WO2013152321A1 - Procédé et appareil pour déterminer la rigidité d'une chaussée - Google Patents

Procédé et appareil pour déterminer la rigidité d'une chaussée Download PDF

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Publication number
WO2013152321A1
WO2013152321A1 PCT/US2013/035504 US2013035504W WO2013152321A1 WO 2013152321 A1 WO2013152321 A1 WO 2013152321A1 US 2013035504 W US2013035504 W US 2013035504W WO 2013152321 A1 WO2013152321 A1 WO 2013152321A1
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WO
WIPO (PCT)
Prior art keywords
modulus
layer
estimated
roadway
compaction
Prior art date
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PCT/US2013/035504
Other languages
English (en)
Inventor
Sesh Commuri
Musharraf Zaman
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The Board Of Regents Of The University Of Oklahoma
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by The Board Of Regents Of The University Of Oklahoma filed Critical The Board Of Regents Of The University Of Oklahoma
Priority to EP13772090.0A priority Critical patent/EP2844798A4/fr
Priority to CN201380029906.XA priority patent/CN104364445A/zh
Priority to US14/390,936 priority patent/US20150030392A1/en
Publication of WO2013152321A1 publication Critical patent/WO2013152321A1/fr

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
    • E01C19/00—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving
    • E01C19/22—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving for consolidating or finishing laid-down unset materials
    • E01C19/23—Rollers therefor; Such rollers usable also for compacting soil
    • E01C19/28—Vibrated rollers or rollers subjected to impacts, e.g. hammering blows
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
    • E01C19/00—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving
    • E01C19/22—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving for consolidating or finishing laid-down unset materials
    • E01C19/23—Rollers therefor; Such rollers usable also for compacting soil
    • E01C19/28—Vibrated rollers or rollers subjected to impacts, e.g. hammering blows
    • E01C19/288—Vibrated rollers or rollers subjected to impacts, e.g. hammering blows adapted for monitoring characteristics of the material being compacted, e.g. indicating resonant frequency, measuring degree of compaction, by measuring values, detectable on the roller; using detected values to control operation of the roller, e.g. automatic adjustment of vibration responsive to such measurements
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
    • E01C19/00—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving
    • E01C19/22—Machines, tools or auxiliary devices for preparing or distributing paving materials, for working the placed materials, or for forming, consolidating, or finishing the paving for consolidating or finishing laid-down unset materials
    • E01C19/23—Rollers therefor; Such rollers usable also for compacting soil

Definitions

  • the current disclosure is directed to methods and apparatus for the compaction of roadway materials, and more particularly, to methods and apparatus for calibrating a compaction analyzer, and determining stiffness during construction.
  • Asphalt is often used as pavement.
  • various grades of aggregate are used.
  • the aggregate is mixed with asphalt cement (tar) and sand, and heated to approximately 1 0 °C to 169 °C , and a paver lays down the hot asphalt mix and levels the asphalt mix with a series of augers and scrapers.
  • the material as laid is not dense enough due to air voids in the asphalt mix. Therefore, a roller makes a number of passes over the layer of asphalt material, referred to herein as the asphalt mat, driving back and forth, or otherwise creating sufficient compaction to form asphalt of the strength needed for the road surface, or an individual pavement layer.
  • One of the key process parameters that is monitored during the compaction process is the compacted density of the asphalt mat. While there are many specifications and procedures to ensure that the desired density is achieved, most of these specifications require only 3-5 density readings per lane mile. Typically, the density readings will be from extracted roadway cores.
  • the process of measuring density of the asphalt mat during the compaction process is cumbersome, time-consuming, and is not indicative of the overall compaction achieved unless measurements are taken at a large number of points distributed in a grid fashion, which is difficult to achieve in the field due to cost considerations alone. Failure to meet the target density is unacceptable and remedial measures may result in significant cost overruns. Because the density cannot be measured directly, researchers have attempted different methods for indirect measurements.
  • Stiffness is likewise a key design factor that directly impacts the load bearing capacity of roadway pavements. Early deterioration of pavements due to rutting, fatigue cracking, and other types of distresses may be attributed to inadequate stiffness achieved during the compaction process. The stiffness of a pavement is typically expressed in terms of modulus. While the dependence of pavement performance on stiffness is well known, stiffness is rarely measured or monitored in the field during pavement construction. Remedial measures to rectify inadequate compaction after pavement has cooled down are costly and time consuming.
  • the apparatus disclosed herein comprises a vibratory compactor, or roller, with sensors, and a compaction analyzer associated therewith.
  • the compaction analyzer has a feature extraction module, a neural network module and an analyzer module.
  • the sensors may comprise accelerometers for measuring vibratory response signals of the roller, and the compaction analyzer utilizes the characteristics of the vibratory response signals to generate, in real time, a modulus signal representative of the density of the material being compacted.
  • the compaction analyzer will generate signals representative of the dynamic modulus of the pavement.
  • a method of compacting a roadway section with a roller having a compaction analyzer operably associated therewith comprises entering initial input parameters into the compaction analyzer and making a plurality of passes with the roller over a layer of a portion of the roadway section.
  • the method may further comprise applying a vibratory energy to the portion of the roadway section with the roller as it moves over the layer of the portion of the roadway section and repeatedly gathering responsive vibration signals of the roller as it moves over the layer portion of the roadway section. Additional steps may comprise generating, with the compaction analyzer, estimated modulus signals representative of estimated dynamic moduli based upon the responsive vibration signals of the roller and the initial input parameters entered into the compaction analyzer and measuring the modulus of the layer of the roadway section at a pluralit of locations on the portion of the roadway section.
  • the measured moduli may be compared to the estimated moduli at the plurality of locations to determine the difference between the measured and the estimated moduli, Selected ones of the initial input parameters to the analyzer can then be adjusted based on the difference between the measured moduli and the estimated moduli.
  • the compaction analyzer will generate an adjusted modulus output signal which will more closely approximate an actual modulus of the roadway section than does the estimated modulus signal.
  • the remainder of the layer of the roadway section is rolled until the compaction analyzer with the adjusted input parameters generates a desired adjusted output modulus signal.
  • the method is performed on each layer of a multi-layer roadway section and an effective modulus for the multi-layer roadway section is determined using the modulus for each layer.
  • Another method may comprise entering initial input parameters into the compaction analyzer and making a plurality of passes over a layer of a portion of the roadway section.
  • Vibratory energy may be applied to a portion of layer the roadway section as the plurality of passes are made, responsive vibratory signals of the roller generated in response to the applied vibratory energy are gathered.
  • Selected responsive vibratory signals may be designated as corresponding to specified compaction levels, and the compaction levels of the portion of the roadway section representative of the responsive vibratory signals delivered in real time to an analyzer module in the compaction analyzer as the roller moves along the portion of the roadway section.
  • An estimated modulus is generated in real time with the compaction analyzer based on the delivered compaction level and the initial input parameters as the roller rolls along the portion of the roadway.
  • Modulus measurements of the portion of the roadway section may be taken at a plurality of locations on each layer of the portion of the roadway section to determine a measured modulus at each of the plurality of locations on each layer.
  • the estimated modulus generated by the compaction analyzer at the plurality of locations is compared with the measured modulus at the plurality of locations, and selected ones of the initial input parameters are adjusted based upon the differences between the estimated modulus and the measured modulus.
  • An adjusted modulus of the layer roadway section is generated in real time based upon the delivered compaction levels and the adjusted input parameters that more closely approximate the actual modulus than did the estimated modulus.
  • An effective modulus for the multi layer roadway section is determined based on the modulus of each layer.
  • FIG. 1 is a schematic representation of a roller with a compaction analyzer.
  • FIG. 2 is a schematic representation of the compaction analyzer components.
  • FIG. 3 is exemplary and shows spectral features at an instant in time.
  • FIG. 4 is a spectrogram, and shows a five-second data set for passes made by the roller
  • FIG. 5 shows the power content of the signals represented in FIG. 4.
  • FIG. 6 is a section showing multiple layers of the roadway.
  • the current disclosure is directed to methods and apparatus for compacting a roadway, and for using, and calibrating an Intelligent Asphalt Compaction Analyzer (I AC A).
  • the current disclosure is likewise directed to a method of determining the dynamic modulus of a roadway, which is a measure of the stiffness of the roadway,
  • FIG. 1 schematically shows the IACA 5, a device that can measure the density of an asphalt pavement continuously in real time, over the entire length of the pavement during its construction.
  • Quality control techniques currently used in the field involve the measurement of density at several locations on the completed pavement or the extraction of roadway cores. These methods are usually time-consuming and do not reveal the overall quality of the construction. Furthermore, any compaction issues that are identified cannot be easily remedied after the asphalt mat has cooled down.
  • IACA 5 is a measurement device that does not control any aspect of the machine behavior. Further, IACA 5 is a stand-alone device that can be retrofitted on any existing vibratory compactor. A primary utility of IACA 5 is in providing real-time measurements of the density of the asphalt mat at each location on the pavement under construction. This information can be utilized by the roller operator to ensure uniform compaction, address under-compaction, as well as prevent over-compaction of the pavement.
  • IACA 5 functions on the hypothesis that the vibratory roller, for example vibratory roller 10, and the underlying pavement material, which may be, for example, Hot Mix Asphalt (HMA) form a coupled system.
  • the response of vibratory roller 10 is determined by the frequency of its vibratory motors and the natural vibratory modes of the coupled system. Compaction of an asphalt mat increases its stiffness and as a consequence, the vibrations of the compactor are altered. The knowledge of the properties of the pavement material and the vibration spectrum of the compactor can therefore be used to estimate the stiffness of the asphalt mat.
  • Quality specifications for HMA are generally specified as a percentage of air voids so that, for example, 100% density means no air voids exist, and 90% density means 10% air voids exist. Since the quality specifications are usually specified as percentage air void content or as a percentage of the Maximum Theoretical Density (MTD) of the asphalt mat, IACA 5 can estimate the compacted density of the pavement.
  • MTD Maximum Theoretical Density
  • Vibratory compactor 10 which may be, for example, a DD- 138 HFA lngersoll Rand vibratory compactor, includes forward and rear drums 12 and 14, Forward drum 12 has an eccentric weight 16 mounted therein, and if desired, both forward and rear drums 12 and 14 may have eccentric weights 16 mounted therein. Eccentric weights 16 are rotated by motors (not shown), so that the rotation of the weights 16 within drums 12 and 14 cause an impact at the contact between drums 12 and 14 and a base 18, which may be comprised of HMA. Base 18 may be referred to as asphalt mat 18.
  • Sensor module 22 associated with 1ACA 5 consists of accelerometers 24 mounted to frame 30 for measuring the vibrations of the compactor 10 during operation and may include infrared temperature sensors 26 for measuring the surface temperature of the asphalt base. Accelerometer 24 and temperature sensors 26 may be mounted to a frame 30 of roller 10. Sensors 26 essentially comprise a real-time data acquisition system. IACA 5 may include a user interface 28 which may be an Intel Pentium based laptop for specifying the amplitude and frequency of the vibration motors, and to input mat properties such as the mix type and lift thickness.
  • Accelerometer 24 may be a CXL10HF3 tri-axial accelerometer manufactured by Crossbow, capable of measuring lOg acceleration up to a frequency of 10 kHz.
  • the surface temperature of asphalt mat 18 may be measured using an infrared temperature sensor 26 mounted on the frame 30.
  • a global positioning system (GPS) 32 may also be mounted to roller 10.
  • GPS will, as is known in the art provide locations of roller 10 and will be coordinated with IACA 5 so that the location of the densities generated by IACA 5 will be known.
  • GPS receiver 32 may be, for example, a Trimble Pro XT GPS receiver used to record the location of the roller 10 as it moves.
  • IACA 5 includes a feature extraction (FE) module 34 which computes the Fast Fourier Transform (FFT) of the input signal and extracts features corresponding to vibrations at different salient frequencies.
  • the input signals are the responsive vibratory signals of roller 10, which results from the impacts made by the eccentric weights 16.
  • the responsive vibratory signals are measured, or gathered by accelerometer 24.
  • IACA 5 also includes a Neural Network (NN) Classifier 36 which is a multi-layer Neural Network that is trained to classify the extracted features into different classes, where each class represents a vibration pattern specific to a pre-specified level of compaction.
  • Compaction analyzer module 38 in IACA 5 post-processes the output of the neural network and estimates the degree of compaction in real time. Each component of IACA 5 will be described in more detail hereinbelow.
  • Feature extractor module 34 implements a Fast Fourier Transform to efficiently extract the different frequency components of the responsive vibratory signals of roller 10.
  • the output of the FFT is a vector with 256 elements, where each element corresponds to the normalized signal power at the corresponding frequency.
  • the normalized signal power is the square of the amplitude at the frequency, so the extracted features are frequencies, and amplitudes at the frequencies.
  • FIG. 3 is an example of the spectral features of vibratory signals, and shows frequencies, and the normalized power (i.e., squares of amplitudes) of the frequencies.
  • the vibration signal of the roller 10 is sampled at a rate of 1 kHz (1000 Hz/sec), Because the responsive vibration signal of the roller 10 is sampled at 1 kHz, it is understood that the frequency spectrum is uniformly distributed from 0 to 500 Hz. Since the FFT output is a sector with 256 elements, the features are extracted in frequency bands of approximately 2 Hz. Features may be extracted eight times per second in an overlapping fashion, such that the input to the neural network 36 will include 128 elements from the previous instant at which features were extracted, and 128 elements from the current or immediate feature extraction.
  • Neural network classifier 36 is a three layer neural network with 200 inputs, 10 nodes in the input layer, 4 nodes in the hidden layer, and 1 node in the output layer.
  • the inputs of the neural network correspond to the outputs of the feature extraction module, i.e., in this case 200 features in the frequency spectrum.
  • 200 features in the frequency spectrum i.e., from 100-500 Hz
  • Those in the lower range represent the frequency of roller 10 and may be ignored.
  • Neural network 36 will classify the vibratory response signals of roller 10 into classes representing different levels of compaction.
  • the output of feature extraction module 34 is analyzed over several roller passes during the calibration process and the total power content in the responsive vibration signal of roller 10 is calculated at each instant in time.
  • the power calculation is set forth hereinbelow.
  • a minimum power level, a maximum power level, and equally spaced power levels are identified and the features of the vibratory response signal that correspond to the identified power levels are used to train the neural network 36.
  • the identified minimum, maximum and equally spaced power levels are designated as corresponding to specified levels of compaction.
  • the neural network 36 observes the features of the responsive vibration signals of the roller and classifies the features as corresponding to one of the levels of compaction.
  • the plurality of pre-specified compaction levels will be identified, or designated with a number.
  • a minimum compaction level can be identified, or designated as compaction level 0, and a maximum compaction level can be designated as compaction level 4.
  • the compaction levels therebetween can be designated as compaction levels 1, 2 and 3 which correspond to the equally spaced power levels between the minimum and maximum power levels.
  • FIG. 3 is exemplary, and shows features corresponding to five different compaction levels, with the lowest level corresponding to the case where the roller is operating with the vibration motors turned on and designated as level 0, level 4 designated as corresponding to the case where the maximum vibration is observed, and levels 1 through 3 corresponding to spaced levels therebetween.
  • compaction level 0 corresponds to a lay-down density of the asphalt mat and the compaction level 4 corresponds to the target density as specified in the mix design sheet (designed at 100 gyrations of the superpave gyratory compactor).
  • the lay-down density of asphalt is generally assumed to be, for example, 85% to 88%, and the target or maximum density will generally be 94-97%.
  • Compaction levels 1 , 2 and 3 are designated as corresponding to equally spaced densities therebetween.
  • Asphalt mat 18 may include a portion 40 of a roadway section 42 to be compacted.
  • the portion 40 will comprise a defined length, for example, thirty feet.
  • Locations will be identified on the portion of the roadway, marked as locations A, B, C, D and E on FIG. 1 . The locations will be used to obtain actual measured densities of the portion 40 of the roadway section 42.
  • roadway section 42 may extend for several miles and that once the calibration described herein has occurred, rolling of the remainder of the roadway section 42 can occur without further actual measurement of the density so long as the roadway section is comprised of the same roadway material as portion 42, based upon the output of the IACA 5 as indicated on an IACA display 44.
  • roller 10 makes a plurality of passes over the portion 40 of roadway section 42, eccentric weights 16 will generate impacts as described herein. Responsive vibratory signals of roller 10 are gathered by accelerometer 24 as roller 10 moves along portion 40 by accelerometer 24.
  • Roller 10 will cease making passes when the responsive vibratory signals become consistent, which indicates that no further change in compaction is occurring. Roller 10 should stop, for example, before rollover occurs,
  • the power content of the responsive vibratory signals of roller 10 are calculated using the extracted features by feature extractor 34.
  • the power content is calculated each time a feature extraction occurs, which as described herein, may be eight times per second.
  • the spectrogram of the vibration signal can be represented by a matrix of «, rows and n j columns, where each element of the spectrogram V represents the normalized power in a given feature at a particular instant in time (i.e., the square of the amplitude of the frequency). For example, the element in the i th row and f h column represents the normalized power contained in the i th feature at the j*T s instant in time, where T s is the sample time.
  • FIG. 4 An example showing the power contained in the vibration signal over successive roller passes over a stretch of pavement during its compaction is shown in FIG. 4.
  • the power index is set to three (3), that is the power content over three successive time instants is averaged to determine the average power content at a given instant.
  • the three successive time instants may be, for example, three consecutive intervals of .125 seconds since as explained earlier, features may be extracted every .125 seconds.
  • a spectrogram like the one shown in FIG. 5, can be used to identify the locations on portion 40 where the maximum and minimum power occurred, and the locations of equally spaced power levels, for example, three equally spaced power levels therebetween.
  • five identified power levels are designated as corresponding to minimum compaction level 0, equally spaced compaction levels 1 , 2 and 3, and maximum compaction level 4.
  • neural network 36 will classify the features and identify the features as corresponding to one of the compaction levels 0, 1 , 2, 3 or 4.
  • 200 features representative of the responsive vibration signal of the roller at that time namely, the 200 frequencies and the normalized power (squares of the amplitudes) of those frequencies are provided as inputs to the neural network. Only 200 features are utilized and those features in the lower range (i.e., 0-100 Hz) are ignored.
  • the network will be trained so that the output of the neural network is one of compaction levels 0, 1, 2, 3, 4.
  • the neural network will be trained to recognize the extracted features as being the same, or most similar to the features that correspond to one of the identified power levels, and will be classified accordingly. Thus, if the extracted features are most similar to the features that correspond to the minimum power level, the output of the neural network will be the indicator 0, for the minimum compaction level. If the extracted features are most similar to those contained in the maximum power signal, the output of the neural network will be the number 4, which indicates that the maximum compaction has been reached. The same process will occur when the extracted features are features that are most similar to those at one of the equally spaced power levels, in which case the output of the neural network will be one of the numbers 1 , 2 or 3. During the training process, the interconnection weights of the neural network are modified to minimize the error between the output of the neural network and the level of compaction corresponding to each data set.
  • IACA 5 can be calibrated to estimate the dynamic modulus E, or M of the constructed road 40.
  • the modulus M of each layer of the road 40 as it is constructed can be determined using the IACA, and thereafter the overall effective modulus E eff oi the road can be determined.
  • the manner and method of calibration is like that disclosed with respect to density applied to dynamic modulus.
  • the initial calibration of IACA 5 thus assumes that compaction level 0 corresponds to the modulus of the asphalt mat at the lay-down density and the compaction level 4 corresponds to the target dynamic modulus M T at the density specified in the mix design sheet (designed at 100 gyrations of the superpave gyratory compactor).
  • the IACA is trained to classify the vibrations into estimated levels of compactors, it is thus calibrated to reflect the modulus M of pavement layers, so that the overall E eff for the constructed multi-layer pavement can be determined.
  • dynamic modulus tests are conducted in a lab for the mix used in the construction of each layer.
  • w is assumed to correspond to the dynamic modulus at the lay-down density of the layer, and thus corresponds to the lowest compaction level.
  • the modulus value M r at the target density of the compacted mix is assumed to be the highest modulus that can be achieved and corresponds to the highest compaction level.
  • MM and M T are determined by performing dynamic modulus tests for the asphalt mix being used in accordance with the AASHTO TP 62-03 test method. Master curves for the mix used are developed using the test to correlate the modulus with density. Curves are developed for the mix at different densities, for example 6%, 8%, 10% and 12% air voids.
  • M !d will be the modulus at 12% voids, and Mr will be the modulus at 6% voids - or 94% density.
  • the construction of the master curves is explained in more detail below.
  • the lay-down density of asphalt is generally assumed to be, for example, 85% to 88%, and the target or maximum density will generally be 94-97%.
  • Compaction levels 1 , 2 and 3 are designated as corresponding to equally spaced densities therebetween.
  • the curves are generated at 88%, 90%, 92% and 94% density.
  • the asphalt mixtures are thermorheologically simple materials and the time- temperature superposition principle is applicable in the linear viscoelastic state.
  • Dynamic modulus and phase angle of asphalt mixtures can be shifted along the frequency axis to form single characteristic master curves at a desired reference temperature or frequency.
  • the master curve for a mix is generated at a reference temperature of 21°C using the procedure outlined in Practical Procedure for Developing Dynamic Modulus Master Curves to Pavement Structural Design, Transportation Research Record (2005) No. 1929, by R, Bonaquit and D. W.
  • the shift factor used here was of the following form:
  • Max is the maximum ]E*
  • f r is the reduced frequency at reference temperature
  • f is the frequency at a particular temperature
  • ⁇ ⁇ is the viscosity of binder at reference temperature
  • A is the regression intercept of viscosity-temperature curve
  • VTS is the regression slope of viscosity-temperature susceptibility
  • a (T) is the shift factor as a function of temperature and age
  • ⁇ , ⁇ , ⁇ , c are fitting parameters.
  • Asphalt mat 18 may include portion 40 of the roadway section 42, which is a multi layer roadway, to be compacted.
  • roadway section 42 is a three layer roadway section.
  • the portion 40 will comprise a defined length, for example, thirty feet. Locations will be identified on the portion of the roadway, marked as locations A, B, C, D and E on FIG. 1. As shown in FIG. 6 the locations may be identified with a subscript to correspond to the individual layers.
  • Locations for layer LI may be identified as Ai , Bj, C
  • Locations for layer L2 may be identified as A 2 , B 2 , C 2 , D 2 , and E 2 .
  • Locations for layer L I may be identified as A3, B3, C3, D3, and E3.
  • the pattern will follow if more layers are used so that the locations will be Aj-Ej, where i is the layer number.
  • the locations A-E are exemplary and more or less locations my be used in the methods described herein.
  • the modulus of each layer, LI , L2 and L3 will be used to obtain actual measured, or independently determined modulus of the layers of portion 40 of the roadway section 42.
  • roadway section 42 may extend for several miles and that once the calibration described herein has occurred, rolling of the remainder of the roadway section 42 can occur without further actual measurement of the modulus so long as the roadway section is comprised of the same roadway material as portion 42, based upon the output of the IACA 5 as indicated on an IACA display 44.
  • a plurality of initial inputs are entered into the compaction analyzer module 38.
  • the initial inputs include the mix parameters of the roadway materials which may include, for example, type of construction (full depth, overlay, etc.), mix type, pavement lift, and lift thickness.
  • Other initial inputs include the estimated target modulus Mr at the maximum density, and a minimum estimated modulus / y which may be the modulus at the lay-down density. Mr will be the target density as described herein. Mr and MM from the master curves will be at 21°C. The modulus value at other temperatures can be calculated by applying the correction factors developed in the master curves.
  • Additional initial inputs to be entered into analyzer module 38 include an initial offset (offin) which is an estimated, or assumed offset, or difference between the assumed modulus Mi d at the lay-down density and the actual modulus at the lay-down density, and an initial slope k in .
  • the slope constant is simply the slope of a line running through Mr and w and the compaction levels.
  • kt n is ( ⁇ - tf)/(ncL-l) , where ncL is the number of compaction layers.
  • the GPS sensor 32 will trigger accelerometer 24 to begin collecting vibration data when location A is reached.
  • the coordinates at the beginning A and end E of the portion 40 may be, for example, at the center of the width of the roadway portion 40. The coordinates will be utilized to start and stop the collection of responsive vibration signals of roller 10 as roller 10 passes over portion 40.
  • the additional locations B, C and D may be, for example, at five, fifteen and twenty-five feet and are marked as well, at the center of the width of the portion 40 of the roadway section.
  • the compaction level will be an input to the analyzer module 38, which will utilize the initially entered input parameters and will generate a display of an estimated modulus in MegaPascals (MPa).
  • Analyzer module 38 will convert the compaction level into an estimated modulus in MPa.
  • an actual measured, or independently determined modulus is determined.
  • One method for measuring is by using a Falling Weight Deflectometer (FWD) to determine the modulus. Measurements are taken using an FWD, for example, at locations Ai, ⁇ , C ⁇ , ⁇ ⁇ and Ei which were previously marked on the center of layer 1 portion 40 of roadway section 42. The measured modulus of each location is compared to the estimated modulus (i.e., M est ) at each of the identified locations.
  • FWD Falling Weight Deflectometer
  • the locations and estimated level of compaction at each of the locations is determined through GPS measurements and the output of the neural network 36 as described.
  • the location of the estimated modulus is available from the display, since the GPS unit 32 will provide the location at which the estimated modulus occur.
  • the slope and offset are then adjusted, or modified to minimize the square of the error between the estimated and measured modulus.
  • the adjusted or modified slope and offset are represented by k ad j and offadj.
  • the adjusted offset is calculated as the mean error between the estimated and the measured densities so that
  • Offadj - ⁇
  • n the number of locations at which a modulus is measured in this case at five locations.
  • off adj the average error.
  • Mi d - modulus at lay-down density [00049] Ci or / tillrind - output of the neural network (compaction level) [00050] M est - estimated density of the neural network, and [00051] mefl i - measured modulus.
  • the calibration scheme using the measured modulus is as follows.
  • the new offset, of adj is calculated as set forth above.
  • the initial input parameters are adjusted to utilize off adj and k aclj in the density calculation in the analyzer module.
  • the adjusted modulus M adj is a more reliable indicator of actual stiffness of roadway portion 40 than is the estimated modulus.
  • the roller 10 can make passes on roadway section 42 until the IACA display indicates a predetermined desired final stiffness, at which point roller 10 can be moved to another roadway section. If the additional roadway section has the same mix parameters as roadway section 42, there is no need for recalibration.
  • the adjusted modulus is determined using the initial input parameters, except for the selected adjusted input parameters, namely, k adj and offad j, along with the compaction level delivered to the analyzer module from the neural network.
  • ELj, EL 2 and EL 3 are the dynamic modulus for layers LI, L2 and L3, respectively, and hi, h 2 and h 3 are the thickness of the respective layers.
  • Ci and C 2 are correction factors utilized to obtain better agreement with the exact theory of elasticity as explained I P. Ullidtz, Modeling Flexible Pavement Response and Performance, Narayana Press, Odden, denmark, pp. 38-43 (1998). The value depends on layer thicknesses, modulus ratios, Poisson ratios and the number of layers. It is understood that the process can be extended using the Odemark method for transformation of a layered system.
  • master curves are developed based on the mix used in the construction of the road.
  • the dynamic modulus master curves for the asphalt mix must be determined before the IACA calibration can occur.
  • the master curves can be developed as described above by compacting the asphalt mix in a laboratory to obtain compacted specimens of 6%, 8%, 10% and 12% air voids. To obtain the desired results, a plurality of samples may be compacted for each target void. As described earlier, the mix may be preheated and compacted using a superpave gyratory compactor.
  • Dynamic modulus tests may be conducted on the specimens using a MTS servo-hydraulic system in accordance with the test protocol set forth in AASHTO (2002) TP62-03. Testing is preferably performed at a plurality of temperatures, for example, 4°C, 21 °C, 40°C and 55°C. For each temperature level, tests may be conducted at a plurality of frequencies. As described above, master curves are generated for the different air void levels (6%, 8%, 10% and 12%) at a reference temperature of 21 °C using the procedure outlined in Practical Procedure
  • ELi, EL 2 and EL 3 are preferably determined using an FWD, there are occasions in which testing can not be conducted. There are at least two other methods that can be used to determine EL] , EL 2 and EL 3 . If it is not possible to conduct FWD tests, cores can be cut at locations Aj-E and the densities of the cores determined using known methods. The master curves can then be utilized to find the corresponding modulus, which will be used as the M meas in the calculations expressed herein. If it is not possible to construct Dynamic Modulus Master Curves for estimating modulus from density, there are known empirical models developed for known mixes, which can be used to supply M meas .
  • the 2 nd and 3 rd layer mixes contained approximately 22 percent 1 " rock, 50 percent manufactured sand, 13 percent sand, and 15 percent recycled asphalt pavement (RAP), and 4.1 percent PG 76-28 OK binder.
  • the gradations and other volumetric properties of all HMA mixes are given in Table 1 and Table 2.
  • the loose HMA mixes were preheated in an oven, and samples were compacted using a Superpave Gyratory Compactor (SGC). Three replicates of samples were compacted at 6, 8, 10, and 12 % ⁇ 1 % target air voids levels. Initially, samples having 150 mm in diameter x 167.5 mm in height were prepared.
  • test specimens of 100 mm in diameter were cored from the center of the gyratory compacted specimens, and sawed from each end of the specimen to get final sample of size 100 mm in diameter x 150 mm in height.
  • the volumetric analysis was conducted to obtain effective binder content (V b eir), the voids in mineral aggregates (VMA), the voids filled with asphalt (VFA), and the air voids (V a ) for all the mixes (Table 3).
  • VMA Voids in Mineral Aggregates
  • V, Air Voids Table 3 Specimens Volumetric Properties for all HMA Mixes .1
  • Dynamic modulus was measured for all collected mixes at four different air voids: 6 %, 8 %, 10 %, and 12 %, All dynamic modulus tests were performed using a MTS servo- hydraulic testing system.
  • the test specimen was placed in an environmental chamber and allowed to equilibrium to the specified testing temperature ⁇ 0.5° C.
  • the specimen temperature was monitored using a dummy specimen with a thermocouple mounted at the center.
  • Two linear variable differential transducer (LVDTs) were mounted on the specimen. Friction reducing end treatment, two teflon papers were placed between the specimen ands and loading plates. To begin testing, a minimal contact load was applied to the specimen. A sinusoidal axial compressive load was applied to the specimen without impact in a cyclic manner.
  • test was run on each test specimen at four different temperatures, including 4, 21 , 40, and 55° C, and the test started from the lowest temperature to the highest temperature. For each temperature level, the test was run at different frequencies from the highest to the lowest, including 25 , 10, 5, 1 , 0.5, 0.1 Hz. Prior to testing, the sample was conditioned by appiying 200 cycles of load at a frequency of 25 Hz. The load magnitude was adjusted based on the material stiffness, air void content, temperature, and frequency to keep the strain response within 50-150 micro-strains. The data was recorded for the last 5 cycles of each sequence. The dynamic modulus tests were performed according to the AASHTO TP62-03.
  • Air Voids (%) Ma s E* (MPa) ⁇ P ⁇ Fit
  • the use of the IACA in estimating the stiffness of a multi layer HMA pavement was investigated during the construction of interstate 1-35 in Norman, OK. A test section of 450 feet was selected and seven test locations, approximately 20 meters apart, were marked on the center line of the lane for verification analysis. The test procedure and results are now discussed. [00072] The 1ACA data was collected during the compaction of the all three layers (base, 2 nd layer, and 3 rd layer). First, the test points were marked on base layer, and the IACA data was collected. The GPS location of these points was recorded to locate these test locations on each pavement layer. Similar points were marked on 2 nd and 3 rd layers and the IACA data was collected during the compaction of each of these layers.
  • Odemark method is used to transform a system consisting of layers with different moduli into an equivalent system where the thicknesses of the layers are altered but all layers have the same modulus.
  • Ei , E 2 and E 3 are dynamic modulus of top, 2 nd , and base layer, and hi, h 2 , and h 3 are the thickness of respective layers.
  • Ci, and C 2 are the correction factors to obtain better agreement with exact theory of elasticity, (21 , 26). The value of correction factors depend on the layer thicknesses, modular ratios, Poisson ratios and the number of layers in pavement structure. In the present study correction factors were taken as C
  • the verification of the TACA measured modulus was done by conducting FWD testing on seven test locations that were marked before for estimating density using the IACA.
  • the FWD is a nondestructive test device used to characterize the in-situ pavement moduli (15, 27). It drops a transient load from a specified height on a 300 mm diameter circular plate with a thin rubber pad mounted underneath. The load and deflection is measured using load cell and sensors set on the ground. Seven sensors were placed at 0, 200, 300, 450, 600, 900, and 1 00 mm away from the center of the loading plate.
  • the FWD test was conducted on 3 rd layer of pavement using a Dynatest FWD test system.
  • Numerical back-calculation software MODULUS 6.0 was used to process the FWD raw data so as to determine the modulus value (28).
  • the back-calculated modulus is often called the effective modulus because the value represents the effect of the layer within the whole pavement structure.
  • the effective modulus was calculated at 21° C to compare this modulus with laboratory measured effective dynamic modulus. Since, the FWD loading induces a pulse of duration of 0.03 s (29), which is equivalent to a test frequency of 5.3 Hz (1/0.03/2 ⁇ ), hence, the comparisons in this paper are performed using modulus values calculated at 21 °C and 5 Hz frequency.
  • Table 6 shows the results of the FWD test.
  • FIG 3. below shows that the modulus estimated by the proposed method is in good agreement with the FWD measurements.

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Abstract

L'invention porte sur un appareil qui permet de compacter des matériaux de chaussée et qui comprend un analyseur de compactage pour calculer la rigidité pendant la construction de la chaussée. L'appareil génère un module dynamique pour chaque couche d'une chaussée, qui peut être utilisé pour calculer le module effectif global. L'invention porte également sur un procédé qui permet de déterminer la rigidité et qui comprend la génération d'un module dynamique pour chaque couche et le calcul du module effectif global à l'aide du module de chaque couche.
PCT/US2013/035504 2012-04-06 2013-04-05 Procédé et appareil pour déterminer la rigidité d'une chaussée WO2013152321A1 (fr)

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CN201380029906.XA CN104364445A (zh) 2012-04-06 2013-04-05 用于测定道路硬度的方法和设备
US14/390,936 US20150030392A1 (en) 2012-04-06 2013-04-05 Method and apparatus for determining stiffness of a roadway

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