Crushed stone 40 70 Application. Fractions rubble

This optimization is performed by minimizing the summation function, i.e. The amounts of the curves of the matrix that do not correspond to the row of optimization criteria. Figure 11 shows this optimization. The results of this optimization are presented in Table 9 in terms of values. The maximum level achieved by the number of curves corresponding to two criteria for each of the levels and expressed as a percentage is presented in the table. It has been found that on average, less than 5% of the curves of the complete matrix can perform two criteria selected for the summation function.

On average, you can also see that the greatest percentage is found for a basalt source, none of which is 96, 7%. On the contrary, the lowest percentages are found for a lime source whose variability of the porosity of the shallow fraction to optimal more depending on the percentage of small particles. Probably due to the fact that? In this case, very close to the average value of the percentage of small particles of the complete matrix, i.e. 5, 07%.

The number of curves having a percentage of small particles near the average, naturally higher, it becomes more difficult to simultaneously satisfy the two criteria. Compared to these two methods, it seems that the methods give quite close results, as can be seen from the table. They are also expressed in absolute value in this table.

It should be noted that in general the difference between the two methods is about one tenth, which is on the scale of an interesting parameter, which represents the percentage of small particles, small. Table 11: Difference between? obtained from the summation function and? The resulting linear straight lines.

Meaning in italics: Differences expressed in absolute values. Values? The resulting summation, were built in relation to the values? obtained from regression equations. For which both methods give the same result, amount to 3, 5%. For meaning? From 2 to 3, 5%, the summation function method gives higher values, while it gives lower values \u200b\u200bfor the value? from 3, 5 to 7%.

Based on the results presented, it can be concluded that the use of regression lines ensures sufficient accuracy in most cases, despite the characteristics of said relationships. The values \u200b\u200bthat can be calculated using regression lines are relatively consistent with the values \u200b\u200bobtained from the summation function, seeking to ensure that the minimum curve does not correspond to two optimization criteria associated with the porosity of the fraction. Thin to optimal and percentage of small particles.

However, both methods could be improved, in addition to achieving more equal results, if the matrix was improved by adding many other curves of the real form. It would make it even better to take into account the role of all grain size in addition to the percentage of small particles on the creation of porosity in the fine fraction of materials. Values? can be seen in the picture? The summation obtained from the function is considered in this case.

The presented approach differs from the usual approach, since, as a rule, the effect of small particles per performance is especially valued on mass conditions. In this research project, a complex β-variable was proposed describing the resistance to erosion. It consists of the coefficient of uniformity, the porosity of the shallow fraction and the blue value of the materials. The porosity of the shallow fraction is a variable associated with the state of materials, which is best describing this phenomenon of erosion.

Results corresponding to optimization of erosion resistance depending on the coefficient of homogeneity are presented in the table. This is naturally associated with the nature of the trend equations that have a power type. This follows from the conclusions about the percentages of subtle critics, where higher levels of performance are associated with a higher percentage of the range of changes in the spindle. As discussed earlier, mechanical behavior is analyzed in two aspects, namely, reversible module and susceptibility to constant deformation.

Due to the more standardized nature of tests with a reversible module, these tests should relate to higher relative importance. However, satisfactory results were obtained from tests for susceptibility to constant deformation, which makes it possible to complete the mechanical characteristic and optimize it from a more global point of view.

In contrast to the optimization of the porosity of the fine fraction, the reflection of which on this subject led to the definition of percent of small critical values, the optimization of mechanical behavior leads to the determination of areas of granulometry curves that limit areas associated with certain levels of performance. Indeed, although only one explains the variable to the source can be used to determine the boundaries between performance levels, a good level of correlation of several efficiency indicators for a specific range of explanatory variables can consider the determination of the part. Granulometric curves that limit performance levels.

It is even more fair for crushed materials for which several explanatory variables showing high levels of correlation have been identified. As mentioned, it is less correct in the case of a partially crushed source. In addition, consideration of the mechanical characteristics on two fronts, reversible module and susceptibility to constant deformation makes it possible to consider at least two optimization points in the spindle and, thus, separating parts of the grain size curves optimized for each of the performance levels.

In this regard, due to the more experimental nature of the tests for susceptibility to constant deformation, only one explaining the variable to the source will be saved to optimize the goals. Moreover, in general, relations associated with these studies seem slightly less accurate.

As shown in the section on optimizing the porosity of the shallow fraction, the first step of optimizing mechanical behavior is to determine the maximum change in the parameters of the mechanical characteristics inside the time. Unlike the porosity of the shallow fraction, there is usually no recognized performance indicator for evaluating mechanical behavior. In addition, the results show that these indicators vary from one source to another and do not necessarily include fractions of the same size. Therefore, it is proposed for each of the test sources to fill three relationships with the highest rating level of the reversible module to identify the minimum and maximum mean values.

However, it should be noted that this exercise excludes explaining variables whose "realistic" borders is difficult to identify in the time zone. Therefore, this type of variable includes two points on the grain size curve. Therefore, in this case, only variables are used, the boundaries of which are easily identified. However, given the above results, it is known that these three meanings are interconnected. They take shape. As mentioned, in the case of a granite gneisse source, you can use several explanatory variables.

However, in the case of two crushed sources, several explanatory variables were identified. For a basalt source, several variables were identified, but it should be noted that there is redundancy in these variables. However, to use the first, it is necessary to determine the percentage of gravel to determine their value. These data are shown in Table 14. However, it is possible to consider the definition of productivity zones for this source in more detail, taking into account the effect of susceptibility to constant strain.

Further details follow further on this issue. Finally, it can also be noted that in some cases the values \u200b\u200bobtained from two different equations give very close results, which lead to some inaccuracies, including the error of the evaluation equations generated by the fields. It should also be noted that the analysis made for the limestone source allows you to extrapolate the percentage of small particles, since the percentage of gravel and sand is known. However, these values \u200b\u200bare presented for informational purposes.

It is important to understand that, apparently, there is no significant tendency between the reversible mechanical behavior and the fine part of the materials for this source. As for susceptibility to constant deformation, only one indicator is used, since few showed high levels of correlation with a constant deformation rate, and since obtaining this value is not the result of standardized tests in the case of this study. Equations describing these relations take shape.

These values \u200b\u200bare presented in the table. These values \u200b\u200bare also included in the table. Combining the analyzes carried out in order to optimize the mechanical behavior of granular materials based on road coat, that is, the values \u200b\u200bobtained from the optimization process for testing the reversible module and susceptibility to constant deformation. You can display areas corresponding to each of the performance levels. In fig. 14, Fig. 15 and rice. 16 shows a graphic illustration of the optimization of mechanical behavior achieved in this section.

However, it should be remembered that this chart is drawn by considering mechanical characteristics on two fronts. However, according to the analysis of reversible modules, the values \u200b\u200bof homogeneity coefficients are proposed. It should also be reminded that, as shown in Fig. 35 It seems preferable that the large unit does not make a matrix for the materials of this source, but was made of small aggregate. The next section will be devoted to this aspect. As for limestone, in Figure 15 it can be seen that the mechanical performance quite well corresponds to the form of the spindle in the gravel part.

This drawing illustrates the importance of a large unit for the operation of this source. Therefore, it leaves a place for materials with some spread. However, from the point of view of sensitivity to constant deformation, it was shown that this characteristic is undesirable, which is potentially associated with a high percentage of small particles. Finally, in the case of basalt, the performance quite well corresponds to the form of a spindle in the case of a gravel fraction, as well as a limestone source.

However, in the case of a basalt source, if the mechanical characteristics approach two fronts, it is determined that it is preferable that the sand fraction is more thin. The mechanical optimization process leaves a place to select those available for estimating or optimizing grain size. Indeed, depending on the criteria, which is preferable to consider optimization only with a part of a reversible module, only a part of a constant deformation or a combination of two, as it was presented.