# Utilization of Six Sigma in Quality Improvement of Recycled Aggregates Concrete

Authors: Mohamad Terro; Moetaz El-Hawary; Rana Al-Fares; Mark Goldstein
Journal: Journal of Waste Management
 (2013)

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## Abstract

The use of recycled demolished concrete in producing new concrete is an established method to improve sustainability through reducing the environmental impact of using virgin aggregates and through reducing construction waste. Six sigma is a set of tools and strategies for process improvement. In this paper, the six sigma DMAIC methodology is utilized to optimize the design parameters in order to improve and assure the quality of the resulting recycled aggregate concrete. The project aims to produce concrete with compressive strength of 25 MPa without using additives. Five parameters were used in the initial analysis that were reduced to three after refinement. The refined parameters are the ratio of recycled coarse aggregates, the ratio of recycled fine aggregates, and the water/cement ratio. It was concluded that the optimum values for the three parameters are 26%, 30%, and 0.5, in order.


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## Body

## 1. Introduction

Despite the many advances in technology including material technology, concrete is still the premier construction material in all types of civil engineering works, including low- and high-rise building, water retaining structures, bridges, and general infrastructure. Concrete is a manufactured product, essentially consisting of cement, aggregates, water, and possibly admixtures, additives, or agents. Among these constituents, aggregates including sand, crushed stone, or gravel form the major part by volume. Traditionally, aggregates have been readily available at economic prices and of qualities to suit all purposes. However, there has been a rising awareness to the environmental damage caused by quarries producing such primary aggregates. Therefore, countries with major cycles of demolition and construction works, such as the Arabian Gulf region, including Kuwait, are exploring ways to use recycled aggregates produced from such activities.Since its establishment in 2001, the Environment Preservation Industrial Company (EPIC) in Kuwait has been cooperating with major stakeholders in the private and government construction sectors to encourage the utilization of recycled construction materials in projects around the country. The company receives the debris from demolished civil constructions and utilizes global production technologies with standard specifications to provide the construction market with quality recycled material mainly consisting of fine and coarse recycled aggregates.In addition to the many benefits to the environmental preservation envisaged by the use of recycled aggregates in civil works, financial savings are also incurred from reduction in transport and production energy costs. Reduction in waste landfills is a further important benefit to this trend.The subject of the use of recycled materials as aggregate replacement in concrete had received considerable research attention over the last decade. Richardson et al. [1] investigated the possibility of achieving concrete made with un-graded recycled aggregates with a comparable strength to that of concrete made with virgin aggregates (control sample).Dosho [2] developed a recycling system in which he replaced normal aggregates with recycled aggregates from construction wastes whilst ensuring safety, quality, and cost effectiveness. In his study, he stressed on the tremendous impact on the environment from applying his proposed method to waste generated from the demolition of large scale buildings such as powerhouses.Acknowledging that concrete from construction demolition sites apportions more than half of the total wastes in Hong Kong, Tam and Wang [3] utilized a series of laboratory tests to set out some guidelines that would facilitate the use of RAC in the construction industry. They concluded their paper with highlights on potential reduction in the quality of RAC, a classification of RA for various construction applications, and alerts users of possible extra slump loss due to the use of RAC.Moreover, a number of statistically based approaches have emerged in many studies on the mixture design of concrete [4, 5]. In such research studies, mostly a full- or fractional-factorial experimental setup approach is used to calibrate optimal values of concrete strength. Simon used a statistical approach to develop an internet-based software program to optimize concrete mixture proportions [6]. Soudki et al. used a full-factorial experimental study to optimize mix proportions that would produce concretes least sensitive to temperatures in hot climate [7]. They used the water/cement ratio, coarse/total aggregate ratio, total aggregate/cement ratio, and temperature as their independent variables for calibrating the strength of concrete.In almost all literature studies above, focus on the average value obtained from sample repetition of quality indicators (i.e., compressive strength) has been maintained. However, latest quality assurance (QA) methods such as six sigma stress the importance of controlling both average values and dispersion of the data for achieving quality levels of the output or “response variable,” where reaching a preset average value ensures accuracy, while maintaining a small standard deviation precision ensures precision. In other words, previous research has used parts of QA methods, namely, mathematical optimizations, to reach desired results rather than using an overall holistic approach to their studies.Six sigma is a structured data-driven statistical approach used to improve processes and reduce defects that would eventually lead to poor products. Originaing in 1985, by Bill Smith, a senior quality assurance engineer at Motorola, six sigma was responsible for earning the company the “Malcolm Baldrige National Quality Award” in 1988. It was not until Jack Welch, the CEO of General Electric (GE), who adopted six sigma in January 1996 and implemented it at a company-wide scale, that the methodology gained its wide reputation and popularity for making breakthroughs in quality and profits and reducing defects. The methodology is acclaimed to be applicable to almost all processes that are repetitive, with a measurable input and well-defined output. Although it is more adopted to processes in manufacturing and business environments, six sigma application has been successfully applied to many other fields including services and scientific deterministic research.This research aims to recruit a holistic QA approach using the six sigma methodology to find optimal concrete mixes for concretes made with recycled aggregates from building construction wastes. The report will be organized according to the phases and steps of the six sigma methodology (referred to as DMAIC).


## 2. Define

### 2.1. Articulate the Problem

Concrete mixture proportion is a major factor in controlling the behavior and properties of concrete structures during their service life. Currently, the use of recycled aggregates from concrete building construction is not as popular as it should be to achieve the sustainability trends in new construction industries. This could be due to the lack of understanding and confidence in its mechanical properties and behavior under service and ultimate conditions.This research undertakes sheding light on the behavior of recycled aggregate concrete made with various mix proportions in addition to finding optimal conditions which would improve its average strength. Statistical tools of the six sigma methodology will be utilized to achieve this purpose.From a six sigma perspective, typically, compressive strength average values of concrete mixed at the Environment Preservation Industrial Company (EPIC) in Kuwait have an average of nearly 15 MPa. This project aims at increasing this average value to above 25 MPa without using admixtures, additives, or agents.


### 2.2. Define Response Variables

The properties of concrete are divided into fluid or plastic and hardened phase properties. In its fluid or plastic phase, concrete properties include setting times, specific gravity, and slump value. The compressive strength, tensile or modulus of fracture, and permeability are properties of its hardened phase. All of the aforementioned properties are quality indicators of concrete as a building material and may independently or collectively serve as output or response variable for specific uses of construction during its erection or service life. However, as supported by the literature, the compressive strength is the most indicative value of the quality of performance of concrete in almost all tests.Therefore, the response variable for this project has been selected as the compressive strength of concrete at 28. For further scientific investigations, future projects might bring different response variable under the scrutiny of the six sigma method.


### 2.3. Set Project Goals

In general, the project goals aim at encouraging the use of concrete made with aggregates recycled from construction wastes. This could be achieved by conducting studies that demonstrate the possibility of obtaining concretes with adequate quality through carefully considered mix design. In the research subject of this paper, based on previous literature studies in Kuwait, the direct goal is to obtain an average compressive cube strength of concrete made with recycled aggregates from construction wastes of 25 MPa whilst keeping the standard variation to a minimal value. Since previous studies did not consider the values of the standard deviation, it was not possible to establish a preset value to target at this stage.


### 2.4. Draw Process Map

A simple typical process map describing the various steps involved in the process of producing the concrete made with recycled aggregates is given in Figure1.Figure 1
Process map of producing concrete made with recycled Aggregates.


## 2.1. Articulate the Problem

Concrete mixture proportion is a major factor in controlling the behavior and properties of concrete structures during their service life. Currently, the use of recycled aggregates from concrete building construction is not as popular as it should be to achieve the sustainability trends in new construction industries. This could be due to the lack of understanding and confidence in its mechanical properties and behavior under service and ultimate conditions.This research undertakes sheding light on the behavior of recycled aggregate concrete made with various mix proportions in addition to finding optimal conditions which would improve its average strength. Statistical tools of the six sigma methodology will be utilized to achieve this purpose.From a six sigma perspective, typically, compressive strength average values of concrete mixed at the Environment Preservation Industrial Company (EPIC) in Kuwait have an average of nearly 15 MPa. This project aims at increasing this average value to above 25 MPa without using admixtures, additives, or agents.


## 2.2. Define Response Variables

The properties of concrete are divided into fluid or plastic and hardened phase properties. In its fluid or plastic phase, concrete properties include setting times, specific gravity, and slump value. The compressive strength, tensile or modulus of fracture, and permeability are properties of its hardened phase. All of the aforementioned properties are quality indicators of concrete as a building material and may independently or collectively serve as output or response variable for specific uses of construction during its erection or service life. However, as supported by the literature, the compressive strength is the most indicative value of the quality of performance of concrete in almost all tests.Therefore, the response variable for this project has been selected as the compressive strength of concrete at 28. For further scientific investigations, future projects might bring different response variable under the scrutiny of the six sigma method.


## 2.3. Set Project Goals

In general, the project goals aim at encouraging the use of concrete made with aggregates recycled from construction wastes. This could be achieved by conducting studies that demonstrate the possibility of obtaining concretes with adequate quality through carefully considered mix design. In the research subject of this paper, based on previous literature studies in Kuwait, the direct goal is to obtain an average compressive cube strength of concrete made with recycled aggregates from construction wastes of 25 MPa whilst keeping the standard variation to a minimal value. Since previous studies did not consider the values of the standard deviation, it was not possible to establish a preset value to target at this stage.


## 2.4. Draw Process Map

A simple typical process map describing the various steps involved in the process of producing the concrete made with recycled aggregates is given in Figure1.Figure 1
Process map of producing concrete made with recycled Aggregates.


## 3. Measure

### 3.1. Validate Measurement Systems

Many measurement-related factors could affect the physical, mechanical, and chemical properties of concrete in its plastic or hardened stage. A measurement validation study that would consider most of those factors would be an excellent candidate for a future research. This should include issues related to operators, mixing and testing tools and machines, environment, and quality inspection methods at the source (e.g., EPIC). In this research, however, we shall restrict our measurement validation study to the definition of factors that are used in the testing.Testing of the concrete strength involved cubes of dimension 100 × 100 × 100 mm3, tested at 28 days. Despite the fact that many of the previous researches employed admixtures to their mixes to increase the value of the compressive strength, it was decided to exclude the use of admixture since it might mask the various effects from other variables on the concrete in addition to the impracticality of admixtures in large scale concrete projects.As for the current visual inspection method employed at EPIC, it involves a rudimentary inspection by an operator at the gate of the company site who searches by eye for the extent of impurities in the content of loaded trucks while standing in his post. The operator then passes a judgment on the quality of the construction waste as best, average, or worst quality. This inspection method is hardly accurate since it is highly dependent on individual operators who could pass different judgments and does not account for other highly important qualities that are not visible to the naked eye. Such invisible properties include environmental condition of the source of the construction wastes, their age, chloride and sulfate contents, and initial quality of concrete such as type of aggregate and mix proportions. The inspection for classification of quality and properties of construction waste is a highly important factor and worthy of a research study on its own.


### 3.2. Collect Data on Response Variable(s)

There exist a large number of factors that could affect the properties of RAC which could be considered as independent variables for this study. Possible independent variables include the factors shown in Table1. After a careful examination of the independent variables in Table 1 and taking into consideration the scope of work set out in the define stage, the list for possible major impact factors has been reduced to curing, % coarse RA, % fine RA, w/c ratio, and quality of RA (as shown in the italic cells in Table 1).Table 1
Independent variables affecting the strength of RAC.
Quality
Proportions
Mixing
Testing
Age of demolished construction
w/c ratio
Environmental temperature
Curing
Chloride and  sulfate content of RA
% of coarse RA
Mixing tools
Sample size
% of impurities in RA
% of fine RA
Mixing method
Testing tools and  machines
Quality of RA
Cement content
Operators
Operators
Type of RA
Cement type
Water saturation condition of RA
Age at testing
Additives
Admixtures or agents
Size of RA
Aspect ratio of RA


### 3.3. Establish Baseline

Many researches, including those made at Kuwait University, employed admixtures to boost the compressive strength above values that are marketed by aggregates recycling companies. Such values exceeded 40 MPa for the compressive strength with standard deviations around 20% of the strength value [8]. The average value of cube strength marketed values by EPIC ranges roughly between 10 and 20 MPa or 25 MPa at best. Since we are only interested in the basic or raw values of the compressive strength, which are the values used for marketing RA product by EPIC, we did not include any additives, admixtures, or agents in the concrete mixes to augment the average value of the strength results. Therefore, the results of any previous studies at Kuwait University or from EPIC were only used as indicative values to demonstrate relations and trends and to help in approximating baseline values for our six sigma study. The authors could not find strong supportive results from EPIC based on a systematic scientific approach to help in establishing baseline.The authors were not successful in obtaining reliable previous test results from EPIC to get current defect levels. Therefore, based on private communication with EPIC, a current average value of 15 MPa is assumed as a baseline prior to the implementation of six sigma.


## 3.1. Validate Measurement Systems

Many measurement-related factors could affect the physical, mechanical, and chemical properties of concrete in its plastic or hardened stage. A measurement validation study that would consider most of those factors would be an excellent candidate for a future research. This should include issues related to operators, mixing and testing tools and machines, environment, and quality inspection methods at the source (e.g., EPIC). In this research, however, we shall restrict our measurement validation study to the definition of factors that are used in the testing.Testing of the concrete strength involved cubes of dimension 100 × 100 × 100 mm3, tested at 28 days. Despite the fact that many of the previous researches employed admixtures to their mixes to increase the value of the compressive strength, it was decided to exclude the use of admixture since it might mask the various effects from other variables on the concrete in addition to the impracticality of admixtures in large scale concrete projects.As for the current visual inspection method employed at EPIC, it involves a rudimentary inspection by an operator at the gate of the company site who searches by eye for the extent of impurities in the content of loaded trucks while standing in his post. The operator then passes a judgment on the quality of the construction waste as best, average, or worst quality. This inspection method is hardly accurate since it is highly dependent on individual operators who could pass different judgments and does not account for other highly important qualities that are not visible to the naked eye. Such invisible properties include environmental condition of the source of the construction wastes, their age, chloride and sulfate contents, and initial quality of concrete such as type of aggregate and mix proportions. The inspection for classification of quality and properties of construction waste is a highly important factor and worthy of a research study on its own.


## 3.2. Collect Data on Response Variable(s)

There exist a large number of factors that could affect the properties of RAC which could be considered as independent variables for this study. Possible independent variables include the factors shown in Table1. After a careful examination of the independent variables in Table 1 and taking into consideration the scope of work set out in the define stage, the list for possible major impact factors has been reduced to curing, % coarse RA, % fine RA, w/c ratio, and quality of RA (as shown in the italic cells in Table 1).Table 1
Independent variables affecting the strength of RAC.
Quality
Proportions
Mixing
Testing
Age of demolished construction
w/c ratio
Environmental temperature
Curing
Chloride and  sulfate content of RA
% of coarse RA
Mixing tools
Sample size
% of impurities in RA
% of fine RA
Mixing method
Testing tools and  machines
Quality of RA
Cement content
Operators
Operators
Type of RA
Cement type
Water saturation condition of RA
Age at testing
Additives
Admixtures or agents
Size of RA
Aspect ratio of RA


## 3.3. Establish Baseline

Many researches, including those made at Kuwait University, employed admixtures to boost the compressive strength above values that are marketed by aggregates recycling companies. Such values exceeded 40 MPa for the compressive strength with standard deviations around 20% of the strength value [8]. The average value of cube strength marketed values by EPIC ranges roughly between 10 and 20 MPa or 25 MPa at best. Since we are only interested in the basic or raw values of the compressive strength, which are the values used for marketing RA product by EPIC, we did not include any additives, admixtures, or agents in the concrete mixes to augment the average value of the strength results. Therefore, the results of any previous studies at Kuwait University or from EPIC were only used as indicative values to demonstrate relations and trends and to help in approximating baseline values for our six sigma study. The authors could not find strong supportive results from EPIC based on a systematic scientific approach to help in establishing baseline.The authors were not successful in obtaining reliable previous test results from EPIC to get current defect levels. Therefore, based on private communication with EPIC, a current average value of 15 MPa is assumed as a baseline prior to the implementation of six sigma.


## 4. Analyze

### 4.1. Design Experiments and Collect Data on Potential Causes and Response Variable(s)

Further to the identification of potential impact factors in the “measure” phase earlier, a preliminary design of experiment (DOE) program has been set up to study the various effects on the compressive strength. The variables considered are described below.Quality of RA. Based on communication with EPIC, 2 RA qualities are generally considered, “best” and “worst.” The identification between the two types is performed through visual inspection by an operator, with the “worst” being the batches containing lots of impurities such as woods and plastics.Percentage of Coarse and Fine RA. The percentages of RA used in the mix will be for each of the fine and coarse recycled aggregates as follows: 25% and 75%.Water/Cement Ratios. Two w/c ratios will be considered 0.5 and 0.7, in the study.Curing. Air and 100% RH curing conditions are considered.The two-level DOE setup is summarized in Table2.Table 2
Summary of the two-level preliminary DOE design.
Levels
Impact  factor
Quality of RA aggregates
w/c
% Recycled coarse aggregate
% Recycled fine aggregate
Curing
High
1 (best)
0.5
25%
25%
RH100%
Low
2 (worst)
0.7
75%
75%
AirThe total number of samples used will be, therefore, 25 = 32. Seven repetitions have been used for each set of variables, which brings the total number of samples to 224. The sizes of the tested cube samples were 100 × 100 × 100 mm3. Based on Table 2 and since curing and quality of aggregates do not affect the mix design, a total of 8 (23) mix proportions have been designed, as shown in Table 3.Table 3
Factors for the different mix design used in the preliminary DOE program.
Mix
w/c
% Coarse RA
% Fine RA
1
0.5
25
25
2
0.5
25
75
3
0.5
75
25
4
0.5
75
75
5
0.7
25
25
6
0.7
25
75
7
0.7
75
25
8
0.7
75
75The designs for each of the mixes in Table4 have been pretested in the laboratory before being finalized. The mix proportions of each mix design are shown in Table 4.Table 4
Mix proportion by weight of the mixes used in the preliminary DOE program.
Mix
Cement
Water
Normal coarse aggregate (1/2)
Normal coarse aggregates(3/4)
Normal coarse aggregates(3/8)
Recycled coarse aggregates (1/2)
Recycled coarse aggregates (3/4)
Recycled coarse aggregates (3/8)
Normal fine aggregates
Recycled fine aggregates
1
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
2
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
3
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
4
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
5
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
6
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
7
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
8
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5


### 4.2. Identify Major Impact Factors

The experiment begins as a five-factor, two-level, full-factorial experiment. Each experimental run had the benefit of 7 repetitions. Using this data for the analysis, it was possible to compute an average and a standard deviation to represent the performance of the process for each run of the experiment.The results from this first batch of the experiment using two-level full-factorial design described in the previous section are presented in Figure2. A descriptive statistical summary of the result data points is shown in Table 5.Table 5
Descriptive statistics: C2.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C2
224
0
31.247
0.589
8.810
5.800
24.300
31.150
38.075
51.000Figure 2
Histogram of compressive strength results from the two-level first test batch.A histogram and descriptive statistics of the standard deviation of the results from the first batch of test are shown in Figure3 and Table 6.Table 6
Descriptive statistics: C3.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C3
32
0
2.981
0.226
1.276
1.038
2.098
2.708
3.796
6.973Figure 3
Histogram of the standard deviation results from the two-level first test batch.The initial analysis focused on the process average. Because only one replicate was used to represent the model, the first analysis does not include aP value. The statistical analysis is done using the Pareto of effects. In the first pass, the threshold for statistical significance was set at α=0.05. Due to the amount of variation observed in the results, the threshold was changed to α=0.10.Figures4 and 5 show the effect Pareto charts for the average and the standard deviation standardized values, respectively.Figure 4
Main effect Pareto chart of the compressive strength.Figure 5
Main effect Pareto chart of the standard deviation.Pareto charts for the strength and standard deviation including more terms are shown in Figures6 and 7, respectively.Figure 6
Main effect Pareto chart of the compressive strength showing 30 largest terms.Figure 7
Main effect Pareto chart of the compressive strength including second order terms.Based on the analysis of the graphs above, the significant main effect terms on the compressive strength were B, C, and D (w/c, % recycled coarse aggregates, and % recycled fine aggregates, resp.). Term A (quality) appears to be significant in affecting standard deviation. This is understandable due to the unpredictability of the quality of recycled aggregates and looseness in the definition of “best” and “worst” qualities. Second order terms that appeared to be statistically significant included AB and BD, where A refers to “quality.” Because there was also a significant 2nd order effect that included A (quality), we elected to include the main effect of A as a term in the next analysis with reduced terms.Figures8 and 9 show a further analysis of the results based on the main effect plots and interaction plots, respectively, for the compressive strength means. The plots in those figures indicate the correlation between the four shown effects on the compressive strength, the strongest being with the w/c ratio, whilst the lowest appeared to the effect “quality.”Figure 8
Main effect plots of the compressive strength.Figure 9
Interaction plots of the compressive strength.Therefore, as a final conclusion of the analysis presented above, it could be concluded that the effects considered in this study could be reduced to three major impact factors as follows:(i)
% recycled fine aggregates,(ii)
% recycled coarse aggregates,(iii)
w/c ratio.Therefore, a second study has been designed to complement the previous study as described in Table7 with 3 levels to account for nonlinearities in the relations between the factors above and the average and standard variations of the compressive strength.Table 7
Summary of the three-level full-factorial DOE design.
Levels
Impact  factor
Quality of aggregates
w/c
% Recycled coarse  aggregate
% Recycled fine aggregate
Curing
High
BEST
0.5
25%
25%
RH100%
Medium
0.6
50%
50%
Low
0.7
75%
75%The study above necessitated 27 different mix designs, the details of which are summarized in Table8.Table 8
Mix proportions by weight of the mixes used in the 3-level full-factorial DOE program.
Mix
Cement
Water
Normal coarse aggregate (1/2)
Normal coarse aggregates(3/4)
Normal coarse aggregates(3/8)
Recycled coarse aggregates (1/2)
Recycled coarse aggregates (3/4)
Recycled coarse aggregates (3/8)
Normal fine aggregates
Recycled fine aggregates
1
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
2
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
3
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
4
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
5
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
6
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
7
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
8
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
9
2.16
4.33
2.2
2.2
2.2
0.725
0.725
0.725
2
2
10
2.16
4.33
0.725
0.725
0.725
2.2
2.2
2.2
2
2
11
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
3
1
12
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
2
2
13
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
1
3
14
1.8
3.05
2.2
2.2
2.2
0.725
0.725
0.725
3
2
15
1.8
3
2.2
2.2
2.2
0.725
0.725
0.725
3
1
16
1.8
3
2.2
2.2
2.2
0.725
0.725
0.725
1
3
17
1.8
3
1.5
1.5
1.5
1.5
1.5
1.5
3
1
18
1.8
3.05
1.5
1.5
1.5
1.5
1.5
1.5
2
2
19
1.8
3
1.5
1.5
1.5
1.5
1.5
1.5
1
3
20
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
3
1
21
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
2
2
22
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
1
3
23
1.8
2.6
2.2
2.2
2.2
0.725
0.725
0.725
2
2
24
1.8
2.6
0.725
0.725
0.725
2.2
2.2
2.2
2
2
25
1.8
2.6
1.5
1.5
1.5
1.5
1.5
1.5
3
1
26
1.8
2.6
0.725
0.725
0.725
2.2
2.2
2.2
2
2
27
1.8
2.6
1.5
1.5
1.5
1.5
1.5
1.5
1
3


## 4.1. Design Experiments and Collect Data on Potential Causes and Response Variable(s)

Further to the identification of potential impact factors in the “measure” phase earlier, a preliminary design of experiment (DOE) program has been set up to study the various effects on the compressive strength. The variables considered are described below.Quality of RA. Based on communication with EPIC, 2 RA qualities are generally considered, “best” and “worst.” The identification between the two types is performed through visual inspection by an operator, with the “worst” being the batches containing lots of impurities such as woods and plastics.Percentage of Coarse and Fine RA. The percentages of RA used in the mix will be for each of the fine and coarse recycled aggregates as follows: 25% and 75%.Water/Cement Ratios. Two w/c ratios will be considered 0.5 and 0.7, in the study.Curing. Air and 100% RH curing conditions are considered.The two-level DOE setup is summarized in Table2.Table 2
Summary of the two-level preliminary DOE design.
Levels
Impact  factor
Quality of RA aggregates
w/c
% Recycled coarse aggregate
% Recycled fine aggregate
Curing
High
1 (best)
0.5
25%
25%
RH100%
Low
2 (worst)
0.7
75%
75%
AirThe total number of samples used will be, therefore, 25 = 32. Seven repetitions have been used for each set of variables, which brings the total number of samples to 224. The sizes of the tested cube samples were 100 × 100 × 100 mm3. Based on Table 2 and since curing and quality of aggregates do not affect the mix design, a total of 8 (23) mix proportions have been designed, as shown in Table 3.Table 3
Factors for the different mix design used in the preliminary DOE program.
Mix
w/c
% Coarse RA
% Fine RA
1
0.5
25
25
2
0.5
25
75
3
0.5
75
25
4
0.5
75
75
5
0.7
25
25
6
0.7
25
75
7
0.7
75
25
8
0.7
75
75The designs for each of the mixes in Table4 have been pretested in the laboratory before being finalized. The mix proportions of each mix design are shown in Table 4.Table 4
Mix proportion by weight of the mixes used in the preliminary DOE program.
Mix
Cement
Water
Normal coarse aggregate (1/2)
Normal coarse aggregates(3/4)
Normal coarse aggregates(3/8)
Recycled coarse aggregates (1/2)
Recycled coarse aggregates (3/4)
Recycled coarse aggregates (3/8)
Normal fine aggregates
Recycled fine aggregates
1
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
2
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
3
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
4
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
5
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
6
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
7
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
8
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5


## 4.2. Identify Major Impact Factors

The experiment begins as a five-factor, two-level, full-factorial experiment. Each experimental run had the benefit of 7 repetitions. Using this data for the analysis, it was possible to compute an average and a standard deviation to represent the performance of the process for each run of the experiment.The results from this first batch of the experiment using two-level full-factorial design described in the previous section are presented in Figure2. A descriptive statistical summary of the result data points is shown in Table 5.Table 5
Descriptive statistics: C2.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C2
224
0
31.247
0.589
8.810
5.800
24.300
31.150
38.075
51.000Figure 2
Histogram of compressive strength results from the two-level first test batch.A histogram and descriptive statistics of the standard deviation of the results from the first batch of test are shown in Figure3 and Table 6.Table 6
Descriptive statistics: C3.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C3
32
0
2.981
0.226
1.276
1.038
2.098
2.708
3.796
6.973Figure 3
Histogram of the standard deviation results from the two-level first test batch.The initial analysis focused on the process average. Because only one replicate was used to represent the model, the first analysis does not include aP value. The statistical analysis is done using the Pareto of effects. In the first pass, the threshold for statistical significance was set at α=0.05. Due to the amount of variation observed in the results, the threshold was changed to α=0.10.Figures4 and 5 show the effect Pareto charts for the average and the standard deviation standardized values, respectively.Figure 4
Main effect Pareto chart of the compressive strength.Figure 5
Main effect Pareto chart of the standard deviation.Pareto charts for the strength and standard deviation including more terms are shown in Figures6 and 7, respectively.Figure 6
Main effect Pareto chart of the compressive strength showing 30 largest terms.Figure 7
Main effect Pareto chart of the compressive strength including second order terms.Based on the analysis of the graphs above, the significant main effect terms on the compressive strength were B, C, and D (w/c, % recycled coarse aggregates, and % recycled fine aggregates, resp.). Term A (quality) appears to be significant in affecting standard deviation. This is understandable due to the unpredictability of the quality of recycled aggregates and looseness in the definition of “best” and “worst” qualities. Second order terms that appeared to be statistically significant included AB and BD, where A refers to “quality.” Because there was also a significant 2nd order effect that included A (quality), we elected to include the main effect of A as a term in the next analysis with reduced terms.Figures8 and 9 show a further analysis of the results based on the main effect plots and interaction plots, respectively, for the compressive strength means. The plots in those figures indicate the correlation between the four shown effects on the compressive strength, the strongest being with the w/c ratio, whilst the lowest appeared to the effect “quality.”Figure 8
Main effect plots of the compressive strength.Figure 9
Interaction plots of the compressive strength.Therefore, as a final conclusion of the analysis presented above, it could be concluded that the effects considered in this study could be reduced to three major impact factors as follows:(i)
% recycled fine aggregates,(ii)
% recycled coarse aggregates,(iii)
w/c ratio.Therefore, a second study has been designed to complement the previous study as described in Table7 with 3 levels to account for nonlinearities in the relations between the factors above and the average and standard variations of the compressive strength.Table 7
Summary of the three-level full-factorial DOE design.
Levels
Impact  factor
Quality of aggregates
w/c
% Recycled coarse  aggregate
% Recycled fine aggregate
Curing
High
BEST
0.5
25%
25%
RH100%
Medium
0.6
50%
50%
Low
0.7
75%
75%The study above necessitated 27 different mix designs, the details of which are summarized in Table8.Table 8
Mix proportions by weight of the mixes used in the 3-level full-factorial DOE program.
Mix
Cement
Water
Normal coarse aggregate (1/2)
Normal coarse aggregates(3/4)
Normal coarse aggregates(3/8)
Recycled coarse aggregates (1/2)
Recycled coarse aggregates (3/4)
Recycled coarse aggregates (3/8)
Normal fine aggregates
Recycled fine aggregates
1
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
2
5.52
11.04
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
3
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
4
5.52
11.04
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
5
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
2.5
7.5
6
4.6
6.6
5.6
5.6
5.6
1.8
1.8
1.8
7.5
2.5
7
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
2.5
7.5
8
4.6
6.6
1.8
1.8
1.8
5.6
5.6
5.6
7.5
2.5
9
2.16
4.33
2.2
2.2
2.2
0.725
0.725
0.725
2
2
10
2.16
4.33
0.725
0.725
0.725
2.2
2.2
2.2
2
2
11
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
3
1
12
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
2
2
13
2.16
4.33
1.5
1.5
1.5
1.5
1.5
1.5
1
3
14
1.8
3.05
2.2
2.2
2.2
0.725
0.725
0.725
3
2
15
1.8
3
2.2
2.2
2.2
0.725
0.725
0.725
3
1
16
1.8
3
2.2
2.2
2.2
0.725
0.725
0.725
1
3
17
1.8
3
1.5
1.5
1.5
1.5
1.5
1.5
3
1
18
1.8
3.05
1.5
1.5
1.5
1.5
1.5
1.5
2
2
19
1.8
3
1.5
1.5
1.5
1.5
1.5
1.5
1
3
20
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
3
1
21
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
2
2
22
1.8
3
0.725
0.725
0.725
2.2
2.2
2.2
1
3
23
1.8
2.6
2.2
2.2
2.2
0.725
0.725
0.725
2
2
24
1.8
2.6
0.725
0.725
0.725
2.2
2.2
2.2
2
2
25
1.8
2.6
1.5
1.5
1.5
1.5
1.5
1.5
3
1
26
1.8
2.6
0.725
0.725
0.725
2.2
2.2
2.2
2
2
27
1.8
2.6
1.5
1.5
1.5
1.5
1.5
1.5
1
3


## 5. Improve

### 5.1. Set Major Impact Factors at Their Optimal Values/Work on the Major Impact Factors and Eliminate Them

Having reduced the impact factors to the three major ones: % recycled coarse aggregates, % recycled fine aggregates, and w/c ratio, the results of all DOE full-factorial study are summarized on the histograms in Figures10 and 11 for the average and standard deviation values for the compressive strength.Figure 10
Histogram of compressive strength results from all test results.Figure 11
Histogram of the standard deviation results from all test results.As improvements to the values above, an average target value for the compressive strength could be chosen as the third quartile value in the descriptive statistics in Table9 (35.1 MPa), whilst reducing the standard deviation value to the first quartile in Table 10 (1.921).Table 9
Descriptive statistics: C2.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C2
357
0
30.654
0.391
7.385
5.800
25.650
29.900
35.100
51.000Table 10
Descriptive statistics: C4.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C4
51
0
2.638
0.160
1.142
1.038
1.921
2.303
3.286
6.973The optimizer function in minitab is implemented to find optimal values for the major impact factors to reach the target values indicated above. It should be noted that the result table had to be reduced to two levels in order to utilize the optimizing function in minitab. The closest results to the desired targeted values are shown in Table11.Table 11
Response optimization.
Parameters
Goal
Lower
Target
Upper
Weight
Import
Fc.avg
Maximum
32.000
35.100
35.1
1
1
Fc.stdv
Minimum
1.921
1.921
2.5
1
1
Global solution
% Coarse = 26.0101
% Fine = 30.5556
w/c ratio = 0.5
Predicted responses
Fc.avg = 34.1753, desirability = 0.701712
Fc.stdv = 2.0957, desirability = 0.698315
Composite desirability = 0.700012Therefore, optimal values for the percentages of coarse and fine recycled aggregates are 26% and 30.6%, respectively, with a water/cement ratio of 0.5. This set of values would yield an average compressive strength of 34.2 MPa at a standard deviation of 2.1. Reducing the standard deviation to the preselected and optimized target value of 2.1 resulted in a 95% confidence interval for the mean compressive strength as 34 MPa : 35 MPa.


## 5.1. Set Major Impact Factors at Their Optimal Values/Work on the Major Impact Factors and Eliminate Them

Having reduced the impact factors to the three major ones: % recycled coarse aggregates, % recycled fine aggregates, and w/c ratio, the results of all DOE full-factorial study are summarized on the histograms in Figures10 and 11 for the average and standard deviation values for the compressive strength.Figure 10
Histogram of compressive strength results from all test results.Figure 11
Histogram of the standard deviation results from all test results.As improvements to the values above, an average target value for the compressive strength could be chosen as the third quartile value in the descriptive statistics in Table9 (35.1 MPa), whilst reducing the standard deviation value to the first quartile in Table 10 (1.921).Table 9
Descriptive statistics: C2.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C2
357
0
30.654
0.391
7.385
5.800
25.650
29.900
35.100
51.000Table 10
Descriptive statistics: C4.
Variable
N
N
*
Mean
SE mean
StDev
Minimum
Q
1
Median
Q
3
Maximum
C4
51
0
2.638
0.160
1.142
1.038
1.921
2.303
3.286
6.973The optimizer function in minitab is implemented to find optimal values for the major impact factors to reach the target values indicated above. It should be noted that the result table had to be reduced to two levels in order to utilize the optimizing function in minitab. The closest results to the desired targeted values are shown in Table11.Table 11
Response optimization.
Parameters
Goal
Lower
Target
Upper
Weight
Import
Fc.avg
Maximum
32.000
35.100
35.1
1
1
Fc.stdv
Minimum
1.921
1.921
2.5
1
1
Global solution
% Coarse = 26.0101
% Fine = 30.5556
w/c ratio = 0.5
Predicted responses
Fc.avg = 34.1753, desirability = 0.701712
Fc.stdv = 2.0957, desirability = 0.698315
Composite desirability = 0.700012Therefore, optimal values for the percentages of coarse and fine recycled aggregates are 26% and 30.6%, respectively, with a water/cement ratio of 0.5. This set of values would yield an average compressive strength of 34.2 MPa at a standard deviation of 2.1. Reducing the standard deviation to the preselected and optimized target value of 2.1 resulted in a 95% confidence interval for the mean compressive strength as 34 MPa : 35 MPa.


## 6. Control

### 6.1. Monitor Response Variables so that Benefits Are Sustained and Problems Once Fixed Will Stay Fixed

This step is related to the analysis and design of a control system that would ensure maintaining the quality levels reached in the analysis presented in this study. Control charts for future mixes using recycled aggregates and the optimization results above should be analyzed to detect any deviation for the target compressive strength or standard deviation. Continuous efforts should be devoted to further study and improve the performance of the system. The foregoing study would shed light on factors that could be further investigated to maintain quality and lay the foundation for further research work.It should be noted that aggregates obtained under “best quality” originally contained cement which increased the cement content beyond the values stated in the mixes. This improved the behavior of mixes containing higher percentages of recycled aggregates, in particular those in batch 2 which only consisted of “best quality” recycled aggregates.Since admixtures have not been used, the cement content in mixes with low w/c ratio was increased in the laboratories in order to allow for increasing the water content for workability, while maintaining the value of w/c ratio. This further boosted the strength of the mixes withw/c=0.5. Future studies should include the cement content as a factor affecting the compressive strength of recycled aggregates.Better inspection measures should be implemented at the recycled aggregates factory to present a finer distinction on the quality of recycled aggregates like specific weight, water absorption PH level, and so forth. It is believed that the quality of aggregates coming from the factory should be a major impact factor and deserves a more detailed study.Finally, since many aspects of concrete mechanical properties are based on statistical findings, the six sigma methodology is a powerful and adequate tool that ensures both accuracy (mean value) and preciseness (standard deviation) of results. More studies must be encouraged in this field to discover more about the behavior of concretes made with recycled aggregates.


## 6.1. Monitor Response Variables so that Benefits Are Sustained and Problems Once Fixed Will Stay Fixed

This step is related to the analysis and design of a control system that would ensure maintaining the quality levels reached in the analysis presented in this study. Control charts for future mixes using recycled aggregates and the optimization results above should be analyzed to detect any deviation for the target compressive strength or standard deviation. Continuous efforts should be devoted to further study and improve the performance of the system. The foregoing study would shed light on factors that could be further investigated to maintain quality and lay the foundation for further research work.It should be noted that aggregates obtained under “best quality” originally contained cement which increased the cement content beyond the values stated in the mixes. This improved the behavior of mixes containing higher percentages of recycled aggregates, in particular those in batch 2 which only consisted of “best quality” recycled aggregates.Since admixtures have not been used, the cement content in mixes with low w/c ratio was increased in the laboratories in order to allow for increasing the water content for workability, while maintaining the value of w/c ratio. This further boosted the strength of the mixes withw/c=0.5. Future studies should include the cement content as a factor affecting the compressive strength of recycled aggregates.Better inspection measures should be implemented at the recycled aggregates factory to present a finer distinction on the quality of recycled aggregates like specific weight, water absorption PH level, and so forth. It is believed that the quality of aggregates coming from the factory should be a major impact factor and deserves a more detailed study.Finally, since many aspects of concrete mechanical properties are based on statistical findings, the six sigma methodology is a powerful and adequate tool that ensures both accuracy (mean value) and preciseness (standard deviation) of results. More studies must be encouraged in this field to discover more about the behavior of concretes made with recycled aggregates.


## 7. Conclusions

The six sigma DMAIC methodology was utilized as a quality control method for recycled aggregates concrete. The 28-day compressive strength was used as the response variable. Five parameters were used in the initial analysis that were reduced to three parameters, namely, water to cement ratio, percentage of recycled coarse aggregates, and percentage of recycled fine aggregates. Using six sigma, the optimized values for the selected parameters were found to be 0.5%, 26%, and 30%, in order. The use of those optimum values resulted in recycled aggregates concrete of average strength of 34.2 MPa at a standard deviation of 2.1. Reducing the standard deviation to the preselected and optimized target value of 2.1 resulted in a 95% confidence interval for the mean compressive strength as 34 MPa : 35 MPa without using additives. It is, therefore, concluded that the six sigma method may be utilized to optimize design parameters and assure quality of recycled aggregates concrete. The method may be used to optimize other parameters. Different response variables that govern durability or workability may be selected.



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*Source: 394912-2013-11-30.xml*