Method and device for estimating a characteristic quantity of a bituminous mixture and associated computer program

Abstract

Method and device for estimating a characteristic quantity of a bituminous mix and associated computer program. The invention relates to a method for estimating a characteristic quantity (GC) of a bituminous mix in a predefined environment, the bituminous mix comprising a mixture of aggregates and a bituminous binder. The method comprises a step of receiving a set of variable(s) (Vl) relating to the bituminous binder, a composition (C) of the bituminous mix, a set of variable(s) (VE) relating to the predefined environment, and a set of variable(s) (Ve) relating to the bituminous mix; a step of calculating a dynamic modulus (E*) of the bituminous mix from the set of variable(s) (Vl) relating to the bituminous binder and the composition (C) of bituminous coating, and a step of estimating the characteristic quantity (GC) from the calculated dynamic modulus (E*), of the set of variable(s) (VE), (Vl), and (Ve).Figure for the abbreviation: Figure 1.

Description

Title of the invention: Method and device for estimating a characteristic quantity of a bituminous mixture and associated computer program

[0001] The present invention relates to a method for estimating a characteristic quantity of a bituminous coating.

[0002] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such an estimation method.

[0003] The invention also relates to an electronic estimation device configured to implement such an estimation method.

[0004] The invention is therefore in the field of methods for modeling characteristic quantities of a material, and particularly methods for estimating characteristic quantities of a bituminous mixture, typically to select an optimal bituminous mixture having the highest estimated lifespan among a plurality of estimated lifespans associated with a plurality of bituminous mixtures, in order to manufacture a pavement from the optimal bituminous mixture.

[0005] It is well known that bitumen is an essential material for many applications, and particularly for the construction of road infrastructure.

[0006] Indeed, bitumen is the main material of bituminous binders used in bituminous mixes which are the basis of road surfaces.

[0007] Thus, the bituminous binder ensures the strength, watertightness and durability of bituminous coatings, and therefore ultimately of our roads, highways, and runways.

[0008] For example, a category of bituminous mixtures particularly used for these applications are mixtures consisting of 90% to 95% aggregates (such as sand, gravel or pebbles of different sizes) and 5% to 10% bituminous binder.

[0009] It is therefore crucial to be able to accurately estimate the physical and chemical properties of binders and bituminous coatings, in order to adapt their uses, optimize their implementation, and above all ensure the safety, longevity and performance of the structures in which they are used.

[0010] These properties are generally estimated through experimental tests carried out on binders and bituminous coatings.

[0011] For bituminous binders, one can notably mention tests carried out using DSR (Dynamic Shear Rheometer) equipment, such as frequency sweep tests allowing characterization their rheological properties (complex modulus, phase angle, ...) at different temperatures, or LAS (Linear Amplitude Sweep) tests to evaluate their fatigue resistance.

[0012] For bituminous mixtures, we can mention the 2PB (for 2 Tipping Points) or 4PB (for 4 Tipping Points) bending tests or the cyclic direct tensile tests, all of which allow us to measure their resistance to cyclic stresses and therefore to fatigue, the ITSR (Indirect Tensile Strength Ratio) tests characterizing their sensitivity to water, or the tension-compression modulus tests, which allow us to measure their dynamic modulus.

[0013] It is known from the prior art that the measurements from these experimental tests are then used in models implementing methods to estimate different characteristic quantities of bituminous mixtures.

[0014] These characteristic quantities are notably estimated from the calculation of their dynamic modulus, denoted E* and also called complex modulus.

[0015] However, these devices implement this calculation from a large amount of data, which proves to be resource-intensive in terms of material and time to carry out all the experimental measurements upstream of the calculation.

[0016] There is therefore a need to propose a method for estimating a characteristic quantity of a bituminous mixture, based on a calculation of its dynamic modulus which is simplified and less resource-intensive.

[0017] To this end, the invention relates to a method for estimating at least one characteristic quantity of a bituminous mixture in a predefined environment, the bituminous mixture comprising a mixture of aggregates and a bituminous binder, the method being implemented by an electronic estimation device and comprising the following steps:

[0018] - reception of a set of variable(s) relating to the bituminous binder, of a composition of the bituminous mixture, of a set of variable(s) relating to the predefined environment, and of a set of variable(s) relating to the bituminous mixture, said set of variable(s) relating to the bituminous mixture being distinct from the composition of the bituminous mixture;

[0019] - calculation of a dynamic modulus of the bituminous mixture;

[0020] - estimation of at least one characteristic quantity of the bituminous mixture from the calculated dynamic module, the set of variable(s) relating to the predefined environment, the set of variable(s) relating to the bituminous binder, and the set of variable(s) relating to the bituminous coating;

[0021] said calculation step being implemented from the set of variable(s) relating to the bituminous binder and the composition of the bituminous mix, independently of the set of variable(s) relating to the bituminous mix

[0022] With the method according to the invention, the calculation of the dynamic modulus of the bituminous mix is ​​carried out solely from the composition of the bituminous mix and variables relating to the bituminous binder. Since experimental tests carried out on the bituminous mix (allowing the determination of all the variables relating to the bituminous mix) are much more complex to implement than experimental tests carried out solely on the bituminous binder, the method according to the invention therefore exhibits a significantly reduced consumption of material and time resources.

[0023] According to other advantageous aspects of the invention, the estimation method comprises one or more of the following features, taken individually or in all technically possible combinations:

[0024] - the set of variable(s) relating to the bituminous binder includes at least a set of variable(s) chosen from the group comprising: a set of thermal variable(s) relating to the bituminous binder, a set of fatigue variable(s) relating to the bituminous binder, a set of rheological variable(s) relating to the bituminous binder, a set of hydrolysis kinetics variable(s) relating to the bituminous binder, and a set of oxidation kinetics variable(s);

[0025] - the set of variable(s) relating to the bituminous coating includes at least one set of relative fatigue variable(s) to the bituminous coating;

[0026] - the set of variable(s) relating to the predefined environment includes at less a set of variable(s) chosen from the group comprising: a set of climatic variable(s) relating to the predefined environment and a set of traffic variable(s) relating to the predefined environment;

[0027] - the composition of the bituminous mix is ​​defined based on a percentage of each granular fraction relative to the aggregate mixture, a percentage of bituminous binder, and a percentage of air;

[0028] - at least one estimated characteristic quantity is the service life of the asphalt bituminous;

[0029] - the estimation process includes, after the estimation step, a step of selection of an optimal bituminous mixture having the highest estimated service life among a plurality of estimated service lives associated with a plurality of bituminous mixtures, in order to manufacture a pavement in the predefined environment from the optimal bituminous mixture;

[0030] - the estimation process comprises, between the calculation step and the estimation step, a modeling step of damage to the bituminous coating as a function of the time in the predefined environment, from the calculated dynamic module, from the set of variable(s) relating to the predefined environment, from the set of variable(s) relating to the bituminous binder and from the set of variable(s) relating to the bituminous coating, the estimation of the life of the bituminous coating being implemented from the modeled damage.

[0031] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement an estimation method as defined above.

[0032] The invention also relates to an electronic device for estimating at least one characteristic quantity of a bituminous mixture in a predefined environment, the bituminous mixture comprising a mixture of aggregates and a bituminous binder, the electronic device comprising:

[0033] - a receiving module configured to receive a set of variable(s) relating to the bituminous binder, a composition of the bituminous mix, a set of variable(s) relating to the predefined environment, and a set of variable(s) relating to the bituminous mix, said set of variable(s) relating to the bituminous mix being distinct from the composition of the bituminous mix.

[0034] -a calculation module configured to calculate a dynamic modulus of the bituminous mixture;

[0035] -an estimation module configured to estimate at least one characteristic quantity of the bituminous mixture from the calculated dynamic module, the set of variable(s) relating to the predefined environment, the set of variable(s) relating to the bituminous binder, and the set of variable(s) relating to the bituminous mixture;

[0036] said calculation module being further configured to calculate the dynamic modulus of the bituminous mix from the set of data relating to the bituminous binder and the composition of the bituminous mix, independently of the set of variable(s) relating to the bituminous mix.

[0037] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0038] [Fig-1] [Fig.1] is a schematic representation of an electronic device according to the invention of estimating a characteristic quantity of a bituminous coating; and

[0039] [Fig.2] [Fig.2] is a flowchart of an estimation method according to the invention, implemented by the electronic device of [Fig.1].

[0040] An electronic device 10 for estimating a characteristic quantity Gc of a bituminous mixture in a predefined environment is shown in [Fig.1].

[0041] Bituminous mix comprises a mixture of aggregates and a bituminous binder. VBC (from the English Binder Content) denotes the percentage of bituminous binder relative to the bituminous mix.

[0042] The aggregate mixture is a mixture of air and different types of aggregates, where each type of aggregate belongs to a predefined size range.

[0043] For a given type of aggregate, its size range is defined based on a sieve having openings with a predefined diameter value. In other words, each type of aggregate corresponds to a predefined range of aggregate diameter values.

[0044] For example, an asphalt mixture comprises a bituminous binder and an aggregate mixture comprising an amount of air and aggregates belonging to four predefined size ranges: a type of aggregate passing through a No. 200 sieve having openings of 0.075 mm in diameter, a type of aggregate passing through a No. 4 sieve having openings of 4.75 mm in diameter, a type of aggregate passing through a 3 / 8 inch sieve having openings of 9.5 mm in diameter, and a type of aggregate passing through a 3 / 4 inch sieve having openings of 19 mm in diameter.

[0045] For a given aggregate mixture and for each type of aggregate in the mixture, a percentage of this type of aggregate is defined in relation to the aggregate mixture by a percentage of granular fraction denoted pt, where t is the type of sieve defining the size range of this type of aggregate.

[0046] In addition, Va is also noted as the percentage of air included in the aggregate mixture.

[0047] For example, for the asphalt mixture described above, p2oo, P4, Pas, P34 are noted as the percentages of the granular fractions of the aggregates passing respectively through the No.200, No.4, 3 / 8 inch and 3 / 4 inch sieves.

[0048] Thus, the bituminous mix comprises VBc% of bituminous binder, Va% of air, and for each type of aggregate in the aggregate mixture, a certain percentage of granular fraction pt%, so that:

[0049] VBC + Va + Ept = 100%

[0050] Furthermore, the values ​​VBc, Va and each percentage pt of granular fraction of the aggregate mixture define a composition of the bituminous mix.

[0051] In addition, at least one characteristic quantity Gc is at least one characteristic variable of the bituminous mixture, which is preferably not directly measurable from an experimental test carried out on the bituminous mixture.

[0052] Advantageously, at least one characteristic quantity Gc estimated by the electronic device 10 is the service life of the bituminous coating.

[0053] The electronic device 10 is suitable for being connected to an external electronic system 12, in order to transmit at least one estimated characteristic quantity Gc to the external system 12.

[0054] The external system 12 is for example an electronic data processing device, or a display configured to display at least one characteristic quantity Gc estimated to an operator.

[0055] The electronic estimation device 10 comprises a receiving module 14, a computing module 16 connected to the receiving module 14, and an estimation module 18 connected to the computing module 16, as shown in [Fig.1].

[0056] The electronic estimation device 10 includes an information processing unit (not shown), formed, for example, of a memory and a processor associated with the memory. The electronic device 10 also includes a storage memory.

[0057] In the example of [Fig. 1], the receiving module 14, the calculating module 16, and the estimating module 18 are each implemented as a software program, or a software component, executable by the processor. The memory is then capable of storing a receiving program, a calculating program, and an estimating program. The processor is then capable of executing each of these programs: the receiving program, the calculating program, and the estimating program.

[0058] In an alternative not shown, the receiving module 14, the computing module 16 and the estimation module 18 are each implemented as a programmable logic component, such as an FPGA (Field Programmable Gate Array), or as a dedicated integrated circuit, such as an ASIC (Application-Specific Integrated Circuit).

[0059] When the electronic estimation device 10 is implemented in the form of one or more software programs, i.e., in the form of a computer program, it is also capable of being stored on a computer-readable medium (not shown). The computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. By way of example, the readable medium is an optical disc, a magneto-optical disc, a ROM, a RAM, any type of non-volatile memory (e.g., FLASH or NVRAM), or a magnetic card. A computer program comprising software instructions is then stored on the readable medium.

[0060] The receiving module 14 is configured to receive a set of variable(s) Vi relating to the bituminous binder, a composition C of the bituminous mix, a set of variable(s) VE relating to the predefined environment, and a set of variable(s) Ve relating to the bituminous mix, said set of variable(s) Ve relating to the bituminous mix being distinct from the composition C of the bituminous mix. For clarity, the concatenation of all these variables is noted V on [Fig.1].

[0061] In this description, the expression "variable(s) relating to", respectively "variable(s) relating to", also means "variable(s) characteristic of", respectively "variable(s) characteristic of the".

[0062] Advantageously, the set of variable(s) Vi relating to the bituminous binder comprises at least one set of variable(s) chosen from the group comprising: a set of thermal variable(s) Vthji relating to the bituminous binder, a set of fatigue variable(s) Vf,i relating to the bituminous binder, a set of rheological variable(s) Vrj relating to the bituminous binder, a set of hydrolysis kinetics variable(s) Vhj relating to the bituminous binder, and a set of oxidation kinetics variable(s) Voj relating to the bituminous binder.

[0063] Typically, each set of variable(s) included in the set of variable(s) Vi relating to the bituminous binder is a set of experimental measurement(s) from at least one experimental test carried out on the bituminous binder.

[0064] For example: - the set of thermal variable(s) Vth,i relating to the bituminous binder is derived from at least one ABCD test (from the English Ageing by the Binder in a Compact Device) and / or at least one BBR test (from the English Bending Beam Rheometer) and / or at least one thermal contraction test carried out on the bituminous binder, - The set of fatigue variable(s) Vfj relating to the bituminous binder is derived from at least one LAS test carried out on the bituminous binder, - The set of rheological variable(s) Vr4 relating to the bituminous binder is derived from at least one "frequency sweep" test carried out on the bituminous binder, and includes in particular a dynamic shear modulus G* of the bituminous binder, - the set of hydrolysis kinetic variable(s) Vhji relating to the bituminous binder is derived from at least one hydrolysis test carried out on the bituminous binder, and - the set of oxidation kinetic variable(s) VOji relating to the bituminous binder is derived from at least one RTFOT test (Rolling Thin Film Oven Test) and / or at least one PAV test (Pressure Aging Vessel) followed by FTIR (Fourier Transform InfraRed spectroscopy).

[0065] Advantageously, the set of variable(s) Ve relating to the bituminous coating includes at least one set of fatigue variable(s) Vfje relating to the bituminous coating.

[0066] Similarly, the set of variable(s) Ve relating to the bituminous coating is typically a set of experimental measurement(s) from at least one experimental test carried out on the bituminous coating.

[0067] For example, the set of fatigue variable(s) Vfje relating to the bituminous mixture is derived from at least one flexural test and / or at least one tensile-compression test carried out on the bituminous mixture.

[0068] The set of VE variable(s) relating to the predefined environment includes at least one set of variable(s) chosen from the group comprising: a set of climatic variable(s) relating to the predefined environment and a set of traffic variable(s) relating to the predefined environment.

[0069] The set of climatic variable(s) relating to the predefined environment is preferably taken from at least one database listing environmental and traffic data relating to a plurality of environments.

[0070] For example, the set of climatic variable(s) relating to the predefined environment is taken from the ASHRAE database (from the English American Society of Heating, Refrigerating and Air-Conditioning Engineers).

[0071] According to another example, the set of traffic variable(s) relating to the predefined environment is derived from assumptions made about the nature of the road on which the bituminous coating will be installed.

[0072] Thus, if the bituminous coating is intended to be installed on a motorway, for example, a frequency of passage of light vehicles on this motorway and a frequency of passage of heavy goods vehicles on this motorway is estimated, and the set of traffic variable(s) relating to the predefined environment is obtained from these estimates.

[0073] As described above, the composition C of the bituminous mix is ​​preferably defined from a percentage pt of each granular fraction relative to the aggregate mixture, a percentage of bituminous binder VBc, and a percentage of air Va.

[0074] The calculation module 16 is configured to calculate a dynamic module E* of the bituminous mixture from the set of variable(s) Vi relating to the bituminous binder and the composition C of the bituminous mixture, independently of the set of variable(s) Ve relating to the bituminous mixture.

[0075] Advantageously, the computing module 16 is configured to implement this calculation via the implementation of a previously trained machine learning algorithm.

[0076] In addition, the machine learning algorithm is pre-trained from datasets listing dynamic modulus values ​​E* for a plurality of bituminous mixtures, each bituminous mixture being characterized only by its variables Vi relating to the bituminous binder and by its composition C.

[0077] In other words, the machine learning algorithm is trained with training data comprising n-tuples of values, each n-tuple being formed of an expected dynamic modulus value E* at output and corresponding value(s) of variable(s) Vi relating to the bituminous binder and composition C provided as input to the machine learning algorithm.

[0078] For example, the machine learning algorithm is pre-trained from an LTPP (Long-Term Pavement Performance) dataset, an ASU-UMD (Arizona State University-University of Maryland) dataset, a South Carolina Department of Transportation (South Carolina Department of Transportation) dataset, and a University of New Hampshire dataset.

[0079] As an optional complement, the machine learning algorithm is previously trained from different datasets comprising n-tuples of values, each n-tuple consisting of only a part of the set of variable(s) Y] relating to a bituminous binder, the composition C of the associated bituminous mix, and the value of the dynamic modulus E* expected at the output of the machine learning algorithm.

[0080] According to another optional complement, the prior training of the machine learning algorithm is supplemented by at least one training dataset from a micromechanical model of the bituminous coating.

[0081] Alternatively, the calculation module 16 is configured to calculate the dynamic module E* only via the implementation of the micromechanical model of the bituminous mix.

[0082] The micromechanical model of the bituminous mixture is an algorithm suitable for representing the microscopic behavior of the bituminous mixture.

[0083] In particular, the micromechanical model is able to predict the dynamic modulus E* of the bituminous mixture from the set of variable(s) Vj relating to the bituminous binder and the composition C of the bituminous mixture.

[0084] As an optional complement or alternative, the micromechanical model is also configured to predict the fatigue properties of the bituminous mix, from the set of variable(s) Vi relating to the bituminous binder and the composition C of the bituminous mix.

[0085] The micromechanical model of the bituminous mixture is, for example, suitable for predicting these fatigue properties of the bituminous mixture from a finite element method.

[0086] Preferably, the calculation module 16 is further configured to calculate the dynamic module E* in the absence of the set of variable(s) Ve relating to the bituminous coating, i.e. without taking into account the set of variable(s) Ve relating to the bituminous coating.

[0087] Advantageously, the calculation module 16 is configured to calculate the dynamic module E* only from the set of variable(s) Vi relating to the bituminous binder and the composition C of the bituminous mix.

[0088] The estimation module 18 is configured to estimate at least one characteristic quantity Gc of the bituminous mixture from the calculated dynamic module E*, the set of variable(s) VE relating to the predefined environment, the set of variable(s) Vi relating to the bituminous binder, and the set of variable(s) Verelative(s) relating to the bituminous mixture.

[0089] In other words, the estimation module 18 is configured to estimate at least one characteristic quantity Gc of the bituminous mixture from the dynamic module E* and the set V of the received variable(s), as represented in [Fig.1].

[0090] For example, in the case where at least one characteristic quantity Gc is the service life of the bituminous mixture, the estimation module 18 is configured to model damage to the bituminous mixture in the predefined environment as a function of time.

[0091] In addition, the estimation module 18 is configured to implement, upstream of the modeling of the damage of the bituminous mixture as a function of time, a plurality of models of different physical or chemical behaviors of the bituminous mixture.

[0092] For example, the estimation module 18 is configured to model, from predefined equations, thermal diffusion phenomena relating to bituminous asphalt, oxygen diffusion and oxidation phenomena relating to bituminous asphalt, thermal damage or fatigue damage phenomena relating to bituminous asphalt, or even a viscoelastic behavior of bituminous asphalt.

[0093] In particular, by way of example, the estimation module 18 is capable of modeling these different phenomena from at least the following equations:

[0094] - the thermal diffusion phenomena relating to bituminous coatings in the Pavement layers are modeled using the following equations: Dr. Ot? pC« —+ — = G £?£ ar $ = -Ar «te

[0095] where T denotes the temperature, X denotes the thermal conductivity, q denotes the heat flux density, p denotes the density of the bituminous coating, Cp denotes the specific heat capacity of the bituminous coating, z denotes the height, and t the time;

[0096] - the phenomena of oxygen diffusion and oxidation relating to the coating Bituminous materials are modeled using the following equations: [00971 ae=^{Do^P).CoTR*^

[0098] where P denotes the partial pressure of oxygen in the bituminous binder, Do denotes the oxygen diffusion coefficient in the bitumen, T denotes the temperature, R denotes the ideal gas constant, h denotes a solubility constant of oxygen in the bituminous mix, c denotes a parameter that relates the carbonyl formation rate to oxygen consumption, and 100991 ^ = M RTFO k t ^ +kc

[0100] where CA denotes quantity of carbonyl in the bituminous binder, MRTF0 denotes the initial carbonyl content after RTFO (Rolling Thin Film Oven) test, kf denotes an oxidation rate constant, kc denotes an oxidation constant, and t the time;

[0101] - thermal damage phenomena or fatigue damage The properties related to bituminous asphalt are modeled using the following equations:

[0102] aR pR *pR uijkl ^ijkl

[0103] where oijkiR denotes the components of the reduced stress tensor, CijkR denotes the reduced stiffness modulus, ekR denotes the components of the reduced strain tensor, 101041 ^^(tr^dr

[0105] where E(tr) denotes the relaxation function of the material, ER denotes the reference relaxation modulus, and h denotes the reduced time, and

[0106] S = f(WR,S, a, ...)

[0107] where S denotes a damage variable, WR denotes the reduced dissipated energy, and a denotes an intrinsic parameter of the material.

[0108] The operation of the electronic device 10 will now be explained, in particular with the help of [Fig. 2] representing a flowchart of the process estimation of at least one characteristic quantity Gc according to the invention, the method being implemented by the electronic estimation device 10.

[0109] In advance, the bituminous coating and the bituminous binder are both subjected to a plurality of experimental tests allowing to measure respectively the set of variable(s) Ve relating to the bituminous coating and the set of variable(s) Vi relating to the bituminous binder.

[0110] The estimation process includes a receiving step 100, a calculation step 110, and an estimation step 130.

[0111] In the case where at least one characteristic quantity Gc is the service life of the bituminous mixture, then the estimation process further includes, between the calculation step 110 and the estimation step 130, a modeling step 120, and also includes a selection step 140 following the estimation step 130, as shown in [Fig.2].

[0112] The person skilled in the art will understand that in the case of an estimation of a characteristic quantity Gc other than the service life of the bituminous coating, the modeling step 120 and the selection step 140 described below are for example not included in the process according to the invention.

[0113] During the reception step 100, the electronic device 10 receives, via its reception module 14, the set V of variable(s), including the set of variable(s) Verelative(s) relating to the bituminous mix and the set of variable(s) Vi relating to the bituminous binder from the experimental tests carried out upstream of the process, as well as the set of variable(s) VE relating to the predefined environment, and the composition C of the bituminous mix.

[0114] During the calculation step 110, carried out after the acceptance step 100, the calculation module 16 calculates the dynamic modulus E* of the bituminous mixture from the set of variable(s) Vi relating to the bituminous binder and the composition C of the bituminous mixture, independently of the set of variable(s) Ve relating to the bituminous mixture.

[0115] Preferably, calculation step 110 is implemented in the absence of the set of variable(s) Ve relating to the bituminous coating.

[0116] Advantageously, calculation step 110 is implemented only from the set of variable(s) Vi relating to the bituminous binder and the composition C of the bituminous mix.

[0117] Advantageously still, the calculation step 110 is implemented via the previously trained machine learning algorithm and / or via the implementation of the micromechanical model of the bituminous mix, as described above.

[0118] According to this advantageous addition, the fatigue properties of the bituminous mixture are also calculated during this calculation step 110.

[0119] During the modeling step 120, the estimation module 18 of the electronic device 10 models damage to the bituminous mixture as a function of time in the predefined environment, from the calculated dynamic module E* and advantageously from the fatigue properties of the bituminous mixture calculated during the calculation step 110, the set of variable(s) VE relating to the predefined environment, the set of variable(s) Vi relating to the bituminous binder and the set of variable(s) Ve relating to the bituminous mixture, the estimation of the life of the bituminous mixture being implemented from the modeled damage.

[0120] For example, during this modeling step 120, the estimation module 18 models a plurality of physical and chemical phenomena relating to the bituminous mixture, and models the damage to the bituminous mixture as a function of time in the predefined environment from this plurality of modeled physical and chemical phenomena.

[0121] During the estimation step 130, in the case of an estimation of at least one characteristic quantity Gc other than the service life, the estimation module 18 estimates the at least one characteristic quantity Gc of the bituminous mixture from the dynamic module E* calculated during the calculation step 110, the set of variable(s) VE relating to the predefined environment, the set of variable(s) Vi relating to the bituminous binder, and the set of variable(s) Ve relating to the bituminous mixture.

[0122] In the case of estimating the service life of the bituminous mixture, the estimation module 18 estimates during the estimation step 130 the service life from the damage modeled during the modeling step 120.

[0123] During the optional selection step 140, carried out after the estimation step 130, the estimation module 18 selects an optimal bituminous mixture having the highest estimated service life during the estimation step 130 from among a plurality of estimated service lives associated with a plurality of bituminous mixtures, in order to manufacture a pavement in the predefined environment from the optimal bituminous mixture.

[0124] Preferably, the selection step 140 is implemented by the estimation module 18 when the electronic device 10 has previously implemented, on several occasions, the reception 100, calculation 110, modeling 120 and estimation 130 steps, with respect to said plurality of bituminous mixtures, in order to be able to implement this selection. In other words, according to this preferential aspect, the process The estimation process includes several successive iterations of the reception 100, calculation 110, modeling 120 and estimation 130 steps.

[0125] Thus, the estimation method according to the invention makes it possible to estimate a characteristic quantity Gc of a bituminous mixture from a calculation of the dynamic modulus E* which consumes few resources.

[0126] Indeed, tests carried out on the scale of the bituminous binder are simpler to carry out, less time-consuming and less numerous than tests carried out on the scale of the bituminous mix.

[0127] Moreover, because the calculation step 110 is advantageously implemented by a machine learning algorithm previously trained from different datasets and / or by the micromechanical model, the electronic device 10 is more robust: indeed, the calculation of the dynamic modulus E*, and optionally of the fatigue properties of the bituminous mix, can be carried out with the composition C and only a part of the set of variable(s) Vi relating to the bituminous binder.

[0128] Moreover, the implementation of this machine learning algorithm and / or this micromechanical model allows for the simultaneous implementation of a multitude of estimates of characteristic quantities Gc relating to a multitude of bituminous mixtures, without significant degradation of performance.

Claims

Demands

1. A method for estimating at least one characteristic quantity (Gc) of an asphalt mixture in a predefined environment, the asphalt mixture comprising a mixture of aggregates and a bituminous binder, the method being implemented by an electronic estimation device (10) and comprising the following steps: - receiving (100) a set of variable(s) (VJ) relating to the bituminous binder, a composition (C) of the asphalt mixture, a set of variable(s) (VE) relating to the predefined environment, and a set of variable(s) (Ve) relating to the asphalt mixture, said set of variable(s) (Ve) relating to the asphalt mixture being distinct from the composition (C) of the asphalt mixture; - calculating (110) a dynamic modulus (E*) of bituminous coating;- estimation (130) of at least one characteristic quantity (Gc) of the bituminous mixture from the calculated dynamic modulus (E*), the set of variable(s) (VE) relating to the predefined environment, the set of variable(s) (Vi) relating to the bituminous binder, and the set of variable(s) (Ve) relating to the bituminous mixture; characterized in that the calculation step (110) is implemented from the set of variable(s) (VJ) relating to the bituminous binder and the composition (C) of the bituminous mixture, independently of the set of variable(s) (Ve) relating to the bituminous mixture.;

2. Estimation method according to claim 1, wherein the set of variable(s) (VJ) relating to the bituminous binder comprises at least one set of variable(s) selected from the group comprising: a set of thermal variable(s) (Vth,i) relating to the bituminous binder, a set of fatigue variable(s) (Vf>1) relating to the bituminous binder, a set of rheological variable(s) (Vrj) relating to the bituminous binder, a set of hydrolysis kinetic variable(s) (Vh,i) relating to the bituminous binder, and a set of oxidation kinetic variable(s) (Voj).

3. Estimation method according to any one of the preceding claims, wherein the set of variable(s) (Ve) relating to the bituminous coating includes at least one set of fatigue variable(s) (Vf,e) relating to the bituminous coating.

4. Estimation method according to any one of the preceding claims, wherein the set of variable(s) (VE) relating to the predefined environment comprises at least one set of variable(s) selected from the group comprising: a set of climatic variable(s) relating to the predefined environment and a set of traffic variable(s) relating to the predefined environment.

5. Estimation method according to any one of the preceding claims, wherein the composition (C) of the bituminous mix is ​​defined from a percentage of each granular fraction relative to the aggregate mixture, a percentage of bituminous binder, and a percentage of air.

6. Estimation method according to any one of the preceding claims, wherein the at least one estimated characteristic quantity (Gc) is the service life of the bituminous coating.

7. Estimation method according to claim 6, further comprising, after the estimation step (130), a selection step (120) of an optimal bituminous mixture having the highest estimated service life among a plurality of estimated service lives associated with a plurality of bituminous mixtures, in order to manufacture a pavement in the predefined environment from the optimal bituminous mixture.

8. Estimation method according to claim 6 or 7, further comprising, between the calculation step (110) and the estimation step (130), a modeling step (120) of damage to the bituminous mix as a function of time in the predefined environment, from the calculated dynamic module (E*), the set of variable(s) (VE) relating to the predefined environment, the set of variable(s) (Vi) relating to the bituminous binder and the set of variable(s) (Ve) relating to the bituminous mix, the estimation (130) of the service life of the bituminous mix being implemented from the modeled damage.

9. Computer program, comprising software instructions which, when executed by a computer, implement a method according to any one of the preceding claims.

10. An electronic device (10) for estimating at least one characteristic quantity (Gc) of an asphalt mixture in a predefined environment, the asphalt mixture comprising a mixture of aggregates and a bituminous binder, the electronic device (10) comprising: - a receiving module (14) configured to receive a set of variable(s) (VJ) relating to the bituminous binder, a composition (C) of the bituminous mix, a set of variable(s) (VE) relating to the predefined environment, and a set of variable(s) (Ve) relating to the bituminous mix, said set of variable(s) (Ve) relating to the bituminous mix being distinct from the composition (C) of the bituminous mix; - a calculation module (16) configured to calculate a dynamic module (E*) of the bituminous mixture; - an estimation module (18) configured to estimate at least one characteristic quantity (Gc) of the bituminous mixture from the calculated dynamic module (E), the set of variable(s) (VE) relating to the predefined environment, the set of variable(s) (Vi) relating to the bituminous binder, and the set of variable(s) (Ve) relating to the bituminous mixture; characterized in that the calculation module (16) is further configured to calculate the dynamic module (E) of the bituminous mix from the data set(s) (Vi) relating to the bituminous binder and the composition (C) of the bituminous mix, independently of the variable set(s) (Ve) relating to the bituminous mix.

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