MULTIGRADE, MULTIVARIABLE, CUSUM QUALITY CONTROL

Transcription

1 MULTIGRADE, MULTIVARIABLE, CUSUM QUALITY CONTROL K. W. Day*, Consultant, Australia 32nd Conference on OUR WORLD IN CONCRETE & STRUCTURES: August 2007, Singapore Article Online Id: The online version of this article can be found at: This article is brought to you with the support of Singapore Concrete Institute All Rights reserved for CI Premier PTE LTD You are not Allowed to re distribute or re sale the article in any format without written approval of CI Premier PTE LTD Visit Our Website for more information

2 32 nd Conference on OUR WORLD IN CONCRETE & STRUCTURES: August 2007, Singapore MULTIGRADE, MULTIVARIABLE, CUSUM QUALITY CONTROL K. W. Day*, Consultant, Australia Synopsis The paper presents a method of quality control of concrete claimed to detect quality changes and detect their cause substantially faster and more accurately than any other. It took 25 years from the early 1950s to the late 1970s to perfect my technique for analysing concrete compression test data. In the 25 years since then, the technique has spread widely, but it is still not fully incorporated in any national or international code of practice. So I want to take this opportunity to present its exact basic concepts. I need to present each of the three major components: Multigrade, Multivariable and Cusum and to add to them one further item: prediction from early age tests. I will take the three major components in reverse order: Cusum I did not invent cusum (cumulative sum analysis). It was developed in the chemical industry (Woodward and Goldsmith, 1964) and first used for concrete QC in the UK in the 1970s (Testing Services, 1970). I started to apply it some six months later (independently of the Testing Services development). Cusum involves subtracting a target value from each result and maintaining a cumulative sum of the remainders. Its main value is that it detects a change in the mean value of a string of results about three times as quickly as a normal Shewhart Chart. Detection can be by mathematical analysis of the string of cumulative differences, or by graphing the cumulative sums. The graphical method is preferable because it is easier to detect and eliminate false changes due to testing error or abnormal circumstances. My major contribution is to use the continuously updated current average of a variable as the target. This focuses the cusum on detection of change rather than adherence to a selected target. It also has huge significance for the ease of combining very large numbers of grades of concrete in a single cusum analysis. Multivariable Multivariable relates to including cusum graphs of other variables (such as density, workability, and temperature, tests on constituent materials such as cement strength and sand grading, and also average pair difference of 28day results to detect any deterioration in testing quality) on the

3 same display as concrete strength. I started to do this in my first year in the concrete business (1952) but the idea still does not seem to have been fully accepted by the UK QSRMC (Quality Scheme for Ready Mix Concrete), even though I have presented papers there on two occasions. The concept is that changes in concrete strength will be mirrored in, and so confirmed and explained by, changes in one or more of the other variables. A substantial point is that density data is very valuable and should be measured and entered in the system on receipt of the specimens at the lab, rather than at test. This allows a density cusum to run six days ahead of a 7day strength cusum, which is more valuable than a statistical assessment of strength itself (e.g. a V-mask) in assessing whether an observed downturn is or is not significant. Multigrade Multigrade relates to combining the results of several (or many) grades of concrete in a single analysis. If done effectively, this gives an equivalent effect to increasing the frequency of testing many times over. EN206, the European quality scheme, does this to a limited extent but makes hard work of it and has to limit it to a few similar grades of concrete. EN206 combines grades by adjusting or converting the results of other grades so that they can be analysed as though from a selected control grade. This requires initial and continued effort to set up and continually adjust the conversion process to be fully effective. I have been happily applying my version to combining the results from hundreds of grades of widely different character with no human effort required (the computer does it all) for more than 20 years. My technique is to accumulate departures from the current average value of any variable in each individual grade as though these were all from the same average value. While I was initially dubious of this, I have found that it works beautifully for strengths of 20MPa or less to strengths of 100MPa or more, and including normal dense and structural lightweight concrete in the same analysis. Early Age Prediction Normally 'early age' means something of the order of 3 to 7days but I have also originated a means of extending this to cover predictions from less than 24hours where the additional cost of temperature history monitoring is considered justified. My contribution for normal results has been to recognize that a more useful prediction is obtained by adding the average gain to the early result than by assuming a percentage increase. This is because the early age result tends to be of a single specimen and to be subject to more than usual error (e.g. in curing). Applying a percentage increase multiplies the effect of any error. Note that it is low 7day results that result in mix modification, so unwarranted pessimism about subsequent gain is to be avoided. For very early results it is necessary to have a temperature history record and to express the age as an Arrhenius Equivalent Age (EA) rather than a physical age. The concept (and a competing but less accurate concept of temperature x time maturity) is well known, but the usual technique is to construct a strength v maturity (or EA) graph, measure the maturity (or EA), and read off the strength. Construction of the calibration graph requires substantial effort, but a more serious fault is that this technique assumes that the concrete being assessed is the same as that used to construct the graph and so cannot react to changes in concrete quality. I have devised a simple program that continuously and automatically feeds back and corrects the Arrhenius constants so that predictions of 28day strength (and any other desired age) based on the actual concrete in question can be obtained within a few hours. Combined Effect The real power of my overall system lies in the way that these separate elements combine together. It is the way I do cusum that enables such widespread multigrading, the use of cusum that links multivariables, and the use of multivariables to confirm and explain the detection of

4 change. Combined with early age prediction, these features enable the detection and cause of change to be established several weeks earlier than most (all?) other control systems. The earlier detection and trend rectification itself reduces the overall variability of the concrete being produced - and I have demonstrated that the number of results required to detect a given change is directly proportional to their basic variability, so that if you have double the standard deviation, it will require twice as many results to confirm a change. Supplementary Analysis The system presented works well in detecting change in either a single grade of concrete or a mass of results from hundreds of different grades of concrete. However the possibility exists that some factor affecting only a single relatively obscure grade of concrete, generating few results, will be swamped by the mass of other results. The system therefore displays a tabulation of statistical analyses of each separate grade arranged in order of departure from target strength. Any problem grade will therefore be clearly highlighted, even amongst thousands of results from hundreds of other grades. Aspects of Control not Considered Here There are techniques affecting the control of quality that are not included in the system as discussed here. These include batch plants that automatically telephone or selected individuals in the event of batching errors outside a nominated range and the use of appropriately monitored delivery vehicles to act as rheometers. If provided, data from such devices can be included in the graphed variables of the control system here described. Possible Future Advances in Control Technology It is always possible that new testing techniques will be devised, such as an instant measurement of a sand grading and/or moisture content, or an accurate measurement of the total water content of concrete in a mixer. Such developments could probably be integrated into the control system described here, if and when they are realised. However it is contended that the techniques presented represent the ultimate in the analysis of control data as currently available. Availability of the Technique The technique was made substantially available as a Lotus spreadsheet in a series of 10 articles appearing bi-monthly in Concrete International in Apart from this it has only been available as part of the ConAd computer program, marketed by the author's company, Concrete Advice Pty. Ltd. in the 1990s, and now, since the sale of that company, by Command Alkon Inc. The author is currently engaged in producing an improved version together with his new associates Contek and Shilstone. To enable the basic technique to become a standard item, available to all, there is now a free program available on my website This program falls far short of ConAd in many respects but it does enable all the features presented here to be employed, with the exception that the 'multivariables' available are limited to strength, density, slump, and temperature and only eight cusum graphs can be drawn. Specification of Concrete While not the subject of the current address, a few words on this subject may be helpful. Although it has taken 50 years in some cases, it seems that the whole world is coming to accept a view I have been presenting since the early 1950s. In 1958(1) I wrote 'The only rational objective for any but 100% testing is not to discover and reject faulty products but to ascertain the minimum quality level of the production'. The article went on to assert that the only really fair

5 and effective basis for quality regulation is the imposition of a cash penalty for marginally defective concrete based on a statistical analysis of test data. I am still of that opinion, even though (so far!) it does not appear to be shared by anyone else in the world - maybe another 50 years? I have also expressed the view that an effective control system must have two quite separate features. One is to form a very accurate view of the mean strength and variability of the concrete supplied to date (no hurry). The other is to detect as quickly as possible when the quality of the concrete being supplied changes (no requirement for accuracy or infallibility). Any attempt to combine the two is likely to fail to accomplish either. A requirement that is absent from most specifications is 'the concrete shall be produced under an approved control system'. In this respect it should be noted that ISO 9001 certification establishes that the producer is correctly operating his nominated control system, but not whether that system is effective in the early detection of change. Summary and Conclusion I believe that it is important to get the detail exactly right and that this is currently not happening. I list again the important features of the system, making clear where this differs from some current practices: 1. Test data (including specimen density) should be entered in the system on the day it is obtained and visually assessed daily using automatically generated multigrade, multivariable, cusum graphs. 2. Cusum analyses should use the constantly updated average values of all variables as a target rather than a specified target. 3. Data should be 'multigraded' by cusuming these differences as though from the same mean rather than transforming results to a control grade. 4. Data should include strength at 7days or earlier transformed into a 28day strength prediction by adding the current average gain. Prediction should NOT involve any assumption that a low early age result is likely to also show a lower subsequent gain. 5. The daily assessment should include at least the first few rows of an automatically generated table ranking all grades in order of departure of current and predicted mean strengths from their target values. 6. Cusums should include average pair difference of 28day results as an indication of testing quality. References 1. Day, KW Statistical quality control of Concrete and Concrete Products, 3part series, Commonwealth Engineer Melbourne, Australia, Dec 1958, Jan and Feb Day, KW Concrete mix design quality control and specification, Taylor and Francis, London (Editions 1995,1999 and 2006)