The use of recycled demolished concrete in producing new concrete is an established method to improve sustainability throughreducing the environmental impact of using virgin aggregates and through reducing construction waste. Six sigma is a set oftools and strategies for process improvement. In this paper, the six sigma DMAIC methodology is utilized to optimize the designparameters in order to improve and assure the quality of the resulting recycled aggregate concrete. The project aims to produceconcrete with compressive strength of 25MPa without using additives. Five parameters were used in the initial analysis that werereduced to three after refinement. The refined parameters are the ratio of recycled coarse aggregates, the ratio of recycled fineaggregates, and the water/cement ratio. It was concluded that the optimum values for the three parameters are 26%, 30%, and 0.5,in order.
1. Introduction
Despite the many advances in technology including materialtechnology, concrete is still the premier constructionmaterialin all types of civil engineering works, including low- andhigh-rise building, water retaining structures, bridges, andgeneral infrastructure. Concrete is a manufactured product,essentially consisting of cement, aggregates, water, and pos-sibly admixtures, additives, or agents. Among these con-stituents, aggregates including sand, crushed stone, or gravelform themajor part by volume. Traditionally, aggregates havebeen readily available at economic prices and of qualities tosuit all purposes. However, there has been a rising awarenessto the environmental damage caused by quarries producingsuch primary aggregates. Therefore, countries with majorcycles of demolition and construction works, such as theArabian Gulf region, including Kuwait, are exploring ways touse recycled aggregates produced from such activities.
Since its establishment in 2001, the Environment Preser-vation Industrial Company (EPIC) in Kuwait has beencooperating with major stakeholders in the private andgovernment construction sectors to encourage the utilization
of recycled construction materials in projects around thecountry. The company receives the debris from demolishedcivil constructions and utilizes global production technolo-gies with standard specifications to provide the constructionmarket with quality recycled material mainly consisting offine and coarse recycled aggregates.
In addition to the many benefits to the environmentalpreservation envisaged by the use of recycled aggregates incivil works, financial savings are also incurred from reductionin transport and production energy costs. Reduction in wastelandfills is a further important benefit to this trend.
The subject of the use of recycled materials as aggregatereplacement in concrete had received considerable researchattention over the last decade. Richardson et al. [1] inves-tigated the possibility of achieving concrete made with un-graded recycled aggregates with a comparable strength to thatof concrete made with virgin aggregates (control sample).
Dosho [2] developed a recycling system in which hereplaced normal aggregates with recycled aggregates fromconstruction wastes whilst ensuring safety, quality, and costeffectiveness. In his study, he stressed on the tremendousimpact on the environment from applying his proposed
2 Journal of Waste Management
method to waste generated from the demolition of large scalebuildings such as powerhouses.
Acknowledging that concrete from construction demo-lition sites apportions more than half of the total wastes inHong Kong, Tam andWang [3] utilized a series of laboratorytests to set out some guidelines that would facilitate theuse of RAC in the construction industry. They concludedtheir paper with highlights on potential reduction in thequality of RAC, a classification of RA for various constructionapplications, and alerts users of possible extra slump loss dueto the use of RAC.
Moreover, a number of statistically based approaches haveemerged 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 calibrateoptimal values of concrete strength. Simon used a statisticalapproach to develop an internet-based software programto optimize concrete mixture proportions [6]. Soudki et al.used a full-factorial experimental study to optimize mixproportions that would produce concretes least sensitive totemperatures in hot climate [7]. They used the water/cementratio, coarse/total aggregate ratio, total aggregate/cementratio, and temperature as their independent variables forcalibrating the strength of concrete.
In almost all literature studies above, focus on the averagevalue obtained from sample repetition of quality indicators(i.e., compressive strength) has been maintained. However,latest quality assurance (QA) methods such as six sigmastress the importance of controlling both average valuesand dispersion of the data for achieving quality levels ofthe output or “response variable,” where reaching a presetaverage value ensures accuracy, while maintaining a smallstandard deviation precision ensures precision. In otherwords, previous research has used parts of QA methods,namely, mathematical optimizations, to reach desired resultsrather than using an overall holistic approach to their studies.
Six sigma is a structured data-driven statistical approachused to improve processes and reduce defects that wouldeventually lead to poor products. Originaing in 1985, by BillSmith, a senior quality assurance engineer at Motorola, sixsigma was responsible for earning the company the “MalcolmBaldrige National Quality Award” in 1988. It was not until JackWelch, the CEO of General Electric (GE), who adopted sixsigma in January 1996 and implemented it at a company-widescale, that the methodology gained its wide reputation andpopularity for making breakthroughs in quality and profitsand reducing defects. The methodology is acclaimed to beapplicable to almost all processes that are repetitive, witha measurable input and well-defined output. Although it ismore adopted to processes in manufacturing and businessenvironments, six sigma application has been successfullyapplied to many other fields including services and scientificdeterministic research.
This research aims to recruit a holistic QA approach usingthe six sigma methodology to find optimal concrete mixesfor concretes made with recycled aggregates from buildingconstruction wastes. The report will be organized accordingto the phases and steps of the six sigma methodology(referred to as DMAIC).
2. Define
2.1. Articulate the Problem. Concrete mixture proportion isa major factor in controlling the behavior and properties ofconcrete structures during their service life. Currently, the useof recycled aggregates from concrete building constructionis not as popular as it should be to achieve the sustainabilitytrends in new construction industries. This could be due tothe lack of understanding and confidence in its mechanicalproperties and behavior under service and ultimate condi-tions.
This research undertakes sheding light on the behavior ofrecycled aggregate concrete made with various mix propor-tions in addition to finding optimal conditions which wouldimprove its average strength. Statistical tools of the six sigmamethodology will be utilized to achieve this purpose.
From a six sigma perspective, typically, compressivestrength average values of concretemixed at the EnvironmentPreservation Industrial Company (EPIC) in Kuwait have anaverage of nearly 15MPa. This project aims at increasing thisaverage value to above 25MPa without using admixtures,additives, or agents.
2.2. Define Response Variables. Theproperties of concrete aredivided into fluid or plastic and hardened phase properties. Inits fluid or plastic phase, concrete properties include settingtimes, specific gravity, and slump value. The compressivestrength, tensile or modulus of fracture, and permeability areproperties of its hardened phase. All of the aforementionedproperties are quality indicators of concrete as a buildingmaterial and may independently or collectively serve asoutput or response variable for specific uses of constructionduring its erection or service life. However, as supported bythe literature, the compressive strength is the most indicativevalue of the quality of performance of concrete in almost alltests.
Therefore, the response variable for this project has beenselected as the compressive strength of concrete at 28. Forfurther scientific investigations, future projects might bringdifferent response variable under the scrutiny of the six sigmamethod.
2.3. Set Project Goals. In general, the project goals aimat encouraging the use of concrete made with aggregatesrecycled from construction wastes. This could be achievedby conducting studies that demonstrate the possibility ofobtaining concretes with adequate quality through carefullyconsidered mix design. In the research subject of this paper,based on previous literature studies in Kuwait, the direct goalis to obtain an average compressive cube strength of concretemade with recycled aggregates from construction wastes of25MPa whilst keeping the standard variation to a minimalvalue. Since previous studies did not consider the values ofthe standard deviation, it was not possible to establish a presetvalue to target at this stage.
2.4. Draw Process Map. A simple typical process mapdescribing the various steps involved in the process of
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Table 1: Independent variables affecting the strength of RAC.
Quality Proportions Mixing TestingAge of demolished construction w/c ratio Environmental temperature CuringChloride and sulfate content of RA % of coarse RA Mixing tools Sample size% of impurities in RA % of fine RA Mixing method Testing tools and machinesQuality of RA Cement content Operators OperatorsType of RA Cement type Water saturation condition of RA Age at testing
AdditivesAdmixtures or agents
Size of RAAspect ratio of RA
Process map for mixing concrete made with recycled aggregates
EPIC CE LABs
Start
Watering of aggregates(surface saturated)
(%wt)
Concrete blocksbrought by trucks
(origin)
Visual screeningof quality of
concretetype I or II
Grinding intofines and coarse
Production ofbatches
Coarse aggregates3/4 and 3/8
(kg/m3)
Fine aggregates(kg/m3)
Cement(kg/m3)
w/c ratio(%wt)
Admixture(%wt)
Mixing (time mins)
Curing(air 100% RH)
Concrete strength28 and 90 days
(MPa)
Figure 1: Process map of producing concrete made with recycledAggregates.
producing the concrete made with recycled aggregates isgiven in Figure 1.
3. Measure
3.1. Validate Measurement Systems. Many measurement-related factors could affect the physical, mechanical, andchemical properties of concrete in its plastic or hardenedstage. A measurement validation study that would considermost of those factors would be an excellent candidate for afuture research. This should include issues related to oper-ators, 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 measurementvalidation study to the definition of factors that are used inthe testing.
Testing of the concrete strength involved cubes of dimen-sion 100 × 100 × 100mm3, tested at 28 days. Despite the fact
that many of the previous researches employed admixtures totheir mixes to increase the value of the compressive strength,it was decided to exclude the use of admixture since it mightmask the various effects from other variables on the concretein addition to the impracticality of admixtures in large scaleconcrete projects.
As for the current visual inspection method employed atEPIC, it involves a rudimentary inspection by an operatorat the gate of the company site who searches by eye for theextent of impurities in the content of loaded trucks whilestanding in his post. The operator then passes a judgmenton the quality of the construction waste as best, average, orworst quality.This inspectionmethod is hardly accurate sinceit is highly dependent on individual operators who could passdifferent judgments and does not account for other highlyimportant qualities that are not visible to the naked eye.Such invisible properties include environmental conditionof the source of the construction wastes, their age, chlorideand sulfate contents, and initial quality of concrete such astype of aggregate and mix proportions. The inspection forclassification of quality and properties of construction wasteis a highly important factor and worthy of a research study onits own.
3.2. Collect Data on Response Variable(s). There exist a largenumber of factors that could affect the properties of RACwhich could be considered as independent variables forthis study. Possible independent variables include the factorsshown in Table 1. After a careful examination of the indepen-dent variables in Table 1 and taking into consideration thescope of work set out in the define stage, the list for possiblemajor impact factors has been reduced to curing, % coarseRA, % fine RA, w/c ratio, and quality of RA (as shown in theitalic cells in Table 1).
3.3. Establish Baseline. Many researches, including thosemade at Kuwait University, employed admixtures to boostthe compressive strength above values that are marketedby aggregates recycling companies. Such values exceeded40MPa for the compressive strengthwith standard deviationsaround 20% of the strength value [8]. The average valueof cube strength marketed values by EPIC ranges roughlybetween 10 and 20MPa or 25MPa at best. Since we areonly interested in the basic or raw values of the compressive
4 Journal of Waste Management
Table 2: Summary of the two-level preliminary DOE design.
Levels Impact factorQuality 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% Air
strength, which are the values used formarketing RA productby EPIC, we did not include any additives, admixtures, oragents in the concrete mixes to augment the average valueof the strength results. Therefore, the results of any previousstudies at Kuwait University or from EPIC were only usedas indicative values to demonstrate relations and trends andto help in approximating baseline values for our six sigmastudy. The authors could not find strong supportive resultsfrom EPIC based on a systematic scientific approach to helpin establishing baseline.
The authors were not successful in obtaining reliableprevious test results from EPIC to get current defect levels.Therefore, based on private communication with EPIC, acurrent average value of 15MPa is assumed as a baseline priorto the implementation of six sigma.
4. Analyze
4.1. Design Experiments and Collect Data on Potential Causesand Response Variable(s). Further to the identification ofpotential impact factors in the “measure” phase earlier, apreliminary design of experiment (DOE) program has beenset 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 RAqualities are generally considered, “best” and “worst.” Theidentification between the two types is performed throughvisual inspection by an operator, with the “worst” being thebatches containing lots of impurities such as woods andplastics.
Percentage of Coarse and Fine RA.The percentages of RAusedin the mix will be for each of the fine and coarse recycledaggregates as follows: 25% and 75%.
Water/Cement Ratios. Two w/c ratios will be considered 0.5and 0.7, in the study.
Curing. Air and 100% RH curing conditions are considered.The two-level DOE setup is summarized in Table 2.The 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 sizesof the tested cube samples were 100 × 100 × 100mm3. Basedon Table 2 and since curing and quality of aggregates do notaffect the mix design, a total of 8 (23) mix proportions havebeen designed, as shown in Table 3.
The designs for each of the mixes in Table 4 have beenpretested in the laboratory before being finalized. The mixproportions of each mix design are shown in Table 4.
Table 3: Factors for the differentmix design used in the preliminaryDOE program.
Figure 2: Histogram of compressive strength results from the two-level first test batch.
4.2. Identify Major Impact Factors. The experiment beginsas a five-factor, two-level, full-factorial experiment. Eachexperimental run had the benefit of 7 repetitions. Using thisdata for the analysis, it was possible to compute an averageand a standard deviation to represent the performance of theprocess for each run of the experiment.
The results from this first batch of the experiment usingtwo-level full-factorial design described in the previoussection are presented in Figure 2. A descriptive statisticalsummary of the result data points is shown in Table 5. Ahistogram and descriptive statistics of the standard deviationof the results from the first batch of test are shown in Figure 3and Table 6.
The initial analysis focused on the process average.Because only one replicate was used to represent the model,
Journal of Waste Management 5
Table 4: Mix proportion by weight of the mixes used in the preliminary DOE program.
Figure 3: Histogram of the standard deviation results from the two-level first test batch.
the first analysis does not include a 𝑃 value. The statisticalanalysis 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, thethreshold was changed to 𝛼 = 0.10.
Figures 4 and 5 show the effect Pareto charts for theaverage and the standard deviation standardized values,respectively.
86420
Pareto chart of the standardized effects(response is FC avg, 𝛼 = 0.10)1.71
Quality
Curing
Recycled coarse aggregates (%)
Recycled fine aggregates (%)
Term
1210Standardized effect
W/C
Figure 4: Main effect Pareto chart of the compressive strength.
Pareto charts for the strength and standard deviationincluding more terms are shown in Figures 6 and 7, respec-tively.
Based on the analysis of the graphs above, the significantmain effect terms on the compressive strength were B, C, andD (w/c, % recycled coarse aggregates, and % recycled fineaggregates, resp.). Term A (quality) appears to be significantin affecting standard deviation. This is understandable dueto the unpredictability of the quality of recycled aggregatesand looseness in the definition of “best” and “worst” qual-ities. Second order terms that appeared to be statisticallysignificant included AB and BD, where A refers to “quality.”Because there was also a significant 2nd order effect that
6 Journal of Waste Management
Table 7: Summary of the three-level full-factorial DOE design.
Levels Impact factorQuality of aggregates w/c % Recycled coarse aggregate % Recycled fine aggregate Curing
HighBEST
0.5 25% 25%RH100%Medium 0.6 50% 50%
Low 0.7 75% 75%
Table 8: Mix proportions by weight of the mixes used in the 3-level full-factorial DOE program.
Pareto chart of the standardized effects(response is standard deviation,
Quality
Curing
Term
1.81.61.41.21.00.80.60.40.20.0
W/C
𝛼 = 0.10)
Recycled coarse aggregates (%)
Recycled fine aggregates (%)
Figure 5: Main effect Pareto chart of the standard deviation.
included A (quality), we elected to include the main effect ofA as a term in the next analysis with reduced terms.
Figures 8 and 9 show a further analysis of the results basedon the main effect plots and interaction plots, respectively,for the compressive strength means. The plots in thosefigures indicate the correlation between the four showneffects on the compressive strength, the strongest beingwith the w/c ratio, whilst the lowest appeared to the effect“quality.”
Therefore, as a final conclusion of the analysis presentedabove, it could be concluded that the effects considered inthis study could be reduced to three major impact factors asfollows:
Therefore, a second study has been designed to complementthe previous study as described in Table 7 with 3 levelsto account for nonlinearities in the relations between thefactors above and the average and standard variations of thecompressive strength.
The study above necessitated 27 different mix designs, thedetails of which are summarized in Table 8.
5. Improve
5.1. Set Major Impact Factors at Their Optimal Values/Workon the Major Impact Factors and Eliminate Them. Havingreduced the impact factors to the threemajor ones:% recycledcoarse aggregates, % recycled fine aggregates, and w/c ratio,the results of all DOE full-factorial study are summarizedon the histograms in Figures 10 and 11 for the average andstandard deviation values for the compressive strength.
As improvements to the values above, an average targetvalue for the compressive strength could be chosen as thethird quartile value in the descriptive statistics in Table 9(35.1MPa), whilst reducing the standard deviation value tothe first quartile in Table 10 (1.921).
The optimizer function inminitab is implemented to findoptimal values for themajor impact factors to reach the targetvalues indicated above. It should be noted that the resulttable had to be reduced to two levels in order to utilize theoptimizing function in minitab. The closest results to thedesired targeted values are shown in Table 11.
Therefore, optimal values for the percentages of coarseand fine recycled aggregates are 26% and 30.6%, respectively,with awater/cement ratio of 0.5.This set of values would yieldan average compressive strength of 34.2MPa at a standarddeviation of 2.1. Reducing the standard deviation to thepreselected and optimized target value of 2.1 resulted in a95% confidence interval for the mean compressive strengthas 34MPa : 35MPa.
6. Control
6.1. Monitor Response Variables so that Benefits Are Sustainedand Problems Once Fixed Will Stay Fixed. This step is relatedto the analysis and design of a control system that wouldensure maintaining the quality levels reached in the analysispresented in this study. Control charts for future mixes usingrecycled aggregates and the optimization results above should
8 Journal of Waste Management
BTe
rm
2.17
Effect1614121086420
Lenth’s PSE = 1.20134
A QualityW/CB
C Recycled coarse aggregates (%)D Recycled fine aggregates (%)E Curing
Factor Name
Pareto chart of the effects(response is FC avg, 𝛼 = 0.10, only 30 largest effects shown)
CECDAC
ABEBEBC
ABDEAD
CDEBCE
BCDEDE
ABCBDE
ABCEACDE
AEACE
ABCDEACDBCD
ABCDAE
ABDCD
BDAB
Figure 6: Main effect Pareto chart of the compressive strength showing 30 largest terms.
be analyzed to detect any deviation for the target compressivestrength or standard deviation. Continuous efforts should bedevoted to further study and improve the performance of thesystem. The foregoing study would shed light on factors thatcould be further investigated to maintain quality and lay thefoundation for further research work.
It should be noted that aggregates obtained under “bestquality” originally contained cement which increased thecement content beyond the values stated in the mixes. Thisimproved the behavior of mixes containing higher percent-ages of recycled aggregates, in particular those in batch 2which only consisted of “best quality” recycled aggregates.
Since admixtures have not been used, the cement contentin mixes with low w/c ratio was increased in the laboratoriesin order to allow for increasing the water content for worka-bility, while maintaining the value of w/c ratio. This furtherboosted the strength of the mixes with w/c = 0.5. Futurestudies should include the cement content as a factor affectingthe compressive strength of recycled aggregates.
Better inspectionmeasures should be implemented at therecycled aggregates factory to present a finer distinction on
the quality of recycled aggregates like specific weight, waterabsorption PH level, and so forth. It is believed that thequality of aggregates coming from the factory should be amajor impact factor and deserves a more detailed study.
Finally, since many aspects of concrete mechanical prop-erties are based on statistical findings, the six sigma method-ology is a powerful and adequate tool that ensures bothaccuracy (mean value) and preciseness (standard deviation)of results. More studies must be encouraged in this field todiscover more about the behavior of concretes made withrecycled aggregates.
7. Conclusions
The six sigma DMAIC methodology was utilized as a qualitycontrol method for recycled aggregates concrete. The 28-daycompressive strength was used as the response variable. Fiveparameters were used in the initial analysis that were reducedto three parameters, namely, water to cement ratio, percent-age of recycled coarse aggregates, and percentage of recycledfine aggregates. Using six sigma, the optimized values for the
Journal of Waste Management 9
C
1.746
A QualityBC Recycled coarse aggregates (%)D Recycled fine aggregates (%)E Curing
Factor Name
Pareto chart of the standardized effects(response is Std-Dev, 𝛼 = 0.10)
E
AE
C
AB
BC
A
DE
D
CD
AC
B
CE
BE
BD
AD
Term
Standardized effect2.01.51.00.50.0
W/C
Figure 7: Main effect Pareto chart of the compressive strength including second order terms.
W/CQuality
Main effects plot for FC avgData means
BestWorst
40353025
0.70.5
Mea
n
40353025
Mea
n
Recycled coarseaggregates (%) aggregates (%)
Recycled fine
7525 7525
Figure 8: Main effect plots of the compressive strength.
selected parameters were found to be 0.5%, 26%, and 30%, inorder. The use of those optimum values resulted in recycledaggregates concrete of average strength of 34.2MPa at astandard deviation of 2.1. Reducing the standard deviation tothe preselected and optimized target value of 2.1 resulted in
0.50.7
Recycled coarse aggregates (%)
Recycled fine aggregates (%)
w/c
Quality
40
30
2040
30
2040
30
20
w/cQuality
BestWorst 25
75
Recycled coarse aggregates (%)
0.70.5 7525 7525
Interaction plot for FC avgData means
Figure 9: Interaction plots of the compressive strength.
a 95% confidence interval for the mean compressive strengthas 34MPa : 35MPa without using additives. It is, therefore,concluded that the six sigma method may be utilized to
10 Journal of Waste Management
484236302418126
40
30
20
10
0
Freq
uenc
y
Histogram
Compressive strength (MPa)
Mean 30.65StDev 7.385N 357
Figure 10: Histogram of compressive strength results from all testresults.
76543210
8
6
4
2
0
Freq
uenc
y
Histogram
Mean 2.638StDev 1.142N 51
18
16
14
12
10
Standard deviation
Figure 11: Histogram of the standard deviation results from all testresults.
optimize design parameters and assure quality of recycledaggregates concrete. The method may be used to optimizeother parameters. Different response variables that governdurability or workability may be selected.
Acknowledgments
The authors would like to acknowledge Engineer AnwarAl-Suraij for supervising the tests, EPIC for providing theraw materials, and Kuwait University for providing the labfacilities and technicians.
References
[1] A. E. Richardson, K. Coventry, S. Graham, and University ofNorthumbria, “Concrete manufacture with un-graded recycledaggregates,” Structural Survey, vol. 27, no. 1, pp. 62–70, 2009.
[2] Y. Dosho, “Development of a sustainable concrete waste recy-cling system: application of recycled aggregate concrete pro-duced by aggregate replacing method,” Journal of AdvancedConcrete Technology, vol. 5, no. 1, pp. 27–42, 2007.
[3] W. Tam and K. Wang, “Ways to facilitate the use of recycledaggregate concrete,” Journal of Waste and Resource Manage-ment, no. 3, pp. 125–129, 1996.
[4] M. Kessal, P.-C. Nkinamubanzi, A. Tagnit-Hamou, and P.-C.Aı̈tcin, “Improving initial strength of a concrete made with type20M cement,” Cement, Concrete and Aggregates, vol. 18, no. 1,pp. 49–54, 1996.
[5] M. Crowder, “A statistical approach to a deterioration process inreinforced concrete,”Applied Statistics, vol. 40, no. 1, pp. 95–103,2009.
[6] M. J. Simon, “Concrete mixture optimization using statisticalmethods: final report,” A Final Report FHWA-RD-03-060, TheFederal Highway Administration and the National Institute ofStandards and Technology, Gaithersburg, Md, USA, 2003.
[7] K. A. Soudki, E. F. El-Salakawy, and N. B. Elkum, “Full factorialof optimization of concretemix design for hot climates,” JournalofMaterials in Civil Engineering, vol. 13, no. 6, pp. 427–433, 2001.
[8] M. Naseer Haque and A. Al-Yagout, “Characterization of recy-cled aggregates produced from construction waste treatmentplant in Kuwait,” Final Report Submitted for Funded ResearchEV02/06, Kuwait University, Kuwait City, Kuwait, 2010.
