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  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

    1

    Research Article

    ANALYSIS AND EXPERIMENTAL DESIGN OF SLUMP DRY

    CONCRETE MIX IN WARM AND HOT HUMID ZONES, SOUTH

    EASTERN NIGERIA

    John U. Ezeokonkwo1, Chukwuemeka Daniel Ezeliora

    2, F. O. Ezeokoli

    1

    1Department of Building, NnamdiAzikiwe University, Awka, Anambra State 2Department of Mechanical Engineering, NnamdiAzikiwe University, Awka, Anambra State

    Correspondence should be addressed to John U. Ezeokonkwo

    Received August 14, 2015; Accepted August 22, 2015; Published September 05, 2015;

    Copyright: © 2015 John U. Ezeokonkwo et al. This is an open access article distributed under the Creative

    Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,

    provided the original work is properly cited.

    Cite This Article: U. Ezeokonkwo, J., Daniel Ezeliora, C., Ezeokoli, F.(2015). Analysis and Experimental Design of Slump Dry Concrete Mix in Warm and Hot Humid Zones, South Eastern Nigeria. Advances in Engineering &

    Scientific Research, 1(1).1-12

    ABSTRACT

    In the research work, the use of factorial mathematical model was adopted for the slumps dry of concrete mix in a Hot and

    Warm humid zones as functions of quantity of cement, water-cement ratio and quantity of aggregates, the composition of

    the concrete mix was optimized by varying the independent factors (variables) for various seasons within the zones through

    Box Wilson’s composite mathematical method. The optimum value for factors X1 and X2 and X3 and X4 were obtained for

    the Hot and Warm humid zones as Y2 = 106.8221. The electronic (computer) manipulations of the data generated from the

    experiments, the following graphs (1 – 8) were generated for a better understanding of interactions between the factors and

    value generated as a result.

    KEYWORDS:Concrete mix, matlab, Climatic Conditions, factorial design, Quality, Production

    INTRODUCTION

    Conceptual framework is the operationalization of the

    variables that hold the research together. It helps one to

    make logical sense of the relationship among variables or

    factors that have been identified as significant to the

    problem under investigation. The quality control

    management of building materials with emphases on

    concrete works is examined based on the following:

    Concept of Quality Control in Building Production,

    Factors affecting Quality of Concrete Work.

    Climatic conditions and their effect on Quality of Concrete Work

    Concrete Mix Design and Production

    Quality Control Measures of Concrete Works

    Concept Of Quality Control In Building Production

    Quality Control is a process employed in other to ensure

    that a product or service conforms to established standards

    or specifications. It may include whatever actions a

    business deems necessary to provide for control and

    verification of certain characteristics of a product or

    service. The basic goal of quality control is to ensure that

    the products, services, or processes provided meet specific

    requirements and are dependable, satisfactory and fiscally

    sound.

    Essentially, quality control involves the examination of a

    product, services, or process for certain minimum levels of

    quality. The goal of a quality control team is to identify

    products or services that do not meet a specified standard

    of quality. If a problem is identified, the job of a quality

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    Open Access Scientific Publisher

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  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

    2

    k k k

    control team or professional may involve stopping

    production temporarily. Depending on the particular

    service or product as well as the type of problem identified,

    production or implantation may not cease entirely.

    Wisegeek, (2010).

    The concept of quality control in building production is the

    quality of the production which involves the quality of

    integrated action due to human, material, machinery,

    process methodology and work environment. This is also

    known as process quality which reflects the quality of the

    finished work or product. In order to ensure the quality of

    production, the quality of each process must be controlled,

    which is the focus of quality control during construction

    Shilian, (2004).

    HUMAN FACTORS IN QUALITY CONTROL

    Since human activities, form part of production process,

    the overall quality control and individual ability of humans

    would determine to large extent the results of all quality

    control activities. Therefore, human are considered as both

    the controlled targets and controlling motivation of other

    quality control activities Cheng, (2004). The contents of

    human control includes the overall quality of the set up or

    company and individual knowledge, ability, physical

    condition, psychological state, quality consciousness,

    behaviour, concept of organizational discipline, and

    professional ethics. The main measures and approach of

    human control on production sites are summarized as

    follows:

    The management objectives and responsibilities of the project manager or

    supervisor being considered as the centre the

    organization of project management should be

    set up reasonably with appropriate

    management personnel.

    The operating workers should be asked to have relevant qualifications, particularly

    important technical trades, special trades etc.

    There should be very strict on-site management system and production

    discipline and the standard of operation

    technology and management activities.

    Incentives and communication activities should be

    promoted to arouse staff’s enthusiasm.

    REGRESSION MODEL

    In accordance with the experimental method (Box

    Wilson’s Mathematical Theory of Experiment), 25

    experiments were carried out for effective study of the

    mutual interactions of various factors (variables)

    considered in the experiment. Both the experimental and

    theoretical values of the slump in mm, density and

    compressive strength of the concrete measured during the

    wet and dry seasons obtained as contained in tables 1 and 2

    for the two zones (Hot and Warm humid zones). From the

    results obtained regressional model for the factors –

    dependent variables were derived in the form.

    Y =bo + ΣbiXi+ΣbijXi2

    +Σbij XiXj 1

    i=1 i=1 i-j

    The main objective in the regression analysis is to determine the statistical relevance of the derived mathematical

    expression for the studied subject of this research, which in summary is as a check and balance apparatus for the reliability

    of measured results of the experiment on one hand, and the adequacy of the derived Mathematical Model (MM) for the

    observations made.

    Critical Values of Regression Coefficient based on the Regression Mathematical Model is shown in the equation 1.1.

    Y2=208–0.035X1–9.12X2+0.0268X3–0.0501X4 ..... Equation 1.1

    The equation 1.1 is the regression equation of the slump dry season under which the experiments were performed.

    THE RESEARCH METHOD

    used in this work is the application of Factorial design Analysis of Mathematical Models for Variables in the Zones. The

    method is used to study the relative influence of each of the factors on the slumps (workability) of concrete, density and

    compressive strength for each climatic season, quasi or mono factorial models were obtained. From the analysis, it is

    possible to make the following deductions on the influence of the different factors over the workability density and strength

    of concrete.

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    Computer Analysis Of The Experimental Results From The Two Zones

    Table 1:Values of Results from Hot Humid Zone (Awka)

    Level of factors and test X1 = C Cement kg/m3

    X2= w water content kg/m

    3

    X3 = Fa fine paragraph kg/m3

    X4 = Ca coarse Aggregate kg/m0

    Slump dry mm

    Xnar Highest level (+)

    Xim Lowest level (-)

    Xer Central Level (0) average

    𝜹Interval of Change Δ

    300

    207

    254

    46

    7

    5

    6

    1

    690

    414

    552

    138

    1380

    953

    1167

    213

    Test No X1 X2 X3 X4 Y2

    1 207 5 414 953 85

    2 207 7 690 953 103

    3 207 5 690 953 157

    4 207 5 690 953 150

    5 300 7 414 953 63

    6 300 5 690 1380 80

    7 207 7 690 1380 97

    8 207 7 690 1380 51

    9 207 6 552 1167 64

    10 300 7 552 1167 58

    11 254 5 552 1167 78

    12 254 7 552 1167 94

    13 254 6 414 953 159

    14 300 5 690 953 152

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    15 207 7 414 1380 112

    16 254 6 552 1167 170

    17 207 5 414 953 100

    18 207 5 690 953 98

    19 254 7 552 1167 92

    20 254 5 552 1167 92

    21 254 7 690 953 99

    22 254 6 414 1167 99

    23 254 6 552 1380 101

    24 254 6 552 953 97

    25 254 6 552 1167 142

    Source: Researcher’s Field Work

    Table 2:Values of Result obtained from Experiment in Warm Humid Zone (Owerri)

    Level (of Factors and tests)

    X1 = C Cement Kg.m3

    X2 = c

    Water Cement Kg/m3

    X3 =

    Fine Aggregate

    Kg/m3

    X4

    Coarse

    Aggregate

    Slump

    SDRY

    Highest Level (+) 300 .7 690 1380

    Xmin Lowel level (-) 207 .5 414 953

    Xmin Control level(0) 254 .6 552 1167

    Interval of Change 46 .1 138 213 Y2

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    Source: Researcher’s Field Work

    After experimentally generating data on Tables 1 and 2, the data was subjected to electronic manipulation with Minitab

    software and the following results with appropriates tables and figures were obtained.

    S/N0

    1 - - - - 85

    2 + + - - 110

    3 - + - - 159

    4 - + - - 157

    5 + + - - 125

    6 + - + + 73

    7 - + + + 101

    8 + + + + 163

    9 - 0 0 0 72

    10 + 0 0 0 58

    11 0 - 0 0 87

    12 0 + 0 0 68

    13 0 0 - - 159

    14 + - + - 157

    15 - + - + 109

    16 0 0 0 0 167

    17 - - - 0 105

    18 - + - 0 97

    19 0 + 0 0 91

    20 0 - 0 0 99

    21 + + 0 0 98

    22 0 0 - 0 101

    23 0 0 0 + 94

    24 0 0 0 0 102

    25 0 0 0 0 99

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    Regression Analysis: Y2 versus X1, X2, X3, X4

    The regression equation is

    Y2 = 208 - 0.035 X1 - 9.12 X2 + 0.0268 X3 - 0.0501 X4

    Predictor Coef SE Coef T P

    Constant 207.59 83.75 2.48 0.022

    X1 -0.0352 0.2026 -0.17 0.864

    X2 -9.118 8.272 -1.10 0.283

    X3 0.02685 0.06349 0.42 0.677

    X4

    -0.05011

    0.04300

    -1.17

    0.258

    S = 33.2274 R-Sq= 16.8% R-Sq(adj) = 0.1%

    Analysis of Variance

    Source DF SS MS F P

    Regression 4 4452 1113 1.01 0.427

    Residual Error 20 22081 1104

    Total 24 26533

    Source DFSeq SS

    Source DF Seq SS

    X1 1 132

    X2 1 2694

    X3 1 126

    X4 1 1500

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    AESR 12|Volume 1|Issue 1|2015

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    Unusual Observations

    Obs X1 Y2 Fit SE Fit Residual St Resid

    16 254 170.00 100.29 7.43 69.71 2.15R

    R denotes an observation with a large standardized residual.

    Figure 1:Effects Plot for Y2

    3210-1-2-3

    99

    95

    90

    80

    70

    60

    50

    40

    30

    20

    10

    5

    1

    Standardized Effect

    Pe

    rce

    nt

    A X1

    B X2

    C X3

    D X4

    Factor Name

    Not Significant

    Significant

    Effect Type

    Normal Plot of the Standardized Effects(response is Y2, Alpha = 0.05)

    Figure 2 :Residual Plots for Y2

    50250-25-50

    99

    90

    50

    10

    1

    Residual

    Per

    cent

    1501251007550

    60

    30

    0

    -30

    Fitted Value

    Res

    idua

    l

    6040200-20-40

    8

    6

    4

    2

    0

    Residual

    Freq

    uenc

    y

    24222018161412108642

    60

    30

    0

    -30

    Observation Order

    Res

    idua

    l

    Normal Probability Plot Versus Fits

    Histogram Versus Order

    Residual Plots for Y2

  • Advances in Engineering & Scientific Research

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    Figure 3:Main Effects Plot for Y2

    300207

    120

    115

    110

    105

    100

    75

    690414

    120

    115

    110

    105

    100

    1380953

    X1

    Me

    an

    X2

    X3 X4

    Main Effects Plot for Y2Data Means

    Figure 4:Interaction Plot for Y2

    75 690414 1380953

    120

    100

    80

    120

    100

    80

    120

    100

    80

    X1

    X2

    X3

    X4

    207

    300

    X1

    5

    7

    X2

    414

    690

    X3

    Interaction Plot for Y2Data Means

  • Advances in Engineering & Scientific Research

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    Figure 5:Contour Plots of Y2

    X2*X1

    300260220

    7.0

    6.5

    6.0

    5.5

    5.0

    X3*X1

    300260220

    650

    600

    550

    500

    450

    X4*X1

    300260220

    1300

    1200

    1100

    1000

    X3*X2

    765

    650

    600

    550

    500

    450

    X4*X2

    765

    1300

    1200

    1100

    1000

    X4*X3

    640560480

    1300

    1200

    1100

    1000

    X1 207

    X2 5

    X3 414

    X4 953

    Hold Values

    >

    < 20

    20 70

    70 120

    120 170

    170 220

    220

    Y2

    Contour Plots of Y2

    Figure 6: Surface Plots of Y2

    7100

    6

    150

    200

    200250 5

    300

    Y2

    X2

    X1

    720

    600100

    150

    200

    200 480250

    300

    Y2

    X3

    X1

    1400

    50 1200

    100

    150

    200

    200 1000250300

    Y2

    X4

    X1

    720

    600100

    120

    140

    5

    160

    4806

    7

    Y2

    X3

    X2

    1400

    1200100

    120

    140

    5

    160

    100067

    Y2

    X4

    X2

    1400

    0 1200

    50

    100

    150

    480 1000600 720

    Y2

    X4

    X3

    X1 207

    X2 5

    X3 414

    X4 953

    Hold Values

    Surface Plots of Y2

    RESPONSE OPTIMIZATION

    Parameters

    Goal Lower Target Upper Weight Import

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    Y2 Target 50 106.58 200 1 1

    Local Solution

    X1 = 285.194

    X2 = 5.16162

    X3 = 656.545

    X4 = 1181.60

    Predicted Responses

    Y2 = 106.82 , desirability = 0.997409

    Composite Desirability = 0.989775

    Figure 7:optimization plot

    CurHigh

    Low0.98977D

    Optimal

    d = 0.99835

    Targ: 112.460Y1

    y = 112.6046

    d = 0.99741

    Targ: 106.580Y2

    y = 106.8221

    d = 0.99915

    Targ: 7.4072Y3

    y = 7.4069

    d = 0.99448

    Targ: 2223.5020Y4

    y = 2230.5515

    d = 0.95765

    Targ: 11.8322Y5

    y = 11.7123

    d = 0.99228

    Targ: 28.6832Y6

    y = 28.7706

    0.98977Desirability

    953.0

    1380.0

    414.0

    690.0

    5.0

    7.0

    207.0

    300.0X2 X3 X4X1

    [285.1943][5.1616][656.5455][1181.5960]

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    Figure 8:Root Mean Square Test for Non linear Regression Analysis

    Non-linear results/graphs using matlabY2 results

    Figure 9:Coefficient of relationship Test for Non linear Regression Analysis

    Model Fitting And Validation For Strength After assessing the data graphically, the second step in analysis is to estimate an appropriate model for each

    response.

    1 2 3 4 5 6 784

    86

    88

    90

    92y

    Data point

    RMS training set error: 0.09279 Variation explained: 99.8299 %

    Predicted y (training values)

    Actual y (training values)

    1 2 3 4 5 6 7 860

    70

    80

    90

    100

    y

    Data point

    RMS test set error: 6.9458 Variation explained: -704.0647 %

    Predicted y (test values)

    Actual y (test values)

    Bias Gene 1 Gene 2-50

    0

    50

    100Gene weights

    Bias Gene 1 Gene 20

    0.5

    1

    1.5

    2x 10

    -5 P value (low = significant)

    R squared = 0.9983 Adj. R squared = 0.99745

  • Advances in Engineering & Scientific Research

    AESR 12|Volume 1|Issue 1|2015

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    The adequacy of each fitted model was validated

    quantitatively by calculating statistical measures such as

    residual standard deviation and (PRESS), and graphically

    by examining residual plots. The residual standard

    deviation S, for this model is O.99mpa. A value of s near

    the repeatability value (replicate standard deviation

    calculated from centre points) is an indication of an

    adequately fitting model.

    CONCLUSION AND RECOMMENDATION

    Factorial design mathematical model and a non-linear

    matlab least square regression model were developed to

    study and analyze the results. it is possible to analyze

    accurately the positive effects of the various factors

    responsible for better slumps dry (workability) and

    strength of concrete produced and to optimize those factors

    for quick determination of the optimum factorial

    composition of concrete for any given climatic condition.

    The factorial design shows the optimal production mix of

    the concrete production in hot and warm climatic condition

    while thmatlab non-linear regression approach was used to

    see the effect of linearity and non-linearity of the data.

    From the results, it shows that the dat is more of non-linear

    with the coefficient of determination of the dependent and

    independent variables (R2

    ) of 0.9963. also when adjusted

    the R2

    , it shows a coefficient of 0.99745. The results

    however, where recommended to the construction

    industries, builders and Civil engineers for their

    applicability of the Optimal results and its relationships in

    hot and warm humid zones of south east Nigeria.

    REFERENCES

    [1] Chang Hu, 2004: Construction Project Management. Second Edition, China Construction Industry Publisher,

    22-32.

    [2] Koehler, E.P. and Fowler D.W. (2003): ICAR 105, Measuring the Workability of High Fines, concrete

    International Centre for Aggregates research. The

    University of Texasat Austin.

    [3] Liang, Shilian, (2004): Engineering Project Management, Second edition, China: Dongbei

    University of Finance and Economics. 71-79.

    [4] Mahmood, K.(2005): Factors Affecting Reinforced Concrete Construction Quality in Pakistan. CBM-CI

    International Workshop, Karachi, Pakistan.

    [5] Mcpharlin, (2012): Weather and How to Minimize the Effect on Concrete Qranite Rock.

    [6] Scanton, J. M. (1994): Factors Influencing Concrete Workability P. Klieger, and J. F. Lamond, ed.

    Significance of Tests Properties of Concrete and

    Concrete-Making. American Society for Testing and

    Materials. STP 169c, Philadelphia. P. A.

    [7] WiseGeek, http://www.wisegeek.com/what-is-quality-control.htm (14.04.2010).

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