3.303, 4.158, and 21.630 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), and calcium (Ca), respectively [48] (Tables 3 and 4).The XRF results presented in Tables6 and 7 indicate 67.293%, 15.423%, 5.278%, 4.260%, 3.082%, and 2.764% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, and CaO, respectively. The element table showed 46.871, 26.257, 11.563, 6.610, and 1.539 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), and calcium (Ca), respectively [49] (Tables 5–7).


### 3.2. Physical Properties of the Test Materials

The preliminary test results of soil for this experimental investigation are presented in Table8 and indicate a highly plastic soil with high swelling potential as a result of a plasticity index of >17% and shrinkage limit of 12%, poorly graded, and possess a high content of clay minerals that enabled the soil exhibits soft and expansive behavior. According to AASHTO and the unified soil classification system, the experimented soil is further classed as A7 and CH soil group (USCS). The strength properties also observed from the result imply below standards acceptable for foundation works and hence the need for reinforcement. The soil’s particle size distribution and additives are presented in Figure 2. The uniformity coefficient (Cu) and curvature coefficient (Cc) results are derived from the plot, where D10, D30, and D60 are the sizes of particles obtained from the semilog plot that 10%, 30%, and 60% of the test particles are finer, respectively [50]. From the result, we observe that Cu for the test materials is > 1 that indicates that the values of D10and D60are closer to each other that indicates that the particles are in a similar size range or uniformly graded but poorly graded because Cu < 4 and Cc < 1. The gradation parameters provide valuable details about the particle size distribution and for soil classification purposes, which indicates the effects on the gradation properties when the additives were mixed with the soil samples [32, 51].Table 8
General test soil properties.
Test soil properties and unitsResultsMoisture content12.36%% passing sieve 0.075 mm6.26LL56%PL33%PI23%Specific gravity2.17SL12%AASHTOA-7USCSCHOMC15.3%MDD1.72 mg/m3pH8.6ColorBrownish ashCBR18.64%Fineness modulus3.26The gradation parameters computation results for the soil and COCM is presented in the following equations.Contaminated soil:(3)coefficientofcurvatureCc=0.4220.8×0.3=0.735;coefficientofuniformityCu=0.80.3=2.67.Test soil:(4)coefficientofcurvatureCc=0.3520.75×0.2=0.82;coefficientofuniformityCu=0.750.2=3.75.


#### 3.2.1. Effects of Admixtures on Atterberg Limit Behavior

The plasticity characteristics of soil-lime-COCM stabilized blend indicated plasticity index (PI) have declined; the limit of plastic (PL) and liquid limit (LL) results are shown in Figure3. The decrement was observed to be linear as the addition of COCM (%) increased and produced the minimum result at a 20% fraction of COCM with PI, PL, and LL values of 11.94, 17.89, and 29.83, respectively. The observed decrease in the consistency limits of the blended mix is attributed to the colloidal phase structure of the mixture, which is due to a rapid rise in pH and ionic concentration level [52]. The product of this reaction results in the formation of attractive forces within the fabric of the weak soil due to pozzolanic reaction, particle flocculation, and the exchange of cation. The rise in pore water ionic concentration and exchange of cation leads to double diffuse layer shrinkage and a sharp reduction in the reliability limits values. The plastic and liquid and limit results of the treated soil stood at <30 and 50% in that order; consequently, they remained good as a subgrade resource in road surface building agreeing to the Federal Ministry of Works and Housing [53] specifications [36, 54].Figure 3
Consistency limit test results.


#### 3.2.2. Effects of Admixtures on Compaction Behavior

The consequence of COCM and lime additives regarding the compaction possessions of the blended mixture was evaluated using a proctor compaction test, and the results obtained are presented in Figure4. The laboratory outcomes got proved that the addition of lime resulted in a rise in maximum dry density (MDD) and optimal moisture content (OMC) from 1.72 to 1.78 mg/m3 and from 15.3 to 17.4%, respectively. Generally, the more lime + COCM (%) fractions in the blended mixture led to OMC increment and reduction in MDD properties with the OMC increasing from 15.8 to 18.23%, while the MDD rose from 1.78 to 1.85 mg/m3 at 5% of COCM addition and then decreased linearly to 1.76 mg/m3 at 20% of COCM. This trend is in agreement with the findings of Sharma et al. [55], where the decrease in MDD is attributed to the role of chemical reactions in decreasing the density of lime-COCM blend in a stabilization protocol. This is a result of the mixture ingredients’ surface area increase and the cation exchange mechanism because the reaction utilizes much quantity of water to dissociate hydroxyl (OH−) and calcium (Ca2+) ions in the mixture to achieve a pozzolanic reaction, which improves its mechanical properties [49].Figure 4
Proctor compaction test results.


#### 3.2.3. CBR (California Bearing Ratio) Test Result

CBR is a laboratory test on the soil used for strength assessment for pavement subbase and subgrade purposes. From the experimental response concerning the experimental runs as it is in Figure5, the CBR records amplified from 12.86 to 16.34% and 18.64 to 23.5% for soaked and unsoaked CBR, respectively, due to the addition of 6.5% lime. The CBR increased further to the maximum response of 32.21 and 64.3% for soaked and unsoaked CBR, respectively when 6.5% lime and 5% COCM were added to the blended mixture. The CBR result started to decrease linearly with a further increase in the additive concentration from 10 to 20% of COCM + 6.5% lime with a CBR response of 27.15–17.03% and 53.18–21.34%, respectively. The derived strength properties for the unblended mixture were observed to fall short of the requirement for pavement construction materials (<30%) per Nigerian general specification for highways [56]. To improve the strength and durability performance, stabilizing agents or additives are needed to reinforce the weak for civil construction purposes. The increase in CBR results observed is a result of adequate provision of calcium from COCM and lime, which led to the gradual formation of cementitious hydration product of C-S-H and C-A-S-H connected with pozzolanic responses from alumina-silicate materials for strength properties improvement. Furthermore, the acquired results suggested that the quantity of crude oil contamination had no negative effect on the soil's strength, but rather increased it due to the addition of lime as a binder and interaction with the soil-COCM [57].Figure 5
CBR test results.


### 3.3. Spectrum Electron Microscopy Result

The independent stabilizing effect of lime and the calcium ions sufficient in the contaminated soil components is responsible for the significant increase in mechanical characteristics of the soil for construction applications. Further microstructural analysis was carried out on the untreated soil, as well as the soil that had been optimally treated with lime and contaminated materials, to explicate the significant strength enhancement. This was accomplished using a scanning electron microscope (SEM) with embedded FiberMetric software and electron dispersive X-ray (EDX) at 15 kV mapping with a full backscatter electron detector (BSD) that helps detect elastically scattered electrons, as well as an electron dispersive X-ray (EDX) at 15 kV mapping with a full backscatter electron detector (BSD). These investigations are intended to rationalize the underlying micro to macro physicochemical variations in the soil fabric. The performance of any organic and/or inorganic substance is determined by the functional groups involved. For a specifically identified specimen, such functional groups are recognized to coincide with a standard range of wavenumber (cm−1) in the absorbance or transmittance scale. For an inorganic substance, functional groups are related to particular characteristics and distinctiveness. By relating the fiber-metric distribution and supplying elemental compositions from electron dispersive X-ray (EDX) of a mapped surface, the SEM is utilized to illustrate morphological changes in the structural fabric of the specimens. The natural soil (NS) micrograph reveals a solid and compact-like structure with a smooth to the rough surface (Figure 6(a)). Because of the probable interaction of the admixtures within the surface structure of the clay mineral layer, the lime-treated specimens (NS + lime) in Figure 6(b) have an aggregated and flocculated structure. The NS + limes +5% contaminated material specimen (Figure 6(c)) had some apparent cracks and fewer microholes to macroholes, while the NS + limes +20% contaminated material specimen (Figure 6(d)) had macropores (Figure 6(d)). The pores and cracks could be caused by the specimen not receiving enough moisture to achieve complete hydration [35, 58].Figure 6
Micrograph of treated and untreated samples.
(a)(b)(c)(d)


### 3.4. X-Ray Diffraction Test Result

The qualitative X-ray diffraction investigation and spectrum indicating mineralogical analysis compositions of crude oil contaminated materials (COCM) are reported in this paper as it is in Figure7. The details derived from the spectra results indicate the presence of Quartz (SiO2), corrosive aluminum phosphate (AlPO4), microcline, galena, anglesite, and garnet. The intensities vary significantly due to the influence of crude oil and impurities present in the COCM. The graphical result showed 26.5° and 21° as the derived peak intensities that implies that COCM is predominantly a characteristic quartz; microcline, which is a potassium-rich alkali feldspar; and aluminum phosphate minerals. The undulating peaks observed in the spectra signify that prism and basal were gotten as varying degrees of 2-theta. The obtained result indicates the presence of lead, sulfate, silicon, and aluminum bonded crystals [59].Figure 7
XRD of the crude oil contaminated soil (COCM).Figure8 presents the phase data view for the X-ray diffraction test results of the natural soil, which indicate the presence of osumilite, quartz, nacrite, sepiolite, and ilmenite soil minerals. The graphical result showed 27° and 12.5° as the derived peak intensities that implies that the test soil is predominantly a characteristic quartz (SiO2) and osumilite, which is a potassium-sodium-iron-magnesium-aluminum silicate mineral with a figure of merit of 1.368 and 0.647, respectively [60, 61].Figure 8
XRD of the test soil.


### 3.5. Statistical Analysis

The experimental results derived from the mechanical properties evaluation exercise of the stabilized clayey soil lime contaminated sand mixture were tabulated for analysis purposes as presented in Table9. The outcomes of the laboratory experiments presented the system database for evaluation of the multiple constraints between the consistency properties, compaction behavior, and CBR responses with varying percentage treatment of soil, lime, and COCM. Statistical analysis of the experimental results will help assess the behavior of the blended mixture understudy and also aid in the derivation of the optimum combination level of the soil-lime-additive blend. Multiple linear regression analysis (MLR) is adapted in this research to efficiently measure the effects of lime and COCM in the stabilized clayey soil mixture. The computed results in respect to the experimental runs indicated the third run with natural soil (NS) + 6.5% lime + 5% COCM as the best characteristic performance that satisfies standard specification for road construction at 64.3% CBR, 15.8% OMC, and MDD of 1.85 [30, 62].Table 9
Data set for statistical computation.
Model variablesCBROMCMDDLLPLControl18.6415.31.725633.65NS + 6.5% lime23.517.41.7850.8932.08NS + 6.5% lime + 5% COCM64.315.81.8547.2428.19NS + 6.5% lime + 10% COCM53.1816.051.8241.3524.77NS + 6.5% lime + 15% COCM42.7616.831.8034.7620.14NS + 6.5% lime + 20% COCM21.3418.231.7629.8317.89Table10 presents the connection coefficients of the factor variables derived from experimental results for the multiple linear regression (MLR) model developments. The results indicate a positive correlation of 0.9367 for MDD and negative correlation coefficients of −0.1485, −0.4031, and −0.1039, respectively, for PL, OMC, and LL variables compared to the CBR value. Also, LL and PL variables possess a strong positive correlation of 0.995 to indicate the linear relationship between the model parameters. The 3-D surface plot showing the combined effects of two parameters on the CBR response of the stabilized soil-lime-COCM blend is shown in Figure 9 [63, 64].Table 10
Pearson correlation results.
CBROMCMDDLLPLCBR1OMC−0.403051MDD0.936694−0.112141LL−0.1039−0.67125−0.205881PL−0.14849−0.59645−0.217180.994551Figure 9
3-D surface plot.


#### 3.5.1. Regression Analysis

The examination of several linear regressions (MLR) is carried out using the fit regression model option in Minitab 18 software. Analysis of variance (ANOVA) was implored to assess the statistical important difference between the system data sets as shown in Table11. From the result, the F-value, adjusted mean squared error, and the sum of squared error values of 173.16, 2.601, and 2.6 respectively, were calculated. The obtained computation results showed that there is statistical significance between the variables at a 95% confidence interval with a P value of 0.037, which is less than the critical value of 0.05 [64].Table 11
Analysis of variance.
SourceDFAdj SSAdj MSF-valueP valueRegression41801.74450.435173.160.037OMC117.0717.0716.560.237MDD1767.19767.190294.920.037LL10.090.0910.040.882PL10.420.4170.160.757Error12.602.601Total51804.34Table12 presents the generated regression model summary with a standard regression error of 1.613 and a coefficient of determination of 99.86%, which indicates very good prediction performance as it represents the variation (%) in the response explicated by the developed regression model presented in Table 13.Table 12
Model summary.
S (%)R-sq (%)R-sq (adj)R-sq (pred) (%)1.6128699.8699.2891.73Table 13
Regression equation.
CBR=−449.1 − 7.30 OMC + 348.6 MDD + 0.43 LL − 1.32 PLFigure10 presents the multiple linear regression (MLR) residual plot that ensures that the regression model assumptions are satisfied; the statistical plots show the normal probability plot of the residuals in percent, the fitted value vs. residuals ranging from −1.0 to 1.0, frequency distribution histogram of the residuals and the residuals against the observation order (%) [65, 66].Figure 10
Residual plots for the MLR model.


## 3.1. Material Characterization and Classification

For natural expansive soil geotechnical characterization, lime and contaminated soil samples were carried out here. The results obtained from the laboratory exercise are detailed in Tables2–7 using the Gaussian intensity method. The obtained experimental results as shown in Tables 2 and 3 for the lime samples indicates 2.750%, 2.439%, 0.083%, 0.353%, 0.651%, 5.833%, and 87.157% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, MgO, and CaO, respectively (Table 2). The element table showed 30.550, 1.354, 2.308, 0.691, 2.484, and 61.257 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), magnesium (Mg), and calcium (Ca), respectively [46, 47].Table 2
XRF result for lime characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO22.8970.331wt.%2.7500.314V2O50.0390.020wt.%0.0120.006Cr2O30.0000.000wt.%0.0000.000MnO0.0100.009wt.%0.0080.007Fe2O30.9870.034wt.%0.3530.012Co3O40.0630.012wt.%0.0150.003NiO0.0250.006wt.%0.0190.005CuO0.0460.007wt.%0.0330.005Nb2O30.0090.020wt.%0.0020.005MoO30.0150.025wt.%0.0060.010WO30.0430.032wt.%0.0110.008P2O50.0000.000wt.%0.0000.000SO30.5670.069wt.%0.4040.049CaO85.7100.469wt.%87.1570.477MgO4.1237.423wt.%5.83310.503K2O0.0000.000wt.%0.0000.000BaO0.0780.078wt.%0.0290.029Al2O34.3621.468wt.%2.4390.821Ta2O50.0240.028wt.%0.0030.004TiO20.1160.028wt.%0.0830.020ZnO0.0260.007wt.%0.0180.005Ag2O0.0000.000wt.%0.0000.000Cl0.4050.035wt.%0.6510.056ZrO20.0200.017wt.%0.0090.008SnO20.4351.151wt.%0.1650.435Table 3
XRF element result for lime characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000030.550MgKa0.9411.69362.486AlKa10.1203.40652.308SiKa35.3014.03731.354PKa0.0004.0050.000SKa40.6464.96880.227ClKa96.3928.28480.405KKa0.00010.62170.000CaKa20019.690109.627261.257TiKa15.4143.77930.069VKa6.6273.31440.022CrKa0.0003.104880.000MnKa3.7893.41140.008FeKa398.98113.91680.691CoKa30.8965.81890.046NiKa14.9503.51140.020CuKa31.2134.87260.037ZnKa19.2435.17190.021ZrKa5.6404.84640.015NbKa2.0014.37040.007MoKa2.4674.04640.010AgKa0.0002.02480.000SnLa25.48567.41820.343BaLa3.8243.80590.070TaLa4.4035.10300.020WLa7.9345.90990.034Table 4
XRF result for contaminated soil characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO230.6611.061wt.%28.6800.992V2O50.0950.032wt.%0.0290.01Cr2O30.1160.017wt.%0.0430.006MnO0.0420.011wt.%0.0330.009Fe2O35.9450.061wt.%2.0920.021Co3O40.0430.019wt.%0.0100.004NiO0.0000.000wt.%0.0000.000CuO0.0960.009wt.%0.0680.006Nb2O30.0300.012wt.%0.0070.003MoO30.0110.016wt.%0.0040.006WO30.0030.033wt.%0.0010.008P2O50.0000.000wt.%0.0000.000SO31.2300.155wt.%0.8640.109CaO30.2650.297wt.%30.3320.297MgO2.6169.239wt.%3.64812.883K2O1.2170.084wt.%0.7260.050BaO1.5610.140wt.%0.5720.051Al2O36.2412.334wt.%3.4401.286Ta2O50.0750.033wt.%0.0100.004TiO21.6070.053wt.%1.1310.037ZnO0.0140.007wt.%0.0100.005Ag2O0.0850.091wt.%0.0210.022Cl17.7540.235wt.%28.1450.373ZrO20.2950.016wt.%0.1350.007SnO20.0000.000wt.%0.0000.000Table 5
XRF element result for contaminated soil characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000032.704MgKa0.5471.93271.578AlKa13.0334.87403.303SiKa310.54910.741514.332PKa0.0005.49590.000SKa54.1676.81530.493ClKa2301.45530.503217.754KKa141.2069.72381.010CaKa4183.82041.028221.630TiKa278.2149.20690.963VKa20.9467.08880.053CrKa39.2555.87260.079MnKa19.7075.22010.032FeKa3041.86031.10074.158CoKa27.48111.94850.032NiKa0.0006.42840.000CuKa80.4697.40930.077ZnKa12.9846.4950.011ZrKa219.29811.61890.218NbKa21.2048.58890.024MoKa6.5948.93380.008AgKa6.5697.08330.079SnLa0.00026.54520.000BaLa99.5798.90331.398TaLa17.7037.71580.061WLa0.6547.92400.002Table 6
XRF result for natural soil characterization.
ComponentConcentrationErrorUnitsMole %ErrorSiO256.1721.679wt.%67.2932.011V2O50.2570.026wt.%0.1020.010Cr2O30.0680.012wt.%0.0320.005MnO0.0790.010wt.%0.0810.01Fe2O39.4500.064wt.%4.2600.029Co3O40.0460.019wt.%0.0140.006NiO0.0100.009wt.%0.0100.009CuO0.0710.007wt.%0.0640.007Nb2O30.0600.010wt.%0.0180.003MoO30.0050.014wt.%0.0020.007WO30.0000.000wt.%0.0000.000P2O50.0990.368wt.%0.0500.187SO30.3440.109wt.%0.3090.098CaO2.1540.077wt.%2.7640.099MgO0.0000.000wt.%0.0000.000K2O0.6430.063wt.%0.4920.048BaO0.1470.205wt.%0.0690.096Al2O321.8483.364wt.%15.4232.375Ta2O50.0440.027wt.%0.0070.004TiO25.8560.082wt.%5.2780.074ZnO0.0140.006wt.%0.0130.005Ag2O0.0510.067wt.%0.0160.021Cl1.5180.084wt.%3.0820.170ZrO21.0650.021wt.%0.6220.012SnO20.0000.000wt.%0.0000.000Table 7
XRF element result for natural soil characterization.
ElementLine codeIntensity (c/s)Error (c/s)ConcentrationOKa0.0000.000046.871MgKa0.0002.24300.000AlKa40.8266.285411.563SiKa416.41412.446926.257PKa1.5655.84960.043SKa9.6743.06250.138ClKa141.6637.83491.518KKa76.5257.52650.534CaKa336.10111.97651.539TiKa1624.34522.73803.511VKa91.0499.14660.144CrKa33.9795.80630.046MnKa54.1526.74700.061FeKa6911.67446.60926.610CoKa41.82917.23110.034NiKa8.9348.26560.008CuKa73.4337.74680.057ZnKa16.6906.93360.012ZrKa975.67518.94320.789NbKa51.8168.54590.047MoKa3.42110.22130.003AgKa4.6226.07460.048SnLa0.0009.99600.000BaLa15.08721.12190.131TaLa12.9398.02060.036WLa0.0008.20080.000The obtained experimental results presented in Tables4 and 5 indicate 28.680%, 3.440%, 1.131%, 2.092%, 28.145%, and 30.332% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, and CaO, respectively. The element table showed 32.704, 14.322, 3.303, 4.158, and 21.630 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), and calcium (Ca), respectively [48] (Tables 3 and 4).The XRF results presented in Tables6 and 7 indicate 67.293%, 15.423%, 5.278%, 4.260%, 3.082%, and 2.764% by mole concentration of SiO2, Al2O3, TiO2, Fe2O3, Cl, and CaO, respectively. The element table showed 46.871, 26.257, 11.563, 6.610, and 1.539 concentration values for oxygen (O), silicon (Si), aluminum (Al), iron (Fe), and calcium (Ca), respectively [49] (Tables 5–7).


## 3.2. Physical Properties of the Test Materials

The preliminary test results of soil for this experimental investigation are presented in Table8 and indicate a highly plastic soil with high swelling potential as a result of a plasticity index of >17% and shrinkage limit of 12%, poorly graded, and possess a high content of clay minerals that enabled the soil exhibits soft and expansive behavior. According to AASHTO and the unified soil classification system, the experimented soil is further classed as A7 and CH soil group (USCS). The strength properties also observed from the result imply below standards acceptable for foundation works and hence the need for reinforcement. The soil’s particle size distribution and additives are presented in Figure 2. The uniformity coefficient (Cu) and curvature coefficient (Cc) results are derived from the plot, where D10, D30, and D60 are the sizes of particles obtained from the semilog plot that 10%, 30%, and 60% of the test particles are finer, respectively [50]. From the result, we observe that Cu for the test materials is > 1 that indicates that the values of D10and D60are closer to each other that indicates that the particles are in a similar size range or uniformly graded but poorly graded because Cu < 4 and Cc < 1. The gradation parameters provide valuable details about the particle size distribution and for soil classification purposes, which indicates the effects on the gradation properties when the additives were mixed with the soil samples [32, 51].Table 8
General test soil properties.
Test soil properties and unitsResultsMoisture content12.36%% passing sieve 0.075 mm6.26LL56%PL33%PI23%Specific gravity2.17SL12%AASHTOA-7USCSCHOMC15.3%MDD1.72 mg/m3pH8.6ColorBrownish ashCBR18.64%Fineness modulus3.26The gradation parameters computation results for the soil and COCM is presented in the following equations.Contaminated soil:(3)coefficientofcurvatureCc=0.4220.8×0.3=0.735;coefficientofuniformityCu=0.80.3=2.67.Test soil:(4)coefficientofcurvatureCc=0.3520.75×0.2=0.82;coefficientofuniformityCu=0.750.2=3.75.


### 3.2.1. Effects of Admixtures on Atterberg Limit Behavior

The plasticity characteristics of soil-lime-COCM stabilized blend indicated plasticity index (PI) have declined; the limit of plastic (PL) and liquid limit (LL) results are shown in Figure3. The decrement was observed to be linear as the addition of COCM (%) increased and produced the minimum result at a 20% fraction of COCM with PI, PL, and LL values of 11.94, 17.89, and 29.83, respectively. The observed decrease in the consistency limits of the blended mix is attributed to the colloidal phase structure of the mixture, which is due to a rapid rise in pH and ionic concentration level [52]. The product of this reaction results in the formation of attractive forces within the fabric of the weak soil due to pozzolanic reaction, particle flocculation, and the exchange of cation. The rise in pore water ionic concentration and exchange of cation leads to double diffuse layer shrinkage and a sharp reduction in the reliability limits values. The plastic and liquid and limit results of the treated soil stood at <30 and 50% in that order; consequently, they remained good as a subgrade resource in road surface building agreeing to the Federal Ministry of Works and Housing [53] specifications [36, 54].Figure 3
Consistency limit test results.


### 3.2.2. Effects of Admixtures on Compaction Behavior

The consequence of COCM and lime additives regarding the compaction possessions of the blended mixture was evaluated using a proctor compaction test, and the results obtained are presented in Figure4. The laboratory outcomes got proved that the addition of lime resulted in a rise in maximum dry density (MDD) and optimal moisture content (OMC) from 1.72 to 1.78 mg/m3 and from 15.3 to 17.4%, respectively. Generally, the more lime + COCM (%) fractions in the blended mixture led to OMC increment and reduction in MDD properties with the OMC increasing from 15.8 to 18.23%, while the MDD rose from 1.78 to 1.85 mg/m3 at 5% of COCM addition and then decreased linearly to 1.76 mg/m3 at 20% of COCM. This trend is in agreement with the findings of Sharma et al. [55], where the decrease in MDD is attributed to the role of chemical reactions in decreasing the density of lime-COCM blend in a stabilization protocol. This is a result of the mixture ingredients’ surface area increase and the cation exchange mechanism because the reaction utilizes much quantity of water to dissociate hydroxyl (OH−) and calcium (Ca2+) ions in the mixture to achieve a pozzolanic reaction, which improves its mechanical properties [49].Figure 4
Proctor compaction test results.


### 3.2.3. CBR (California Bearing Ratio) Test Result

CBR is a laboratory test on the soil used for strength assessment for pavement subbase and subgrade purposes. From the experimental response concerning the experimental runs as it is in Figure5, the CBR records amplified from 12.86 to 16.34% and 18.64 to 23.5% for soaked and unsoaked CBR, respectively, due to the addition of 6.5% lime. The CBR increased further to the maximum response of 32.21 and 64.3% for soaked and unsoaked CBR, respectively when 6.5% lime and 5% COCM were added to the blended mixture. The CBR result started to decrease linearly with a further increase in the additive concentration from 10 to 20% of COCM + 6.5% lime with a CBR response of 27.15–17.03% and 53.18–21.34%, respectively. The derived strength properties for the unblended mixture were observed to fall short of the requirement for pavement construction materials (<30%) per Nigerian general specification for highways [56]. To improve the strength and durability performance, stabilizing agents or additives are needed to reinforce the weak for civil construction purposes. The increase in CBR results observed is a result of adequate provision of calcium from COCM and lime, which led to the gradual formation of cementitious hydration product of C-S-H and C-A-S-H connected with pozzolanic responses from alumina-silicate materials for strength properties improvement. Furthermore, the acquired results suggested that the quantity of crude oil contamination had no negative effect on the soil's strength, but rather increased it due to the addition of lime as a binder and interaction with the soil-COCM [57].Figure 5
CBR test results.


## 3.2.1. Effects of Admixtures on Atterberg Limit Behavior

The plasticity characteristics of soil-lime-COCM stabilized blend indicated plasticity index (PI) have declined; the limit of plastic (PL) and liquid limit (LL) results are shown in Figure3. The decrement was observed to be linear as the addition of COCM (%) increased and produced the minimum result at a 20% fraction of COCM with PI, PL, and LL values of 11.94, 17.89, and 29.83, respectively. The observed decrease in the consistency limits of the blended mix is attributed to the colloidal phase structure of the mixture, which is due to a rapid rise in pH and ionic concentration level [52]. The product of this reaction results in the formation of attractive forces within the fabric of the weak soil due to pozzolanic reaction, particle flocculation, and the exchange of cation. The rise in pore water ionic concentration and exchange of cation leads to double diffuse layer shrinkage and a sharp reduction in the reliability limits values. The plastic and liquid and limit results of the treated soil stood at <30 and 50% in that order; consequently, they remained good as a subgrade resource in road surface building agreeing to the Federal Ministry of Works and Housing [53] specifications [36, 54].Figure 3
Consistency limit test results.


## 3.2.2. Effects of Admixtures on Compaction Behavior

The consequence of COCM and lime additives regarding the compaction possessions of the blended mixture was evaluated using a proctor compaction test, and the results obtained are presented in Figure4. The laboratory outcomes got proved that the addition of lime resulted in a rise in maximum dry density (MDD) and optimal moisture content (OMC) from 1.72 to 1.78 mg/m3 and from 15.3 to 17.4%, respectively. Generally, the more lime + COCM (%) fractions in the blended mixture led to OMC increment and reduction in MDD properties with the OMC increasing from 15.8 to 18.23%, while the MDD rose from 1.78 to 1.85 mg/m3 at 5% of COCM addition and then decreased linearly to 1.76 mg/m3 at 20% of COCM. This trend is in agreement with the findings of Sharma et al. [55], where the decrease in MDD is attributed to the role of chemical reactions in decreasing the density of lime-COCM blend in a stabilization protocol. This is a result of the mixture ingredients’ surface area increase and the cation exchange mechanism because the reaction utilizes much quantity of water to dissociate hydroxyl (OH−) and calcium (Ca2+) ions in the mixture to achieve a pozzolanic reaction, which improves its mechanical properties [49].Figure 4
Proctor compaction test results.


## 3.2.3. CBR (California Bearing Ratio) Test Result

CBR is a laboratory test on the soil used for strength assessment for pavement subbase and subgrade purposes. From the experimental response concerning the experimental runs as it is in Figure5, the CBR records amplified from 12.86 to 16.34% and 18.64 to 23.5% for soaked and unsoaked CBR, respectively, due to the addition of 6.5% lime. The CBR increased further to the maximum response of 32.21 and 64.3% for soaked and unsoaked CBR, respectively when 6.5% lime and 5% COCM were added to the blended mixture. The CBR result started to decrease linearly with a further increase in the additive concentration from 10 to 20% of COCM + 6.5% lime with a CBR response of 27.15–17.03% and 53.18–21.34%, respectively. The derived strength properties for the unblended mixture were observed to fall short of the requirement for pavement construction materials (<30%) per Nigerian general specification for highways [56]. To improve the strength and durability performance, stabilizing agents or additives are needed to reinforce the weak for civil construction purposes. The increase in CBR results observed is a result of adequate provision of calcium from COCM and lime, which led to the gradual formation of cementitious hydration product of C-S-H and C-A-S-H connected with pozzolanic responses from alumina-silicate materials for strength properties improvement. Furthermore, the acquired results suggested that the quantity of crude oil contamination had no negative effect on the soil's strength, but rather increased it due to the addition of lime as a binder and interaction with the soil-COCM [57].Figure 5
CBR test results.


## 3.3. Spectrum Electron Microscopy Result

The independent stabilizing effect of lime and the calcium ions sufficient in the contaminated soil components is responsible for the significant increase in mechanical characteristics of the soil for construction applications. Further microstructural analysis was carried out on the untreated soil, as well as the soil that had been optimally treated with lime and contaminated materials, to explicate the significant strength enhancement. This was accomplished using a scanning electron microscope (SEM) with embedded FiberMetric software and electron dispersive X-ray (EDX) at 15 kV mapping with a full backscatter electron detector (BSD) that helps detect elastically scattered electrons, as well as an electron dispersive X-ray (EDX) at 15 kV mapping with a full backscatter electron detector (BSD). These investigations are intended to rationalize the underlying micro to macro physicochemical variations in the soil fabric. The performance of any organic and/or inorganic substance is determined by the functional groups involved. For a specifically identified specimen, such functional groups are recognized to coincide with a standard range of wavenumber (cm−1) in the absorbance or transmittance scale. For an inorganic substance, functional groups are related to particular characteristics and distinctiveness. By relating the fiber-metric distribution and supplying elemental compositions from electron dispersive X-ray (EDX) of a mapped surface, the SEM is utilized to illustrate morphological changes in the structural fabric of the specimens. The natural soil (NS) micrograph reveals a solid and compact-like structure with a smooth to the rough surface (Figure 6(a)). Because of the probable interaction of the admixtures within the surface structure of the clay mineral layer, the lime-treated specimens (NS + lime) in Figure 6(b) have an aggregated and flocculated structure. The NS + limes +5% contaminated material specimen (Figure 6(c)) had some apparent cracks and fewer microholes to macroholes, while the NS + limes +20% contaminated material specimen (Figure 6(d)) had macropores (Figure 6(d)). The pores and cracks could be caused by the specimen not receiving enough moisture to achieve complete hydration [35, 58].Figure 6
Micrograph of treated and untreated samples.
(a)(b)(c)(d)


## 3.4. X-Ray Diffraction Test Result

The qualitative X-ray diffraction investigation and spectrum indicating mineralogical analysis compositions of crude oil contaminated materials (COCM) are reported in this paper as it is in Figure7. The details derived from the spectra results indicate the presence of Quartz (SiO2), corrosive aluminum phosphate (AlPO4), microcline, galena, anglesite, and garnet. The intensities vary significantly due to the influence of crude oil and impurities present in the COCM. The graphical result showed 26.5° and 21° as the derived peak intensities that implies that COCM is predominantly a characteristic quartz; microcline, which is a potassium-rich alkali feldspar; and aluminum phosphate minerals. The undulating peaks observed in the spectra signify that prism and basal were gotten as varying degrees of 2-theta. The obtained result indicates the presence of lead, sulfate, silicon, and aluminum bonded crystals [59].Figure 7
XRD of the crude oil contaminated soil (COCM).Figure8 presents the phase data view for the X-ray diffraction test results of the natural soil, which indicate the presence of osumilite, quartz, nacrite, sepiolite, and ilmenite soil minerals. The graphical result showed 27° and 12.5° as the derived peak intensities that implies that the test soil is predominantly a characteristic quartz (SiO2) and osumilite, which is a potassium-sodium-iron-magnesium-aluminum silicate mineral with a figure of merit of 1.368 and 0.647, respectively [60, 61].Figure 8
XRD of the test soil.


## 3.5. Statistical Analysis

The experimental results derived from the mechanical properties evaluation exercise of the stabilized clayey soil lime contaminated sand mixture were tabulated for analysis purposes as presented in Table9. The outcomes of the laboratory experiments presented the system database for evaluation of the multiple constraints between the consistency properties, compaction behavior, and CBR responses with varying percentage treatment of soil, lime, and COCM. Statistical analysis of the experimental results will help assess the behavior of the blended mixture understudy and also aid in the derivation of the optimum combination level of the soil-lime-additive blend. Multiple linear regression analysis (MLR) is adapted in this research to efficiently measure the effects of lime and COCM in the stabilized clayey soil mixture. The computed results in respect to the experimental runs indicated the third run with natural soil (NS) + 6.5% lime + 5% COCM as the best characteristic performance that satisfies standard specification for road construction at 64.3% CBR, 15.8% OMC, and MDD of 1.85 [30, 62].Table 9
Data set for statistical computation.
Model variablesCBROMCMDDLLPLControl18.6415.31.725633.65NS + 6.5% lime23.517.41.7850.8932.08NS + 6.5% lime + 5% COCM64.315.81.8547.2428.19NS + 6.5% lime + 10% COCM53.1816.051.8241.3524.77NS + 6.5% lime + 15% COCM42.7616.831.8034.7620.14NS + 6.5% lime + 20% COCM21.3418.231.7629.8317.89Table10 presents the connection coefficients of the factor variables derived from experimental results for the multiple linear regression (MLR) model developments. The results indicate a positive correlation of 0.9367 for MDD and negative correlation coefficients of −0.1485, −0.4031, and −0.1039, respectively, for PL, OMC, and LL variables compared to the CBR value. Also, LL and PL variables possess a strong positive correlation of 0.995 to indicate the linear relationship between the model parameters. The 3-D surface plot showing the combined effects of two parameters on the CBR response of the stabilized soil-lime-COCM blend is shown in Figure 9 [63, 64].Table 10
Pearson correlation results.
CBROMCMDDLLPLCBR1OMC−0.403051MDD0.936694−0.112141LL−0.1039−0.67125−0.205881PL−0.14849−0.59645−0.217180.994551Figure 9
3-D surface plot.


### 3.5.1. Regression Analysis

The examination of several linear regressions (MLR) is carried out using the fit regression model option in Minitab 18 software. Analysis of variance (ANOVA) was implored to assess the statistical important difference between the system data sets as shown in Table11. From the result, the F-value, adjusted mean squared error, and the sum of squared error values of 173.16, 2.601, and 2.6 respectively, were calculated. The obtained computation results showed that there is statistical significance between the variables at a 95% confidence interval with a P value of 0.037, which is less than the critical value of 0.05 [64].Table 11
Analysis of variance.
SourceDFAdj SSAdj MSF-valueP valueRegression41801.74450.435173.160.037OMC117.0717.0716.560.237MDD1767.19767.190294.920.037LL10.090.0910.040.882PL10.420.4170.160.757Error12.602.601Total51804.34Table12 presents the generated regression model summary with a standard regression error of 1.613 and a coefficient of determination of 99.86%, which indicates very good prediction performance as it represents the variation (%) in the response explicated by the developed regression model presented in Table 13.Table 12
Model summary.
S (%)R-sq (%)R-sq (adj)R-sq (pred) (%)1.6128699.8699.2891.73Table 13
Regression equation.
CBR=−449.1 − 7.30 OMC + 348.6 MDD + 0.43 LL − 1.32 PLFigure10 presents the multiple linear regression (MLR) residual plot that ensures that the regression model assumptions are satisfied; the statistical plots show the normal probability plot of the residuals in percent, the fitted value vs. residuals ranging from −1.0 to 1.0, frequency distribution histogram of the residuals and the residuals against the observation order (%) [65, 66].Figure 10
Residual plots for the MLR model.


## 3.5.1. Regression Analysis

The examination of several linear regressions (MLR) is carried out using the fit regression model option in Minitab 18 software. Analysis of variance (ANOVA) was implored to assess the statistical important difference between the system data sets as shown in Table11. From the result, the F-value, adjusted mean squared error, and the sum of squared error values of 173.16, 2.601, and 2.6 respectively, were calculated. The obtained computation results showed that there is statistical significance between the variables at a 95% confidence interval with a P value of 0.037, which is less than the critical value of 0.05 [64].Table 11
Analysis of variance.
SourceDFAdj SSAdj MSF-valueP valueRegression41801.74450.435173.160.037OMC117.0717.0716.560.237MDD1767.19767.190294.920.037LL10.090.0910.040.882PL10.420.4170.160.757Error12.602.601Total51804.34Table12 presents the generated regression model summary with a standard regression error of 1.613 and a coefficient of determination of 99.86%, which indicates very good prediction performance as it represents the variation (%) in the response explicated by the developed regression model presented in Table 13.Table 12
Model summary.
S (%)R-sq (%)R-sq (adj)R-sq (pred) (%)1.6128699.8699.2891.73Table 13
Regression equation.
CBR=−449.1 − 7.30 OMC + 348.6 MDD + 0.43 LL − 1.32 PLFigure10 presents the multiple linear regression (MLR) residual plot that ensures that the regression model assumptions are satisfied; the statistical plots show the normal probability plot of the residuals in percent, the fitted value vs. residuals ranging from −1.0 to 1.0, frequency distribution histogram of the residuals and the residuals against the observation order (%) [65, 66].Figure 10
Residual plots for the MLR model.


## 4. Conclusion

Experimental investigation of crude oil contaminated soil materials’ suitability for soil stabilization purposes were investigated, and from the result obtained, the following can be concluded [67]:(i)
The examined clayey soil’s overall engineering properties testing results and classified accordingly A-7 and CH, GP in line with AASHTO and USCS specifications which indicated poorly graded silty clayey soil. Also, Atterberg limit test results showed moderately shrinkage-plastic behavior. The result for the contaminated soil showed poorly graded sand with Cu and Cc of 3.75 and 0.82, respectively.(ii)
The independent addition of lime and crude oil contaminated soil materials (COCM) into the soil generally resulted in a fall in the Atterberg limit test results as the quotient of lime and COCM additives fractions in the blended mixture increases. The compaction properties were observed to be positively affected to reinforce the soil for construction purposes as the OMC tends to increase with a higher percentage of the additive. At 5% COCM and 6.5% of lime addition, the MDD produced the maximum density result of 1.85 mg/m3, and the MDD decreased linearly with further increase in COCM fractions in the mix to 1.76 mg/m3 at 20% of COCM and 6.5% of lime.(iii)
The mechanical strength abilities (CBR) of the test clayey soil experienced a tremendous improvement due to the incorporation of lime and COCM additives in the mixture. The maximum CBR values of 64.3% were yielded at 5% contaminated soil +6.5% lime addition, whereas the control produced a CBR result of 18.64% to enhance the soil’s strength requirement that conforms to the required standard for road paving material specifications (Nigeria General Specification, NGS).(iv)
The quantitative and qualitative microstructural and mineralogical studies of XRF, XRD, and SEM have confirmed that the surge in strength of stabilized specimens was vital because of the accumulation and creation of C–S–H and C-A-S–H compounds. Furthermore, the mineralogical analysis result signifies little or limited effects of hydrocarbon contamination on the environment or as a retarder during the soil stabilization reaction.(v)
Analysis of the results obtained was finally carried out to find out the effects of the mixture proportion variations on the mechanical toughness behavior of the blended mixture using multiple linear regression (MLR) at a 95% confidence interval. The coefficient of determination of 99.86% was calculated in the process that signifies a satisfactory model.



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*Source: 7207842-2022-07-05.xml*