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The Falling Weight Deflectometer (FWD) is a widely used non-destructive test device for estimating the pavement stiffness properties. However, the conventional elastostatic interpretation of FWD measurements is generally associated with a number of inconsistencies. The purpose of this project is to develop a reliable and effective dynamic backcalculation method capable of estimating the location and properties of the permanent or seasonal stiff layer (as well as other pavement stiffness properties) from FWD measurements. The backcalculation method is implemented in the form of a user-friendly software that allows unedited deflection time histories from the FWD test to be used as an input to the back-analysis. The backcalculation scheme developed in this study is based on the Artificial Neural Network (ANN) approach and employs a three-dimensional multilayer viscoelastic dynamic model as a predictive tool.

In this study, the accuracy of the stiffness estimate from portable deflectometers is investigated, based upon the example of a particular device, PRIMA 100. The Beam Verification Tester (BVT) apparatus was developed at the University of Minnesota for the Minnesota Department of Transportation to: (i) verify the performance of the PRIMA device and (ii) to check the calibration factors of the sensors of the PRIMA device. The objective of such tests is to detect the potential occurrence of deterioration of the sensor's accuracy. Associated with the BVT apparatus, an enhanced setup for the portable device is examined. The inconsistency of the traditional data interpretation method using peak values of load and displacement time histories is pointed out by comparing the stiffness estimated from the PRIMA device against the known stiffness of the beam. An alternative method using Frequency Response Functions, spectral average, Single Degree of Freedom System analog, zero frequency estimates and curve fitting is proposed to extract the static stiffness from PRIMA measurements. Test results show good agreement between estimates based on the modified analysis and true beam stiffness. Implementation of both the alternative data interpretation method and the enhanced device setup to quality assurance field measurements are proposed.

This Technical Summary relates to Report 2007-34, "Implementation of Ground Penetrating Radar," published August 2007.

This study deals with the experimental investigation of the effects of moisture and density on the elastic moduli and strength of four subgrade soils generally representing the range of road conditions in Minnesota. The testing approach involved i) reduced-scale simulation of field compaction, ii) field-type testing on prismatic soil volumes, and iii) element testing on cylindrical soil specimens. The field-type testing included: i) the GeoGauge, ii) the PRIMA 100 device, iii) the modified light weight deflectometer (LWD) device, iv) the portable vibratory deflectometer (PVD) and v) the Dynamic Cone Penetrometer (DCP). To compare the Young's modulus values stemming from the field-type and laboratory experiments, cylindrical specimens were extracted from the prismatic soil volumes and tested for the resilient modulus (Mr), small-strain Young's modulus using bender elements. The results reveal that both moisture and density have a measurable effect on the elastic modulus and strength of all four soils. On the element testing side, the small strain estimates from the bender element tests were in good agreement with the resilient modulus values. In the context of field testing, there was significant scatter of the estimated Young's moduli depending upon the particular testing device.

Resilient modulus, shear strength, dielectric permittivity, and shear and compressional wave speed values were determined for 36 soil specimens created from the six soil samples. These values show that the soils had larger stiffnesses at low moisture contents. It was also noted during testing that some non-uniformity was present within the axial displacement measurements; larger levels of non-uniformity were associated with low moisture contents, possibly due to more heterogeneous moisture distributions within these specimens. Lastly, the data collected during this study was used to recommend a relationship between granular materials' small strain modulus and their resilient modulus. This relationship was given in the form of a hyperbolic model that accurately represents the strain-dependent modulus reduction of the base and subgrade materials. This model will enable field instruments that test at small strains to estimate the resilient modulus of soil layers placed during construction.

The objective of this project was to render the Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) road assessment methods accessible to field engineers through a software package with a graphical user interface. The software implements both methods more effectively by integrating the complementary nature of GPR and FWD information. For instance, the use of FWD requires prior knowledge of pavement thickness, which is obtained independently from GPR.

The objective of this project was to provide a qualitative assessment of the Minnesota Department of Transportation's Intelligent Compaction (IC) Specifications. IC is an attractive approach to evaluate the compaction quality because it involves continuous and instantaneous evaluation of the soil through machine-drive power or drum vibration monitoring. Four construction sites utilizing IC were visited: (1) TH 36 in North St. Paul, involving both granular and nongranular soils; (2) US 10 in Staples, with granular soil; (3) TH 60 in Bigelow, with nongranular soil; (4) US 10 in Detroit Lakes, involving both granular and nongranular soils. The report integrates comments from the four site visits and provides an interpretation on the use of IC at each site. As the technology now exists on the equipment used at these locations, IC provides only an index, which is specific to the conditions associated with a particular site. An interpretation of comments provided the basis for the following recommendations: Use light weight deflectometers (LWD) for quality assurance of stiffness; Establish a procedure to determine the target LWD value; Eliminate calibration areas (control strips); Simplify IC data evaluation and presentation; Calibrate the IC roller and related transducers; Support development of alternative IC methodologies; Simplify or eliminate moisture corrections.

The primary objective of this study was to quantify stiffness (resilient modulus) of aggregate base containing recycled asphalt and concrete pavements. After a survey of other state's specifications and implementation guidelines, Minnesota recycling projects were selected based on the availability of laboratory resilient modulus (MR) tests and field measurements from FWD. The projects were County State Aid Highway 3, Trunk Highway 23 and Trunk Highway 200. Based on the results of a parametric study, it was found that traditional peak-based analysis of FWD data can lead to significant errors in elastostatic backcalculation. A procedure for extracting the static response of the pavement was formulated and implemented in a software package called GopherCalc. Laboratory resilient modulus measurements were compared with moduli backcalculated from the FWD data. The FWD data was analyzed using conventional (peak-based) and modified (FRF-based) elastostatic backcalculation (Evercalc) as well as a simplified mechanistic empirical model called Yonapave. Laboratory values from sequences in the MR protocol that produced a similar state-of-stress were used. Additionally, a seasonal analysis of FWD test data revealed a significant increase in stiffness when the pavement is in the frozen state.

The objective of this project was to develop an efficient and accurate algorithm for the back analysis of pavement conditions measured by ground penetrating radar (GPR). In particular, more reliable information about the thickness of the asphalt concrete (AC) layer and the dielectric constants of the AC and base layers were obtained from the electromagnetic field measurements performed on roads using GPR. A brief introduction to the existing methodology for interpreting GPR images is reviewed, and the theory associated with electromagnetic wave propagation in layered structures is described. Utilizing the full waveform solution, algorithms for back analysis of pavement conditions were developed based on the artificial neural network approach and the frequency response function concept. Software called ''GopherGPR'' uses the GPR signal from one antenna to interpret the characteristics of the AC layer with no assumptions on material properties. Thus, the new technique has the capability of providing information not previously available.

The goal of the project is to establish a non-destructive field testing technique, including a data analysis algorithm, for determining in-place pile lengths by way of seismic waves. The length of each pile supporting a high-mast light tower (HMLT) will be identified through a systematic sensing approach that includes (i) collection and classification of the pertinent foundation designs and soil conditions; (ii) use of ground vibration waveforms captured by a seismic cone penetrometer; (iii) three-dimensional visco-elastodynamic finite element analysis (FEA) used as a tool to relate the sensory data to in situ pile length; (iv) use of machine learning (ML) algorithms, trained with the outputs of FEA simulations, to solve the germane inverse problem; (v) HMLT field testing; and (vi) analysis-driven data interpretation. Several hundred HMLTs throughout Minnesota have foundation systems, typically concrete-filled steel pipe piles or steel H-piles, with no construction documentation (e.g., pile lengths). Reviews of designs within current standards suggest that some of these foundations may have insufficient uplift capacity in the event of peak wind loads. Without knowledge of the in situ pile length, an expensive retrofit or replacement program would need to be conducted. Thus, developing a screening tool to determine in situ pile length — as compared to a bulk retrofit of all towers with unknown foundations — would provide significant cost savings.