Development of a Methodological Approach for Mapping Granular Soils for Pavement Layers ()
1. Introduction
Growing populations are creating a need for connections to link growing towns, ports and villages. The response to this need is infrastructure development, in particular road links, for which construction and maintenance require large quantities of materials to be extracted from the ground. As the road industry seeks out and exploits areas where materials can be extracted, these areas become increasingly scarce, alongside the need for agricultural land and urbanization. Materials used in the construction of road pavements have specific geomechanical qualities. For their exploitation, geotechnical surveys are carried out beforehand to identify the zones. This work takes place during the feasibility phase of each project. It can take from 6 to 13 months, depending on the scale of the project. Geotechnical maps are therefore recommended to facilitate access to the basic data needed to decide on the feasibility of projects.
A geotechnical map is a cartography that provides a generalized representation of all the components of a geological environment that are important for land development and for the design, construction and maintenance of civil engineering works and mines [1]. As an art form for representing spatial phenomena, cartography is subject to a process of abstraction involving selection, classification, simplification and symbolization. It is established by collecting data, processing the data to be mapped and producing the map. The development of Geographic Information System (GIS) software has made it easier to produce maps, saving time and money.
However, as far as the geotechnical map is concerned, data collection depends largely on the data collection and processing methodology. The southern part of Benin, which is already facing a booming urban dynamic [2], is facing a gradual disappearance of areas where road construction materials are collected, and a search for new areas in more remote parts. To prevent this situation in the country, the development of geotechnical maps could be a solution [3]. There are several approaches to acquiring geotechnical data for mapping: systematic inventory of materials [3], superimpositions of existing data (existing maps) and new data [4] [5]. In the case of Benin, which has no geotechnical map, this study aims to develop a methodological approach that collects the geotechnical data collected during geotechnical missions by means of an information platform.
2. Study Area
This study focuses on southern Benin, which largely overlaps with the coastal sedimentary basin and extends across the continental shelf [6] from the Atlantic Ocean up to latitude 7˚30'N. The entire region is subject to a subequatorial climate. The geomorphology is characterized by seven plateaus separated by large rivers and the Lama depression. To the north of these plateaus, the landscape consists of a vast peneplain interspersed with inselbergs, plateaus, and narrow valleys. The plateaus are composed of sediments deposited on the crystalline basement, which dates from the Quaternary, Tertiary, and Cretaceous periods. The crystalline basement of southern Benin can be considered a portion of continental crust, made up of meta-sedimentary series (quartzites, marbles, and parts of gneiss from various units) associated with granites of different ages (See Figure 1).
Figure 1. Localization bay of Benin (Litho library).
3. Materials and Methods
The methodological approach is in 3 phases as follows:
Comparative analysis of four methods from the literature review.
Presentation of the data recording platform.
Implementation of the data extraction and granular soil mapping approach.
3.1. Comparative Analysis of Four Methods
A comparative summary of four methods from the literature review was carried out. It covers:
The study for the establishment of geotechnical maps in Quebec [7], the study to produce a geotechnical map of the town of Ain Témouchent in Algeria [1].
The study to produce a geotechnical map of the city of Lubumbashi [3].
The study for the 2D and 3D stratigraphic modeling of the subsoil of the Brussels-Capital Region [4].
This review covers several regions of the world: North America, North Africa, Central Africa and Europe. No similarly studied sites were found in West Africa. The comparison covers data collection methods, data processing and the production of thematic maps.
Based on their nature, the data originate from 4 sources:
- Base maps [1] [4].
- Aerial images [7].
- Geotechnical, geological and environmental study reports [1] [4] [7].
- Databases [3] [4].
Recourse to any one of these data sources depends on its availability in the country’s historical archives. These data are generally available in developed countries: Quebec [7], Algeria [1] and Belgium [4]. However, this basic data is lacking in developing countries such as Congo [3].
Data processing leads to the determination of the parameters to be mapped: in the four cases studied, [1] and [3] carried out the geotechnical classification of soils according to several international systems (LCPC, GTR or AASHTO). In 2006, [4]. Digitized and modeled soil stratigraphy using borehole data. [7] also determined soil stratigraphy.
In other words, there are two key parameters for understanding soil: soil class and stratigraphy.
Except for [7], who drew up a manual map, all three authors drew up the map using ArcGIS GIS and cartography software.
This comparative study shows that there is convergence in the methods used to collect, process and establish the maps. The use of particular data depends on the data sources and the means made available for data collection. Once the data has been collected, two parameters can be used to identify the soil. These are soil class and stratigraphy. These parameters, together with the geodetic coordinates, are used to draw up the maps.
3.2. Methodological Approach for Developing a Map in Benin
In Benin, there is no geotechnical mapping that could be updated, and the existing geological map has not been updated. On the other hand, geotechnical prospecting data is available from a number of players: geotechnical laboratories, contractors and project owners. This data is acquired repeatedly for each infrastructure construction project. The methodological approach is therefore as follows:
Design of an open platform to feed the database. Each operator is approved and provides data to the database in a predefined format.
The zoning unit is within the boundaries of the department. Benin has twelve
The processing incorporated into the database enables soil classes and stratigraphy to be determined. The classification system is adapted to granular soils, in particular laterite, which is most commonly used in road layers in Benin.
The maps are produced on ArcGIS 10.8 using data from the database.
3.3. Materials
The materials are:
Data source: 6 road geotechnical study reports.
Database design software: MySQL Workbench.
Web development software and API: WINDEV & Node.js.
Software and mapping: ArcGIS 10.8.
Storage hardware: VPS cloud model server 3-PRODUCT CONFIGURATION: 8v CPU CORES 24 GB RAM STORAGE: 300 GB NVMe, 2 Snapshots, 1.2 TB SSD-32TB OUT + Unlimited In.
4. Results & Discussion
4.1. Design of a Data Recording Platform
The selection of a platform is guided by the need for it to be open and accessible to any operator approved by the national geotechnical authority to access and record their borehole data in real time. The platform design consists of a database deployed on a secure cloud server and processing and mapping applications.
Table 1 below describes the entities in the conceptual data model.
Table 1. Description of the entities in the conceptual data model.
Data type |
Entity |
Description |
Metadata |
Operator/ Laboratory |
Data provider (laboratory or firm that carried out the tests). The data provider enters its address and certification. They receive autorisation to record the data and autorisation to access all the other data in the database |
File |
References of the document that reports on the tests: project title—report (paper or digital format); archive location—validation level (preliminary design, detailed design, provisional or final)—legal ownership (project owner)—report production date |
Site |
Type of site (laterite quarry, rock quarry, sand quarry, roadbed, etc.) Site information (description, name and location) |
Borehole |
Description of the geospatial location of the survey point (zone code, GPS coordinates, etc.); |
Sample parameters |
Type of sample (intact or reworked—location in coordinates (x, y and z)—results of tests carried out on the sample (AG, LA, Proctor, CBR, etc.) |
Geotechnical data |
Lithological section |
Position of cut—depth—thickness, geotechnical name, color, natural water content—image of cut |
Granulometric analysis data |
Sieves (record standard diameters) Percentage passing the sieve of diameter D |
Proctor |
Percentage of optimum Proctor records the different percentages of optimum (100%; 95%; 90%) Proctor detail records the dry density (Dsopt) at the Proctor optimum modified according to the Proctor percentage |
Optimum water content |
Optimum water content Proctor |
ICBR Index |
CBR corresponding to Proctor’s percentage |
Soil classes |
Classification: different classification systems (HRB, GTR, LCPC, etc.) Soil class: class of soil (e.g. class A1a for HRB, the common name) Classification details: values for each parameter that classifies the soil |
The diagram of the conceptual model of the database is shown in the following figures (see Figure 2).
Figure 2. Schema of the conceptual database model.
Figure 3. ROAD MAT home screen capture.
The ROAD MAT platform is a user-friendly application for recording data in the database. It has an authentication interface, a welcome interface and interfaces for recording detailed data. Figure 3 shows a model interface for the ROAD MAT platform. It can be used to sort data and export results to different file formats (Word, Excel, KMZ, Shapefile).
Figure 4. CEBTP classification curve [8].
To process the data, the geotechnical classes and the California Bearing Ratio (CBR) of the materials taken from the soil quarries were determined. Soil classification and bearing capacity are used to assess the geomechanical quality of granular materials that can be used for pavement layers. The classification method used is that of the Centre Expérimental de Recherches et d’Etudes des Bâtiments et des Travaux Publics (CEBTP) [8]. This classification was used to classify laterites from Côte d’Ivoire and the Democratic Republic of Congo [9]. It has the advantage of dividing laterites into three classes according to their decreasing geomechanical qualities (G1, G2, G3). The materials sampled in the borehole are assigned to each class as follows:
Considering:
The percentage passing the 0.08 mm sieve obtained by particle size analysis tests (standard NF P 94-056), noted (f);
- The plasticity index obtained by the Atterberg limit test in accordance with standard NF P 94-051, noted IP.
- The percentage passing the 0.08 mm sieve obtained by particle size analysis tests (standard NF P 94-056), noted (f);
- The plasticity index obtained by the Atterberg limit test in accordance with standard NF P 94-051, noted IP.
The class of the material: G1 - G2 - G3 (see Figure 4); a material is of class
o G1 if f ≤ 15% for IP < 16 and f*IP < 250 when IP > 16
o G2 if 15% < f < 25%, and 250 < f*IP < 600 and f*IP < 250 when IP > 16
o G3 if 25% < f < 35%, and 600 < f*IP < 1000 when IP > 28
Type G1 is the best laterite gravel with a CBR of over 30, an OPM dry density of over 2.1 and a water content of between 5 and 8; Group G2 is made up of lower quality laterites with a CBR of between 15 and 30, an OPM dry density of between 2 and 2.1 and an optimum compaction water content of between 7 and 10, while Group G3 is made up of laterites with a CBR of less than 15, an OPM dry density of between 1.9 and 2.2 and an optimum compaction water content of between 8 and 12 (see Figure 5).
Figure 5. Class and CBR maps for Mono department.
In addition, three classes of CBR were defined by test in accordance with standard NF P 94-093 as follows:
- 5 < CBR < 30, materials accepted as subgrade; noted P1.
- 30 < CBR > 80, materials accepted as sub-base layer; noted P2.
- CBR > 80 materials accepted as base course, noted P3.
4.2. Test Results of the Methodological Approach
In order to validate the methodology, the data recorded relates to a selection of 714 km of linear roads in six departments, surveyed between 2014 and 2023 by the three main laboratories. However, Table 2 of results for the Mono department is presented below. It covers 16 holes drilled in laterite quarries.
From the table it can be seen that of the 16 boreholes, 4 belong to the G1 class, 11 to the G2 class and 1 to the G3 class, while 15 belong to the CBR P2 class and 1 to the CBR P1 class. We can deduce that the materials in this area of the country are of average geomechanical quality G2 and also of average CBR P2. This result provides geotechnical reference data for pavement design.
It used the ArcGIS 10.8 software to design and produce the granular soil class and CBR class maps. An extract of the results is shown in Figure 5.
Table 2. Soil class and CBR data for the Mono department.
N˚ Sounding |
sieve 0.08 mmm (f) |
IP |
f*IP |
Class of soil |
CBR |
Class of CBR |
Longitude E |
Latitude N |
SEG1 |
15 |
10 |
150 |
G1 |
46 |
P2 |
1.8558333333333 |
6.5939167 |
SEG2 |
14 |
9 |
126 |
G1 |
39 |
P2 |
1.8449833333333 |
6.59685 |
SEG3 |
14 |
8 |
112 |
G1 |
52 |
P2 |
1.9214166666666 |
6.5469333 |
SEG4 |
15 |
9 |
135 |
G1 |
40 |
P2 |
1.9063833333333 |
6.55095 |
SEG5 |
22 |
15 |
330 |
G2 |
45 |
P2 |
1.9093333333333 |
6.5502 |
SEG6 |
26 |
17 |
442 |
G3 |
35 |
P2 |
1.9057666666666 |
6.5333333 |
SEG7 |
21 |
13 |
273 |
G2 |
79 |
P2 |
1.8988999999999 |
6.5699667 |
SEG8 |
23 |
15 |
345 |
G2 |
36 |
P2 |
1.9461666666666 |
6.5904 |
SEG9 |
23 |
16 |
368 |
G2 |
43 |
P2 |
1.95 |
6.6137667 |
SEG10 |
17 |
12 |
204 |
G2 |
58 |
P2 |
1.96265 |
6,61505 |
SEG11 |
18,5 |
10 |
185 |
G2 |
40 |
P2 |
1.95 |
6.62085 |
SEG12 |
21 |
11 |
231 |
G2 |
34 |
P2 |
1.9382000000000 |
6.57915 |
SEG13 |
18 |
13 |
234 |
G2 |
39 |
P2 |
1.9410500000000 |
6.5666667 |
SEG17 |
21 |
15 |
315 |
G2 |
28 |
P1 |
1.9099 |
6.5440833 |
SEG18 |
17 |
12 |
204 |
G2 |
50 |
P2 |
1.8914666666666 |
6.5657333 |
SEG19 |
21,5 |
13 |
279.5 |
G2 |
40 |
P2 |
1.8170000000000 |
6.5938333 |
Analysis of the maps gives an idea of the geographical dispersion of the boreholes associated with their geomechanical quality. Class G2 and P2 are dominant on both maps. It should also be noted that the geotechnical surveys were carried out in the same area (in this case, the commune of Houeyogbe). Surveys should be carried out in areas not covered by a materials inventory campaign to supplement the data provided by the surveys carried out for the roadworks.
5. Conclusion
This study’s methodological approach highlights the systematic and controlled recording, for enhancement purposes, of data produced by laboratories and construction companies during geotechnical and geological surveys for road construction projects. To this end, a web platform for recording the data has been developed. An example of its application in the Mono department was presented, with data from 16 boreholes recorded. The results showed that the G2 soil class dominated (69%), while the CBR P2 class dominated (94%). The ROAD MAT platform and database are constantly being improved with the use of new BIG DATA possibilities. Its use by stakeholders will make it possible to collect and centralize vital data to define geotechnical parameter references for greater control of geotechnical risks. The adoption of the platform will also enable the development of geotechnical mapping, which until now has been non-existent in Benin.