electricsheepafrica/cement-brand-performance

Cement Brand Performance Dataset

Abstract

This dataset provides synthetic but research-grounded data on cement brand performance in Nigerian construction based on BS EN 197 standards and comparative studies of local cement manufacturers. The data encompasses setting time, compressive strength development, fineness properties, and soundness test results. Each record contains detailed information on cement brand, grade classification, and corresponding performance metrics derived from published local research.

Keywords: cement, brand performance, BS EN 197, setting time, compressive strength, Nigeria, Grade 42.5, fineness

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1. Introduction

1.1 Background

Cement is the fundamental binding material in concrete construction, and its performance directly influences the structural integrity and durability of built infrastructure. Nigeria's cement market is dominated by several major manufacturers, each producing Portland limestone cement under the Grade 42.5 classification. Understanding the performance characteristics of different brands is essential for engineers, contractors, and quality control professionals.

1.2 Problem Statement

Significant variations exist in the performance of cement brands available in the Nigerian market. Research has documented differences in:

• Initial and final setting times (ranging from 100-180 minutes)
• Early and ultimate compressive strength development
• Fineness and particle size distribution
• Soundness and dimensional stability
• Consistency between batches

These variations affect concrete workability, strength development, and long-term durability, requiring careful brand selection and quality verification.

1.3 Research Objectives

This dataset aims to provide:

1. Comprehensive performance data on Nigerian cement brands
2. Setting time characteristics (initial and final)
3. Compressive strength development at 7 and 28 days
4. Fineness and soundness test parameters
5. Quality consistency indicators

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2. Methodology

2.1 Data Generation Framework

The synthetic data generation follows a research-grounded approach based on published Nigerian studies comparing cement brand performance. Parameters are derived from empirical laboratory testing data and market surveys.

2.2 Parameter Evidence Table

Parameter	Source	Value/Range	Evidence
BS EN 197-1	European Standard	Cement composition	CEM I, CEM II classification
Grade 42.5	BS EN 197	28-day strength ≥42.5 MPa	Standard Nigerian cement
Burham setting time	ResearchGate study	100-110 min initial	Fastest setting brand
Dangote setting time	ResearchGate study	170-180 min initial	Slowest setting brand
Diamond setting time	ResearchGate study	125-135 min initial	Medium setting time
Elephant setting time	ResearchGate study	135-145 min initial	Medium setting time
Purechem setting time	ResearchGate study	155-165 min initial	Slow setting time
Ashaka brand	Northern Nigeria	Performance data	Regional manufacturer
BUA brand	National distribution	Performance data	Major manufacturer
7-day strength	Standard practice	65-75% of 28-day	IS 456 age relationship
28-day strength	Reference	42.5-58 MPa	BS EN 197 requirement
Fineness (Blaine)	BS EN 197	2800-4500 cm²/g	Particle size indicator
Soundness	BS EN 196-3	≤10mm expansion	Le Chatelier test
Aniweteli (2023)	NAU Journal	Southeast Nigeria brands	Civil Engineering Vol 2
Egwuatu et al.	University study	Brand performance correlation	Compressive strength study
Ojo et al.	OAU study	South West Nigeria quality	Obafemi Awolowo University
Mac-Eteli et al.	NDU/FUO study	Grade 42.5 PLC	Niger Delta University

2.3 Test Standards

• BS EN 197-1:2011: Cement - Composition, specifications and conformity criteria for common cements
• BS EN 196-1: Methods of testing cement - Determination of strength
• BS EN 196-3: Methods of testing cement - Determination of setting times and soundness
• BS EN 196-6: Methods of testing cement - Determination of fineness

2.4 Brand Classifications

Brand	Manufacturer	Location	Market Share
Dangote	Dangote Cement	Obajana, Ibese	Dominant
Elephant	Dangote Cement	Ewekoro	High
Burham	Burham Cement	Sokoto	Medium
Diamond	Diamond Cement	Benin	Medium
Purechem	Purechem Industries	Ogun	Growing
Ashaka	Ashaka Cement	Gombe	Regional
BUA	BUA Cement	Sokoto	Growing

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3. Dataset Description

3.1 Variables

Variable	Type	Description	Range/Values
specimen_id	String	Unique identifier	CEM-YYYY-NNNNN
brand	Categorical	Cement manufacturer	Dangote, Elephant, Burham, Diamond, Purechem, Ashaka, BUA
cement_type	Categorical	Cement classification	CEM I, CEM II/A-L, CEM II/B-L
grade	Categorical	Strength grade	Grade 42.5, Grade 32.5
initial_setting_time_min	Float	Initial setting time	85-200 minutes
final_setting_time_min	Float	Final setting time	150-280 minutes
compressive_strength_7d_mpa	Float	7-day strength	25-40 MPa
compressive_strength_28d_mpa	Float	28-day strength	42.5-58 MPa
fineness_blaine_cm2_g	Float	Blaine fineness	2800-4500
fineness_45um_percent	Float	45μm sieve retained	5-15%
soundness_mm	Float	Le Chatelier expansion	1-10 mm
loss_on_ignition_percent	Float	LOI	1-5%
insoluble_residue_percent	Float	IR content	0.5-3%
source_location	Categorical	Test location	Lagos, Abuja, Port Harcourt, Kano, Ibadan
test_standard	String	Applicable standard	BS EN 197, ASTM C150
test_date	Date	Testing date	2020-2025
batch_number	String	Manufacturing batch	Alphanumeric
quality_rating	Categorical	Overall quality	excellent, good, fair, poor
region	Categorical	Geographic region	Southwest, Southeast, North, Niger Delta

3.2 Statistical Distributions

Compressive strength follows a normal distribution parameterized by brand and age:

$${\sigma }_{28d}\sim N({\mu }_{brand},{\sigma }_{brand}^2)\backslash sigma\_\{28d\}\; \backslash sim\; N(\backslash mu\_\{brand\},\; \backslash sigma\_\{brand\}^2)$$

Brand-Specific 28-day Strength Parameters:

Brand	Mean Strength (MPa)	Std Dev (MPa)	Consistency
Dangote	52	4	High
Elephant	50	4.5	High
Burham	48	5	Medium
Diamond	49	5	Medium
Purechem	47	6	Medium
Ashaka	46	6	Medium
BUA	50	5	High

3.3 Setting Time Classifications

Brand	Initial Setting (min)	Classification
Burham	100-110	Fast
Diamond	125-135	Medium
Elephant	135-145	Medium
Purechem	155-165	Slow
Dangote	170-180	Slow
Ashaka	140-155	Medium
BUA	145-160	Medium

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4. Source Attribution

4.1 Primary Research Sources

1. Aniweteli, I.M. et al. (2023). "Comparative Analysis of Cement Brands in Southeast Nigeria." NAU Journal of Civil Engineering, Vol 2, Federal University of Technology Owerri.

2. Egwuatu, P.I. et al.. "Correlation Between Cement Brand Performance and Concrete Properties." University of Nigeria Research Publications.

3. Ojo, O.A. & Adeyemi, G. (2019). "Quality Assessment of Cement Brands in South West Nigeria." Obafemi Awolowo University Journal of Technology, 12(3), 45-52.

4. Mac-Eteli, C., Amadi, A.A., & Okwudili, M. (2021). "Performance Evaluation of Grade 42.5 Portland Limestone Cement in the Niger Delta Region." Niger Delta University Journal of Engineering and Federal University Otuoke Journal of Engineering.

5. Ayegbo, O. & Igbinomwanhia, D. (2020). "A Comparative Study of Setting Time and Compressive Strength of Different Cement Brands in Nigeria." Nigerian Journal of Technology, 39(2), 512-520.

6. Oluremi, J.R., Adediran, A.A., & Odeyemi, S.O. (2022). "Investigating the Influence of Cement Brands on Concrete Durability in Nigeria." Journal of Building Engineering, 45, 103628.

4.2 Standards Referenced

• BS EN 197-1:2011: Cement - Composition, specifications and conformity criteria for common cements
• BS EN 196-1:2018: Methods of testing cement - Determination of strength
• BS EN 196-3:2016+A1:2019: Methods of testing cement - Determination of setting times and soundness
• ASTM C150/C150M-22: Standard Specification for Portland Cement
• NIS 17:2004: Nigerian Industrial Standard for Cement

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5. Usage Examples

5.1 Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/cement-brand-performance")

5.2 Filtering by Brand

# Get Dangote brand data
dangote = dataset.filter(lambda x: x['brand'] == 'Dangote')

5.3 Regression Example - Predicting 28-day Strength

from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor

X = dataset['train'].remove_columns(['compressive_strength_28d_mpa', 'specimen_id'])
y = dataset['train']['compressive_strength_28d_mpa']

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

5.4 Quality Rating Classification

from sklearn.ensemble import RandomForestClassifier

features = ['initial_setting_time_min', 'compressive_strength_28d_mpa', 
            'fineness_blaine_cm2_g', 'soundness_mm']
X = dataset['train'][features]
y = dataset['train']['quality_rating']

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

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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 market conditions; performance may vary in other regions
3. Batch Variability: Actual cement performance can vary between production batches
4. Storage Conditions: Cement performance degrades with improper storage; dataset assumes proper storage
5. Not for Certification: Should not be used for actual material certification or structural design

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7. License

Creative Commons Attribution 4.0 International (CC BY 4.0)

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8. Citation

@dataset{cement_brand_performance,
  author = {Electric Sheep Africa},
  title = {Cement Brand Performance Dataset - Nigeria},
  year = {2025},
  publisher = {HuggingFace},
  doi = {10.57967/hf.0002},
  url = {https://huggingface.co/datasets/electricsheepafrica/cement-brand-performance}
}

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