USE OF CORRELATIONS AND REGRESSION ANALYSIS AS STATISTICAL TOOLS IN GREEN CONCRETE RESEARCH

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USE OF CORRELATIONS AND REGRESSION ANALYSIS AS STATISTICAL TOOLS IN GREEN CONCRETE RESEARCH

Abstract:

This research explores the application of correlations and regression analysis as statistical tools in the investigation of green concrete. Green concrete, characterized by its eco-friendly composition and production methods, has gained prominence in sustainable construction practices. The study employs correlations to establish relationships between various parameters and regression analysis to model the influence of key factors on the properties of green concrete.

The research methodology involves the formulation of green concrete mixtures with different sustainable additives, such as supplementary cementitious materials, recycled aggregates, and chemical admixtures. Comprehensive testing is conducted to assess the mechanical, durability, and environmental performance of the green concrete. Correlation analysis is employed to identify interdependencies among different variables, while regression analysis is used to develop predictive models for key concrete properties.

The findings from correlations and regression analysis offer insights into the intricate relationships between sustainable additives and the resulting properties of green concrete. These statistical tools contribute to a nuanced understanding of how specific parameters influence the overall performance of green concrete mixes. The research aims to guide future developments in green concrete technology by providing a quantitative basis for optimizing mix designs and enhancing sustainability in the construction industry.

Chapter One:

Introduction

1.1 Background

Concrete, as a ubiquitous construction material, has undergone continuous innovation and development to meet the growing demands of sustainable and environmentally friendly construction practices. The advent of green concrete, incorporating alternative materials and sustainable production methods, marks a significant shift towards reducing the environmental impact of traditional concrete production. In the realm of green concrete research, statistical tools such as correlations and regression analysis play a crucial role in understanding the relationships between various factors influencing concrete properties.

Concrete is a versatile construction material that can be manufactured using locally available materials like crushed stone, river sand and water. It is very popular when the fact is considered that around the world approximately twice as much concrete is used in construction than the total of all other building materials, such as, steel, plastic, wood, and aluminium. Its compressive strength is an important aspect in deciding its load carrying capacity [1]. Concrete according to Worrell as cited by [2] is the second most consumed entity after water and accounts for 5% of the world’s total CO2 emission as a result of the production of cement as one of its major ingredient and hence a threat to the environment, prompting the search for other materials that are environmental-friendly (green concrete).

Green concrete is defined as a concrete which uses waste material as at least one of its components, or its production process does not lead to environmental destruction, or it has high performance and life cycle sustainability [3]. Green concrete has nothing to do with colour. It is a concept of thinking environment into concrete considering every aspect from raw materials manufactured over mixture design to structural design, construction, and service life [4]. Green concrete is very cheap to produce because waste products are often used to make them; also charges for the disposal of waste are avoided as an added benefit [5]. The emergence of green concrete has to a large extent curb the environmental problem arising from unscientific and indiscriminate disposal of municipal solid waste, which is a real menace for the whole society. These wastes are increasing day by day due to increase in population, urbanisation and industrialisation. The characterisation of municipal solid waste according to Sharholy as cited by [6] shows that it contains about 55–65% of compostable material, 25–35% of dry/recyclable materials and 15-20% of inert material.

Efforts have been made in recent decades to develop “green” concretes containing industrial waste [7]. It is well known that in such green concretes cement has been partially replaced by industrial and/or agricultural byproducts such as fly ash, ground granulated blast furnace slag, metakaolin, rice husk ash, etc., which are considered as supplementary cementitious materials (SCMs). The replacement of cement by using SCMs not only decreases the landfills of waste materials and their associated environmental impacts, but also reduces the carbon footprint of concrete. In general, SCMs can be used to improve the mechanical properties of concrete, either in fresh or hardened mixtures [7].

According to [8], correlation measures the degree of linear association between two or more variables when a movement in one variable is associated with the movement in the other variable either in the same direction or the other direction. And regression analysis is the study of the nature and extent of association between two or more variables on the basis of the assumed relationship between them with a view to predict the value of one variable from the other. Correlation and Regression are two analyses that are based on multivariate distribution. Correlation is described as the analysis which lets us know the association or the absence of the relationship between two variables ‘x’ and ‘y’. On the other end, Regression analysis, predicts the value of the dependent variable based on the known value of the independent variable, assuming that average mathematical relationship between two or more variables exist [9]. People use regression on an intuitive level every day. In business, a welldressed man is thought to be financially successful. A mother knows that more sugar in her children’s diet results in higher energy levels. The ease of waking up in the morning often depends on how late you went to bed the night before. Quantitative regression adds precision by developing a mathematical formula that can be used for predictive purposes.

Correlation and regression as analytical tools use in statistics have been taught in so many institutions over the years especially in physical sciences and engineering fields; have been used in different works of life like industries and businesses; a lot of books and articles. have been written and published to make people understand correlation and regression analyses but have been seldomly applied in science and engineering academic research. This paper focuses on using linear correlation and linear and non-linear regression to analyse the results of the mechanical and durability properties of green concrete and to also show science and engineering researchers a possible way of applying these analytical tools in their future research in order to explain and predict the property been researched.

1.2 Rationale

The development and optimization of green concrete formulations involve a complex interplay of multiple variables, including the type and proportion of alternative materials, curing methods, and environmental conditions. Correlations and regression analysis provide quantitative insights into the dependencies and interactions among these variables, allowing researchers and practitioners to make informed decisions during the concrete mix design process. By applying statistical tools, researchers can identify key factors affecting the performance of green concrete and optimize its properties for specific applications.

1.3 Research Aim and Objectives

The primary aim of this research is to explore the use of correlations and regression analysis as statistical tools in green concrete research. The specific objectives include:

To examine the relationships between various components of green concrete mixtures.

To quantify the impact of alternative materials on specific concrete properties using statistical models.

To assess the predictive capabilities of regression analysis in estimating green concrete performance.

1.4 Significance of the Study

This study holds significance in several dimensions:

Optimization of Green Concrete Formulations: Understanding correlations between different components allows for the optimization of green concrete mixtures, maximizing both sustainability and performance.

Data-Driven Decision Making: Statistical analysis provides a data-driven approach to decision-making in green concrete research, offering insights into the most influential factors.

Advancement of Sustainable Construction Practices: By utilizing statistical tools, this research contributes to the advancement of sustainable construction practices, supporting the broader goals of environmentally conscious infrastructure development.

1.5 Scope of the Study

The research focuses on the application of correlations and regression analysis specifically in the context of green concrete research. The scope encompasses the analysis of relationships between alternative materials (such as fly ash, slag, and recycled aggregates), curing methods, and various concrete properties, including compressive strength, durability, and workability.

1.6 Research Methodology

The study adopts a systematic research methodology involving:

Literature Review: A comprehensive review of existing literature on green concrete research and the application of statistical tools.

Data Collection: Collection of experimental data from relevant studies and laboratory tests on green concrete mixtures.

Correlation and Regression Analysis: Application of statistical techniques to analyze relationships and build predictive models.

Validation: Validation of the models through comparison with additional experimental data and industry standards.

1.7 Organization of the Thesis

The thesis is organized into several chapters, each addressing specific aspects of the research. Chapter Two provides an extensive review of the literature on green concrete research and statistical tools. Chapter Three outlines the research methodology, detailing the data collection process and the application of correlations and regression analysis. Subsequent chapters present and analyze the findings, discuss their implications, and conclude with recommendations for further research and practical applications.

In conclusion, this introductory chapter sets the stage for the exploration of correlations and regression analysis as indispensable tools in green concrete research. By delving into the relationships between various components and properties, this research aims to contribute to the ongoing discourse on sustainable construction practices and the optimization of green concrete formulations.

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