Introduction

In any engineering project, the subsoil investigation and geotechnical testing of soil become necessary to acquire the data, essential to design the substructures. Because of the limited availability of time and the lack of data, large estimation errors occur in the design, and hence the risk factor for stability and cost of the project increases (Rwakarehe and Mfinanga 2014). Also, these investigations are usually performed under professional supervision and always require sophisticated instruments thus may prove to be a costly element in any project. On the other hand, the aftermath effects of construction with the improper exploration of the subsoil cause the structure to be ruinously settled (Muhwezi et al. 2014). Therefore, a proper but economical assessment of subsoil strata is always desirable in the construction industry all over the world.

Despite rapid technological progress in the construction industry, the urban underground has remained an enigmatic space (Angin 2016). Plenty of projects carried out in the past in both urban and developing areas, and their subsoil investigation reports (SIRs) based on lethargic laboratory and field tests, and decades of soil behavior observations, eventually has become only the part of the documentation, rather than providing guidelines for potential project designs. Furthermore, the initial feasibility reports for megaprojects are usually based on interrupted data from different SIRs rather than organized data (May et al. 2010; Yoo 2016). Therefore, there is a strong need to integrate such SIRs in the form of soil maps which not only play an important role for quick and economical assessment of subsoil properties during the preliminary stage of new projects but also provide a fair idea about the purpose for which land can be used (Aziz et al. 2017). These maps aid the designers in characterizing the soil strength, stiffness, and other critical engineering properties, which can be used to un-tediously estimate and evaluate the engineering design parameters of the soil (Zeraatpisheh et al. 2019; Tajik et al. 2020; Oda et al. 2013; Padarian et al. 2019). Furthermore, it can serve as a guide for local contractors in terms of foundation design parameters for a variety of infrastructure development projects where the project budget does not allow for an independent subsoil investigation.

There have been numerous studies around the world aimed at the development of the various type of soil maps using different techniques (Poppiel et al. 2021; Rasaei and Bogaert 2019; Pahlavan-Rad et al. 2018; Wang et al. 2016; Taghizadeh-Mehrjardi et al. 2020; Cracknell and Reading 2014; Kidd et al. 2020; Voltz et al. 2020). Among various techniques, Geographic Information System (GIS) proving to be a very effective tool for capturing, storing, retrieving at will, transforming, and displaying spatial data from the real world (Robinson and Metternicht 2006; Juárez-Camarena et al. 2016; Akumu et al. 2019). As per Hennig et al. 2013, the data can be displayed in three distinctive yet overlapping viewpoints: database, spatial analysis, and maps. Using spatial interpolation of subsoil data, some recent studies integrated the practical application of GIS in the field of engineering (Arrouays et al. 2017; Bargaoui et al. 2019; Arrouays et al. 2020). Some other researchers used GIS-based coding and analyzed the subsoil investigation data, and produced geophysical, geological, and subsoil maps (Gabàs et al. 2014; Aldefae et al. 2020; Coelho et al. 2021). Such maps are essential in providing design guidance, as well as the creation of construction and building rules and regulations, which can lead to cost savings in the soil exploration program due to the readily available organized data about sub-soil conditions for the site in question. Furthermore, such maps serve as a reference and solution for dealing with various engineering issues that may arise prior to the project's completion.

Different countries have developed their own soil maps, however, very few studies have been published in the literature for the South Asia region (Aziz et al. 2017; Zeraatpisheh et al. 2019; Tajik et al. 2020). Therefore, the authors place a great emphasis on the development of such maps for this region, in particular for rapidly growing cities or districts in terms of agricultural and commercial trade points of view, such as the district Sialkot. This district planned to connect the China Pakistan Economic Corridor (CPEC), through a trunk road network that will be extended throughout the region. The subject district falls under the flagship project of the “One Belt One Road initiative” that aims to establish a trade route for the region. In addition, the development of a special economic zone named, “Sialkot Export Processing Zone” has also significantly increased the importance of this region (Jahangir et al. 2020). Therefore, due to the rapid industrialization and infrastructure developments across the district, the authorities have recognized the significance of readily available subsoil information as an essential part of cost-effective construction planning and this study is a step forward.

Taking the aforementioned discussion into account the current study focuses on developing an organized database that can be visualized by employing spatial mapping based on different critical geotechnical properties such as strength, stiffness, soil type, soil consistency, seismic parameters, and chemical analysis at every location in the research region of district Sialkot, Pakistan.

Study area

Sialkot district is located in the east of Pakistan, between \(32^{ \circ } \;24^{\prime}\) N–\(32^{ \circ } \;37^{\prime}\) N latitude and \(73^{ \circ } \;59^{\prime}\) E –\(75^{ \circ } \;02^{\prime}\) E longitude, at an average height of approximately 256 m above sea level, between the rivers Ravi and Chenab, with a population density of 903 people per square kilometer (Malik et al. 2010). The climate is hot and wet in the summer and cold in the winter, with an average annual rainfall of about 1000 mm, with the peak rainfall season during the monsoon. Figures 1, 2, and 3 present the location, administrative controlled tehsils, and average climatic condition of the study area.

Fig. 1
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Location of district Sialkot a District Sialkot location on the Pakistan map; b District Sialkot on the Punjab province map; c District Sialkot study area

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Fig. 2
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Administratively controlled Tehsils of District Sialkot

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Fig. 3
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Average climatic condition of the district Sialkot

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Fig. 4 presents the contour map of the Sialkot district. The data for elevations from mean sea level were collected using satellite data which is further transformed into visualization maps to get the idea of undulated landscape. The contour intervals are uniformly spaced which shows the study area lies in a plain landscape. The lowest point falls within the range of 222.03 m whereas the highest points lie in the range of 291.29 m above the mean sea level. The peak ground acceleration of the study area as per Pakistan's revised Building Code (BCP) (2007) ranges between 0.16 and 0.24 g (Quittmeyer and Jacob 1979).

Fig. 4
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Contour map of Sialkot District

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Database description

A large number of subsoil information were gathered from extensive field and laboratory investigation reports carried out for various projects throughout the district. These pieces of information include numerical and alphanumerical data on geographical, geological, and engineering data (Sun 2012; Wadi et al. 2021). The detailed information gleaned from soil investigation reports (SIRs) is presented in Table 1.

Table 1 Data retrieved from subsoil investigation reports
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Subsoil information data from 155 different construction project sites comprise of 282 boreholes executed in the study area were compiled. The borehole's average data were retrieved and recorded for further analyses. Figure 5 depicts the location of each borehole point. The thickness and location of each stratum, as well as the Standard Penetration Test N-values (SPT-N), soil consistency limits, shear wave velocity, and chemical analysis (i.e., sulphate content, and soluble salts) results at various depths, were also extracted against each borehole. Out of 155 sites, the average borehole data of 143 sites have been used to provide accurate lithological and stratigraphical information of each project site, while the remaining 12 SIRs evenly distributed throughout the district were used for validation purposes. It is pertinent to mention here, in terms of sources, the authors ensured that the SIRs selected for validation are from multiple sources and representative of the whole data set used in the development of the soil maps.

Fig. 5
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Study area with locations of data points

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Results and discussion

Statistical evaluation of SPT-N data

To evaluate the homogeneity of the test data of the study region, various statistical techniques were incorporated. Table 2 presents the results of various statistical evaluations that illustrate the mean SPT-N values, their mode, variance, and standard deviation along with depth. In addition, skewness and kurtosis analyses were also incorporated to statistically measures the symmetry and tail-heaviness of the distribution of data. The results show that at 1.5m and 3m depth the data is slightly positively skewed, while with the increase in depth the data tends to be approximately symmetrical as can be seen in Fig 6 and Table 2. On the other hand, the value of kurtosis was found to be very low and negative along with depth showing the uniform distribution of data, i.e., platykurtic kurtosis. The statistical analyses of the database show that a wide range of data spectrum is considered in the development of spatial maps in the current study.

Table 2 Descriptive statistical analysis of database based on SPT-N value
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Fig. 6
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SPT-N histogram at various depths

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Figure 7a, b presents the variation of SPT-N and PI values with depth considering the maximum and minimum ranges and standard deviation. The SPT N-values tend to increase with an increase in depth while the PI value of the soil ranges from high plastic to non-plastic soil. Based on subsoil data, several linear regression models were developed and documented in Tables 3 and 4 to predict the SPT-N value and PI value using the depth factor. Furthermore, as developed regression models exhibit a strong relationship (i.e., R2 value), thus they can be reliably used for quick and economical assessment of SPT-N values and soil consistency with reasonable accuracy during the initial, planning, and design phases of future projects in the study zone.

Fig. 7
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Statistical variation of SPT-N and PI values with depth

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Table 3 Linear Regression analysis of SPT-N with depth
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Table 4 Linear Regression analysis of PI with depth
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Development of SMs

Subsoil data were gathered from various locations from different reliable authorities throughout the district. All four administratively controlled tehsils have a uniform distribution of scattered points (Fig. 2). The coordinates of the site location, elevation from mean sea level, SPT-N values, soil type, soil consistency, and chemical composition at different depths were digitalized and used as input data in the Arc GIS 10.5 by integrating pertinent data from SIRs.

For the current study, ArcMap software (ArcGIS 10.5) (Booth and Mitchell, 2001) was used to create SMs using a spatial analyst and the Inverse Distance Weighting (IDW) interpolation technique. The IDW interpolation technique is based on the idea that the value at an unknown data point can be estimated as a weighted median of values at data points within a certain cut-off distance or from a set of nearby points (Masser and Crompvoets 2015). For GIS-interpolated SPT-N subsoil maps, this technique provides a better representation of data as demonstrated by various pertinent studies (Al-Ani et al. 2013; Aziz et al. 2017). It is pertinent to mention here, prior to the development of SMs using spatial analyst, IDW technique, various trials were made by incorporating different other methods such as Geostatistical analyst (i.e., diffusion, IDW, global polynomial, and Kernel), Spatial analyst (i.e., spline, IDW, universal and ordinary kriging). The interpolation results were then compared to the input value points for validation. The results show that among the different above-mentioned techniques, the IDW technique was found to be quite effective in precisely predicting the various geotechnical soil properties with real-time data. In addition, a number of supportive literature were also found on the efficacy of the IDW technique compared to other methods (Zhou and Michalak, 2009; Lu and Wong, 2008; Al-Ani et al. 2013; Gong et al. 2014).

SMs based on SPT-N values

Figure 8 shows the SPT-N-based SMs at various depths, i.e., 1.5, 3 m, 4.5 m, 6 m, 7.5 m, and 9 m below the existing ground level (EGL). These SMs show the consistency and strength of the soil at various stratigraphic intervals. To evaluate the variation of SPT-N with depth, six maps were created. In general, there is an increasing trend in SPT-N values with an increase in depth highlighting the transformation of soil consistency from soft to hard (Terzaghi et al. 1996). The SMs show different ranges of SPT-N values, i.e., < 2, 2–4, 4–8, 8–16, and >16 of the study area at different depths highlighting soft, medium, and hard consistency of the soil. The results show that up to 3 m depth, the majority of the study area is predominant with SPT-N values falling in the range of 0–8, highlighting soft to medium-hard consistency of the soil as presented by pink, peach, and light brown color.

Fig. 8
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SMs based on SPT-N values

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In addition, at 3 m depth, there are some yellow patches in the study area demonstrating hard consistency soil with SPT-N values ranges between 8 and 16. Further, with the increase in depth up to 9 m, the SPT-N values kept increasing and the study area is primarily displaying hard consistency soil with SPT-N values ranges between 8–16 and >16.

Further, shear wave velocity which is an important seismic design parameter is also taken into account in the current study. It is used to calculate various spectral acceleration under seismic loading which are regarded as a vital seismic design parameter in modern building codes, i.e., ASCE-07, IBC (2006) (Mahmood et al. 2016; Haider and ur Rehman 2021). Therefore, the authors have incorporated the shear wave velocity values corresponding to SPT-N values as shown in Fig. 8. In general, the shear wave velocity increases with an increase in depth corresponding to an increase in stiffness. The shear wave velocity for depths at depths 1.5 m, 3 m, 4.5 m, 6 m, 7.5 m, and 9 m ranges from 0 to > 23 2m/s, respectively.

SMs based on soil type

SMs were developed based on the soil types found below the study area. A Unified Soil Classification System (USCS) was used to classify subsoil types. Various soil types were assigned numerical codes: (1) CH, fat clay; (2) CL, lean clay; (3) CL-ML, silty clay; (4) ML, Silt; (5) SM, silty sand; (6) poorly graded silty sand; (5). Six maps were created based on the observed trend of soil variation with depth, as shown in Fig. 9. SMs were established at depth intervals of 1.5 m, 3 m, 4.5 m, 6 m, 7.5 m, and 9 m, respectively. The maps show that CL and CL-ML were generally dominant at shallower depths (up to 3m), whereas the northern side of the district (Tehsil Sialkot) was dominated by SP-SM type of soil. Beyond the 3m stratum, the SP-SM soil type occupied the majority of the district. Further exploration of the depths reveals the dominance of the SP-SM/SM soil type. Such distribution pattern of soil types conforms with the geology of the study area, i.e., alluvium plain.

Fig. 9
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SMs of district Sialkot based on soil type

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SMs based on soil consistency

Soil consistency, i.e., plasticity index (plasticity index (PI)= liquid limit (LL) – plastic limit (PL)) is an important soil property that defines the moisture-volume relatioship which is critical for the stability of any civil engineering structure built on it. In addition, there are various correlations developed in the literature that use soil consistency as a predicting tool to envisage various geotechnical properties, i.e., soil activity, swelling and shrinkage, compressibility, hydraulic conductivity, strength, etc. These correlations are of significant importance for quick and preliminary assessment of different aforementioned geotechnical properties (Rehman et al. 2017; Ijaz et al. 2020a). Therefore, in the current study, the authors established the SMs based on soil consistency (i.e., PI) for subsoil strata for quick assessment of engineering behavior as shown in Fig 10. The SMs show four ranges of PI; (1) non-plastic soil (PI = 0); (2) low plastic soil (0 > PI ≤7); (3) medium plastic (7> PI ≤ 17); and (4) high plastic soil (PI > 17). In general, the soil consistency (i.e., PI) decreases or tends to exhibit non-plastic behavior with an increase in depth of the soil, and these results are inconsistent with the soil type maps shown in Fig 10. Up to 3 m depth, the PI of the major portion of the study area falls under the medium to high plastic soil range. Beyond 3 m, the high values of PI tend to diminish and gradually reduce and fall in the range of low to the non-plastic range. For example, at 9 m depth, the study area is predominant with non-plastic soil (i.e., PI = 0). This trend is attributed to change in soil type as can be seen in Fig. 9, i.e., with an increase in depth, the study area is predominant with SP-SM type of soil which exhibits non-plastic behavior (i.e., PI = 0) which validates the soil consistency behavior.

Fig. 10
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SMs based on soil consistency (i.e., PI)

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SMs based on chemical analysis

Chemical analysis is of crucial importance in terms of the presence of specific chemicals, i.e., sulphate, soluble salts which may play a critical role and affect the foundations if its concentration is beyond the permissible limits, i.e., > 0.5 (sulphate content). Such chemical analyses are important for engineers to understand the soil behavior based on the presence of hazardous material in the soil. To analyze the distribution of the percentage of total soluble salts and sulphate content at shallow depth, interpolated SMs were developed using the available data at 1.5m depth. The soil contains traces of sulphate contents and total soluble salts, and the majority of the district area falls under the permissible limits, i.e., 0.24% and 0.50%, respectively, as shown in Fig. 11.

Fig. 11
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SMs based on chemical analysis

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Validation of SMs

The borehole logs from 143 of the 155 investigation reports in the study area were used to prepare SMs, while the remaining 12 reports were used for validation. The actual SPT-N values, PI values, and soil type for a given depth and location were compared and validated with the predicted interpolated points using ArcGIS. In general, the validation points were evenly distributed throughout the district, resulting in a negligible difference between actual and predicted values.

A comparison of actual and predicted values based on SPT-N and PI values are shown in Fig. 12a, b. While Table 5 shows a comparison of actual and predicted values based on soil type. The analyses show that the developed SMs exhibit a very good accuracy between actual and predicted values of soil type exhibiting accuracy of more than 83%. Besides, Table 6. presents the strength of predicted SPT-N and PI value using the correlation coefficient approach. The results show relatively good strength between actual and predicted values of SPT-N and PI, which authenticate the practicality of the developed maps.

Fig. 12
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Comparison of actual and predicted values; a SPT-N values; b PI values

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Table 5 Comparison of actual and predicted soil type
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Table 6 Correlation coefficient approach for strength prediction SPT-N and PI value
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Civil engineering applications

The SMs developed in this study are critical for construction in the Sialkot region and could help in making preliminary decisions for future projects. For instance, the SMs based on SPT-N value gives an idea about the ground condition for footing placement. At 1.5 m depth, many weak spots (i.e., SPT-N= 0–2) are identified in the Sialkot district which is not suitable for construction and required ground improvement or engineering fill (Fig. 8). However, these weak spots tend to diminish beyond 3 m depth which means that foundation at/below 3 m depth could be constructed without prior heavy mechanical ground improvement. Similarly, a clear idea of the engineering properties of ground could also be taken using the SMs based on soil type (Fig. 9). For instance, CH is considered to be disastrous soil in terms of its volumetric change behavior for Civil Engineering structures. It is identified that for the Sialkot region at various depths, few spots bear the problematic soil. Thus, prior care must be taken to deal with this soil in the highlighted region in the SMs (Fig. 9). These soils can be dealt with by the replacement with suitable engineering fill or by stabilization using different additives (Rehman et al. 2018; Ijaz et al. 2020a, b, c). The selection of these methods depends on the availability of the additive/filler materials at the site. Moreover, the idea about the soil-moisture volume behavior could be taken using SMs based on soil consistency (Khalid et al. 2015, 2019). For instance, different spots have been identified in the Sialkot region for which soil consistency could be regarded as highly plastic. Furthermore, the chemical analysis results exhibit that total soluble salts and sulphate content within the study area falls within the permissible range with few exceptions (Fig. 11). Therefore, care should be taken while carrying out construction in those spots for which sulphate content is not within the permissible limits, i.e., use of sulphate resistant cement which enhances the durability of the foundation in high sulphate zone.

Summary

The current study presents the spatial maps of district Sialkot by integrating a large database of SIRs. SMs show that CL and CL-ML were generally dominant at shallower depths (up to 3 m) with average SPT-N values ranges between 2-8 and shear wave velocity between 138-195 m/s. While the soil consistency up to 3m exhibits higher values of plasticity index (PI) falls in the range of 7 ≥ PI > 17. Whereas beyond 3m, the soil stratum was enriched with SP-SM soil type having SPT N-value ranges between 9 AND >16 with average shear wave velocity through 195- >232 m/s. While the soil consistency beyond 3 m depth tends to exhibit low plastic and non-plastic behavior. Furthermore, at shallow footing depth (i.e., 1.5m) the sulphate contents and soluble salt presents non-hazardous nature of the soil. In addition, the statistical analyses of the database show that a wide range of uniformly distributed data spectrums is considered in the current study to develop spatial maps.