Investigation of shallow subsurface soil for engineering construction: A case study of Etioro Akoko, Southwestern Nigeria

Abstract

For sustainable development of Etioro Akoko, Southwestern Nigeria, soil materials must meet and exceed the requirements of a good tropical environment soil. This study is aimed at using geotechnical and geophysical investigations to evaluate the subsoil for pre- and post-engineering construction and to achieve sustainable groundwater development. The geotechnical analyses are grain size analysis, Atterberg, linear shrinkage, specific gravity, CBR and compaction tests, while electrical resistivity method was used for geophysical investigation. The geotechnical analysis of liquid limit, plastic limit, plasticity index, linear shrinkage, grain size analysis, specific gravity, CBR and compaction tests range from 24.0%–40.8%, 19.3%–23.7%, 4.3%–17.4%, 1.4%–9.3%, 2.58–2.62, 30%–46%, 1496 kg/m³–1742 kg/m³ and 10%–14.2%, respectively. For the electrical resistivity approach, six traverses in an east–west orientation were created. Four distinct layers were identified, and they are top soil, weathered layer, slightly weathered layer and bedrock. Two wells of moderate to good yield were identified from the different fractures located. Atterberg limit tests and grain size analysis of the soil samples showed that these soil samples met the requirements for a good soil in Nigeria. The CBR and MDD values showed that they are suitable for subgrade courses. We reject the null hypothesis for statistical analysis between the mean resistivity values of the six traverses and the geotechnical properties of the soil and also the ANOVA statistic between resistivity values and the six traverses, while we accept the null hypothesis for variables between geotechnical data.

Keywords:

• dipole–dipole configuration
• geotechnical properties
• strength
• competent soil
• sustainability
• CBR

1. Introduction

Development or failure of buildings and roads has been part of everyday issues that affect all aspects of human life. They serve as a multiplier of economy drivers. Good roads aid interregional connectivity and ease congestion through alternative ways. Construction of good and durable roads is part of the necessary amenities for sustainable development which requires competent subsoil materials. Ale (2022) described good roads as panacea to achieving national development which is desperately lacking in many developing countries of the world. He noted that many of these roads were constructed without necessary testing and analysis. Bello and Atilola (2015); Ogunribido et al. (2015); Olabode (2019); Ademila et al. (2020); Oluwakuse et al. (2020); Adekeye et al. (2021) and Ale et al. (2022) had previously investigated the engineering properties of subsoils in several Nigerian regions. They highlighted poor subsoil materials, poor drainage system (that causes flooding of road), topography and poor materials (clayey soil, adortrated asphalt) used during construction as the causes of some failed portions. According to Adelusi et al. (2013) and Eissa (2021), the most common reason for failure in the construction of many structures and roads is substandard soil. The use of geophysical methods for geotechnical site characterization has proven to be a valuable supplement tool in assessing the subsurface conditions. A fault in the subsurface despite a good surface material can lead to a disaster. Both geotechnical properties of subsoil and geophysical properties of the subsurface must be known before construction and verified after construction for documentation purpose (Ademila, 2017), to provide good quality information for the professional which will help in the design of constructions and utilities (Ozegin et al., 2019) and to monitor the integrity of both the soil material and structure in term of competence (Aigbedion et al., 2019). Obioha et al. (2021) examined the root cause of road failure in a Nigeria state using an integrated geophysical and geotechnical methods and found out that a combination of poor drainage system, topography, poor engineering practice and poor soil material with 80% of fine contents were responsible for the failure. Oluwakuse et al. (2019) evaluated the competence of subsoil in Alagbaka extension, Akure; Oyeyemi et al. (2017) evaluated subsoil properties in Ajah Lagos-island; Adebisi et al. (2016) carried out subsoil investigation in Ago-Iwoye/Ishara highway while Ofomola et al. (2018) investigated a site III of the Delta State University, Abraka, south-western Nigeria using integrated geophysical and engineering geological methods. The subsurface materials in all these locations have good engineering capabilities for foundation and other construction applications. Adeyemo (2004) investigated the cause of road failure along llesha-Akure-Benin federal highway using dipole–dipole array. He noted that the heterogeneous sub-base and sub-grade materials on which the road was built, as well as the topsoil’s clay content, were to blame for the road’s failure. Analyzing the subsurface condition using geophysical methods should not be a one-time event; it should be a continuous exercise before construction, during the construction stages and post construction. To better assess the competence of the subsurface materials using an indirect geophysical method; the merits and demerits of the various geophysical methods should be considered in line with the objectives of the study. Chambers et al. (2002), Loke and Lane (2004) and Eissa et al. (2019) in their different assessments found Wenner and dipole–dipole arrays of electrical resistivity method to be the best for buried foundation studies. Loke and Lane (2004) described dipole–dipole array as the best in resolving horizontal changes with relatively poor resolution in vertical variation in resistivity (conductivity), while Wenner array gives good vertical changes in resistivity resolution and poor horizontal change resolution. Eissa et al. (2019) noted that in the use of dipole–dipole configuration, the most competent layer in the subsurface should have higher resistivity values in the first 10 m. Etioro-Akoko, SW Nigeria is a moderately populated town that has most of its population through the migration of people (mostly staff and students of Adekunle Ajasin University) from a neighbouring town of Akungba-Akoko where the University is located. Akingboye and Osazuwa (2021) described Etioro-Akoko area as having unique and intricate subsurface characteristics, with shallow overburden development which resulted in low groundwater yield. In Etioro-Akoko, old buildings are being renovated and new hostels are being constructed for students and staff of the Institution. Most of the minor roads linking the residential areas with the major road are graded with no drainage and asphalt pavement on the graded roads. For easier accessibility of these residential areas, there is the need to carry out geotechnical assessment on Etioro-Akoko subsoil so as to address pre- and post-engineering construction problems that may arise from using poor subsoil material and achieve sustainable groundwater development in complex geological terrains similar to Etioro Akoko in tropical region. In this study, the geophysical survey method will help to reveal the lithological units and give an overview in terms of lateral continuity of the competent layer in the subsurface.

2. Study area

Etioro-Akoko is situated along the Owo–Ikare-Akoko route in the northern part of Ondo State, Nigeria. It lies between latitudes 07° 25′ 30’’ and 07° 27′ N and longitudes 005° 42′ and 005° 44′ E of the Greenwich Meridian (Figures 1). The adjoining towns are Ayegunle and Oba-Akoko to the South, East of Supare-Akoko and South of Akungba-Akoko. Etioro-Akoko is located in the tropical rainforest belt of Nigeria. The southwestern monsoon winds are responsible for the tropical wet and dry climate. The dry season is usually between October and February, while the rainy season begins in March and ends in September (Akinseye, 2010). The prevalent drainage pattern in this region is dendritic. The physical topography of this region has an average elevation of 345 m above the sea level, and it is made up of three separate landforms of hills, plains and valleys. Etioro Akoko falls within the Precambrian Basement Complex of Southwestern Nigeria under Rahaman (1988) classification. Ogunyele et al. (2019) noted that granite gneiss is the predominant rock type in Etioro Akoko with a few occurrences of migmatite rock. They further stressed that the rocks trend in WNW-ESE to ENE-WSW with moderate to steep dips to the south (Figure 2).

Figure 1. Road map of Akoko Southwest local government of Ondo State. Inset: location map of Nigeria and Ondo state.

Figure 2. Geological map of Etioro-Akoko along with the nearby settlements in Ondo State, Southwestern Nigeria (modified after Akingboye & Osazuwa, 2021; Ogunyele et al., 2019).

3. Methodology

3.1. Geotechnical method

Field reconnaissance was first carried out to visually assess the previous road construction, drainage conditions and types of soil. Ten disturbed subsoil samples were collected from ten trial pits along major and minor roads in Etioro-Akoko, Southwest Nigeria (Figure 3). Six of the ten trial pits (TP1-TP6) were located very close to the six established dipole–dipole traverses. Soil samples were taken at a depth of 2 m along the minor roads that link the residential areas with the major road and were collected into well-labeled polythene bags. The soil’s natural moisture content (500 g each) was determined less than 10 minutes after collection in the Engineering Geology Laboratory. Prior to further investigation, soil samples were air dried for two weeks and all the tests were done in accordance with the British Standard Code of Practice (BS1377: (British Standard, 1990)). The soil samples were run through a test sieve of No. 40 (425 m) in order to conduct the Atterberg limit tests (liquid limit, plastic limit and linear shrinkage). The American Society for Testing and Materials’ (ASTM) D4318 and the American Association of State Highway and Transportation Officials’ (AASHTO) T 89 and T 90 standards, respectively, were followed for the liquid limit test and the plastic limit test. In accordance with ASTM D4943, a linear shrinkage test is conducted. Specific gravity measurements were determined using a water pycnometer in accordance with ASTM D854 and AASHTO T 100 standards. The ASTM D422–63 standard was followed while analyzing the particle size of soil samples. The sizes of the set of sieves that were utilized are as follows: 4.75 mm, 2.36 mm, 1.18 mm, 850 m, 425 m, 300 m, 150 m, 75 m and pan. Free Swell index test is determined following IS: 2720 (Part 40) 1977 specification. The ASTM D 698 and ASTM D1883–05 specifications were followed when conducting a typical Proctor test and CBR test (penetration resistance), respectively.

Figure 3. Data acquisitions map of the study area showing the locations of the ten trial pits vis a vis the six geophysical traverses (modified after Akingboye & Osazuwa, 2021).

3.2. Geophysical method

dipole–dipole array is one of the many geophysical electrical resistivity method configurations. dipole–dipole array survey was conducted in mapping of the subsurface deep weathered/fractured zones and also to delineate their trends and distribution within the study area. Six traverses of about 75 m for traverses 1–3 & 5 and 85 m for traverses 4 & 6 were employed (Figure 3). These traverses were set up roughly in an east-to-west direction. The current and potential electrodes are two sets of electrodes that make up the dipole–dipole electrode array. The standard for a dipole–dipole electrode array is to keep the current and potential electrodes at the same distance (spacing = 5), with a multiple of 5 m difference between the two electrodes (spacing). Aminu et al. (2014) noted that there is no rule stating that electrodes be placed along a common survey line. Using the DIPPROTM 4.0 inversion program, the dipole–dipole data were converted to 2-D subsurface pictures (real component, unnormalized) with references to the colour code below the profiles (undefined). The acquired data were processed qualitatively and quantitatively and results were presented as profiles, maps and inversion models. The interpretations of these profiles were based on the identification of linear conductive features using colour code on the colour scale bar. They were collectively interpreted to clarify the research area’s shallow subsurface geology.

3.3. Statistical analysis

One-way non-parametric Analysis of Variance (ANOVA) is used to compare the mean of at least three conditions. Traverses 1 to 6 are the independent variables, while the resistivity values within each traverse at <5 m depth are the dependent variables. The alpha level used in this study is α = 0.05. The null hypothesis states that nothing is happening, i.e. there is no difference in the means of the resistivity values within the six traverses at the depth of <5 m (H0: φ1= φ2= φ3= φ4= φ5= φ6), while the test hypothesis (alternative hypothesis) states that at least one among the six means is different (H_Alt: 1≠ φ2≠ φ3≠ φ4≠ φ5≠ φ6). The degree of freedom between groups is 5 (k-1, where k is the number of conditions which is six traverses). Degree of freedom within the groups is 66 (N-k; N is the total number of resistivity values and k is the degree of freedom) provided that each traverse starts from station 10 m and ends at station 70 m. A calculated statistic of Pearson’s correlation coefficient (r) is used to assess whether there is a correlation between two variables. These statistics were conducted for the percentage of fine content and strength properties of the soil samples (CBR & Maximum Dry Density) using geotechnical data. Also, for the percentage of clay and strength properties of the soil samples (CBR & MDD). To explain the intensity (strong or weak) and direction (negative (−1.0) or positive (+1.0)) of association between factors, the correlation of the mean resistivity values of the six traverses was plotted against percentage of clay, percentage of fine soil, CBR and MDD values. The mean resistivity values of the six traverses are the independent variables, while the geotechnical properties of the soil samples are the dependent variables. For this statistical model, the degree of freedom (DF) (N-2) is 10 (where N is the number of scores). For a one-tailed test, an alpha level of 0.05 was used, indicating an acceptance of a 5% risk of mistake. If the Critical r value is lower than the correlation coefficient (r) value, the null hypothesis will not be accepted and the test hypothesis will be accepted instead. Regression analysis’s coefficient of determination (r²) makes predictions about how dependent one variable (y) is on another variable (x). Regression analysis is used by researchers to determine which factors (parameters) have a greater impact (high value) and which variables can be disregarded due to low values. Y = mX + b (simple linear regression formula), where X is the predictor (independent) variable, m is the estimated slope, and b is the expected intercept. The better x explains y, the closer the value (x) is to 1. For statistical analysis, Surfer and Excel software tools were utilized.

3.4. Index and strength tests

The values of Natural Moisture Content range from 8.7% to 18.0% (mean & standard deviation values of 12.9 ± 3.77) (Table 1). Eight of the ten soil samples are within the range of 5%-15% recommended by the Federal Ministry of Works and Housing general specification requirements for roads and bridges (FMWH, 2010). Soil sample from trial pit 6 (18%) may cause engineering loss if not properly managed (Table 1). The coarse contents and the fine contents of the soil samples with their mean and standard deviation values range from 65.1% to 92.5% (78.4 ± 11.69) and 7.5% to 34.9% (21.7 ± 11.69) respectively as presented in Table 1. According to the FMWH (1997); all the soil samples with the exception of soil sample from TP4 are suitable for sub-grade, sub-base and base materials as the percentage by weight finer than NO 200BS test sieve are far less than 35% and cannot be affected by seasonal variation change. Soil sample from TP4 cannot singlehandedly affect the soil negatively. All the soil samples are well graded because of the wide range of particle sizes on the grain size distribution curve from fine to coarse (Figure 4). All the soil samples have more than 5% of silt and clay passing through sieve no. #200; and cannot be referred to as clean sand (Table 1). The values of liquid limit, plastic limit, plasticity index and linear shrinkage with their mean and standard deviation values for the ten soil samples range from 24.0% to 40.8% (32.9 ± 6.88), 19.3% to 23.7% (21.9 ± 1.92), 4.3% to 17.4% (10.7 ± 5.15) and 1.4% to 9.3% (5.4 ± 2.81) respectively as presented in Table 1. According to the FMWH (1997), sub-grade materials should have a liquid limit not higher than 80% and a plasticity index not higher than 55%, while sub-base and base materials should have limits not higher than 35% and 12%, respectively. All the soil samples fall within the subgrade specification. Soil samples from trial pits 4, 5, 6, 9 & 10 met the specification of subbase and base materials. According to Sowers (1979) classification; the plasticity index values of six soil samples (TP4, TP5, TP6, TP8, TP9 & TP10) fall within slightly plastic soil (3%-15%) with slightly dry strength while the others fall within medium plastic soil (>15%-30%) with medium dry strength. Brink et al. (1982) stated that a good soil must have its linear shrinkage value below 8% while Gidigasu (1974) put it as below 10%. All the soil samples fall below 10%; this is an indication that they can be utilized as base, subbase and subgrade materials. The obtained values for liquid limit, plastic limit, plasticity index and linear shrinkage are very similar to the values gotten by Ale (2021, 2022), Adekeye et al. (2021), Olofinyo et al. (2022) in the southwest basement complex of Nigeria but are lower than the values gotten in Cameroon by Wouatong et al. (2014) and Hyoumbi et al. (2017). Casagrande plasticity chart helps to reveal what percentage of fine grain soil is clay and what percentage is silt using the Atterberg limit. Based on Casagrande’s plasticity chart classification (Casagrande, 1947); all the soil samples from the ten trial pits are above the A-line with five samples (TP1-TP3, TP7 and TP8) falling within the clay medium compressibility (CI) while the remaining five (TP4-TP6, TP9 and TP10) fall within clay low compressibility (CL) (Figure 5). The activity of clay is defined as the percentage link between the plasticity index and clay fraction. The values of the soil samples activity fall between 0.42 and 2.23 and are displayed in Table 1 and Figure 6 ’s activity chart. In the classification of clay, activity values below 1 indicate kaolinite, between 1 and 2 indicate illite and above 2 indicate montmorillonite. Similar to this, activity values between 0.75 and 1.25 are considered normal, values above 1.25 indicate that the mineral is active, while values below 0.75 indicate that it is inactive. This implies that the clay activities of the soil samples are from inactive to active clays based on Skempton (1953) interpretation (Table 1). The primary clay minerals in all of the soil samples are either kaolinite or illite, and they all have low to moderate expansion potentials (Figure 7). The specific gravity of the ten trial pits with mean and standard deviation values range from 2.58 to 2.62 (2.6 ± 0.02) (Table 1) and fall within the range given by Maignien (1966) for the lateritic soils found in equatorial Africa, which is between 2.5 and 3.6. The comparatively low specific gravity values imply more coarse soils and less weathering action. Also, comparing the obtained results with Bowles (1992) classification, these laterite soils are either sand or silty sand (Table 1). According to the American Association of State Highway and Transportation Officials (AASHTO, 1993), soil classifications, TP 4, 5, 6, 8, 9 & 10, are classified as A-2-4 with good rating, while TP 1, 2 & 3 either fall under the A-2-6 or A-2-7 classifications (granular material with good rating). Only TP7 has A-5 classification with a fair rating. All the ten soil samples are good materials for road constructions having satisfied all the conditions for subgrade and subbase materials (Table 1). Free swell index test is predominantly used for clay soil particles finer than 0.02 mm to determine the volume of soil without any external constraints, on submergence in water. All the soil samples have 11.1% as their values. According to Holtz and Gibbs (1956), all the soil samples have low expansion potential (of <20) (Table 1). The Maximum Dry Density (MDD) and the Optimum Moisture Content (OMC) values range from 1496 kg/m³ to 1742 kg/m³ (mn±std: 1596.4 ± 71.4) and 10.0% to 14.2% (Mn and STD: 12.6 ± 1.45), respectively (Table 1). All the trial pit samples have poor to very poor ratings in both FMWH (1997) and Woods (1937) classifications. The MDD and OMC values are within the range of very poor to fairly good values obtained in the southwestern basement complex of Nigeria and Cameroon by authors like Ale et al. (2022), Olabode (2019) and Wouatong et al. (2014. According to Simon et al. (1973) and Bell (2007), the California Bearing Ratio (CBR) is used to assess the mechanical strength of subgrades and base course materials as well as to establish the required pavement thickness. The unsoaked CBR values of the trial pits soil samples range from 30% to 46% (Mn and STD values: 37.5 ± 4.97) (Table 1). The FMWH (1997) recommended not less than 80%, 30% and 5% values for unsoaked CBR for base, sub-base and subgrade soils, respectively. These values are similar to those obtained in the central region of Cameroon by Kamtchueng et al. (2015). The results of the soil tests suggest that none of the soil samples are acceptable for base courses, but only for sub-grade and subbase courses (Table 1). Nano-silica, polypropylene fiber, sodium silicate, calcium ions generated from eggshell, sawdust ash and palm kernel shell ash can be used independently to better improve these soil samples and contribute to a denser packing of soil particles (Ale, 2022; Kulanthaivel et al., 2020, 2021, 2022a, 2022b). On a general assessment, the soil samples have better engineering ratings. Figure 8 shows a model of geotechnical properties of Etioro soil for characterization.

Figure 4. Grain size analysis of the ten trial pits soil samples from Etioro Akoko.

Figure 5. Casagrande plasticity chart plot of the ten trial pits soil samples (ASTMD 2487).

Figure 6. Plots of the ten trial pits sampled soils from Etioro Akoko on the activity chart.

Figure 7. Plots of the activity character of the ten trial pits soil samples.

Figure 8. A model of geotechnical properties of soil samples in Etioro Akoko.

Table 1. Geotechnical properties of the soil taking from trial pits

Trial pit	NMC (%)	LL (%)	PL (%)	PI (%)	LS (%)	SG	FSI (%)	Coarse (%)	Sand (%)	Fine (%)	Clay (%)	OMC (%)	MDD (kg/m^3)	CBR (%)	AASHTO	Activity	USCS	Clay type
TP1	8.7	38.6	23.4	15.2	3.6	2.62	11.1	85.0	54.2	15.0	13.9	12.9	1507	31	A-2-6	1.09	CI	I
TP2	14.0	39.6	23.7	15.9	4.3	2.59	11.1	92.0	56.0	8.0	7.1	10.0	1742	30	A-2-6	2.23	CI	I
TP3	11.0	40.8	23.4	17.4	1.4	2.58	11.1	84.5	48.7	15.5	13.3	14.0	1607	41	A-2-7	1.31	CI	I
TP4	14.0	27.1	19.3	7.8	5.7	2.59	11.1	78.3	42.3	21.7	14.3	11.0	1607	40	A-2-4	0.55	CL	K
TP5	12.6	28.2	21.2	7.0	7.1	2.59	11.1	65.1	40.4	34.9	14.6	14.0	1570	46	A-2-4	0.47	CL	K
TP6	18.0	24.0	19.7	4.3	9.3	2.59	11.1	71.7	41.4	28.3	9.5	14.2	1560	39	A-2-4	0.45	CL	K
TP7	5.5	38.8	23.3	15.5	8.6	2.58	11.1	56.8	35.8	43.2	13.0	12.6	1635	38	A-5	1.19	CI	I
TP8	12.7	36.2	24.4	11.8	7.9	2.59	11.1	85.0	52.1	15.0	13.1	11.7	1590	35	A-2-4	0.90	CI	K
TP9	17.4	29.3	21.1	8.2	1.4	2.62	11.1	92.5	66.4	7.5	7.0	12.0	1650	34	A-2-4	1.17	CL	I
TP10	14.7	23.2	19.8	3.4	5.0	2.62	11.1	72.6	46.4	27.4	8.0	14.0	1496	41	A-2-4	0.42	CL	K
Mn± STD	12.9±3.77	32.9±6.88	21.9±1.92	10.7±5.15	5.4±2.81	2.6±0.02	11.1±0.00	78.4±11.69	48.3 ±8.61	21.7±11.69	11.4±3.1	12.6±1.45	1596.4 ±71.4	37.5 ±4.97

LL = PL = Plastic limit, Liquid limit, LS = Linear Shrinkage, PI = Plasticity Index, CBR = California Bearing Ratio, FSI = Free Swell Index, SG = Specific Gravity, OMC = Optimum Moisture Content, MDD = Maximum Dry Density, USCS =Unified Soil Classification System, I = Illite, K = Kaolinite, AASHTO = American Association of State Highway and Transportation Officials, CI = Intermediate compressibility, CL = Low compressibility, Mn =Mean, STD = Standard Deviation

3.5. Dipole–dipole pseudosections

The pseudo-sections (Figures 9–14) were created using the computed apparent resistivity of the obtained field data. These sections described the details of the lateral and vertical changes in ground apparent resistivity beneath each specific traverse line. Four major subsurface layer components of topsoil, weathered layer, fractured bedrock and fresh bedrock were identified. TR1 to TR6 (Figures 9–14) show near surface lateral variation in resistivity, suggesting that the materials within this near surface are heterogeneous, thus leading to lateral inhomogeneities. TR1 (Figure 9) shows low resistivity values that range from 24.2 to 127 Ωm at stations 15–20 m and 35–55 m which suggest clayey material. Also, the basement is shallow at stations between 20 m and 35 m and between 60 m and 70 m, with the basement dipping westward. The change in the gradient of the closely spaced contours suggests fracturing/faulting, which is evident between 30–40 m and 55–65 m. TR2 (Figure 10) exhibits low resistivity values ranging from 154 to 216 Ωm which is evident at the distance range of 10–15 m. Shallow basements are observed at stations 20–30 m and 35–70 m with weathered basement at stations between 30 and 35 m. The gradient change of the closely spaced contours suggests fracture/fault/lineament structure which is evident between 25 and 35 m. TR3 (Figure 11) exhibits low resistivity values ranging from 98.1 to 229 Ωm which is evident at the distance range of 20–60 m. Also, shallow basements are observed at both flanks (stations 10–20 m and 60–70 m). The gradient change of the closely spaced contours suggests fracture/fault/lineament structure which is evident between 55 and 65 m. TR4 (Figure 12) exhibits low resistivity values ranging from 106 to 214 Ωm which is evident at stations 10 to 15 m and 25 to 30 m. Also, shallow basements are observed at station 35–70 m. The gradient change of the closely spaced contours suggests fracture/fault/lineament structure which is evident between 30 and 40 m. TR5 (Figure 13) exhibits low resistivity values ranging from 54 to 260 Ωm which is evident at stations 10–15 m, 30–45 m and 55–70 m. Also, shallow basements are observed at the station 15–30 m and 65–80 m. The gradient change of the closely spaced contours suggests fracture/fault/lineament structure which is evident between 25 and 35 m and 65 and 75 m. TR6 (Figure 14) exhibits low resistivity values ranging from 95.8 to 175 Ωm which is evident at the distance range of 105–60 m. Again, shallow basements are observed at the both flanks at station 10–35 m and 55–70 m. The gradient change of the closely spaced contours suggests fracture/fault/lineament structure which is evident between 55 and 65 m.

Figure 9. Pseudo-section composite plot of the 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

Figure 10. Pseudo-section composite plot of the 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

Figure 11. Pseudo-section composite plot of the 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

Figure 12. Pseudo-section composite plot of the 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

Figure 13. Composite plot of pseudo- section of result of 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

Figure 14. Pseudo-section composite plot of the 2D resistivity inversion beneath (a) observed resistivity data, (b) computed resistivity distribution and (c) inverted resistivity structure.

3.6. Geophysical evaluation of the study area for engineering construction

The sustainability of any civil engineering structure is a function of the competent subsoil layer that houses a foundation or supports construction without any underlying fracture. Akingboye and Osazuwa (2021) noted that without proper attention, the complexity of Etioro area (in terms of oscillating bedrock, topography, fracture, thick clayey soil, deep weathered trough and floaters (boulders)) can cause a series of cracks in building and differential settlement without reinforced foundations as evident in many buildings. Soil samples from the trial pits were obtained at 2-meter depth. The geophysical assessment of the study area focuses on stations with a competent layer at the depth of <5 m. Most low-weight building foundations are always within the depth of 5 m except for exceptional cases. A moderately high resistivity values ranging from 107 to 417 Ωm for traverse 1 (Figures 9 and 15) are found at the distance of 55 to 75 m at <5 m depth and 591 to 799 Ωm for traverse 2 (Figures 10 and 15) which is evident at station 45 to 70 m at <5 m depth. For traverses 3 to 6 (Figures 11–15), moderately high resistivity values of 194 to 506 Ωm, 241 to 918 Ωm 709 to 1474 Ωm and 119 to 503 Ωm are found at the following stations 50 to 70 m, 45 to 60 m, 70 to 80 m and 50 to 70 m, respectively, at <5 m depth. Based on the mean resistivity values of the subsoil, the resistivity values of <150 Ωm are rated as poor. The resistivity values between 150 Ωm and 300 Ωm are rated as fair to good, while resistivity values >300 Ωm are rated as excellent.

Figure 15. The litho-structural dynamics, competent layer for construction and proposed hand-dug well through fractured basement in Etioro Akoko.

3.7. Hydrogeophysical evaluation of the study area

TR1 and TR5 (Figures 9, 14 and 15) show viable hydrogeological zones. The viable hydrogeological zone of TR1 is at stations 55–65 m starting from a depth of <5 m and extends beyond 25 m with an average of over 20 m thickness which is rated good (moderate-to-high yield) in the assessment of Akingboye and Osazuwa (2021). For TR5, the viable hydrogeological zone is at stations 65–75 m starting from the depth of <5 m that extend to about 20 m with an average thickness of 15 m (low to moderate yield). These two are in addition to the 14 hand-dug wells (PWLs 1–14) and four boreholes (PBHs 1–4) points that were noted by Akingboye and Osazuwa (2021).

3.8. Landscape design

Road systems provide the opportunity of mobility and transport for people and goods. However, the road is naturally embedded in the issue of urban heat. Materials such as asphalt and concrete have the ability to absorb heat and emit it later at night, contributing to the urban heat island. Heat from sun, vehicles and friction between vehicles and pavements have a significant effect on the air temperature which has a damaging effect on roads, human health and the immediate environment. Due to the fair to good properties of Etioro subsoil, the use of soft landscape by the side of the road will help to reduce the heat wave generated. These soft landscapes are vegetative materials such as trees, shrubs, grasses and climbers which are not constructed but are used to improve the environment. A well-planned and strategically placed soft landscape element by the side of the road can greatly affect the microclimate of the area through shades. This shade will help to reduce the air temperature, provide a more comfortable microclimate and good scenery to the users of the road. This will help to improve the road users’ mood and reduce the stress of driving.

3.9. Statistical analysis of the mean resistivity values and soil geotechnical data

In this study, Fcritical value for one-way non-parametric ANOVA analysis is 2.37, while the test hypothesis value is 7.05. This implies that the test hypothesis exceeded the critical value (Fstat: 7.05 > Fcrit: 2.37) and therefore we reject the null hypothesis that suggests that the resistivity values within each traverse at <5 m depth are similar in all six traverses. The critical r value for Pearson correlation coefficient is 0.497. The Pearson’s correlation coefficient r value between the mean resistivity values of the six traverses and percentage of clay, percentage of fine and MDD values are −0.94, −0.57 and 0.99, respectively. These values are higher than the critical r value except for the CBR values with mean resistivity values (−0.37) (Figure 16). We therefore reject the null hypothesis in favour of the test hypothesis for mean resistivity values and percentage of fine, percentage of clay and MDD values. Excellent positive correlation exists between mean resistivity values and MDD values. An increased resistivity value increases the MDD value. There exists weak negative correlation between resistivity value and CBR value. Strong but negative correlation exists between resistivity values and percentage of clay which implies that an increased resistivity value is a result of reduction in clay contents. The correlation relationship between percentage of fine and resistivity values is an average negative correlation. The correlation coefficient (r) values between percentage of fine and strength properties of the soil samples (CBR and MDD values) are 0.65 and −0.30. The Pearson’s correlation coefficient r values between percentage of clay and the strength properties of the soil samples (CBR and MDD values) are 0.39 and −0.33 (Figure 17). We accept the null hypothesis for the relationship between geotechnical data except for correlation coefficient value (0.65) between fine and CBR that is higher than critical r value. For geotechnical data, strong positive correlation exists between percentage of fine and CBR, while there is a weak correlation between percentage of clay and CBR and a negative weak correlation between percentage of clay and MDD and percentage of fine with MDD.

Figure 16. Regression plots and correlation coefficient (r) values between (a) mean resistivity values and % of fine (b) mean resistivity values and MDD values (c) mean resistivity values and percentage of clay (d) mean resistivity values and CBR values.

Figure 17. Regression plots and correlation coefficient (r) values between (a) fine contents and CBR values (b) % of fine and MDD values (c) % of clay and CBR values (d) % of clay and MDD values.

4. Conclusions

• Migration of people to Etioro Akoko has brought about recent developments; hence, the need to carry out geotechnical analysis of the subsoil and geophysical assessment of the subsurface for characterisation and to identify the most competent layer at the depth of <5 m.

• All the six traverses showed lateral variation in resistivity suggesting that the materials within the near surface were heterogeneous. Four major subsurface layer components of topsoil, weathered layer, fractured bedrock and fresh bedrock were identified.

• Two wells of moderate to good yield were identified from the different fractures in the six traverses. Competent strata that are good for building and road construction were found at the depth of <5 m.

• Atterberg limit tests, grain size analysis and CBR of the ten trial pits soil samples showed that these soil samples met AASHTO (A-2-4 to A-2-7) and Nigerian classification of a good soil which is a yardstick for sustainable development of Etioro Akoko

• The statistical analysis of ANOVA shows that the test hypothesis value exceeded the critical r value (Fstat: 7.05> FCrit: 2.37) i.e. the resistivity values within each traverse at <5 m depth are not similar in all six traverses. We therefore reject the null hypothesis. The correlation analysis between the mean resistivity values of the six traverses and the percentage of fine, percentage of clay and MDD values is higher than the critical r value except for correlation between resistivity values and CBR.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was personally sponsored