Guidelines for Concrete Mixtures Containing Supplementary Cementitious Materials to Enhance Durability of Bridge Decks (2007)

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1Background Premature deterioration of our nation’s concrete bridges has been a persistent and frustrating problem to those respon- sible for maintaining those bridges as well as to the traveling public. The deterioration typically consists of concrete delam- ination and spalling due to various mechanisms, including corrosion of embedded steel reinforcement, repeated freezing and thawing, deicing salt–induced scaling, or reactive aggre- gates. The rate of this deterioration is primarily dependent on the permeability of the concrete to moisture and aggressive substances and on cracking of the concrete. Because nearly all concrete deterioration processes are driven in some manner by the ingress of water and water- borne agents, such as chloride and sulfate ions, one way to minimize problems is to make the concrete less permeable by, for example, densifying the cementitious paste. This densifi- cation is achieved by using lower water–cementitious mate- rials ratio (w/cm) and supplementary cementitious materials (SCMs), such as silica fume, fly ash, ground granulated blast furnace slag, or metakaolin. However, if the concrete cracks, aggressive agents may reach the interior of the concrete and the reinforcing steel regardless of concrete impermeability. Excessive cracking can result from freezing and thawing action, alkali-silica reaction (ASR), corrosion of reinforcement, plastic shrinkage, restrained shrinkage, or thermal stress. Early- age cracking became relatively common with the use of less permeable concrete made with extremely low w/cm and high dosages of some SCMs such as silica fume. These mixtures often produced very high-strength concrete that was prone to thermal, drying shrinkage, and plastic shrinkage cracking. However, researchers and practitioners have developed mate- rials, mixtures, and construction practices to combat these problems. It is now better understood that high strengths are not necessarily required for durable concrete. In fact, high strengths may be detrimental because of the associated high modulus of elasticity, which could result in the development of restraint-induced stress sufficient to produce cracks. Instead, the mixture can be optimized to minimize permeability and shrinkage/thermal cracking while enabling ease of placement, consolidation, and finishing, thus, minimizing construction- related problems and maximizing durability. A “one size fits all” approach to concrete mixtures does not achieve the goal of maximizing long-term durability because the quality of local materials used to produce the concrete strongly influence mixture properties and performance. Large variability within, and interactions between, concrete raw materials may influence the short-term properties and long-term durability of the concrete. Therefore, concrete mixtures cannot be truly optimized without testing local materials. This situation implies that even specifying a mixture, without real knowledge of the currently available materials, does not ensure that the concrete produced is the best alternative for a given situation. Concrete mixtures are commonly designed to achieve minimum specification requirements; optimization is rarely performed. Because accelerated testing for durability prediction requires a minimum of several months to obtain meaningful data, the process of conducting a concrete test program should begin as early as possible in the design stages of con- struction. This early start will help develop better specifica- tions for concrete materials and mixtures and build a more durable structure. Problem Statement and Scope of Research A great deal of research has been performed on properties of concrete containing one or more supplementary cementi- tious materials. However, this research, conducted on specific SCM sources, has not provided clear conclusions concerning the optimum use of these materials. NCHRP Project 18-08A was conducted to develop a statistically based experimental methodology for determining the best possible mixture pro- portions of high-performance concretes. Introduction to Methodology

The methodology for designing concrete mixtures con- taining supplementary cementitious materials presented in these Guidelines is aimed at aiding the user in conducting an experimental study to select the optimum combination of locally available materials. It is intended for use in the development phase of concrete construction projects where durability is a main objective. The objective of this research was to develop a methodol- ogy for designing hydraulic cement concrete mixtures incor- porating supplementary cementitious materials that will result in enhanced durability of cast-in-place concrete bridge decks. The methodology that was developed mainly consid- ers the use of fly ash, silica fume, slag, and natural pozzolans, both singularly and in combination; but it applies to any combination of materials and performance criteria. This methodology relies on established practices of statisti- cal design and analysis of experiments. It provides a framework for comparing varied types of performance simultaneously and obtains useful information while testing a small number of the possible combinations of variables that describe the full test range. This methodology includes a process for determining concrete performance requirements in durability tests based on a selected service environment, as well as a process for selecting durable raw materials. Guidance for SCM types, combinations, and ranges of use for bridge deck applications is provided. Also, a process for selecting mixture variables to put into an orthogonal experimental design matrix is described. Although the user is expected to have a basic understanding of concrete mixture and concrete technology, background specifically related to durability issues and guid- ance for avoiding harmful material interactions is provided for reference. The methodology is particularly valuable because it defines a procedure for optimizing concrete mix- tures relative to locally applicable performance criteria with locally available materials. Products of Research NCHRP Project 18-08A, “Supplementary Cementitious Materials to Enhance Durability of Concrete Bridge Decks,” produced the following: • NCHRP Report 566: Guidelines for Concrete Mixtures Containing Supplementary Cementitious Materials to Enhance Durability of Bridge Decks • NCHRP Web-Only Document 110: Supplementary Cemen- titious Materials to Enhance Durability of Concrete Bridge Decks, the project report that includes a hypothetical case study • A Microsoft® Excel–based computational tool for the con- crete mixture optimization methodology and a user’s guide These Guidelines present the information required to work through the process of developing an optimized concrete mix- ture using locally available materials. It provides a framework and guidance for making the decisions involved in this process and explains how to perform the experiment design and sta- tistical analysis. NCHRP Web-Only Document 110 (available on the TRB website: http://www.trb.org/news/blurb_detail. asp?id=7715) provides a condensed description of the methodology and the process by which it was developed. It also discusses the scope and capabilities of the methodology and how and where it may be best applied. The appendix of NCHRP Web-Only Document 110 presents the details of the test program conducted in parallel with the development of the methodology, including a description of the materials examined, the tests performed, and the results obtained. The tool, Statistical Experimental Design for Optimization of Concrete (SEDOC), was developed to guide the user and to execute the statistical analysis and modeling. The computa- tional tool and the user’s guide are available on the TRB website (http://www.trb.org/news/blurb_detail.asp?id=7714). Relationship of the Methodology to the Implementation of Concrete Mixtures Designed for Durability The recommended process for the implementation of con- crete mixtures designed for durability for a given structure is summarized as follows: 1. Identify targeted performance in terms of general objec- tives and in terms of quantifiable measures 2. Select the best available raw materials 3. Select the best concrete mixture based on concretes produced with the specific raw materials and tested to evaluate performance 4. Produce trial batches of concrete with the selected raw materials by candidate ready-mixed concrete producers to demonstrate that target performance can be achieved in the field 5. Conduct a comprehensive quality assurance/quality con- trol program and monitor construction practices and the concrete itself through trial placements and during construction The methodology developed in this project will aid the user through the first three stages of the implementation process. The methodology consists of the following steps: 1. Define concrete performance requirements. The future service environment of the concrete is assessed and used to define the desired concrete performance. Tests to evaluate these properties are selected and a “desirability function” 2

is created for each test response. The type of SCMs and general ranges of SCM content that are expected to pro- duce this performance are also identified. 2. Select durable raw materials. The raw materials that are under consideration for the project are evaluated in this step. Worksheets are used to compile and then compare the different properties of the candidate raw materials. The materials most likely to produce durable concrete are selected based on this information. 3. Generate the experimental design matrix. An orthogonal experimental design is selected for the investigation that is compatible with the identified types and ranges of materi- als and the scope of the test program. Based on the design, the experimental matrix (the specific mixtures to be tested) is chosen. 4. Perform testing. In this step, concrete mixtures are batched according to the experimental matrix and tests are conducted as defined in Step 1. 5. Statistically analyze the results with the desirability functions and generate the optimal mixture(s). The test results are compared within the framework provided by the desirability functions. The Best Tested Concrete (BTC) is selected based on the overall desirabilities calcu- lated for each mixture. The performance is modeled for each factor and these models are used to identify the Best Predicted Concrete (BPC). 6. Confirm the optimum mixtures by testing. The BPC and BTC are batched and tested to confirm their durability and to select the optimum performer, or Best Concrete (BC). Introduction to Supplementary Cementitious Materials Four types of SCM are commonly used in concrete bridge deck construction: ground granulated blast furnace slag (GGBFS), fly ash, natural pozzolans, and silica fume. GGBFS or slag, a by-product of iron ore processing, is specified in AASHTO M 302 (ASTM C 989), Standard Spec- ification for Ground Granulated Blast-Furnace Slag for Use in Concrete and Mortars. Fly ash, a by-product of coal-burning electric power plants, is specified in AASHTO M 295, Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use as a Mineral Admixture in Concrete, and ASTM C 618, Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete. Fly ash is divided into two classes by this specification based largely on the total combined percentage of silicon dioxide, aluminum oxide, and iron oxide. Fly ashes with this combined percentage greater than 70% are Class F, while those with this combined percentage less than 70% and greater than 50% are Class C. Class F fly ashes usually contain low amounts of calcium oxide (CaO) (less than 10%) while Class C ashes may have more CaO content (between 10% and 30%). The class is largely determined by the type of coal burned during the gen- eration of the fly ash. Natural pozzolans are also governed by AASHTO M 295 (ASTM C 618). Some of the more common materials that fall into this category are metakaolin and calcined clay. These materials are not by-products but are processed from natu- rally occurring raw materials. Silica fume, a by-product of silicon alloy production, is governed by ASTM C 1240, Standard Specification for Silica Fume Used in Cementitious Mixtures. Silica fume is probably the SCM most associated with concrete designed for durabil- ity because of its extremely fine particle size that densifies the microstructure and thus influences strength, permeability, and other properties. SCMs are hydraulic or pozzolanic (or combinations thereof) materials that are combined with portland cement and contribute to the properties of the concrete. The term “hydraulic” means that the material will set and harden by reacting chemically with water. “Pozzolanic” materials, when finely divided and in the presence of water and port- land cement, react chemically with the calcium hydroxide released during the hydration of the cement to form hydra- tion products (e.g., calcium silicate hydrate). GGBFS and Class C fly ash fall in the hydraulic category. AASHTO M 295 Class F fly ash and Class N natural pozzolans and silica fume fall in the pozzolanic category (1). These materials are discussed in much greater detail in Step 2 of this methodology. Statistical Design of Experiments An experiment that is “designed” is one that is based on a test program laid out to produce results that answer a ques- tion or verify a hypothesis. Statistical design of experiments takes the design of the experiment one step further and involves selecting the experimental parameters so that the experiment will produce data that lends itself to analysis and modeling with statistical tools. The great advantage of statis- tical experimental design is that experiments set up in this way are more efficient, i.e., they allow predictions regarding large numbers of possible variations based on a limited num- ber of experiments. Terminology Table I.1 summarizes the terminology associated with the statistical experimental design that can be applied to the design of concrete mixtures. The three most common terms are “factor,” “level,” and “response.” 3

The term “factor” refers to the independent variable, or x-variable, to be varied in the experiment. There are several kinds of factors. “Type factors” and “source factors” are factors that describe the type or source of material that is used and are defined discreetly to be either one type of material or another, or a material from one source (or supplier) or another, respec- tively. “Amount factors” vary the amount of a raw material in the mixture and can be defined continuously over the range to be tested. It is also possible to combine two factors in a “com- pound factor,” which will be discussed later. The term “level” refers to the chosen value of the factor in a particular mixture. For example, if an amount factor for a given experiment was selected to be w/cm, three levels to test could be chosen as 0.38, 0.40, and 0.44. For a source factor, the levels are the actual sources used such as Plant A and Plant B. A type factor is used when it is desired to change the type of cement, SCM, or other raw material. For example, a type fac- tor might be type of fly ash, and the levels of the type factor could be Class F and Class C. One could then also have an amount factor for fly ash (at levels of perhaps 15% and 30%) that would then apply to whichever type of fly ash was used in the mixture. Another term used is “response.” It is the y-variable, or test result when a mixture is tested for a certain characteristic using a specific test method, such as strength or elastic mod- ulus (i.e., “response” equals test result). The “experimental matrix” is the matrix of combinations of factors and levels that is generated by the user with the aid of tables or software. It includes the specified number of “mixtures” to be evaluated and how the levels of each of the factors should be set for each mixture. The “desirability function” refers to a plot or equation that rates a given output from a test on a scale from 0 to 1, where 0 is an unacceptable result and 1 is a result that needs no improvement. For example, for a test of compressive strength at 7 days, an outcome of 1200 psi (8.3 MPa) for a certain mix- ture might be considered unacceptable and that mixture would be assigned a desirability for strength of 0. If a different 4 elpmaxE noitinifeD mreT Factor X-variable or independent variable (see below) Type factor A factor that varies the type of material used in a mixture “Type of fly ash” Source factor A factor that varies the source or supplier of raw material “Cement producer” Amount factor A factor that varies the amount of a material “Amount of GGBFS” Compound Factor Multiple factors where the levels of one factor depend on the level of another factor. (The two factors work together to define the type and amounts of material used in a mixture.) Factor 1 is a type factor for defining the type of SCM and its levels are fly ash or slag. Factor 2 is an amount factor whose levels are low and high. The amounts specified for low and high for each type of SCM are different. For example, low and high for fly ash might be 15% and 40%, but low and high for slag might be 25% and 50%. Thus, the levels of the second factor change (from 15% and 40% to 25% and 50%) depending on the level of the first factor (either fly ash or slag). Levels The values of the factor to be tested • Class C or Class F for type of fly ash • Plant A or Plant B for source of cement • 15% or 25% for amount of GGBFS Response A measured test result Strength at 7 days = 5000 psi Experimental matrix A list of mixtures to be tested linking specific factors and levels that have been chosen to facilitate the statistical analysis. See tables in the “Selected Orthogonal Design Matrices” section in Step 3. Desirability function A function that rates the test result from very good, i.e., non-improvable (desirability=1) to unacceptable (desirability=0). See Figures S1.2 to S1.23 Overall desirability Combined desirability for a single mixture based on all the individual desirabilities. This combination is calculated as the geometric mean of the individual desirability functions for each response. Overall desirability = 0.984 for Mixture #1 Table I.1. Terminology related to statistical design of experiments.

mixture tested at 5000 psi (34.5 MPa) or higher after 7 days, it might be considered highly desirable and that mixture would be given a desirability for 7-day strength of 1. Mixtures with results in between 1200 and 5000 psi (8.3 and 34.5 MPa) could be assigned a desirability between 0 and 1 according to the desirability function. The desirability function for a response covers every possible outcome of the test to a number between 0 and 1. Through the desirability function, the user is able to define and set the relative importance of each test result (response). The desirability function will be discussed in more detail later in this Introduction and in Step 5. The overall performance or “overall desirability” of a mixture is the combined desirability of each test response and allows a direct comparison of one mixture with another to decide which mixture is best overall. This comparison is possible because the overall desirability is derived from the individual desirabilities for each response and thus reflects the individual properties of the mixture and importance of that property in the overall con- crete performance. Overall desirability is calculated from the geometric mean of the values of the desirability functions for each response. Methods of Designing Experiments Through the use of statistical design of experiments, useful information can be obtained regarding a range of mixtures in question without testing every combination of variables at every level. There are several types of designed experiments, including one-factor-at-a-time, orthogonal main effects designs, mixture approaches, and central composite designs. Each type has its advantages and disadvantages. In this methodology, a straightforward design method called fractional orthogonal design is used. The biggest advan- tage of this approach is that it generally requires the testing of a relatively small number of mixtures to cover a large test space. For example, for an experiment of four three-level factors (four materials at three dosages each), careful selection of the combinations of factor levels to be tested would permit conclusions to be made regarding the full test space (all possi- ble combinations within the factor ranges) from tests of only 9 of the discrete combinations of factor levels instead of all 34 (i.e., 81) possible discrete combinations. This method also allows consideration of non-quantitative factors (such as source of material), which are often variables. Also, this method does not limit the number of responses or the form of the desirability functions. Using the tests results based on only the selected combina- tions, the orthogonal design method can provide a prediction of the best level for each of the factors in the experiment. If the optimal level for any factor (e.g., SCM content) substantially changes for different levels of other factors, the predicted optimum level of that factor may be poorly estimated, but it will not affect the evaluation of the concretes that are actually batched and tested. Because the mixtures in an orthogonal design are quite different from each other, the chance of find- ing a good mixture is increased even in the cases where the optimum level for some factors is difficult to predict. The confirmation testing strategy, where the BPC and BTC are tested, addresses this issue. The alternative is to test substan- tially more mixtures as in the mixture or central composite design approaches (at least 24 of the 81 possibilities would need to be tested for these methods). Desirability Functions and Combining Test Results If only one test were to be performed, the performance of the mixtures could be compared based only on the measured value of that test for each concrete mixture. However, because many different tests will be performed, and the selected mixture must perform well in all of these tests, a method of combining the responses (test results) from the different tests is needed. This combination is achieved by describing a desirability function (2) that provides a rating for all potential values of the test response on a scale from 0 to 1, where 0 means an unaccept- able response, and 1 means no more improvement would be required. Each test response has its own desirability function. The advantage of the desirability function is that all test responses are considered using an equivalent scale and can be combined to produce one score or measure of the quality of a given mixture called the overall desirability function. When maximized, the overall desirability identifies the best possible combination of performance in all the tests. Mathematically, the overall desirability function is the geo- metric mean of the desirability functions for each of the tests. For example, if the desirability functions for three different tests are represented by d1, d2, and d3, the overall desirability, D, will be defined as . In general, for n desirabil- ities, the overall desirability is the nth root of the product of the desirability functions. Because the desirabilities range between 0 and 1, the overall desirability function also ranges between 0 and 1, where 0 is unacceptable and 1 is desirable. To build the desirability function for a specific test result, an optimum target for the measured response of each test is specified. At the target, the individual desirability for that test is 1. Then an allowable range for the measured response is specified. Outside of this range, the individual desirability is 0. The shape of the desirability function between the target and the range is also specified to reflect the importance of being near the target. If the measured response of a particular test is to be maximized (or minimized), then the upper (lower) range of the desirability is considered to be perfect, and thus any measured value above (below) this level has a desirability of 1. Figure I.1 demonstrates the shape of three possible d d d1 2 33 × × 5

desirability functions. Because a mixture that receives a desir- ability of 0 on a single test will have an overall desirability of 0, the performance range assigned to 0 by the function will make that mixture unacceptable regardless of performance in all other categories. Because the desirability function provides the link between the test that may be influenced by the method and testing conditions and predicted actual behavior, the accuracy of the desirability function requires subjective interpretation by the engineer or scientist conducting the study. After the desir- ability function has been applied to the test responses and a maximum overall desirability has been selected, users must apply their expertise in concrete technology to carefully study the predicted responses for each test to ensure that the trade- offs made in maximizing the desirability function did not lead to an unexpected (i.e., contrary to well-established princi- ples) result. Examples of desirability functions for a number of prop- erties (response types) are given in the guidance provided in support of Steps 1 and 2. The desirability functions to be used must be chosen carefully because they are a critical part of the analysis and modeling process. The functions must be adjusted based on what tests are conducted and the under- standing of how each response (test result) affects the over- all performance. Functions may be set to avoid problems or define performance not directly measured by the experi- mental program. For example, the desirability function for air content might be adjusted depending on whether cyclic freezing testing is performed. Sometimes functions will be established for two or more responses in the experimental program to define a certain type of performance and thus the importance or weight of this issue will be indirectly increased. Therefore, all functions should be carefully reviewed to ensure that the proper weight is established for each parameter. The desirability functions provided here are examples that may not be consistent with the development objectives of the concrete mixture for the specific structure or structures being considered. Therefore, the individual mixture requirements should be assessed and appropriate desirability functions should be developed. The example functions were developed for use in an experimental setting and have not been designed or tested for establishing degree of compliance or project pay factors. Analyzing the Orthogonal Design Experiment For each experiment, a numeric analysis (Step 5) will be performed. The analysis will consist of two parts: • The concrete mixtures that were tested will be compared to determine which one best matched the performance requirements for the project in question. The best match is the BTC. The identification of the BTC will involve trade- offs between the different performance measures and uses the overall desirability function as a basis for comparison. • Statistical modeling will be employed to predict the com- bination of the levels of the factors that will produce the BPC according to the same desirability functions. This modeling will be accomplished based on individual pre- dictions for each of the responses (performance measures) for all possible combinations of the factors in the range tested. The statistical models will also provide a prediction of results for the BPC on each of the individual tests, such as strength and elastic modulus. Because the experimental design approach involves a rela- tively small number of tested concretes compared with the number of factors and test combinations, the results of the statistical model need to be confirmed by a second round of testing (Step 6). Typically, the BPC will not be among the mixtures that were actually tested in the original matrix; thus, if it is to be used in an application with confidence, a confir- mation batch of the BPC must be mixed and tested. Realisti- cally, the recommended level of testing of the BPC will be based on the amount of time available for confirmation 6 0.5 1.5 2 2.5 3 3.5 0.2 0.4 0.6 0.8 1 0.5 1.5 2 2.5 3 3.5 0.2 0.4 0.6 0.8 1 0.5 1.5 2 2.5 3 3.5 0.2 0.4 0.6 0.8 1 (c)(b)(a) Figure I.1. Individual desirability functions for (a) a response that must be close to a target value, (b) a response that must be in a range, but not necessarily close to the target value, and (c) a target that is considered perfect if it is below 2 and unacceptable if it is above 3.

testing and the predicted performance difference between the BPC and the BTC. Application of Methodology Flowcharts, worksheets for summarizing information, background discussions of the issues relevant to decisions that need to be made, tables of experimental matrices, and an explanation of the statistical analyses are provided in these Guidelines to aid the user in the application of each of these steps. The Process The initial decisions to be made for designing concrete mixes are laid out in Steps 1 and 2. The end products of Step 1 are the laboratory tests to be conducted (the responses) and the associated performance requirements for the concrete to be designed. Guidance is given regarding suitable ranges of vari- ous SCMs that have been shown to improve the responses. The information gathered from Step 1 is collected in a worksheet. This worksheet and others given in these Guidelines are intended to provide a location for the user to record informa- tion relevant to the specific experiment being conducted. Because these worksheets will be marked up as decisions are made, it is recommended that the user photocopy the pages or print the worksheets (from SEDOC) in case the methodology is to be used more than once. The end products of Step 2 are the potential raw materials’ sources, test data regarding these raw materials, and combina- tions that are likely to be durable. The sources or types of raw materials will be the levels of “source or type factors” in the experimental design matrix. The quantities to which these raw materials will be varied are the levels of the “amount factors” in the experimental design matrix. The full set of (source, type, or amount) factors are the independent variables in the study. The information gathered from Step 2 is collected in several worksheets. The information on these worksheets are combined into the set of factors and levels used in Step 3. Step 3 guides the user in the selection of the experimental matrix from a table of orthogonal experimental designs defined by the number of mixtures to be tested and the number of two- and three- level factors to be investigated (note that only certain combinations of specifically sized factors, varied in specific ways, will produce the symmetrically distributed experi- ment necessary for the statistical analysis.) Figure I.2 schematically illustrates the relationship between the first two steps and how they provide information for the experiment design process. This figure illustrates that, during this selection process, there will likely be interaction among the materials selected based on the performance objectives, the cost and scope of the testing program, the selection of the experimental design matrix, and the number of materials that can be tested. User Aids Examples As part of the research conducted to develop this method- ology, the process of identifying an optimum concrete mix- ture was evaluated using real materials and test results and a hypothetical set of performance requirements. The com- plete details of this hypothetical case study can be found in the appendix of NCHRP Web-Only Document 110 (http://www.trb.org/news/blurb_detail.asp?id=7715); select examples from that investigation are presented throughout these Guidelines to demonstrate how each step may be completed. 7 Step 1: Definition of Concrete Performance Requirements (Responses) and Identification of SCM Effect on Responses Step 2: Selection of Candidate Raw Materials Likely to Produce Durable Concrete Step 3: Selection of Orthogonal Experimental Design (Number of Mixtures to be Tested, Factors and Levels) Figure I.2. Relationship between flowcharts and experimental design of concrete mixtures.

Computational Tool In parallel with these Guidelines, SEDOC—a computa- tional tool consisting of Microsoft® Excel–based work- sheets—was developed to automate several of these tasks. SEDOC is described in detail in the user’s guide available on the TRB website (http://www.trb.org/news/blurb_detail. asp?id=7715). The first workbook, titled “SEDOC: Setup,” is based on the flowcharts developed for Steps 1 and 2 of this study to aid in the development of the experimental matrix. The second workbook, titled “SEDOC: Analysis,” helps to perform the statistical modeling and analysis that leads to the prediction of the optimum concrete. Glossary A glossary of terms is included after the six steps of the methodology. This glossary contains definitions for some of the statistical jargon used to discuss the application of this methodology. 8