Geotechnical evaluation of the alluvial soils for urban land management zonation in Gharbiya governorate, Egypt

Journal of African Earth Sciences 101 (2015) 360–374

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Geotechnical evaluation of the alluvial soils for urban land management zonation in Gharbiya governorate, Egypt Alaa A. Masoud Geology Department, Faculty of Science, Tanta University, 31527 Tanta, Egypt

a r t i c l e

i n f o

Article history: Received 22 May 2014 Received in revised form 8 October 2014 Accepted 16 October 2014 Available online 29 October 2014 Keywords: Spatial variability Factor analysis K-means clustering Ordinary kriging Alluvial soils of Gharbiya governorate Egypt

a b s t r a c t Geological and geotechnical information from 534 borehole in-situ- and lab-based measured soil water conditions (Cl and SO2 4 ion concentrations and depth to water), plasticity, unconfined compression, and consolidation parameters for alluvial clays have been analyzed. Multivariate factorial and clustering along with the geostatistical ordinary kriging techniques were used and evaluated in a Geographic Information systems (GIS) environment. The prime objective was to spatially model the geotechnical variability and to derive the loading factors along with recognition of the distinctive spatial geotechnical zones in terms of their likelihood of occurrence. Results have been, for the first time, presented for the alluvial soils of the Gharbiya governorate, Egypt with the principal management zones and their associated geotechnical risks in the main eight districts were characterized and evaluated for their favorability for construction. Plasticity charts indicated that the soils are inorganic cohesive highly plastic clays. Geotechnical parameters showed wide ranges evidenced by their large standard deviations. Principal five factors dominated with good correlations to the swelling potential (0.90), compression index (0.74), depth to water (0.41), soil water salinity contents of Cl (0.64) and SO2 4 (0.60), and the clay layer thickness (0.59), arranged respectively in their decreasing contribution to more than 70% of the total spatial variability. Three distinctive management zones were delineated with reference to construction favorability. The first zone showed the highest favorability for construction being characterized by lowest potentials to swelling and the Cl and SO2 4 contents and hence corrosion. Characterized by a water level approaching the ground surface, largest Cl and SO2 4 contents violating the severity limits, and largest swelling potential, the second zone attained the lowest construction favorability and therefore safety measures should be applied. The third zone clarified intermediate favorability evidenced by the moderate severity from contents, and low swelling potential. Geotechnically at-risk areas characterized the the Cl and SO2 4 main industrial cities; Kafr Al-Zayat, Al-Mahala Al-Kubra, and Tanta. Kafr Al-Zayat attained the most saline water (Cl and SO2 4 contents) and hence high severity to damage associated and the most over consolidated soils. Al-Mahala Al-Kubra possessed the largest compressibility potential associated with the presence of organic silty clay intercalations. Swelling potential was largest in Tanta. The results of the employed approach can help to establish geotechnical land management zones with construction favorability for safe urban expansions. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Managing environmental risks for successful rational land use planning and development in rapidly growing cities with high anthropogenic pressure require in depth knowledge and accurate modeling of the spatial variability of the geological and geotechnical properties of the soils upon which cities were built (Cendrero et al., 1990; Mendes and Lorandi, 2008; Sharafi et al., 2009; Anbazhagan et al., 2010; Kolat et al., 2006, 2012; Donghee et al., E-mail address: [email protected] http://dx.doi.org/10.1016/j.jafrearsci.2014.10.009 1464-343X/Ó 2014 Elsevier Ltd. All rights reserved.

2012). Disparate sources related to the geological and hydrogeological conditions, climatology, and the human practices that may act simultaneously with different intensities pose challenges to the accurate geotechnical variability modeling of soils (Fenton and Griffiths, 2002; Antonio-Carpio et al., 2004; Breysse et al., 2005; Chang et al., 2005; Phien-wej et al., 2006; Chrétien et al., 2007). The alluvial clays, in particular, constitute significant risk to constructions in terms of their ability to swell or to shrink and hence to volume change and are most commonly over-consolidated (Dhowian et al., 1985) that result in land subsidence, differential settlements, and building collapse (Bell and Jermy, 1994;

A.A. Masoud / Journal of African Earth Sciences 101 (2015) 360–374

Bell and Maud, 1995; Stavridakis, 2006; Hyndman and Hyndman, 2009). Also, the soil Cl and SO2 4 contents of when violating the permissible limits can cause severe damages to the concrete contents of the structures (SBC, 2007; ACI, 2008). Yet, changes to the volume and the Cl and SO2 4 contents of these clays are significant in the arid and semi-arid areas justifying their mutual analysis to disclose their geotechnical risks. In these areas, the change of the soil water content is widely related to high evapotranspiration of vegetation that sometimes exceed four times the precipitation (FAO, 1998), brought about by local site changes such as leakage from water supply pipes or drains, or associated with a pattern of short periods of rainfall followed by long dry periods resulting in seasonal cycles of soil swelling and shrinkage (Nelson et al., 2001; Cameron, 2006; Clayton et al., 2010). However, the geotechnical tests that are widely performed to understand the potential problems of soils can be time-consuming, expensive, and limited. The aforementioned challenges therefore make unraveling the accurate spatial geotechnical and geological constraints of these clays and their spatial variability imperative for investigating the land suitability for construction and for land use management on the limited soil resource. This can play a decisive role to help efficiently prioritize management zones with cost effective optimization of the construction times, efficient setting of mitigation measures, and designing projects for safe extension with appropriate and reliable foundation system to compensate for risks or overcosts (e.g. Tilford, 1994; Parsons and Frost, 2002; Hack et al., 2006; De Rienzo et al., 2008). This dictates the use of a multidisciplinary approach that can integrate the multivariate statistical and geostatistical techniques in a Geographical Information Systems (GIS) environment. These techniques proved indispensible in risk assessment of trace elements in agricultural soils in China (Chen et al., 2008), lead in mining site in Ireland (McGrath et al., 2004), and for delineation of agricultural management zones (Moral et al., 2010). The present research therefore aims at evaluating the integrated use of the multivariate statistical (factor and cluster analyses) and the ordinary kriging for spatial variability mapping and analysis of the geotechnical characteristics of the alluvial clays. A special focus is devoted to characterizing the geotechnical risks related to the fluctuating soil water conditions, i.e., Cl and SO2 4 contents, water table, and the clay layer thickness, along with the soil’s swelling and compressibility potentials. Integration of the adopted techniques aimed at answering the question: how can the numerous geotechnical parameters be synthesized into few spatial management zones of specific characteristics to simply assess the suitability for land development? Such study has been rarely addressed in Egypt and therefore the employed techniques have been applied to a case study area, the Gharbiya governorate, despite of the significant socio-economic impacts from the geotechnical risks where the effects of catastrophic events are often amplified by the high anthropic pressure and the ineffective land management.

2. Study area Gharbiya governorate, located in the middle Delta region, consists of eightdistricts: Tanta, Al-Mahala Al-Kubra, Kafr Al-Zayat, Bassiyun, Qutur, Samannoud, Al-Santa, and Zefta, covering an area of about 1942 km2 of the Nile delta (Fig. 1). The landuse varies between cultivated (1658 km2), residential (214 km2), and barren/utility lands (70 km2). The governorate consists of main 8 districts, 317 villages, and 1249 small hamlets. The area overlies Holocene soils forming the flat-lying alluvial plain averaging 8.5 m a.m.s.l ranging between 11 m at the south and 3 m at its northern part (Fig. 2). The soils comprise of Bilqas Formation

361

underlain by Mit Ghamr Formation. Bilqas Formation forms the top layer of the flood plain of the modern Nile made up of silty clay, brown at the top and gray in the lower part, constituting the agricultural soil of the delta (Fig. 3). Black and gray clays dominate in Zifta close to the Dameitta branch. Organic clay seams with coal intercalations prevail with thickness averaging 1 m in Al-Mahala Al-Kubra and 3 m in Samannoud. The clays of the Bilqas Formation are dominated by montmorillonite (Zaghloul et al., 1977), which is characterized by its high shrinking and swelling properties. Mit Ghamr Formation consists of a thick succession of unconsolidated sands and gravels deposited under continental, lagonal, fluviatile and beach environments. The governorate is marked third (2258 person/km2) of the highly populated governorates in Egypt (CAPMAS, 2012). The population has reached 4,439,000 in 2012, which has been doubled since 1976. This urban overgrowth underpinned with high demand for housing and lack of desert hinterland has exceeded the capacity of the governorate stressing the ability of the local government to provide serviced land. Informal settlements had accordingly proliferated, the population of which reached 30.8% out of the total population (GOPP, 2010). These have adversely affected the housing quality indicators at the level of residential units. Yet, the alluvial clays pose significant hazard to constructions that has recently resulted in the incessant occurrence of road pavement failure, land subsidence, differential settlements, and building collapse. The accurate identification of the spatial constraints of the limited soil resource is crucial to help efficiently prioritize areas with geotechnical risks or over-costs. This could help better selection and design of an appropriate and reliable foundation system for safe extension and sustainable urban development that could contribute to solving the problem of housing shortage.

3. Data and methodology 3.1. Geotechnical data analysis The geotechnical data of the soils were collected from the schools and sewage construction projects carried out by the soil mechanics and foundation research laboratory, Faculty of Engineering, Cairo University and by the Arab Contractors company’s Headquarter in Gharbiya. Geological investigations, in-situ, and lab-based geotechnical tests from 911 boreholes were filtered and 534 boreholes were selected according to specific criteria and were homogenized, classified, and archived in a GIS database thus far. Filtering criteria were based on selecting boreholes with a reliable location, a detailed measured stratigraphic logs with continuous coring vertical profile up to a depth of 20 m from the surface, and with complete in-situ and laboratory tests and statistically homogeneous with the whole data above 95% confidence level. Geospatial referencing was carried out where the borehole location and ground surface elevation was field-determined using GARMINGPSmap62SJ device. The selected boreholes have a set of geotechnical information comprising the most relevant 16 properties—thickness of the clay layer, soil water chemistry (Total Dissolved Solids-TDS, and the contents of Cl and SO2 4 ), Atterberg consistency limits (Natural Water Content – NWC %, Liquid Limit – LL %, Plastic Limit – PL %, Plasticity Index – PI %, and the Consistency Index – Ci), Unconfined Compression Tests (Unconfined Compression Strength – UCS, and Dry Density – DD), Consolidation Tests (Initial Void Ratio – IVR, Effective Overburden Pressure – EOP, Over Consolidation Ratio – OCR, Pre-consolidation Pressure – PP, and the Compression Index – Cc). The depth to water table and the ground surface elevation was also measured and included in the analysis. Descriptive

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Fig. 1. Location and physiography of the study area.

summary statistics and the Pearson’s correlation coefficients among variables were derived and interpreted. A GIS database comprising all the selected data was constructed for the area, adopting a procedure developed to predict more reliably the spatial geotechnical information and their layers. This database can enable users to examine the geotechnical data referenced by spatial coordinates which can be easily imported to other numerical tools. XLSTAT – a Microsoft Excel statistical add-on was used for the factor analysis and clustering. ArcGIS 9.3 software package was used for the calculation of both the experimental and theoretical semi-variograms and for producing the spatial distribution maps of all variables. Spatial models produced along with brief description of the employed techniques are described below. 3.2. Severity zonation from soil water chemistry Severity maps have been produced following the requirements for concrete exposed to sulfate- and chloride-containing solutions following the severity limits described in ACI (2008) and SBC (2007). Corrosion mitigation and concrete durability necessitate that the amount of aggressive ion salts (Cl and SO2 4 ) to not to exceed 2000 ppm for Cl (SBC, 2007) and 1500 ppm for the SO2 4 content (ACI, 2008) in the soil water as these severely damage structures. Ions of Cl can lead to corrosion of steel reinforcement by breaking down the normally present protective layer of oxides present on the steel surface. In addition to the deleterious corrosive 2 effects of SO2 4 on concrete, SO4 can soften and crack concrete due to an expansive reaction.

3.3. Swelling and compressibility potentials Swelling properties are rarely employed in the course of routine site investigations in Egypt because few engineering applications have a perceived requirement for these data for design or construction. Reliance has to be alternatively, placed on estimates based on index parameters, such as LL, PI, and density (Reeve et al., 1980; Holtz and Kovacs, 1981; Oloo et al., 1987). LL and PI are broadly used for evaluating the swelling potential of soil (Thomas et al., 2000). Two widely accepted empirical equations were developed for the estimation of swell index Cs; these are Nagaraj and Murty (1985) (Eq. (1)) and Nakase et al. (1988) (Eq. (2)) equations.

  LLð%Þ Gs C s ¼ 0:0463 100

ð1Þ

C s ¼ 0:00194ðPI  4:6Þ

ð2Þ

where Gs is the specific gravity. According to Isik (2009) and Nagaraj and Murthy’s (1985) equation generally overestimates the swelling index and the RMSE is often larger than that from Nakase et al. (1988). The swelling index is then estimated following the equation of Nakase et al. (1988). The Compression Index (Cc) indicates the deformation characteristic of the over consolidated soils and their compressibility (Larson et al., 1980), the higher the Cc the greater the deformation that will develop for the same change in stress. Three consolidation parameters, IVR, OCR, and Cc are commonly interrelated (Gunduz

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Locations of boreholes

Fig. 2. Elevation contours (m) of the Gharbiya governorate with the locations of representative boreholes.

Fig. 3. Vertical distribution of the sedimentary facies in representative boreholes (the horizontal distance is un-scaled). Locations of cities and villages of the representative boreholes are shown on Fig. 2.

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A.A. Masoud / Journal of African Earth Sciences 101 (2015) 360–374

Table 1 Descriptive statistics of the studied geotechnical parameters in the eight districts of the Gharbiya governorate. Thick (m)

TDS

Cl

SO2 4

NWC

LL

PL

PI

Cs

Ci

UCS

DD

IVR

Cc

PP

EOP

OCR

D2W

1.4 15 7.66

650 12,000 2334

234 2980 833

100 2607 773

17.7 49 31.8

39 114.6 74.3

12.8 36.2 27.5

18 82.4 46.8

0.43 1.15 0.89

0.161 0.735 0.418

0.87 3.17 1.46

1.14 1.87 1.39

0.6 1.01 0.9

0.16 0.33 0.22

1.3 3.1 1.78

0.38 0.84 0.56

1.55 8.16 3.33

0 5.5 2.9

St. Dev. 2.78 Mahala 89

2.67

2133

701

757

4.9

15.2

4.3

12.8

0.14

0.114

0.55

0.13

0.1

0.05

0.4

0.12

1.34

1.28

Min. Max. Mean

5.5 15 10.67

860 16,400 4736

265 10,238 2578

130 4040 1276

25.92 58.2 35.7

32.2 133 88.9

10 97 32.7

2.1 91 56.3

0.13 0.90 0.42

0.5 1.11 0.8

0.66 2.15 1.2

1.1 1.55 1.4

0.83 1.13 0.9

0.16 0.4 0.3

1.2 1.9 1.6

0.34 0.68 0.5

2.13 5.28 3.6

3.1 4 1.7

2.2

4334

2656

1099

6.7

54.7

15.8

40.8

0.16

0.1

0.4

0.1

0.1

0.1

0.2

0.1

1.0

1.2

5 18.7 11.04

520 19,220 5392

176 15,327 3006

150 2990 1508

22.42 46.31 31.65

47 103 78.85

21 35 29.06

23 71 49.80

0.21 0.63 0.44

0.53 1.17 0.94

0.83 2.32 1.52

1.21 1.55 1.40

0.73 1.09 0.91

0.14 0.29 0.22

1.35 3.14 1.82

0.34 0.66 0.47

2.05 8.26 4.14

0.1 4.6 2.08

3.56

4742

3851

930

5.05

12.25

3.08

10.64

0.09

0.12

0.36

0.07

0.09

0.05

0.39

0.10

1.46

1.02

7 14.5 10.63 2.38

450 9500 3259 2863

176 4388 1462 1432

130 3540 965 988

22 41 31.0 4.3

27 97 60.9 21.4

15 38 25.4 5.8

7 65 35.5 18.3

0.06 0.58 0.32 0.16

0.69 2.43 1.0 0.4

1.19 2.24 1.6 0.4

0.01 1.51 1.2 0.6

0.9 0.93 0.9 0.0

0.2 0.24 0.2 0.0

1.8 1.8 1.8 0.0

0.49 0.64 0.5 0.1

2.81 3.67 3.4 0.5

1.1 5 3.3 2.0

4.2 10.4 7.85

400 20,200 4794

117 11,700 2473

140 5950 1477

13.53 48.68 32.0

42 110 77.5

20 34 28.9

22 79 48.6

0.20 0.70 0.43

0.6 1.32 0.9

0.85 2.02 1.4

1.11 1.63 1.4

0.77 1.07 0.9

0.18 0.33 0.3

1.55 2 1.8

0.39 0.62 0.5

2.85 4.62 3.5

0.9 4.5 2.1

1.64

4338

2578

1369

6.4

11.2

2.8

10.2

0.09

0.1

0.4

0.1

0.1

0.0

0.1

0.1

0.6

0.8

1.2 15.5 10.64

400 12,000 2334

117 2980 833

130 2607 773

17.7 49.0 31.8

39.0 114.6 74.3

12.8 36.2 27.5

18.0 82.4 46.8

0.12 0.76 0.43

0.43 1.15 0.89

0.87 3.17 1.46

1.14 1.87 1.39

0.60 1.01 0.90

0.16 0.33 0.22

1.30 3.10 1.78

0.38 0.84 0.56

1.55 8.16 3.33

0 5.5 2.90

3.55

2133

701

757

4.9

15.2

4.3

12.8

0.17

0.14

0.55

0.13

0.10

0.05

0.40

0.12

1.34

1.28

5.1 14 9.1

802 5880 3168

176 2633 1338

289 2300 1157

24.05 51 32.0

48 110 75.1

19 33 27.9

27 79 47.2

0.24 0.70 0.42

0.74 2.43 4.9

0.74 2.06 1.3

1.18 1.54 1.4

0.75 0.99 0.9

0.18 0.28 0.2

1.3 2.9 1.8

0.38 0.62 0.5

2.42 6.04 3.8

1.5 4.4 2.9

1.4

2.39

1986

951

784

6.0

15.1

4.5

12.0

0.11

19.0

0.4

0.1

0.1

0.0

0.6

0.1

1.2

0.8

8 18 10.7

2.4 15.2 8.26

400 6320 1717

195 2925 703

140 2520 614

21 42 32.5

25 101 68.0

17 78 29.0

1 67 39.0

0.04 0.60 0.36

0.47 1.31 0.9

0.65 2.03 1.3

1.24 1.53 1.4

0.6 1.12 0.9

0.15 0.31 0.3

1.2 2.9 1.8

0.36 0.64 0.5

2.22 6.04 4.0

1.1 7 3.3

2.95

1614

653

626

4.5

18.5

8.5

16.7

0.14

0.2

0.4

0.1

0.1

0.0

0.5

0.1

1.1

1.7

1.2 18.7 9.3 3.08

400 9500 3652 3645

117 15,327 1810 2390

130 5950 1092 996

13.53 66.02 32.8 6.0

25 133 73.8 31.1

10 97 29.18 9.3

1 91 48.3 23.7

0.04 0.90 0.41 0.13

0.43 2.43 1.2 5.1

0.48 3.17 1.4 0.4

1.1 1.87 1.4 0.1

0.6 1.13 0.9 0.1

0.14 0.4 0.2 0.1

1.2 3.14 1.8 0.3

0.34 0.84 0.5 0.1

1.55 8.26 3.7 1.1

0 7 2.6 1.4

Elev. (m) Tanta 109 Min. 6 Max. 16 Mean 10.7

4 14 8.8

St. Dev. 2.1 K. Zayat 83 Min. 7 Max. 16 Mean 8.89 St. Dev. 2.24 Bassiyun 33 Min. 5 Max. 12 Mean 7.6 St. Dev. 2.0 Qutur 60 Min. 5 Max. 10 Mean 7.4 St. Dev. 1.3 Samannoud 40 Min. 6 Max. 16 Mean 10.7 St. Dev. 2.78 Al-Santa 30 Min. 8 Max. 12 Mean 9.5 St. Dev. Zefta 90 Min. Max. Mean St. Dev. Gharbiya Min. Max. Mean St. Dev.

2.2 534 4 18 9.4 2.5

Bold refers to the largest mean values of variables in the eight districts.

and Arman, 2007 and references therein) and deemed necessary to spatially characterize the compressibility of the studied clays. 3.4. Determination of potential variability factors and management zones The multivariate factor and cluster analyses techniques have been used to derive factors controlling the geotechnical spatial variability, and to delineate zones with intra-zone minimum and inter-zone maximum variance, respectively. Spatial estimates of the factor scores and the potential management zones were produced using the Ordinary Kriging (OK) geospatial techniques. Factor Analysis (FA) was applied to the data to derive and inspect the few common factors explaining the highest variance and the loading scores of the geotechnical variables on these common factors (Wunderlin et al., 2001). Varifactors (VFs), a new group of variables are then obtained by rotating the axis defined by the

derived factors. Varimax rotation with Kaiser normalization, which is frequently applied to increase the participation of the variables with higher contribution and reduce those with lesser contributions, was adopted in this study. K-means Cluster Analysis (CA) was performed for the recognition of the distinctive geotechnical zones. CA is often coupled to FA to confirm results and provide grouping of variables (Facchinelli et al., 2001). CA clusters data samples into separate n groups of equal variance, minimizing a criterion known as the ‘inertia’ of the groups and the number of clusters to be specified. CA chooses centroids C that minimize the within-cluster sum of squares objective function with a dataset X with n samples (Eq. (3)):

JðX; CÞ ¼

n X i¼0

  min kxj  li k2 ; lj 2C

ð3Þ

A cluster separation index based on both the inter- and intracluster variances (Davies and Bouldin, 1979) was applied to the data.

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A.A. Masoud / Journal of African Earth Sciences 101 (2015) 360–374

Ordinary kriging (OK), the most popular geostatistical method, was appraised to define the best spatial estimation conditions and to produce spatial distribution maps of the studied parameters, taking into account all the available information through cross-validation analysis, multiple linear regression. OK uses spatial correlation to estimate a target variable Z0 at a defined point x0 of the space, using a set of values of the same parameter measured on n0 near the estimation points xá(á = 1, n0) (Journel and Huijbregts, 1978; Goovaerts, 1997; Siska et al., 2005). This method consists of a linear combination of the measures (Eq. (4)):

Z 0 ðx0 Þ ¼

n0 X

k0a Z 0 ðxa Þ;

or complex geological/hydrogeological processes affect the spatial variability of the geotechnical properties. The Pearson’s correlation coefficient (r) is used to disclose the degree of dependency among variables. Correlations were set as strong (r P 0.5), moderate (r < 0.5–0.3) and low (r < 0.3). Coefficients were strong within the plasticity, consolidation, soil water chemistry, and the unconfined compression tests, arranged in decreasing order of correlation strength (Table 2). LL strongly correlated (r = 0.97) with both the PI and Cs. The total dissolved solids  (TDS) strongly correlated with the SO2 4 (r = 0.89) and Cl (r = 0.66) contents. Cl and SO2 4 clarified strong correlation (r = 0.72). These parameters are commonly used either combined or individually as a measure of the water salinity. The ground surface elevation and the depth to water affect to a certain extent the soil water salinity parameters. Contents of Cl and SO2 increase in low lands 4 (r = 0.23 Cl, 0.18 SO2 4 ) and small depth to water areas (r = 0.15 Cl, 0.16 SO2 4 ). The swelling potential, Cs, showed strong correlation with the OCR (r = 0.57) and the EOP (r = 0.50). Also, the swelling potential decreases as the water approaches the ground surface (r = 0.25). The Compression Index (Cc) showed strong correlation with the IVR (r = 0.76), and moderate correlation with NWC (r = 0.48) and OCR (r = 0.31). The largest means of interests to this study characterized the main industrial districts: Kafr Al-Zayat, Al-Mahala Al-Kubra, and Tanta. Kafr Al-Zayat attained the largest means of soil water salinity parameters: TDS, Cl, and SO2 4 associated with the smallest depth to water and the largest thickness of the silty clay layer. Soils of Kafr Al-Zayat were the most over consolidated underpinned with the largest initial void ration (IVR), dry density (DD) and pre-consolidation pressure (PP). Al-Mahala Al-Kubra showed the largest plasticity means: NWC, LL, PL, and PI, and attained the largest potential for compressibility (Cc) mostly associated with the presence of organic silty clay intercalations (see Fig. 3). Swelling potential was largest in Tanta.

ð4Þ

a¼1

where the vector of the coefficients k0a is calculated in order to have a correct estimator, and to minimize the difference between the unknown value on the estimation point and the kriged value on the same point. Minimization takes into account the spatial variability of the parameter expressed by the variogram function ~0 ðhÞ, which is the semivariance of the parameter increment with a respect to distance h, the so-called lag, that must be determined (Eq. (5)):

~0 ðhÞ ¼ a

1 v ar½z0 ðx þ hÞ  z0 ðxÞ 2

ð5Þ

Variogram function is adjusted by means of fitting on experimental semi-variograms. OK provides a ‘‘standard error’’ which is useful to quantify the estimation accuracy/uncertainty and to outline spatial areas that need a supplemental sampling. The experimental semi-variograms and the best-fitted theoretical models for all variables were built based on trial and error parameter selection. Models attained the best goodness of fit resulted in minimum mean error (ME), root mean error (RME), and mean squared error (MSE), and attained root mean squared error (RMSE)close to 1 are considered the best-fit models and were selected for further analysis, among which spherical was of major use.

4.2. Plasticity characteristics 4. Results and discussions Casagrande (1932) plasticity chart (Fig. 4) showed that the soils are inorganic cohesive clays of high plasticity (CH) and hence with high capability of volume changes. The majority of samples are plotted above the A-line, with LL greater than 50% and PI exceeding 10. One sample is located belowthe A-line. Twelve samples showed moderate plasticity and two were low in plasticity. The soils tend to show a general increase in PI which can be best determined linearly at R2 = 0.92 against LL.

4.1. Geotechnical properties The geotechnical parameters (Table 1) showed wide ranges: LL (25–133.6%), PL (10–97%), PI (1–91%), Cs (0.04–0.9), Cl content (117–15,327 ppm), SO2 4 content (130–5,950 ppm), depth to water (0–7 m), UCS (0.48–3.17 kg/cm2), Ci (0.14–0.40), and OCR (1.55–8.26). Such wide ranges suggest that multiple sources and/ Table 2 Pearson’s correlation matrix of the studied geotechnical parameters. Variables

Elev.

Thick

Cl

SO2 4

NWC

LL

PL

PI

Cs

Ci

UCS

DD

IVR

Cc

PP

EOP

OCR

D2W

Elev. Thick Cl SO2 4 NWC LL PL PI Cs Ci UCS DD IVR Cc PP EOP OCR D2W

1 0.08 0.23 0.18 0.04 0.15 0.04 0.16 0.16 0.01 0.33 0.30 0.14 0.22 0.05 0.12 0.21 0.21

1 0.50 0.40 0.33 0.06 0.08 0.04 0.24 0.01 0.23 0.02 0.13 0.04 0.18 0.22 0.07 0.41

1 0.72 0.15 0.22 0.13 0.23 0.23 0.03 0.14 0.21 0.29 0.29 0.02 0.21 0.21 0.15

1 0.12 0.31 0.19 0.32 0.32 0.03 0.22 0.23 0.29 0.34 0.04 0.26 0.26 0.16

1 0.22 0.12 0.18 0.18 0.01 0.26 0.26 0.62 0.48 0.25 0.01 0.06 0.07

1 0.28 0.97 0.97 0.01 0.10 0.28 0.25 0.06 0.27 0.51 0.57 0.23

1 0.24 0.24 0.04 0.05 0.02 0.11 0.00 0.09 0.21 0.21 0.11

1 1.00 0.00 0.14 0.31 0.21 0.09 0.25 0.50 0.57 0.25

1 0.00 0.28 0.31 0.21 0.09 0.25 0.50 0.57 0.25

1 0.07 0.02 0.06 0.02 0.03 0.01 0.01 0.09

1 0.46 0.44 0.22 0.21 0.20 0.21 0.25

1 0.26 0.34 0.03 0.17 0.21 0.24

1 0.76 0.47 0.01 0.14 0.03

1 0.34 0.43 0.31 0.00

1 0.03 0.51 0.05

1 0.84 0.16

1 0.16

1

Values in bold correspond for significant coefficients.

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Fig. 4. Plasticity chart for the cohesive soils of Gharbiya governorate.

Fig. 5. Thickness of the silty clay layer.

4.3. Thickness of the silty clay layer The silty clay layer is generally thicker, reaching a maximum of 14 m, close to the Rosetta and Damiette branches of the Nile River and decreases gradually to reach its minimum (5.5–7.5 m) thickness in Tanta and in Qutur (Fig. 5). Thicker layer enhances the salinity levels (Cl (r = 0.5), SO2 4 (r = 0.4)) since these have lower infiltration capacity than thin soil (Fu et al., 2011), raising the

water holding capacity in the active zone that leads to faster salt accumulation from the cyclic seasonal evaporation (Ansal et al., 2001). 4.4. Geotechnical risks of the soil water The depth to water is one of the main factors affecting the stability of foundation and pose serious risks to residential develop-

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ment (Marache et al., 2009; El May et al., 2010; Kolat et al., 2012). The static water level varied from 0 m to 7 m below the ground surface (Fig. 6a). The water table and hence its flow direction is affected by the topography (r = 0.21). The depth to water correlated (r = 0.25) with the plasticity index and the swelling potential as well as with the Cl (r = 0.15) and SO2 (r = 0.16) 4 contents of the soil water. When water table approaches the surface, it raises the risks of the cyclic seasonal evaporation that contributes to the creation of waterlogging conditions favorable to the occurrence of swelling of clays and enhances the soil salinity and hence the severity of damages to the structures resulting from the corrosion potential and ameliorates the construction favorability. On a similar alluvial deposits in Tunisia, areas where water table approached the surface (0–5 m), assigned the least favorability for construction (El May et al., 2010). Similarly, areas where water table coincided with the surface in Turkey were not suitable for settlement (Kolat et al., 2012). The degree of severity on concrete exposed to sulfate- and chloride-containing solutions increases with the increase of the solute contents (Table 3). Severe

Table 3 Degree of severity on concrete exposed to sulfate- and chloride-containing solutions. Severity

SO2 4 ppm ACI 318

Cl ppm SBC 304

Negligible Moderate Severe Very severe

0–150 150–1500 1500–10,000 >10,000

<500 500–2000 2000–10,000 >10,000

zones of damage from the excessive SO2 (>1500 ppm) and Cl 4 (>2000 ppm) contents were congruent and marked the northern districts as well as Kafr Al-Zayat and the northwestern part of Tanta (Fig. 6b and c). The areas marked by this zone showed relatively low depth to water averaged between 0 and 2.5 m (Fig. 6a). 4.5. Swelling potentials and compression strength The unconfined compressive strength (UCS) determines the bearing capacity of foundations that is related to the consistency of the soil where the harder the soil the higher the UCS and the

(a) Depth to water (m)

(b) SO4- 2 ppm

(c) Cl-ppm

 Fig. 6. Soil water conditions showing (a) depth to water (m), (b) SO2 4 content, and (c) Cl content.

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shear strength. Successful stabilization of expansive soils greatly rely, therefore, on increasing the compressive strength by reducing the plasticity index and hence the swelling potential (e.g., ASTM, 2005). The swelling potential correlated with the unconfined compression strength (r = 0.28), justifying their joint interpretation.

Generally, areas having high swelling potential were low in their compression strength (Fig. 7). Swelling potential was highest in Tanta followed by Kafr Al-Zayat, Qutur, Samannoud, and Al-Mahala Al-Kubra. The compression strength was largest in Bassiyun followed by Kafr Al-Zayat, Tanta, and Samannoud.

(a) Cs

(b) UCS (Kg /cm2)

Fig. 7. Spatial maps showing (a) swelling potential (Cs), and (b) unconfined compression strength (UCS).

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(b) OCR

(a) IVR

(c) Cc

Fig. 8. Consolidation maps showing (a) initial void ratio (IVR), (b) over-consolidation ratio (OCR), and (c) Compression Index (Cc). Table 4 Correlations between variables, factors without rotation (F), and the varimax-rotated factors (D). Geotechnical parameters

F1

F2

F3

F4

F5

Comm.

Variance

D1

D2

Elevation (m) Silty Clay Thickness (m) Cl (ppm) SO2 4 (ppm) Natural Water Content (NWC %) Liquid Limit (LL %) Plastic Limit (PL %) Plasticity Index (PI %) Swelling Index (Cs) Consistency Index (Ci) Unconfined Comp. Strength (UCS) Dry Density (DD) Initial Void Ratio (IVR) Compression Index (Cc) Preconsolidation Pressure (PP) Effective Overb. Press. (EOP) Over Cons. Ratio (OCR) Depth to water – D2W (m) Eigenvalue Variability (%) Cumulative (%)

0.33 0.07 0.45 0.52 0.07 0.89 0.81 0.90 0.90 0.83 0.16 0.44 0.04 0.31 0.28 0.70 0.77 0.35 4.70 27.63 27.63

0.28 0.38 0.40 0.40 0.70 0.25 0.16 0.21 0.21 0.05 0.56 0.43 0.91 0.74 0.55 0.00 0.17 0.08 3.28 19.28 46.90

0.29 0.07 0.06 0.05 0.19 0.14 0.20 0.16 0.16 0.23 0.66 0.32 0.13 0.41 0.01 0.55 0.45 0.41 1.61 9.46 56.36

0.06 0.19 0.64 0.60 0.22 0.09 0.18 0.13 0.13 0.26 0.13 0.31 0.05 0.19 0.02 0.17 0.15 0.07 1.24 7.27 63.63

0.58 0.59 0.12 0.01 0.00 0.26 0.07 0.26 0.26 0.20 0.06 0.12 0.06 0.03 0.54 0.01 0.33 0.22 1.06 6.38 70.00

0.61 0.60 0.83 0.83 0.60 0.96 0.79 0.97 0.97 0.89 0.79 0.60 0.85 0.86 0.68 0.85 0.96 0.46

0.39 0.41 0.17 0.17 0.40 0.04 0.21 0.03 0.03 0.11 0.21 0.40 0.15 0.14 0.32 0.15 0.04 0.54

0.26 0.01 0.35 0.43 0.21 0.92 0.83 0.92 0.92 0.84 0.04 0.34 0.22 0.15 0.38 0.68 0.79 0.33

0.34 0.40 0.50 0.50 0.67 0.07 0.06 0.02 0.02 0.15 0.58 0.51 0.88 0.79 0.48 0.14 0.02 0.15

27.29 27.29

19.61 46.90

Values in bold correspond for each variable to the factor for which the squared cosine is the largest.

4.6. Consolidation and compressibility potentials The studied soils are over consolidated attained OCR between 1.55 and 8.26. The Compression Index (Cc) ranged between 0.14

and 0.4. Spatial maps of the consolidation parameters clarified regional conformable zones ofIVR, OCR, and Cc (Fig. 8). Compression index relies strongly on the IVR (r = 0.76) and moderately on the OCR (r = 0.31). High IVR zones characterize the districts of

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Kafr Al-Zayat, Zifta, and Qutur. Largest OCR marks the eastern and western parts of the area. Kafr Al-Zayat showed areas of exceptional local high OCR that were congruent with high IVR attaining the lowest compressibility potential (Cc). These areas attained the highest potential for swelling (Cs) (Fig. 7) where water table is at the surface with severe SO2 4 hazards (>1500 ppm) (Fig. 6) in the thickest clay layer approaching 20 m (see Figs. 3 and 5). Zones of highest compressibility potentials evidenced by the highest Compression Index (Cc) occurred in Al-Mahala Al-Kubra and in Qutur districts that showed the lowest swelling potentials. 4.7. Geotechnical variability factors Due to the redundancy of information related to correlations existing among the parameters, the first five factors with eigenvalues exceeding the unity explain more than 70% of the total

variance (Table 4), which is sufficient to give a good idea of data structure. Variables attained the largest correlation coefficients of the selected factors are interpreted. In addition, the communalities of the variables, the proportion of their variance explained by the extracted common factors, are larger than 0.6 and therefore represents a unique contribution to the discrimination of the processes controlling the geotechnical variability in the area. Depth to water was an exception showed a communality of 0.46. Therefore, FA is assumed to adequately represent the overall variance of the dataset. FA results therefore focused on the first foremost factors and their interrelations to the swelling potential, compression index (0.74), soil water characteristics; Cl and SO2 4 contents and depth to water, and the clay layer thickness. Factor 1, which explains for 27.63% of the total variance, is strongly loaded by the swelling potential, Cs, (0.90). Factor 2 accounted for 19.28% of the total variability and is strongly

Fig. 9. Projection of the geotechnical parameters on the plane spanned by the two varimax-rotated factors.

Fig. 10. Spatial distribution maps of the varimax-rotated factors, (a) D1, and (b) D2.

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controlled by the compression index, Cc (0.74). Factor 3 accounted for 9.46% of the total variability, and is moderately correlated to the depth to water (0.41). Factor 4 contributed to 7.27% of the variability, and is strongly loaded by the soil water salinity contents; Cl (0.64) and SO2 (0.60). Finally, Factor 5 explains 4 6.38% of the total variability, and is strongly controlled by the silty clay layer thickness (0.59).

The varimax-rotated factors, D1 and D2, contributed to a sum of 47.90% of the total variability. These factors are combined loads from the factors lower in variability than F1 and F2 with different scores and signs for various parameters (Table 4). D1 is attributed to 27.29% of the total variability and was correlated, even stronger than F1, to the swelling potential (Cs) (0.92), and moderately correlated to the depth to water (0.33). Depth to water close to the

Table 5 Statistics of the geotechnical management zones derived from K-means statistics. Zone 1 (304)

Zone 2 (130)

Zone 3 (100)

C Obj.

Min.

Max.

Mean

St. Dev.

C Obj.

Min.

Max.

Mean

St. Dev.

C Obj.

Min.

Max.

Mean

St. Dev.

Elevation (m)

14

4

18

9.9

2.62

9

5

16

8.9

2.28

11

5

13

8.4

1.59

Silty Clay Thickness (m)

12

2.5

15

13.98

2.96

11

4.1

14

12.56

3.27

2.5

15

11

3.71

527

117

1500

574

378

4973

1755

15327

4674

3326

10 1755

527

2750

1727

431

420 31.7

100

2530

448

400

1960

460

5950

2321

994

1310

500

2520

1415

404

13.53

51.78

32.41

5.43

27.14

22.42

58.2

32.64

6.69

38

24.86

66.02

33.97

5.97

69

25

114.6

68.83

17.54

79

26.4

144

79.15

17.36

159

80.83

19.23

10

78

28.02

7.69

24

19

97

30.8

10.55

67 25

19.8

28

19

68

30.25

6.72

41

5

82.4

41.97

14.25

55

16.7

92

50

13.34

42

11

101

51.45

15.11

0.37 0.91

0.04

0.74

0.37

0.13

0.49

0.15

0.82

0.45

0.12

0.37

0.1

0.9

0.46

0.13

0.43

2.43

0.9

0.19

0.94

0.53

92

1.88

9.35

0.69

0.56

92

2.29

11.21

0.92

0.65

3.17

1.38

0.48

2.26

1.46

0.37

0.48

0.48

2.32

1.34

0.45

1.1

1.87

1.38

0.11

1.56 1.42

0.83

1.32

0.01

1.55

1.37

0.22

1.5

1.14

1.54

1.38

0.1

1.06

0.6

1.12

0.91

0.1

0.79

0.73

1.13

0.92

0.1

1.06

0.83

1.09

0.94

0.06

0.31

0.15

0.34

0.24

0.05

0.22

0.14

0.4

0.24

0.07

0.31

0.17

0.33

0.25

0.05

1.9

1.2

3.1

1.75

0.34

1.21

2.9

1.74

0.36

1.9

1.2

3.14

1.81

0.42

0.47

0.36

0.84

0.51

0.1

1.63 0.49

0.34

0.66

0.48

0.11

0.47

0.34

0.68

0.48

0.09

4.04

1.55

8.16

3.59

1.04

3.33

2.05

6.04

3.79

1.17

2.13

8.26

3.96

1.3

5.5

0

7

2.8

1.49

1.7

0

7

2.33

1.36

4.04 1.7

0

4.3

2.05

0.76

-

Cl (ppm) SO4-2 (ppm) Natural Water Content (NWC %) Liquid Limit (LL %) Plastic Limit (PL %) Plasticity Index (PI %) Swelling Index (Cs) Consistency Index (Ci) Unconfined comp. strength (UCS) Dry Density (DD) Initial Void Ratio (IVR) Compression Index (Cc) Preconsolidation Pressure (PP) Effective Overb. Press. (EOP) Over Cons. Ratio (OCR) Depth to water (m)

Bold values show the minimum and maximum values among the classes marked in blue and red color, respectively. C obj. is the central objects of the clusters.

Fig. 11. Geotechnical management zones of the Gharbiya governorate.

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Fig. 12. Proportions of the central objects of variables within the geotechnical clusters.

ground surface enhances the soil water content and expansion of the sheet silicate clay minerals such as montmorillonite becomes potential. D2 was attributed to 19.61% of the total variability and was strongly related to the compression index, Cc, (0.79), the contents of Cl (0.50) and SO2 4 (0.50), and moderately correlated to the layer thickness (0.40). Geotechnical parameters are projected by the length (communalities) on the plane (pattern plot) spanned by the two varimax-rotated factors, variables well-correlated to factors and interrelated are located close on the pattern plot (Fig. 9). Spatial distributions of the varimax-rotated factors and hence their loading variables showed distinctive location contrasting confirming their mutual spatial interrelationships (Fig. 10). Areas largely loaded by D1 variables; largest swelling potential (Cs) and smallest depth to water, are located in the northeastern and southwestern parts. Areas with loads on D2: the Compression Index (Cc), the soil water Cl and SO2 contents, and the layer 4 thickness are located in the southeastern parts. Potentials of these parameters on the varimax-rotated factors are of particular interest to this study that conforms well to those depicted on their corresponding maps. 4.8. Management and construction favorability zones Cluster analysis distinguished three homogeneous geotechnical zones of varying statistics (Table 5; Figs. 11 and 12). Clear relationship existed between maps of the management zones and the geotechnical variables where zones were congruent in location with the corresponding variables. Zone 1 (304 samples) attained the largest depth to water, lowest plasticity, swelling potential, and Cl and SO2 4 contents. This zone showed the largest initial void ratio and compressibility potentials. Zone 1 shows therefore high favorability for construction since risks related to swelling potential and the corrosion due to the Cland SO2 4 contents would be low. Despite the compression index were highest for this zone averaging 0.24, the range of which (0.15–0.34) is commonly acceptable for safe construction in Egypt. Zone 2 (130 samples) attained the smallest depth to water, largest Cland SO2 4 contents, and the largest plasticity limits and swelling potential. It also showed the lowest consolidation parameters and compressibility. Favorability for constructions of this zone is the lowest due to the high levels of Cl and SO2 contents exceeding the severity 4 limits and also swelling potential is high, and therefore safety measures should be applied. Zone 3 (100 samples) showed the lowest compression strength, highest dry density, and highest

consolidation and compressibility parameters. Zone 3 showed intermediate favorability evidenced by the moderate severity from the chloride and sulfate contents, low plasticity and swelling potential. Geotechnically hazardous areas dominated by zone 2 (Fig. 11) were congruent with those showed intermixed potentials of swelling (Cs), compressibility (Cc), and severity from the Cl and the SO2 4 contents. The identified zones and their associated geotechnical risks provide the basis for land management where future efforts should be directed. The congruence of the spatial distribution of the geotechnical parameters and the detected zones establish the viability of the adopted methods for kriging, multivariate factor and cluster statistics and their integrated analyses. 5. Conclusions A comprehensive geotechnical data of the alluvial soils in Gharbiya has been analyzed integrating multivariate statistics and ordinary kriging. The resulted few variables synthesized from the considered soil indexes; two principal factors controlling the properties, three homogeneous zones with distinct inter-zone variances, and their Kriging estimates of vivid spatial variation provide crucial basis to assess the suitability for land development. Management zones were spatially congruent with the areas attained the largest loads on the geotechnical factors justifying the joint effective use of the factor and cluster analyses. Results showed that the soils are inorganic cohesive clays of high plasticity (CH) and high capability of volume changes. The soil water conditions: the chloride and sulfate contents and the depth to water along with of the plasticity and consolidation parameters governed the variability of soils evidenced by their wide ranges and high standard deviations. A multi-disciplinary approach employed was fundamental to interpret the different available numerous data sources and extrapolate them to simple land management zones. Results disclosed the geotechnical problems dominated in the study area and the mutual interrelations of the most influential parameters to variability: the swelling and compressibility potentials, zones of high potential for soil water-related risks on the steel and concrete corrosion, i.e., the Cl and SO2 4 contents and the fluctuation of the groundwater table, and the clay thickness. Principal five factors identified with good correlations to the swelling potential, compression index, depth to water, soil water salinity contents (Cl and SO2 4 ), and the clay layer thickness, arranged respectively in their decreasing contribution to more than 70% of the total spatial variability. The two factors resulted from the varimax-rotation

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contributed nearly to about 50% of the variability. The first factor correlated to the swelling potential (Cs) and depth to water, while the second factor was loaded by the Compression Index (Cc), the soil water Cl and SO2 4 contents, and the layer thickness. Spatial distributions of the two factors and hence their loading variables showed distinctive locational contrasting confirming their mutual spatial interrelationships. Kriging estimates of the clusters clarified the spatial zonation of areas with their construction favorability and the risks associated. Results of this research, maps of the individual parameters, the factor analysis, and the geotechnical management zones, can provide a better guidance to prioritize areas with geotechnical risks or over-costs for civil engineering projects. These could help better selection of a suitable foundation type and construction design, prognosis of changes of the engineering geological conditions and prediction of hazardous geological phenomena early to ensure that the correct design strategy is adopted before costly remedial measures are required. Finally, the application of multivariate geostatistics using physical/mechanical parameters from laboratory tests demonstrates its validity for the preliminary geotechnical characterization of alluvial deposits. A governorate-wide GIS database is produced which can be easily updated with new data. This can improve the modeling estimates and can be implemented later in a decision support system for accurate and cost-effective management of the geotechnical hazards in the area. The adopted methodology can be applied on a broader geographical context in the Nile Delta governorates since these share common geological, hydrogeological, and geotechnical conditions. To the best of our knowledge, this is the first attempt to address the spatial variability and the related geotechnical risks of soils in a GIS framework in Gharbiya governorate and in Egypt. Acknowledgements The author greatly acknowledges the efforts of the Editor Prof. Pat Eriksson and the anonymous reviewers for their valuable comments and suggestions that improved the manuscript. References Anbazhagan, P., Thingbaijam, K.K.S., Nath, S.K., Narendara Kumar, J.N., Sitharam, T.G., 2010. Multi-criteria seismic hazard evaluation for Bangalore city, India. J. Asian Earth Sci. 38, 186–198. _ R., Yıldırım, H., 2001. The cyclic behaviour of soils and effects of Ansal, A., Iyisan, geotechnical factors in microzonation. Soil Dynam. Earthquake Eng. 21 (5), 445–452. Antonio-Carpio, R.G., Perez-Flores, M.A., Camargo-Guzman, D., Alanis-Alcantar, A., 2004. Use of resistivity measurements to detect urban caves in Mexico City and to assess the related hazard. Nat. Hazards Earth Syst. Sci. 4, 541–547. ASTM, 2005. American Society for Testing and Materials Annual Book of ASTM Standards, Volume 04.08, Soil and Rock (I): D 420-D 5611 and Volume 04.09, Soil and Rock (II): D 5714-latest. West Conshohocken, Pennsylvania. Bell, F.G., Jermy, C.A., 1994. Building on clay soils which undergo volume changes. Archit. Sci. Rev. 37, 35–43. Bell, F.G., Maud, R.R., 1995. Expansive clays and construction, especially of low-rise structures: a viewpoint from Natal, South Africa. Environ. Eng. Geosci. 1, 41–59. Breysse, D., Niandou, H., Elachachi, S.M., Houy, L., 2005. A generic approach to soil structure interaction considering the effects of soil heterogeneity. Géotechnique 55 (2), 143–150. Cameron D.A., 2006. The role of vegetation in stabilizing highly plastic clay subgrades. In: Ghataora, G.S., Burrow, M.P.N. (Eds.), Proc. of Railway Foundations, RailFound 06, Birmingham, Sept., pp. 165–186. CAPMAS, 2012. Central Agency for Public Mobilization and Statistics, Egypt. . Casagrande, A., 1932. Research on the Atterberg limits of soils. Public Roads 13 (8), 121–136. Cendrero, A., Sanchez, J., Antolin, C., Arnal, S., Diaz de Teran, J.R., Frances, E., Martinez, V., Monino, M., Neito, M., Nogales, I., Perez, E., Rios, C., Robles, F., Romero, A., Suarez, C., 1990. Geoscientific maps for planning in semiarid regions: Valencia and Gran Canaria, Spain. Eng. Geol. 29, 291–319. Chang, M., Chiu, Y., Lin, S., Ke, T., 2005. Preliminary study on the 2003 slope failure in Woo-wan-chai Area, Mt. Ali Road, Taiwan. Eng. Geol. 80, 93–114.

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