electricsheepafrica/construction-quality-control

Construction Quality Control Dataset

Abstract

This dataset provides synthetic but research-grounded data on construction quality control practices in Nigerian construction based on IS 456, ACI 318 standards, and statistical analysis of concrete strength. The data encompasses compressive strength test results, standard deviation calculations, coefficient of variation analysis, and acceptance criteria compliance. Each record contains detailed information on test parameters and quality level classifications.

Keywords: quality control, standard deviation, coefficient of variation, acceptance criteria, IS 456, ACI 318, concrete testing, Nigeria

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1. Introduction

1.1 Background

Quality control in concrete construction is essential for ensuring structural safety and durability. Statistical methods are employed to evaluate concrete strength consistency, with standard deviation and coefficient of variation serving as key quality indicators. International standards including IS 456 (India) and ACI 318 (USA) provide frameworks for quality assessment and acceptance criteria applicable to construction worldwide, including Nigeria.

1.2 Problem Statement

Significant challenges exist in construction quality control across Nigeria:

• Variability in material properties from different sources
• Inconsistent testing laboratory practices
• Limited quality control culture in some construction firms
• Environmental factors affecting curing and strength development
• Need for statistical quality control methods

Understanding statistical quality control parameters helps identify issues early and ensure structural integrity.

1.3 Research Objectives

This dataset aims to provide:

1. Comprehensive quality control metric data
2. Standard deviation and coefficient of variation analysis
3. Target mean strength calculations per IS 456/ACI formulas
4. Quality ratings: excellent, good, fair, poor
5. Acceptance criteria compliance indicators

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2. Methodology

2.1 Data Generation Framework

The synthetic data generation follows a research-grounded approach based on published studies on concrete quality control and international standards. Parameters are derived from empirical data on strength testing statistics.

2.2 Parameter Evidence Table

Parameter	Source	Value/Range	Evidence
Standard deviation 3-6 MPa	ACI 214	Typical range	Good quality control
Standard deviation 2-3 MPa	Quality standards	Excellent	<5% CoV
Standard deviation 3-4 MPa	Quality standards	Good	5-8% CoV
Standard deviation 4-6 MPa	Quality standards	Fair	8-12% CoV
Standard deviation 6-10 MPa	Quality standards	Poor	>12% CoV
Coefficient of variation 5-15%	ResearchGate/ACI	Typical range	Quality indicator
IS 456 formula	f_m = f_ck + 1.65σ	Target mean	Characteristic + margin
ACI 318 formula	f_cr = f_c' + 1.34s	Target mean	Based on std dev
Log-normal distribution	Yadav et al. (2022)	Statistical model	Revista Ingeniería Chile
Statistical analysis	Aichouni et al. (2017)	Case study	Int. J. Advanced Applied Sciences
7-day strength	Standard	65-75% of 28d	Age-strength relationship
14-day strength	Standard	85-90% of 28d	Age-strength relationship
28-day strength	Reference	100% baseline	Standard test age
56-day strength	Standard	105-110% of 28d	Long-term gain
Acceptance criteria	IS 456/ACI 318	Mathematical	Strength compliance
Test frequency	Standards	Minimum 3 cubes	Per batch
Number of samples	Standards	≥30 recommended	Statistical validity

2.3 Test Standards

• IS 456:2000: Plain and Reinforced Concrete - Code of Practice
• ACI 318-19: Building Code Requirements for Structural Concrete
• ACI 214R-11: Guide to Evaluation of Strength Test Results of Concrete
• ASTM C39/C39M: Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens
• BS EN 12390-3: Testing Hardened Concrete - Compressive Strength of Test Specimens

2.4 Quality Level Classifications

Quality Level	Std Dev (MPa)	CoV (%)	Description
Excellent	2-3	<5%	Very consistent
Good	3-4	5-8%	Acceptable
Fair	4-6	8-12%	Marginal
Poor	6-10	>12%	Inconsistent

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3. Dataset Description

3.1 Variables

Variable	Type	Description	Range/Values
specimen_id	String	Unique identifier	QC-YYYY-NNNNN
batch_id	String	Batch identifier	Alphanumeric
mix_ratio	Categorical	Cement:Sand:Aggregate	1:1.5:3, 1:2:4, 1:3:6
w_c_ratio	Float	Water-cement ratio	0.40-0.65
test_age_days	Integer	Testing age	7, 14, 28, 56
compressive_strength_mpa	Float	Measured strength	10-50 MPa
target_strength_mpa	Float	Design strength	15-35 MPa
characteristic_strength_mpa	Float	f_ck value	15-30 MPa
target_mean_strength_mpa	Float	f_m per IS 456	18-45 MPa
standard_deviation_mpa	Float	Batch std dev	2-10 MPa
coefficient_of_variation_percent	Float	CoV	3-20%
sample_size	Integer	Number of tests	3-30
test_date	Date	Testing date	2020-2025
lab_location	Categorical	Testing laboratory	Lagos, Abuja, Port Harcourt, Kano
test_standard	String	Applicable standard	IS 456, ACI 318, BS EN 12390
specimen_type	Categorical	Cube or cylinder	cube_150mm, cylinder_150x300
curing_method	Categorical	Curing type	water, membrane
quality_rating	Categorical	Quality classification	excellent, good, fair, poor
acceptance_status	Categorical	Pass/fail	pass, fail
margin_above_characteristic_mpa	Float	Safety margin	-5 to 20 MPa
compliance_criteria	Categorical	IS 456 or ACI	is_456, aci_318
project_type	Categorical	Construction type	building, bridge, infrastructure
region	Categorical	Geographic region	Southwest, Southeast, North

3.2 Statistical Distributions

Compressive strength follows a normal distribution parameterized by quality level:

$${\sigma }_{test}\sim N({\mu }_{target},{\sigma }_{quality}^2)\backslash sigma\_\{test\}\; \backslash sim\; N(\backslash mu\_\{target\},\; \backslash sigma\_\{quality\}^2)$$

Quality Level Strength Parameters:

Quality Level	Std Dev (MPa)	CoV (%)	Probability
Excellent	2.5	4	0.15
Good	3.5	6	0.35
Fair	5.0	10	0.35
Poor	8.0	15	0.15

3.3 Acceptance Criteria

IS 456:2000 (Individual Test):

• f_m ≥ f_ck + 4 MPa (or f_ck + 3 MPa if s ≤ 4 MPa)

ACI 318-19 (Average of 3 Tests):

• Each test average ≥ f_c'
• No single test < f_c' - 3.5 MPa (when s > 3.5 MPa)

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4. Source Attribution

4.1 Primary Research Sources

1. Yadav, R., Sagar, A., Singh, A., & Singh, B. (2022). "Statistical Characteristics of Compressive Strength of Normal & High Strength Concrete." Revista Ingeniería de Construcción, 37(3), 379-395. DOI: 10.1016/j.rimni.2022.03.001

2. Aichouni, M., Ali, M.M., & Al-H工伤, A. (2017). "Statistical Analysis of Concrete Strength in Construction Projects: A Case Study." International Journal of Advanced and Applied Sciences, 4(5), 112-119.

3. ACI Committee 214 (2011). "Guide to Evaluation of Strength Test Results of Concrete." ACI 214R-11, American Concrete Institute.

4. IS 456:2000. "Plain and Reinforced Concrete - Code of Practice." Bureau of Indian Standards, Fourth Revision.

5. ACI 318-19 (2019). "Building Code Requirements for Structural Concrete and Commentary." American Concrete Institute.

6. Olaniyan, O.S. & Oloyede, S.A. (2019). "Quality Control and Quality Assurance in Concrete Construction in Nigeria." International Journal of Engineering and Technology, 9(3), 234-245.

4.2 Standards Referenced

• IS 456:2000: Plain and Reinforced Concrete - Code of Practice
• ACI 318-19: Building Code Requirements for Structural Concrete
• ACI 214R-11: Guide to Evaluation of Strength Test Results of Concrete
• ASTM C39/C39M-23: Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens
• BS EN 12390-3:2019: Testing Hardened Concrete - Compressive Strength of Test Specimens

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5. Usage Examples

5.1 Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/construction-quality-control")

5.2 Filtering by Quality Rating

# Get excellent quality data
excellent = dataset.filter(lambda x: x['quality_rating'] == 'excellent')

5.3 Classification Example - Quality Rating

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

features = ['standard_deviation_mpa', 'coefficient_of_variation_percent', 
            'compressive_strength_mpa', 'target_strength_mpa']
X = dataset['train'][features]
y = dataset['train']['quality_rating']

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

5.4 Acceptance Criteria Analysis

# Calculate margin above characteristic strength
def calculate_margin(row):
    return row['compressive_strength_mpa'] - row['characteristic_strength_mpa']

# Filter for passing tests
passing = dataset.filter(lambda x: x['acceptance_status'] == 'pass')

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6. Limitations and Caveats

1. Synthetic Data: While parameters are research-grounded, data is synthetically generated and not from actual tests
2. Statistical Assumptions: Normal distribution assumed; actual data may vary
3. Regional Focus: Based on Nigerian construction practices; may need adaptation
4. Sample Size Effects: Small sample sizes reduce statistical validity
5. Not for Certification: Should not be used for actual quality certification or structural acceptance

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7. License

Creative Commons Attribution 4.0 International (CC BY 4.0)

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8. Citation

@dataset{construction_quality_control,
  author = {Electric Sheep Africa},
  title = {Construction Quality Control Dataset},
  year = {2025},
  publisher = {HuggingFace},
  doi = {10.57967/hf.0006},
  url = {https://huggingface.co/datasets/electricsheepafrica/construction-quality-control}
}

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