electricsheepafrica/africa-synth-aggregates-characterization-nigeria-nigeria

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

Aggregates Characterization - Nigeria Dataset

Abstract

This dataset provides synthetic but research-grounded data on aggregate characterization in Nigerian construction based on ASTM C33 standards and local quarry studies. The data encompasses sieve analysis results, fineness modulus, specific gravity, water absorption, and other physical properties of fine and coarse aggregates from various Nigerian sources. Each record contains detailed information on aggregate type, source location, and corresponding test parameters.

Keywords: aggregates, sieve analysis, fineness modulus, specific gravity, water absorption, ASTM C33, Nigeria, laterite, river sand, quarry

---

1. Introduction

1.1 Background

Aggregates constitute approximately 60-75% of concrete volume and significantly influence its properties including strength, durability, workability, and cost. In Nigeria, aggregates are sourced from various quarries and river beds across different geological zones, resulting in significant variability in their physical and mechanical properties. Understanding these properties is essential for concrete mix design and quality control.

1.2 Problem Statement

Significant variability exists in the properties of aggregates available in Nigeria due to:

• Diverse geological formations across the country
• Different quarrying and processing methods
• Variations in aggregate types (river sand, quarry dust, laterite, granite)
• Regional availability and transportation challenges
• Limited standardization in aggregate processing

This variability affects concrete consistency, strength development, and durability, requiring comprehensive aggregate characterization for reliable mix design.

1.3 Research Objectives

This dataset aims to provide:

1. Comprehensive physical property data on Nigerian aggregates
2. Sieve analysis results and gradation characteristics
3. Fineness modulus values for fine aggregates
4. Specific gravity and water absorption data
5. Laterite versus river sand comparisons

---

2. Methodology

2.1 Data Generation Framework

The synthetic data generation follows a research-grounded approach based on published Nigerian studies on aggregate characterization. Parameters are derived from empirical laboratory testing data and quarry surveys across different states.

2.2 Parameter Evidence Table

Parameter	Source	Value/Range	Evidence
ASTM C33	American Society	Concrete aggregate spec	Standard specification
Imo State quarry	Multiple studies	Aggregate sources	Various quarry locations
Niger State	Nigerian study	River sand sources	Riverine deposits
Lagos quarry	Coastal source	Aggregate types	Quarry operations
Laterite aggregate	Widely available	Common across Nigeria	Residual soil
Specific gravity range	ASTM C33	2.4-2.9	Normal weight aggregates
Fineness modulus range	Nigerian practice	2.3-3.1	Fine aggregate FM
FM 2.0-3.5 typical	Regional studies	Overall range	Mix design applicability
Water absorption	ASTM C127/C128	0.5-3.0%	Typical range
Sieve analysis	ASTM C136	Standard procedure	Gradation determination
Nature Scientific Reports	2023 study	Aggregate sizes effect	Laterized concrete innovation
Ghana aggregate minerology	Academia.edu (2018)	Quarry products case	Mineralogical study
Coastal vs Inland	Regional study	Lagos vs Northern	Geographic variation

2.3 Test Standards

• ASTM C33/C33M-18: Standard Specification for Concrete Aggregates
• ASTM C136/C136M-19: Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates
• ASTM C127-15: Standard Test Method for Relative Density (Specific Gravity) and Absorption of Coarse Aggregate
• ASTM C128-22: Standard Test Method for Relative Density (Specific Gravity) and Absorption of Fine Aggregate
• BS EN 12620:2002+A1:2008: Aggregates for Concrete

2.4 Aggregate Classifications

Aggregate Type	Source	Typical FM	Specific Gravity
River Sand	Niger, Benue, Imo rivers	2.5-3.0	2.60-2.70
Quarry Dust	Imo, Ogun, Lagos quarries	2.8-3.5	2.50-2.65
Laterite	Widely available	3.0-4.0	2.40-2.60
Granite	Quarry sources	N/A	2.65-2.80
Crushed Stone	Quarries	N/A	2.60-2.75

---

3. Dataset Description

3.1 Variables

Variable	Type	Description	Range/Values
specimen_id	String	Unique identifier	AGG-YYYY-NNNNN
aggregate_type	Categorical	Material type	river_sand, quarry_dust, laterite, granite, crushed_stone
aggregate_category	Categorical	Fine or coarse	fine, coarse
source_state	Categorical	Origin state	Imo, Niger, Lagos, Ogun, Oyo, Kano, Delta
source_location	Categorical	Specific quarry/river	Detailed location name
fm_fine	Float	Fineness modulus	2.0-3.5
specific_gravity_bulk	Float	Bulk specific gravity	2.40-2.90
specific_gravity_ssd	Float	SSD specific gravity	2.45-2.95
apparent_specific_gravity	Float	Apparent specific gravity	2.50-3.00
water_absorption_percent	Float	Water absorption	0.3-3.5%
moisture_content_percent	Field moisture	1-8%
silt_content_percent	Float	Silt content	2-12%
clay_content_percent	Float	Clay content	1-8%
sieve_475mm_percent	Float	4.75mm retained	0-15%
sieve_236mm_percent	Float	2.36mm retained	5-35%
sieve_118mm_percent	Float	1.18mm retained	10-45%
sieve_600um_percent	Float	600μm retained	15-55%
sieve_300um_percent	Float	300μm retained	20-65%
sieve_150um_percent	Float	150μm retained	25-75%
test_standard	String	Applicable standard	ASTM C33, BS EN 12620
test_date	Date	Testing date	2020-2025
aggregate_source_type	Categorical	Source classification	river, quarry, pit
geological_zone	Categorical	Geological region	Coastal, Inland, Savannah
quality_rating	Categorical	Overall quality	excellent, good, fair, poor
region	Categorical	Geographic region	Southwest, Southeast, North, Niger Delta

3.2 Statistical Distributions

Fineness modulus follows a normal distribution parameterized by aggregate type:

$$FM\sim N({\mu }_{type},{\sigma }_{type}^2)FM\; \backslash sim\; N(\backslash mu\_\{type\},\; \backslash sigma\_\{type\}^2)$$

Aggregate Type-Specific Parameters:

Aggregate Type	Mean FM	Std Dev	Sample Prevalence
River Sand	2.75	0.25	0.35
Quarry Dust	3.10	0.30	0.30
Laterite	3.40	0.35	0.25
Granite (coarse)	N/A	N/A	0.10

3.3 Source Location Characteristics

Location	State	Aggregate Types	Typical Quality
Owerri	Imo	River sand, quarry dust	Good
Onitsha	Anambra	River sand	Good
Minna	Niger	River sand	Excellent
Badagry	Lagos	Quarry dust	Fair
Abeokuta	Ogun	Quarry dust, laterite	Good
Jos	Plateau	Granite	Excellent
Benin	Delta	River sand	Good

---

4. Source Attribution

4.1 Primary Research Sources

1. Oyekan, G.L. & Kamiyo, O.M. (2011). "A Study of the Quality of Sand from Selected Sources in Southwestern Nigeria." Nigerian Journal of Technology, 30(2), 78-84.

2. Uchechukwu, B., Bert-Okonkwor, C.B.N., & Okafor, C.V. (2025). "Comparative Analysis of Compressive Strength of Concrete Produced Using Coarse Aggregates from Selected Locations in Imo State, Nigeria." International Journal of Multidisciplinary Research and Growth Evaluation.

3. Ogunbayo, B.F. & Aigbavboa, C. (2020). "Assessment of Aggregate Properties and Their Effects on Concrete Strength in Nigeria." Construction Economics and Building, 20(3), 45-62.

4. Nature Scientific Reports (2023). "Laterized Concrete: Effect of Aggregate Sizes and Cement Content on Compressive Strength." DOI: 10.1038/s41598-023-XXXXX

5. Adesanya, D.A. & Alaoye, O.O. (2018). "Evaluation of Quarry Dust as Alternative to River Sand in Concrete Production." Nigerian Journal of Technology, 37(1), 145-154.

6. Amhadi, T.S. & Agyeman, S. (2018). "Quality Assessment of Aggregates from Quarry Sites in Ghana." Academia.edu Research Publications.

4.2 Standards Referenced

• ASTM C33/C33M-18: Standard Specification for Concrete Aggregates
• ASTM C136/C136M-19: Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates
• ASTM C127-15(2022): Standard Test Method for Relative Density (Specific Gravity) and Absorption of Coarse Aggregate
• ASTM C128-22: Standard Test Method for Relative Density (Specific Gravity) and Absorption of Fine Aggregate
• BS EN 12620:2002+A1:2008: Aggregates for Concrete
• NIS 21:2004: Nigerian Industrial Standard for Aggregates

---

5. Usage Examples

5.1 Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/aggregates-characterization-nigeria")

5.2 Filtering by Aggregate Type

# Get river sand data
river_sand = dataset.filter(lambda x: x['aggregate_type'] == 'river_sand')

5.3 Regression Example - Predicting Fineness Modulus

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

X = dataset['train'].remove_columns(['fm_fine', 'specimen_id'])
y = dataset['train']['fm_fine']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestRegressor()
model.fit(X_train, y_train)

5.4 Classification Example - Quality Rating

from sklearn.ensemble import GradientBoostingClassifier

features = ['specific_gravity_bulk', 'water_absorption_percent', 
            'silt_content_percent', 'fm_fine']
X = dataset['train'][features]
y = dataset['train']['quality_rating']

model = GradientBoostingClassifier()
model.fit(X, y)

---

6. Limitations and Caveats

1. Synthetic Data: While parameters are research-grounded, data is synthetically generated and not from actual laboratory tests
2. Regional Focus: Primarily reflects Nigerian aggregate sources; other regions may have different characteristics
3. Seasonal Variation: Water content and moisture vary seasonally; dataset represents typical values
4. Processing Effects: Crushing and washing affect properties; dataset assumes standard processing
5. Not for Certification: Should not be used for actual material certification or mix design without verification

---

7. License

Creative Commons Attribution 4.0 International (CC BY 4.0)

---

8. Citation

@dataset{aggregates_nigeria,
  author = {Electric Sheep Africa},
  title = {Aggregates Characterization - Nigeria Dataset},
  year = {2025},
  publisher = {HuggingFace},
  doi = {10.57967/hf.0003},
  url = {https://huggingface.co/datasets/electricsheepafrica/aggregates-characterization-nigeria}
}

---

Dataset Version: 1.0
Last Updated: February 2026
Total Records: 10,000 (across 3 scenarios)