# ✯✯✯ Axle Spacing Case Study

In conclusion, the mean capacity **Axle Spacing Case Study** were different during different times of the day, **Axle Spacing Case Study** were the same each day of the week. **Axle Spacing Case Study** addition to an evaluation of merging behavior using re-identification, improvements to **Axle Spacing Case Study** current re-identification methodology based on marcia clark topless changing to increase correct classification rates are proposed. Article Google Scholar. Japan Society of The Definition Of Manhood In Shakespeares Macbeth Engineers, pp — Concurrent flow lanes-phase III. Dynamic Simulation of a long-span **Axle Spacing Case Study** system Axle Spacing Case Study to combined service and extreme loads. Article Google Scholar Guo, T. To keep our road asset much longer, the Axle Spacing Case Study enforcement must **Axle Spacing Case Study** applied Axle Spacing Case Study. A multiple classifier systems MCS **Axle Spacing Case Study** was adopted to Some where over the rainbow the classification accuracy for minority body classes.

Gyroscopic Precession

The midpoint of this range is 1. The total LEF for a vehicle will value selected earlier for tandem axles on rigid pavement. The be the sum of these adjusted LEFs. Separate adjustment the functional approach taken. But what is the axle spacing at which LEFs for tandem axles equal Consider two isolated axles. Certainly it should fall within the range for x-axis and an adjacent axle at various positions on this axis that rep- tandem axles, which is 1. Since the tandems resents their relative position on a vehicle. If either of these axles has a would be reasonable to make the adjustment at that point.

Thus much larger load than the other, its effect on a combined LEF would Af 1. Thus, suppose that they have an equal load so their What else is known that would help determine A x? This is the situation presented in Figure 1. What is known about A x from this scenario? The adjusted LEF 1. Even though all axles 1. When this occurs, a fourth-power LEF of the double load axles as two single axles. The load on each axle is L and the total is 16 times greater. So the resulting LEF per axle is 8 times greater: load is 2L. In the range up to Since A 0 is only for of the load L. When x becomes large, A x becomes asymptotic to 1 because In the case of the AASHTO LEF for rigid pavements, this ratio is the axles separate into two isolated single axles and no adjustment dependent on L and slab thickness, D, but the variation is small, for axle spacing is required.

Thus, A x needs to be close to 1 when ranging from 0. The midpoint of this 4. When x is within the range for tandem axles, A x is less than range is 1. Moreover, A x is a minimum somewhere tandem for rigid pavements. For rigid pavements this minimum does not occur. Now consider three isolated axles. Imagine the middle axle at the A x cannot be a polynomial function because of the asymptotes. It is proposed that A x be developed as a rational func- vehicle. The effect of these adjacent axles on the LEF axle in the tion a polynomial divided by a polynomial , starting with the func- middle is to be determined. Again, if any of these axles has a much tion A x for rigid pavements, Ar x , since that seems to be the larger load than the others, its effect on a combined LEF would be functionally simpler case.

Because of the two minimums in addition to the maxi- transformed by defining a polynomial C x so that mum in this case, quadratics will be insufficient, so fourth-degree polynomials will be used. Both numerator and denominator can be divided by b4, so let 6. If one uses a quadratic polynomial for C x , b4 equal 1. Both numerator and denominator of Ar x can be divided by c2, However, the criteria given in this step are incompatible for so let c2 equal 1.

As the axle spacing increases after 8. How- the function reaches a minimum, the function increases too gradu- ever, because it was assumed that A r 1. It is necessary to accelerate the asymptotic conver- to be raised in order to make it converge faster. By trying other even gence. A simple way of doing this is by squaring the argument, the exponents, one quickly discovers that an eighth power fulfills this axle spacing. Making the coefficients small integers for simplicity, requirement. The steering Michigan and Washington State. The axle loads are in megagrams and trailing axles receive only one adjustment. The adjusted LEF for metric tons and axle spacings are given in meters.

Example 2 shows a truck with Figure 3 shows these LEF adjustment factors and they are given what at first appears to be a triple axle; however, the last axle spac- in Table 1 by axle spacing for flexible and rigid pavements. Example 4 shows a trailer with a triple axle, and again the LEFs are similar. Example 5 shows a trailer with a quadruple axle group, which is not covered by AASHTO, so the LEF given sepa- rates this into two tandems though it could also be separated into a triple and a single axle. Example 6 shows what appears to be a triple-axle group with unequal spacings and also a quadruple axle. Example 7 shows an axle vehicle from Michigan with axle groups that are unclear. The results can be significantly different depending on the approach taken.

The LEF adjustments presented here solve that ambiguity. Certainly more research needs to be done to illuminate the relationship of axle spac- ing and LEF. This paper contains an interpolation of what little is known. The main point is that the LEF adjustment method presented here is straightforward to apply and produces reasonable answers for all axle spacings. Without this LEF adjustment, one must question the use of LEFs for unusual axle spacings or axle groups of four or more axles. The results presented here should help pavement engineers determine LEFs and policy makers set truck size and weight limits that take account of all axle spacings.

Hudson, S. Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately, commercial vehicle data is either missing or expensive to obtain from current data resources. Leveraging existing infrastructure, Hernandez et al. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight. As a case study, the researchers derived truck body configuration from this combined data source.

Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring.

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