ES2552357T3 - Method of working a bituminous road surface at a desired micro-roughness and a desired macro-roughness - Google Patents

Abstract

A method of working a bituminous road surface (2) at a desired micro-roughness μRTARGET) and a desired macro-roughness (MRTARGET); the method including the step of measuring the initial macro-roughness (MRSTART) of the road surface (2); The method is characterized in that it includes the additional steps of: performing a number of first tasks, which differ from each other at least in a first working parameter (V), in a first limited calibration portion (9) of the road surface (2); measure the final macro-roughness (MREND) of the road surface (2) at the end of each first task; extrapolate the first best working parameter (VTARGET), to achieve the desired macrorrugosidad (MRTARGET), based on the final macrorrugosidad measurements (MREND) of the first tasks; and perform the first task, using the best first working parameter (VTARGET), over the entire road surface (2).

Description

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DESCRIPTION

Method of working a bituminous road surface at a desired micro-roughness and a desired macro-roughness

Technical field

The present invention relates to a method of working a bituminous road surface at a desired micro-roughness and a desired macro-roughness.

Background of the invention

The performance of the vehicle tires depends largely on the characteristics (typically the micro-roughness and the macro-roughness) of the road surface on which the tires roll, so that, to accurately compare the conclusions of tire tests carried out at times different and / or on different test tracks, the surfaces of the test road must have the same characteristics (i.e. the same micro-roughness and macro-roughness).

Consequently, when paving (that is, performing bituminous coating) a new test area, the micro-roughness and macro-roughness of the road surface required for the correct tire test are established in advance. Once the new asphalt is cast, it is allowed to mature, and the new test area is allowed to stand, at least for 2-4 weeks. At the end of the maturation period, the road surface has a more or less thick bitumen surface layer that "hides" the aggregate located below, and thus normally has a very high micro-roughness and a very low macro-roughness. Therefore, at the end of the maturation period, the road surface must be worked to the required micro-roughness and macro-roughness.

"Calibrating" correctly the work of altering the micro-roughness and the macro-roughness of the road surface is, however, extremely complicated, given the effectiveness of the work that depends significantly on numerous factors partially or totally uncontrollable. For example, the effectiveness of the work is strongly affected by sunlight and ambient temperature and humidity, not only when the asphalt is laid, but also during the ripening period. For example, asphalt placed in summer is normally harder and more compact than asphalt placed in Winter, and therefore is usually less sensitive to the work of altering the micro-roughness and macro-roughness of the road surface.

The work of altering the micro-roughness and macro-roughness of the road surface is currently “calibrated” solely on the basis of experience, but very often it is “calibrated” erroneously, with the result that it has to be redone (scenario of the better case) or the asphalt must be removed and replaced (worst case scenario).

US7850395B1 discloses a road surface roughness analysis system, in which a vehicle mounted on wheels advances parallel to the road, and is equipped with an optical profiler directed on the road surface and which has the purpose of measuring The roughness of it continues. Based on the measurements of the profiler, the road surface can be further worked, for example, smoothed, to meet the specifications.

Description of the invention

An object of the present invention is to provide a method of working a bituminous road surface at a desired micro-roughness and a desired macro-roughness, designed to eliminate the above inconveniences, and which is especially cheap and easy to implement.

According to the present invention, a method of working a bituminous road surface is provided at a desired micro-roughness and a desired macro-roughness, as claimed in the accompanying claims.

Brief description of the drawings

A non-limiting embodiment of the present invention will be described by way of example with reference to the accompanying drawings, in which:

Figure 1 represents a scheme of a bituminous road surface worked using the method according to the present invention.

Figure 2 represents a schematic front view of a device for measuring the micro-roughness and macro-roughness of the road surface of Figure 1.

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Figures 3 and 4 show graphs of road surface roughness test results of Figure 1.

Figure 5 represents a graph of macrorrugality mathematical processing according to the method of the present invention.

Figure 6 represents a schematic front view of a first processing device for processing the road surface of Figure 1 and working substantially in macro-roughness.

Figure 7 represents a graph of mathematical processing of micro-roughness according to the method of the present invention.

Figure 8 represents a schematic plan view of a second processing device for processing the road surface of Figure 1 and working substantially in micro-roughness.

Figure 9 represents a schematic side view of the second processing device of Figure 8.

Preferred embodiments of the invention

The number 1 in Figure 1 together indicates a tire test area, which is shaped like a strip (approximately 1.5-3 meters wide and 20-50 meters long) and includes an asphalted road surface 2 , that is, made of a bituminous aggregate conglomerate (substantially pebbles), which forms the solid structure, and bitumen, which joins (that is, 'sticks') the aggregate.

The main characteristics of the road surface 2 are micro-roughness or micro texture, which refers to roughness with a horizontal wavelength A less than 0.5 mm; and macro-roughness or macro texture, which refers to roughness with a horizontal wavelength A of 0.5 to 50 mm.

As depicted in Figure 2, the micro-roughness and macro-roughness of the road surface 2 are measured using an optical measuring device 3, which includes a gantry support frame 4 that extends over the road surface 2; and a laser distance meter 5, which is mounted on the support frame 4, is placed vertically at a small distance from the road surface 2, and points vertically downward to "observe" the road surface 2. An electric motor 6 moves the laser distance meter 5 along the support frame 4 and across the road surface 2, and the position of the laser distance meter 5 along the support frame 4 is recorded by a position sensor 7 (more specifically, a high resolution encoder). Finally, the measuring device 3 includes a processing unit 8 to process the readings of the laser distance meter 5 and the position sensor 7.

In actual use, the processing unit 8 (controlling the electric motor 6) moves the laser distance meter 5 across the road surface 2 in the support frame 4, and, at the same time, records the meter readings laser distance 5 and position sensor 7 at given measurement intervals (typically constant). In other words, for each measurement point, the processing unit 8 records the distance D recorded by the laser distance meter 5, and the corresponding position X of the laser distance meter 5 along the support frame 4. Once recorded, the succession of the distance measurements D as a function of the X positions is processed mathematically (according to ISO standards) to obtain a synthetic indication of the roughness (divided into micro-roughness and macro-roughness) of the road surface 2. The mathematical processing includes typically a Fourier transform in the length domain to determine the spectrum of wavelengths A in the recorded profile of road surface 2.

For example, Figure 3 represents the so-called "height histogram", that is, the distribution of wavelengths A in the registered profile: the x-axis represents the wavelength A, and the axis and the percentage of presence. Figure 4 represents the so-called “PSD - Spectral Power Density” graph: the x-axis represents (in logarithmic scale) the inverse of wavelength A (that is, the “spatial frequency”), and the y-axis represents PSD (in logarithmic scale). From the graph in Figure 4, it is possible to estimate the MR macro-roughness corresponding to the highest PSD value (at a high wavelength A, ie "low spatial frequency"), and the micro-roughness pR corresponding to the lowest PSD value (at a low wavelength, ie "high spatial frequency").

Once the asphalt of the road surface 2 of the test zone 1 is cast, it is allowed to mature, and the test zone 1 is allowed to stand, for at least 2-4 weeks. At the end of the maturation period, the desired micro-purity pRtarget and the desired MRtarget macro-roughness of the road surface 2 required for correct tire tests are established in advance. At the beginning (that is, at the end of the maturation period), the road surface 2 has a more or less thick bitumen surface layer that "hides" the aggregate from below, so that its actual micro-roughness is usually very high (it is that is, much higher than the desired micro-purity pRtarget), and its macro-purity is usually very low (ie, much lower than the desired MRtarget macro-roughness). Therefore, at the end of the maturation period, the road surface 2 must be worked to reduce its micro-roughness to the desired micro-purity pRtarget, and increase its

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macrorrugosidad to the desired macrorrugosidad MRtarget-

First, the initial MRstart macro-roughness of the road surface 2 is measured using the measuring device 3 described above. Next, a limited calibration portion 9 of the road surface 2 (Figure 1) of the test zone 1 is defined. The calibration portion 9 is approximately one meter long, and is typically located at one end of the zone Test 1. Several first tasks are performed, different from each other in at least one work parameter V, in different sections 10 of the calibration portion 9. In other words, a first task is performed, with a first work parameter V, in a first section 10 of the calibration portion 9 of the road surface 2; a first task is performed, with a second work parameter V different from the first work parameter V, in a second section 10 of the calibration portion 9 of the road surface 2; a first task is performed, with a third work parameter V different from the first and second work parameters V, in a third section 10 of the calibration portion 9 of the road surface 2, and so on (they are performed approximately of four to eight first tasks, with respective different working parameters V, in respective sections 10 of the calibration portion 9).

The first tasks have a significant effect on the macro-roughness of the road surface 2, and only a limited (substantially negligible) effect on the micro-roughness of the road surface 2.

The final macrorrugality MRend of the road surface 2 is measured after each first task performed in the calibration portion 9 of the road surface 2 (ie, in each section 10 of the calibration portion 9). And the work parameter chart V / macro-MR MR of Figure 5 can be constructed representing the test points corresponding to the first tasks performed in sections 10 of the calibration portion 9. Using widely documented mathematical techniques, the best parameter of Vtarget work to achieve the desired macrorrugosidad MRtarget can be extrapolated from the test points in the graph of Figure 5 based on the MRend final macrorrug measurements of the first tasks. For example, as shown in Figure 5, a curve is drawn by approximating the test points (that is, the measurements) of the first tasks in the work parameter plane V / MR macro, and then used to determine the Best Vtarget work parameter depending on the desired MRtarget- macro-wrinkle

Once the best Vtarget work parameter is determined, the first task is performed on the entire road surface 2 using the best Vtarget work parameter- After the first task, the entire road surface 2 (except for the calibration portion 9, which is no longer used) therefore has a final MRend macro-roughness substantially equal to the desired MRtarget- macro-roughness

As shown in Figure 6, each first task includes dry blasting the road surface 2 using a dry blasting device 11. The dry blasting device 11 includes a gantry 12 resting on opposite sides of the road surface 2 and extends over it; and a dry blasting head 13 that faces the road surface 2, movably mounted on the portico 12, and moved through the road surface 2 by an electric motor 14. The portico 12 is preferably mobile at along the road surface 2 in two lanes 15 parallel to the road surface 2 and on opposite sides thereof. In actual use, the dry blasting head 13 is moved along the gantry 12, through the road surface 2, to dry blast a strip of the road surface 2 (of a longitudinal dimension of a few centimeters); and, once a "sweep" of the road surface 2 is completed, the gantry 12 travels longitudinally along rails 15 to "sweep" another strip of the road surface 2.

In a preferred embodiment, the working parameter V is the feed rate (transversely) of the dry blasting head 13. As clearly shown in Figure 5, the higher the working parameter V is (ie, faster advances the dry blasting head 13), the lower the MR macro. In other words, increasing the speed of advance of the dry blasting head 13 reduces the amount of bitumen that it removes, thus producing a "smoother" road surface 2 (that is, with a lower MR macro). In a preferred embodiment, the first tasks only differ in the working parameter V, that is, only the working parameter V differs from one first task to another, and all other parameters (for example, the dry blasting pressure, The type and size of the dry blasting material, and the distance between the dry blasting head 13 and the road surface 2 ...) do not change, thus making the calibration process much simpler and more effective (it is that is to say, obtaining a desired MRtarget) more accurate and more repeatable macro.

In a different embodiment not shown, the first task, as opposed to dry blasting, includes directing a high pressure water jet over the road surface 2. As another alternative, the first task, as opposed to dry blasting, may include applying to the road surface 2 chemical solvents (for example, gasoline or diesel), whose action, however, is much more difficult to control.

After completing the first task on the entire road surface 2, the initial micro-roughness ijRstart of the road surface 2 is measured using the device 3. Then a limited calibration portion 16 of the road surface 2 is defined (Figure 1) of the test zone 1. The calibration portion 16 is approximately one meter long, is typically located at one end of the test zone 1, and differs from and

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It is located next to the calibration portion 9 (which is ignored at this stage). Several second tasks are performed, different from each other in at least one work parameter P, in the calibration portion 16. In other words, a second task is performed with different work parameters P in the calibration portion 16 of the surface of road 2 (approximately four to eight second tasks are performed with respective different work parameters P).

The second tasks have a significant effect on the micro-roughness of the road surface 2, and only a limited (substantially negligible) effect on the macro-roughness of the road surface 2.

The final micro-roughness pRend of the road surface 2 is measured after each second task performed in the calibration portion 16 of the road surface 2. And the work parameter graph P / ml cross-contamination pR of Figure 7 can be constructed by representing the test points corresponding to the second tasks performed in the calibration portion 16. Using widely documented mathematical techniques, the best Ptarget work parameter to achieve the desired micro-purity pRtarget can be extrapolated from the test points in the graph in Figure 7 depending on the final microrecurrence measurements pRend of the second tasks. For example, as shown in Figure 7, a curve is drawn by approximating the test points (that is, the measurements) of the second tasks in the work parameter plane P / micro-roughness pR, and subsequently used to determine the Best Ptarget working parameter depending on the desired micro-purity pRtarget.

Once the best Ptarget work parameter is determined, the second task is performed on the entire road surface 2 using the best Ptarget work parameter- After the second task, the entire road surface 2 (except for the calibration portions 9 and 16, which are no longer in use) therefore have a final micro-purity pRend substantially equal to the desired micro-roughness pRtarget (as well as a final MRend macro-roughness substantially equal to the desired macro-roughness MRtarget).

As shown in Figures 8 and 9, each second task includes dragging along the road surface 2 (at a speed of approximately 1-15 cm / s) a truck 17 mounted on wheels with rubber tires 18, which they are locked for sliding along the road surface 2. The truck 17 preferably includes at least two separate axles 19, each of which supports a respective number of wheels 18, and is blocked by a releasable lock to prevent the 18 wheels turn. The wheels 18 of the two axles 19 are offset, so that the footprint of each wheel 18 is complementary and does not overlap those of the other wheels 18. In a preferred embodiment, each second task includes wetting the road surface 2 with water before of the passage of the truck 17, to prevent the tires of the wheels 18 from leaving large amounts of rubber on the road surface 2 (ie, so as not to "rubberize" the road surface 2).

In a preferred embodiment, the working parameter P is the number of passes of the truck 17 along the road surface 2. For example, the truck 17 is dragged 30 times along the entire calibration portion 16 with the 18 wheels locked, and the final micro-roughness pRend is measured after 30 passes; the truck 17 is then dragged another 30 times along the entire calibration portion 16 with the wheels 18 locked, and the final micro-roughness pRend is measured after 60 passes; the truck 17 is then dragged another 30 times along the entire calibration portion 16 with the wheels 18 locked, and the final micro-roughness pRend is measured after 90 passes, and so on.

As clearly shown in Figure 7, the higher the working parameter P (ie, the greater the number of passes of the truck 17), the lower the micro-roughness pR. In other words, the increase in the number of passes of the truck 17 increases the "smoothing" effect, thus producing a "smoother" road surface 2 (that is, with a lower micro-roughness pR). In a preferred embodiment, the second tasks only differ from one another in the work parameter P, that is, only the work parameter P differs from one second task to another, and all other parameters (i.e., the inflation pressure of tire, the total mass of the truck 17, the drag speed of the truck 17 ...) does not change, thus making the calibration process much simpler and more effective (i.e., obtaining a desired micro-roughness pRtarget) more accurate and more repeatable

In a different embodiment not shown, as opposed to a truck 17 with locked wheels 18, the second task includes mechanically smoothing or scratching the road surface 2 with a smoothing tool.

In a possible embodiment, the desired MRtarget macro-roughness is slightly increased (approximately 5-10%) to allow the effect of the second task on the macro-roughness of the road surface 2 by extrapolating the best Vtarget working parameter- In other words, although limited, the second task performed on the entire road surface 2 to achieve the desired micro-roughness pRtarget of the road surface 2 still has some effect on the macro-roughness of the road surface (i.e., reduces it slightly). The effect of the second task on road surface macro-roughness can be ignored or taken into consideration by slightly increasing the desired MRtarget macro-roughness by extrapolating the best Vtarget work parameter (that is, after the first task, macro-roughness is slightly more high than the desired MRtarget macro-roughness, and, after the second task, it is substantially equal to the desired MRtarget macro-macro).

The described method of working the road surface 2 has numerous advantages.

First of all, it allows to achieve exactly both the desired MRjarget macro-roughness and the desired micro-roughness 5 ijRtarget of the road surface 2 of the test area 1. This is possible by the preliminary calibration work performed on the calibration portions 9 and 16, which allows to determine exactly the best working parameters Vt and Pt, taking into account all the possible variables that more or less affect the characteristics of the road surface 2.

10 Second, the described method is quick and easy to implement, because it simply implies, with respect to known methods, a few additional tasks (identical to those performed on the entire road surface 2) and a few additional roughness measurements (also identical to those performed on road surface 2) in very limited calibration portions 9 and 16.

Claims (16)

5 10 fifteen twenty 25 30 35 40 Four. Five fifty 55 60 65 1. A method of working a bituminous road surface (2) at a desired micro-roughness (pRtarget) and a desired macro-roughness (MRtarget); the method including the step of measuring the initial macro-roughness (MRstart) of the road surface (2); The method is characterized in that it includes the additional steps of: perform a number of first tasks, which differ from each other at least in a first working parameter (V), in a first limited calibration portion (9) of the road surface (2); measure the final macro-roughness (MRend) of the road surface (2) at the end of each first task; extrapolate the first best working parameter (Vjarget), to achieve the desired macrorrugosidad (MRtarget), based on the final macrorrugosidad (MRend) measurements of the first tasks; Y perform the first task, using the best first working parameter (Vtarget), over the entire road surface (2). 2. A method according to claim 1, wherein each first task includes dry blasting the road surface (2) with a dry blasting device (11). 3. A method according to claim 2, wherein the dry blasting device (11) includes: a porch (12) resting on opposite sides of the road surface (2) and extending through it; Y a dry blasting head (13) directed on the road surface (2) and mounted on the gantry (12) for movement through the road surface (2). 4. A method according to claim 3, wherein the gantry (12) is mounted for movement along the road surface (2) on two parallel rails (15) and on opposite sides of the road surface (2). 5. A method according to claim 2, 3 or 4, wherein the first working parameter (V) is the feed rate of the dry blasting head (13). A method according to claim 1, wherein each first task includes directing a high pressure water jet on the road surface (2). 7. A method according to one of claims 1 to 6, wherein the first tasks differ from each other only in a first working parameter (V). A method according to one of claims 1 to 7, and including, after performing the first task on the entire road surface (2), the additional steps of: measure the initial micro-roughness (pRstart) of the road surface (2); perform a number of second tasks, different from each other in at least a second working parameter (P), in a second limited calibration portion (16) of the road surface (2); measure the final micro-roughness (pRendj of the road surface (2) at the end of each second task; extrapolate the best second working parameter (Ptarget), to achieve the desired micro-roughness (pRtarget), based on the final micro-roughness measurements (pRendj of the second tasks; and perform the second task, using the best second work parameter (Ptarget), on the entire road surface (2). 9. A method according to claim 8, wherein each second task includes dragging along the road surface (2) a truck (17) mounted on wheels with rubber tires (18), which are locked to skate on the road. road surface (2). 10. A method according to claim 9, wherein each second task includes the wetting of the road surface (2) with water before the passage of the truck (17). 11. A method according to claim 9, wherein the second working parameter (P) is the number of passes of the truck (17) along the road surface (2). 12. A method according to claim 8, 10 or 11, wherein the second tasks differ from one another only in a second work parameter (P). 5 13. A method according to claim 8, wherein each second task includes subjecting the road surface (2) to mechanical smoothing or abrasion using a smoothing tool. 14. A method according to one of claims 8 to 13, wherein the second calibration portion (16) differs from the first calibration portion (9). 15. A method according to one of claims 8 to 14, and including the additional step of slightly increasing the desired macro-wrinkle (MRtarget) to extrapolate the best first working parameter (Vtarget), in order to take into account the effect of the second task in the macro-roughness of the road surface (2). fifteen 16. A method according to one of claims 1 to 15, wherein extrapolating the best first / second work parameter (Vtarget; Ptarget) includes determining a curve that approximates the measurements of the first / second tasks in the work parameter plane ( V; P) / roughness (MR; pR).