Abstract:
The present disclosure considers a system and method that can predict soil compaction and machine-specific productivity rate across multiple soil conditions without requiring site-specific samples and multi-variable lab testing. The method and system disclosed here can utilize predictive algorithms combined with a soils database to predict soil response to compaction energy across a range of soil moistures for the range of compaction machines available to predict compaction performance.

Description:
TECHNICAL FIELD 
     This patent disclosure relates generally to soil compaction machines, systems, and methods, and, more particularly, to soil compaction forecasting. 
     BACKGROUND 
     Calculating the time and resources necessary to reach a desired compaction density may be beneficial for earthworks compaction projects for numerous reasons, including but not limited to for utilization during the bidding process for earthworks compaction projects in addition to further applications in relation to the planning, management, and completion of earthworks compaction projects. In addition to further characteristics, fast and reliable systems and methods for determining the effort necessary to compact a soil region to the requested density may be valuable. 
     Many currently available methods and systems for forecasting compaction performance rely on performing soil compaction response measurements on soils from the specific site to be compacted. These soil compaction response measurements may be conducted in a laboratory, wherein specific sample may be tested at multiple compaction energies and moisture content levels to create a multivariable output of compaction result due to energy input at varying moisture. These laboratory results may then be compared to field response for a compaction machine operating on the same site specific soil to forecast the machine performance capability across the range of soil moisture. Such methods and systems for forecasting compaction performance are site specific, which may thus require extra time for taking sample and sending them to the laboratory in addition to multi-variable tab testing for each sample. The required extra time and resources which may characterize many currently available compaction forecasting methods and systems may present drawbacks and limitations for the planning, management, and completion of earthworks compaction projects, and particularly during the bidding and soil analysis process. 
     EP 0761886A1 to (the &#39;886 patent) to Froumentin discloses a method and machine where a compacting machine is linked to a computer that provides the geographical coordinates that guides the compacting machine&#39;s path, the number of passes to made over each point by the compacting machine, and the speed at which the compacting machine will travel. The &#39;886 relies upon site specific data and the method and the machine disclosed in the &#39;886 are not predictive. Therefore, while the method and machine disclosed in the &#39;886 patent may make the compacting more efficient it cannot predict the effort necessary to reach a desired soil density. 
     The present disclosure is directed to mitigating or eliminating one or more of the drawbacks discussed above. 
     SUMMARY 
     The present disclosure considers a system and method that may predict soil compaction and machine-specific productivity rate across multiple soil conditions without requiring site-specific samples and multi-variable lab testing. The method and system disclosed here may utilize predictive algorithms combined with a soils database to predict soil response to compaction energy across a range of soil moistures for the range of compaction machines available to predict compaction performance. The method and system of the present disclosure may provide a machine-specific response surface in order to predict performance both in degree of compaction as well as productivity rate with variation in field moisture, depth of the soil, and the number of repeated passes of the machine over the soil. The method and system of the present disclosure may not require testing at all energy levels and moisture contents, because it may predict a complete response surface from a limited number of test points of energy and moisture content. 
     In one aspect of the present invention, a method of managing soil compaction is disclosed. The method includes the steps of inputting a soil characteristic, a machine characteristic, and a desired soil compaction to determine a site-specific machine performance characteristic. 
     In another aspect of the present invention, a system configured to manage soil compaction is disclosed. The system includes a controller configured to determine a site-specific machine performance characteristic based on user input of soil characteristics, machine characteristics, and desired soil compaction. The system also includes at least a user interface to receive input of soil characteristics, machine characteristics, and desired soil compaction and a display to show one or more of the machine performance characteristics. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flowchart of an exemplary method that may be used to calculate a machine performance characteristic. 
         FIG. 2  is a schematic illustration of an exemplary system that may calculate one or more machine performance characteristics. 
         FIG. 3  is a schematic illustration of an exemplary user interface. 
     
    
    
     DETAILED DESCRIPTION 
     Now referring to the drawings, wherein like reference numbers refer to like elements,  FIG. 1  illustrates an exemplary method  100  of forecasting soil compaction. The method  100  can include selection of a soil  101 . The select a soil step  101  can include ascertaining composition characteristics of the soil or predictive compaction characteristics of the soil. The composition characteristics of the soil can include components such as gravel, silt, sand, asphalt, or dirt as well as other soil components. The predictive compaction characteristic of the soil can be based on a Proctor model which can determine a predictive compaction density of the particular soil as a function of water content. Other procedures to determine the predictive compaction characteristics of the soil can include determining compaction density as a function of energy level and water content. For example, instead of analyzing a predictive compaction density of the soil at a single energy level, multiple energy levels and multiple water content levels are used to establish more detailed predictive compaction density associated with the soil. The selection of a soil  101  can also include entering the geographic information associated with the soil to be compacted. For example, the select a soil step  101  can include GPS coordinates specifying the location of the soil to be compacted. The select a soil step  101  can be a default input or can be an input by a user. 
     The exemplary method  100  can include an input desired or target soil density step  102 . The desired or target soil density can also be based on the select a soil step  101  and the input water content step  103 . A Proctor model can be used to provide the information for the input target soil density step  102 . The input desired soil density step  102  can be a default input, can be an input from a computer based on calculations using other data, or can be an input by a user. 
     The exemplary method  100  may include an input water content step  103 . The input water content step  103  may be a default input, may be an input from a computer based on calculations using other data, or may be an input by a user. 
     The exemplary method  100  may include a select machine step  104 . The select machine step  104  may include selecting multiple machines in order to determine the machine performance characteristic  107  for multiple machines or it may include selecting one machine and determining the machine performance characteristic  107  for just one machine. The machine characteristics may include yoke mass, drum mass, drum diameter, drum width, eccentric features, drum frequency, or isomount stiffness. The machine characteristics may conic from a database within a module coupled to the machine or from a remote database that is in communication with a component of the machine. 
     The exemplary method  100  may also include a select lift thickness step  105 . The select lift thickness step  105  may be a default input, may be an input from a computer based on calculations using other data, or may be an input by a user. The default settings for the lift thickness may be associated with the size of the machine. Likewise, the input from the user concerning lift thickness may be approximate ranges based on the size of the machine. 
     The exemplary method  100  may also include an input productivity parameters step  106 . The input productivity parameters step  106  may be a default input, may be an input from a computer based on calculations using other data, or may be an input by a user. The productivity parameter may include the speed of the machine or the efficiency of the machine. The speed of the machine may be directly inputted by the user or determined from default settings. The efficiency of the machine may be directly inputted by the user or determined from default settings. 
     The exemplary method  100  may also output a machine performance characteristic  107 . In one embodiment, the machine performance characteristic may be the number of passes necessary for a specific machine to reach the desired soil density. The present disclosure contemplates a method where the number of passes may be determined by inputting at least one of the following: desired soil density, characteristics of the soil to be compacted, machine characteristics, water content of the soil, lift thickness, and productivity parameters. 
     The desired soil density may be directly inputted by the user or determined from default settings. The soil characteristics may include some of the following: initial soil density, predetermined soil identification based on prior laboratory or field tests, classification based on the soil components. The soil components may include gravel, silt, sand, asphalt, or dirt as well as other soil components. The water content may be directly inputted by the user or determined from default settings. The optimal water content may be determined using a Proctor model. The machine characteristics may include yoke mass, drum mass, drum diameter, drum width, eccentric features, drum frequency, or isomount stiffness. The machine characteristics may come from a database within a module coupled to the machine or from a remote database that is in communication with a component of the machine. The machine characteristics may also come from direct input from the user or the machine. The lift thickness may come from default settings associated with the machine or from direct input from the user. The default settings for the lift thickness may be associated with the size of the machine. Likewise, the input from the user concerning lift thickness may be approximate ranges based on the size of the machine. The productivity parameter may include the speed of the machine or the efficiency of the machine. The speed of the machine may be directly inputted by the user or determined from default settings. The efficiency of the machine may be directly inputted by the user or determined from default settings. 
     After inputting at least one of the characteristics of the soil to be compacted, the machine characteristics, the water content of the soil, the lift thickness, or the productivity parameters, a response surface value may be calculated. With the response surface value the number of passes necessary to reach the desired soil density may be calculate using the following calculation where ρ is the desired soil density, “ρ init ” is the initial soil density, “Δρ” is the difference between the maximum soil density and the initial soil density, “Pass” is the number of passes made by the machine, and “a” is response surface value:
 
ρ=ρ init +(Pass/( a +(Pass/Δρ)))
 
     The “Δρ” and “a” values above may be unique for each machine under a particular lift thickness. The “Δρ” and “a” values may be determined by field test data and the response surfaces. 
     After calculating the number of passes, the productivity of a specific machine may be calculated based on the machine characteristics and the productivity parameters of the machine. After calculating the number of passes an optimal compaction machine may be suggested based on the calculated number of passes, default machine characteristics, and default productivity parameters of the machines. 
     In one embodiment of the disclosed method, the machine performance characteristic  107  may be the machine identification number. In this embodiment the machine identification number may be determined by inputting at least one of the following: desired soil density, characteristics of the soil to be compacted, or water content of the soil. The desired soil density may be directly inputted by the user or determined from default settings. The soil characteristics may include some of the following: initial soil density, predetermined soil identification based on prior laboratory or field tests, classification based on the soil components. The water content may be directly inputted by the user or determined from default settings. The optimal water content may be determined using a Proctor model. The number of passes required for multiple machines based on the machine characteristics, lift thickness, or productivity parameters may be calculated as disclosed above. Based on the number of passes required for each machine the user may select a machine with the optimal productivity. If no machine identification number is predicted to achieve the desired soil density, the user may be notified. 
     In one embodiment of the disclosed method, additional analysis may be performed to assess whether the addition of soil additives, changes in lift thickness, or changes in moisture content would result in one or more of the machines being able to achieve the desired soil density. If so, user may be notified of the additional compaction process characteristics needed to achieve the desired soil density for a specific machine. If multiple machines are able to achieve the desired soil density, then additional analysis may be performed to recommend a particular machine based on predicted compaction results, and productivity characteristics. For example, a machine that weighs more may have more operational costs (e.g., fuel costs, maintenance cost etc.) associated with it than a lighter machine. If both can achieve the desired compaction, then the machine having lower operating cost may be recommended. Other productivity characteristics that may be accounted for include the speed at which a machine can go, the width of the roller, the number of passes needed by the machine etc. 
     In another embodiment of the disclosed method, the machine performance characteristic may be a designated route of the machine. The designated route may be based on GPS coordinates and may be determined by the machine characteristics, productivity parameters, and the number of passes needed to reach the desired soil density. The machine performance characteristic may also be a designated routes of multiple machines based on each machine&#39;s characteristics and productivity parameters. 
     In one embodiment, the machine performance characteristics may be updated based upon a rainfall that occurred after the soil sample(s) was taken. This update may enable a more reliable prediction regarding compaction capability. In addition, the compaction prediction, including machine selection, may be reviewed in light of a current moisture level, or predicted rainfall etc. For example, in bid analysis, predicted rainfall may be used to plan the compaction process, e.g., the type(s) of machines needed, the impact of rain on achieving the desired compaction density etc. If the soil sample was taken in a dry season, and compaction is to occur in a more humid or rainy season, then this may be taken into account with productivity and compaction predictions, based on the sensitivity of the ability to compact the soil to moisture, and the ability of a machine to compact the soil based on the moisture content. 
       FIG. 2  illustrates an exemplary system  200  configured to forecast soil compaction. The system  200  may include a user interface  210  configured to receive inputs associated with the soil compaction from a user, and a display  203  configured to display information associated with the soil compaction. The system  200  may include also include a controller  202  configured to perform calculations relevant to the soil compaction forecast. In addition, the system  200  may include a database  205  configured to store information associated with the soil compaction. For example, the database  205  may include data associated with previously analyzed soil. The data may include lab analysis of the soil, compaction predictions associated with the soil, and actual compaction characteristics associated with the soil. As will be described below, the system  200  may include a communication device  204  configured to communicate with a database  205  and a machine module  207  within a machine  206  used for soil compaction. The communication device  204  includes a wireless communication network and/or a landline. For example, the system  200  may communicate compaction information to a machine module  207  within a machine  206  involved in the compaction process. In addition, the system  200  may include a web-based interface such that users at the remote data facility or the machine module  207  within a machine  206  may access the web site and obtain desired compaction information. The user interface  210 , controller  202 , display  203 , and communication device  204  may form a machine module  207  incorporated into the machine  206  or may be remote from the machine  206 . Furthermore, the database  205  may be incorporated into the machine  206  or remote from the machine  206 . The database may also be also be included with the user interface  210 , controller  202 , display  203 , and communication device  204  in the machine module. 
     In one embodiment, the controller  202  may determine the number of passes necessary for a specific machine to reach the desired soil density. The present disclosure contemplates a system where the number of passes may be determined by inputting at least one of the following: desired soil density, characteristics of the soil to be compacted, machine characteristics, water content of the soil, lift thickness, and productivity parameters. 
     The desired soil density may be directly inputted by the user through the user interface  210  or determined from default settings provided by the database  205  via the communication device  204 . The soil characteristics may include some of the following: initial soil density, predetermined soil identification based on prior laboratory or field tests, classification based on the soil components. The soil components may include gravel, silt, sand, asphalt, or dirt as well as other soil components. The water content may be directly inputted by the user through the user interface  210  or determined from default settings provided by the database  205  via the communication device  204 . The optimal water content may be determined using a Proctor model. The machine characteristics may include yoke mass, drum mass, drum diameter, drum width, eccentric features, drum frequency, or isomount stiffness. The machine characteristics from a user through the user interface  210  or from the database  205  via the communication device  204 . The lift thickness may come from the user through the user interface  210  or the database  205  via the communication device  204 . The productivity parameter may include the speed of the machine or the efficiency of the machine. The speed of the machine may be directly inputted by the user through the user interface  210  or determined from default settings provided by the database  205  via the communication device  204 . The efficiency of the machine may also be directly inputted by the user through the user interface  210  or determined from default settings provided by the database  205  via the communication device  204 . 
     After inputting at least one of the characteristics of the soil to be compacted either by the user through the user interface  210  or from database  205  via the communication device  204 , the controller  202  may calculate a response surface value. With the response surface value the controller  202  may calculate the number of passes necessary to reach the desired soil density using the following calculation where “ρ” is the desired soil density, “ρ init ” is the initial soil density, “Δρ” is the difference between the maximum soil density and the initial soil density, “Pass” is the number of passes made by the machine, and “a” is response surface value:
 
ρ=ρ init +(Pass/( a +(Pass/Δρ)))
 
     The “Δρ” and “a” values above may be unique for each machine under a particular lift thickness. The “Δρ” and “a” values may be determined by field test data and the response surfaces. 
     After calculating the number of passes, the controller  202  may calculate the productivity of a specific machine based on the machine characteristics and the productivity parameters of the machine. After calculating the number of passes an optimal compaction machine may be suggested based on the calculated number of passes, default machine characteristics, and default productivity parameters of the machines. 
     In one embodiment of the disclosed system  200 , the controller  202  may determine the optimal machine  206  for the compaction project. In this embodiment the controller  202  may use at least one of the following: desired soil density, characteristics of the soil to be compacted, or water content of the soil to select the optimal machine  206  for the compaction project. 
     In another embodiment of the disclosed system  200 , the controller  202  may perform additional analysis may be performed to assess whether the addition of soil additives, changes in lift thickness, or changes in moisture content would result in one or more of the machines being able to achieve the desired soil density. If so, user may be notified, through the display  203 , of the additional compaction process characteristics needed to achieve the desired soil density for a specific machine. If multiple machines  206  are able to achieve the desired soil density, then additional analysis may be performed to recommend a particular machine  206  based on machine characteristics and productivity parameters. 
     In another embodiment of the disclosed system  200 , the controller  202  may designated a route for the machine  206 . The designated route may be based on GPS coordinates and may be determined by the machine characteristics, productivity parameters, and the number of passes needed to reach the desired soil density. The controller  202  may also be a designated routes of multiple machines based on each machine&#39;s characteristics and productivity parameters. Therefore the system  200  is capable of performing route planning and route management. 
       FIG. 3  illustrates an exemplary user interface  210 , which can be used in the exemplary system  200 . The exemplary user interface  210  can include multiple fields for inputs  211 - 217 , a field for desired output  218 , and an output  219 . The fields for inputs  211 - 217 , can set to receive information including desired soil density, machine selection, characteristics of the soil to be compacted, machine characteristics, water content of the soil, lift thickness, or productivity parameters. The user can provide information to one or more of the fields for inputs  211 - 217 . The user can provide a desired output  218 , including number of passes, selection of optimal machine(s), lift thickness, estimated machine productivity, or optimum water content. The user interface can show the output  219 , which can be the number of passes, selection of optimal machine(s), lift thickness, estimated machine productivity, or optimum water content. 
     The exemplary user interface  210  can be incorporated into a compaction machine or it can be in a wireless device in communication with the controller  202  through the communication device  204 . The fields for inputs  211 - 217  can include drop-down menus to select different preset inputs or the fields for inputs  211 - 217  can allow the user to search for preset inputs or enter a new input. The user interface  210  can be embodied, in one embodiment, as a graphical, digital, or other type of user interface such as a touchscreen. The user interface  210  can also be embodied in a computing device  220 . The computing device  220  containing the user interface  210  can be permanently separate from or detachably connected to the machine  206 . The computing device  220  can be a personal or mobile computing device such as a smartphone, tablet, or other type of suitable device. 
     The present invention also contemplates a machine  206  used for soil compacting which includes an user interface  210  configured to receive compaction data, a controller  202  configured to determine a machine performance characteristic based on compaction data, and a communication device configured to communicate the compaction data between a database or with a second machine. Again the compaction data can include desired soil density, machine selection, characteristics of the soil to be compacted, machine characteristics, water content of the soil, lift thickness, or productivity parameters. The database  205  that provides the compaction data can be incorporated into the machine or remote from the machine  206 . 
     INDUSTRIAL APPLICABILITY 
     The present disclosure includes a system  200  and method  100  of forecasting soil compaction. The method  100  includes a select soil step  101 , an input desired soil density step  102 , an input water content step  103 , a select machine step  104 , a select lift thickness step  105 , an input productivity parameters step  106 , and determining a machine performance characteristic  107 . In the present disclosure the soil characteristics do not have to some from site-specific samples. Instead the soil characteristic may come from a database  205  of soil characteristics. The soil characteristics may include composition properties of the soil and predictive compaction characteristics of the soil. 
     In the present disclosure a user may enter desired compaction characteristics into the system  200 , such as desired compaction density etc. The user may request that a machine  206  be recommended that is capable of achieving the desired compaction characteristics. The system  200  may responsively recommend one or more machines  206  capable of achieving the desired compaction characteristics. The system  200  may recommend multiple machines to accomplish the compaction, assign compaction routes to the machines  206 , and predict productivity characteristics associated with the machines. These route assignments may be delivered to compaction machines  206 , and used by the machines  206  (or operators of the machine) to begin compaction. 
     The present disclosure may apply to all compaction machines  206  and across the range of earthworks construction soils. Additional soils and machines may be added to a database as additional compaction data becomes available. The present disclosure contemplates that as more soil response to compaction energy data is compiled and as more machine data on compaction productivity is compiled the present disclosure may also apply to other machines not specifically design as compaction machines. 
     The present disclosure may provide improvements to the compaction forecasting process. One improvement may provide algorithms that predict soil response to compaction energy across a range of soil moisture. These algorithms may make it unnecessary to perform testing at all energy levels and moisture contents. The algorithms may predict a complete response surface from a limited number of test points of energy and moisture content. Other algorithms may predict compaction performance in the field for specific soils tested and/or specific soils previously tested available in a database. Again a response surface may be provided predicting performance both in degree of compaction produced as well as productivity rate with variation in field moisture content, depth of the soil lift (the amount of new soil added over previously compacted soil during the productivity cycle), and the number of repeated passes of the machine over the soil. 
     The algorithms may also predict the response of soil to compaction energy and predict the compaction machine capability  206  to produce compaction along with an anticipated rate of productivity. The predictive output is a “response surface” that shows both the maximum compaction and productivity along with reduced levels when soil conditions such a moisture content are less than at the optimal level. The present disclosure may be captured in analytical models combined with a soils database to provide a user tool for earthworks construction. 
     The present disclosure may provide forecasting of compaction machine performance and capabilities of machines to meet compaction requirements set by contracting authorities at the time of contract bids, and may also allow customers to ascertain optimal machine  206  selection and operation. Capabilities to meet compaction requirements are often a significant source of uncertainty for earthworks construction estimating. The present disclosure may reduce the degree of uncertainty for earth works construction estimating. The present disclosure may provide earlier compaction forecasting to meet contract bids, may also do so based upon machine  206  availability and selection parameters, as provided above. 
     Other aspects, objects, and advantages of the present invention can be obtained from a study of the drawings, the disclosure, and the claims.