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Publication Number:  FHWA-HRT-15-036    Date:  December 2015
Publication Number: FHWA-HRT-15-036
Date: December 2015

 

Long-Term Pavement Performance Program Determination of In-Place Elastic Layer Modulus: Backcalculation Methodology and Procedures

Long Description

Figure 30. Flowchart. Major steps and decisions in linear elastic backcalculation process. This flowchart identifies the six major steps and decisions used in the linear backcalculation process. The major steps include subtasks that flow systematically for the execution of the backcalculation process. The process flow can be described as follows:

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Figure 81. Illustration. Transformation of design structure to effective structure used by the neural networks to compute mechanistic responses. This illustration shows the transformation of the design structure or individual layers to the effective structure used by the neural networks to compute mechanistic responses as part of the portland cement concrete pavements analysis. The illustration is grouped into two segments. The left side of the illustration includes boxes that notes the steps and pavement cross sections for the neural networks, while the right side of the illustration identifies the inputs and resulting pavement cross section. The process and steps for this transformation can be described as follows:

The top of the illustration shows the pavement structure consisting of a concrete slab (jointed plain concrete pavement (JPCP) or continuously reinforced concrete pavement (CRCP)) over a base course with different interface condition between the concrete slab and base. A subbase layer is identified beneath the base layer which is supported by a compacted subgrade layer over the subgrade. This represents the original pavment structure identified by the first box on the left side of the illustration noted as Structure 1.

Step 1 includes the transformation of the elastic modulus, identified as E to the k-value for each month and is independent of friction, which translates to structure 2. Structure 2 is shown as a concrete slab (JPCP or CRCP) over a base course with different interface condition and foundation with a defined effective k-value from the backcalculation process.

Step 2 includes defining the effective slab on grade for each month that is friction dependent, which translates to structure 3. The interface condition can include bond and no bond between the concrete slab and foundation. Structure 3 is shown as a concrete slab (JPCP or CRCP) over the foundation with an effective k-value.

The inputs for the process used by the neural networks for computing pavement respones are identified as: elastic modulus of the portland cement concrete (PCC) (EPCC) for the elastic modulus of the concrete slab; elastic modulus of the base layer (Ebase) for the elastic modulus of the base layer; thickness of the PCC layer (hPCC) for the thickness of the concrete slab; thickness of the base layer (hbase) for the thickness of the base layer; effective k-value for the foundation, compacted subgrade, and subbase layers; and effective thickness of the pavement (heff) for the effective layer thickness for the concrete slab combined with the base layer.

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