The in-situ dynamic modulus properties of asphalt mixture play a significant role in assessing pavement mechanical responses under traffic loading, determining the pavement performance and condition, and making optimized maintenance decisions. Several methods, such as the falling weight deflectometer (FWD), have been utilized as a non-destructive test to back-calculate the in-situ pavement modulus and conditions; however, the FWD test can only be performed periodically and has the disadvantage of disturbing traffic due to lane-closure needs. With the recent advancement in data science and sensing technologies, the application of micro-electromechanical system (MEMS) sensors and machine learning techniques in pavement nondestructive tests has attracted more research attention. This research aims to develop an in-situ evaluation system that can automatically collect, process, and interpret data to determine the in-situ dynamic modulus of the asphalt mixture under traffic loads using embedded wireless sensors and machine learning techniques. The proposed system is a self-adaptive process and can predict in-situ dynamic modulus based only on mechanical responses and environmental conditions. Ultimately, the well-trained predictive model can be integrated into the pavement management system for the automated and cost-effective assessment of pavement conditions, facilitating informed decision-making. The research program encompasses three types of dynamic modulus experiments: laboratory uniaxial dynamic modulus tests, the one-third scale model mobile load simulator (MMLS3) tests, and in-situ dynamic modulus tests. Particle-size wireless sensors, SmartKli sensors, were implemented in the laboratory specimens and the pavements to collect data from sine wave loads and moving loads. Finite element models (FEM) were also developed and calibrated to generate pavement mechanical response data for more pavement types. The collected data and the FEM simulations were integrated into a database for a proposed adaptive data processing procedure. In addition, because the data collected by embedded sensors in infrastructure health monitoring are inevitably contaminated with noise, and the data features have a distinct discrepancy in different types of tests, a secondary objective of this research is to propose a data processing method capable of removing noises, recognizing data feature discrepancies, and extracting hidden features. An adaptive data processing procedure was developed by combining an empirical mode decomposition (EMD) method and an intrinsic mode function (IMF) selection processing to enhance the reliability of the pavement dynamic modulus prediction. Different EMD techniques were applied to decompose signals from wireless sensors embedded in the pavements. The maximum normalized cross-correlation (MNCC) and signal noise ratio (SNR) were selected as indices in the K-means classification to select the effective IMFs. The results indicated that ensemble EMD (EEMD) and multivariant EMD (MEMD) methods can extract more information from the mechanical responses and extend data dimensions. The EEMD method gives the lowest mean relative error (MRE). Therefore, the EEMD method was recommended for infrastructure data processing. The K-means method can adaptively select the effective IMFs based on the MNCC and SNR. Finally, three dynamic modulus predictive models were developed for different situations. An artificial neural network (ANN) model was developed based on the laboratory test data. This model verified that the ANN model can predict in-situ dynamic modulus. The second dynamic modulus predictive model was developed using the ensemble ANN model to improve the stability of the ANN model, which was trained and tested by the data collected from the MMLS3 test. The third model was developed to predict the dynamic modulus of various asphalt mixtures by fusing a transfer learning approach and Transformer architecture. Besides, the training database was extended with the FEM simulations. The results indicated that the ensemble ANN model is feasible and robust in predicting the dynamic modulus of the asphalt mixture in the MMLS3 test. The transfer learning model is reasonable and robust in predicting the in-situ dynamic modulus of the asphalt pavement.

This synthesis report will be of interest to pavement and geotechnical design and research engineers, geologists and engineering geologists, and related laboratory personnel. It describes the current practice for measuring in situ mechanical properties of pavement subgrade soils. The tests conducted to measure the mechanical properties of soil strength and stiffness are the primary topics, and these are discussed in the context of design procedures, factors affecting mechanical properties, and the variability of measurements. Information for the synthesis was collected by surveying U.S., Canadian, and selected European transportation agencies and by conducting a literature search. This TRB report provides information on existing and emerging technologies for static and dynamic, and destructive and nondestructive testing for measuring in situ mechanical properties of pavement subgrade soils. Correlations between in situ and laboratory tests are presented. The effects of existing layers on the measurement of subgrade properties, and soil spatial and seasonal variability are discussed. Most importantly, the use of soil properties in pavement design and evaluation are explained. New applications or improvements to existing test methods to support the use of mechanistic/stochastic-based pavement design procedures are also explained.

Asphalt pavements suffer various failures due to insufficient quality within their design lives. The American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG) has been proposed to improve pavement quality through quantitative performance prediction. Evaluation of the actual performance (quality) of pavements requires in situ nondestructive testing (NDT) techniques that can accurately measure the most critical, objective, and sensitive properties of pavement systems. The purpose of this study is to assess existing as well as promising new NDT technologies for quality control/quality assurance (QC/QA) of asphalt mixtures. Specifically, this study examined field measurements of density via the PaveTracker electromagnetic gage, shear-wave velocity via surface-wave testing methods, and dynamic stiffness via the Humboldt GeoGauge for five representative paving projects covering a range of mixes and traffic loads. The in situ tests were compared against laboratory measurements of core density and dynamic modulus. The in situ PaveTracker density had a low correlation with laboratory density and was not sensitive to variations in temperature or asphalt mix type. The in situ shear-wave velocity measured by surface-wave methods was most sensitive to variations in temperature and asphalt mix type. The in situ density and in situ shear-wave velocity were combined to calculate an in situ dynamic modulus, which is a performance-based quality measurement. The in situ GeoGauge stiffness measured on hot asphalt mixtures several hours after paving had a high correlation with the in situ dynamic modulus and the laboratory density, whereas the stiffness measurement of asphalt mixtures cooled with dry ice or at ambient temperature one or more days after paving had a very low correlation with the other measurements. To transform the in situ moduli from surface-wave testing into quantitative quality measurements, a QC/QA procedure was developed to first correct the in situ moduli measured at different field temperatures to the moduli at a common reference temperature based on master curves from laboratory dynamic modulus tests. The corrected in situ moduli can then be compared against the design moduli for an assessment of the actual pavement performance. A preliminary study of microelectromechanical systems- (MEMS)-based sensors for QC/QA and health monitoring of asphalt pavements was also performed.

At present, there is no direct solution that provides the pavement in-situ layer moduli from deflection measurements. Current methods evaluate the pavement layer moduli from deflection measurements using either empirical approaches or layered elastic computer programs with iterative solutions. In this study mechanistically based typical curves are developed to evaluate the moduli of the pavement layers: surface, base, subbase, and subgrade from surface deflection measurements. The curves are developed using the Chevron N-layer computer program with a large number of typical combinations of layer thicknesses and material moduli. The load is assumed to be uniformly applied on a single circular plate with a 304.8-mm (12-in.) diameter, a typical condition of the falling weight deflectometer and the road rater with a single circular plate. Twenty-four sets of curves are developed representing a wide range of layer thicknesses and deflection basin shapes. Therefore, if the layer thicknesses are known and the surface deflection measurements are determined, the four moduli of the pavement layers can be evaluated. The developed curves are simple to use without the need for previous empirical relationships or computer analysis. The curves developed here are based on static analysis.

Pavement designs, materials and uses vary around the world, but engineers typically employ the resilient moduli of pavement materials as the primary means of evaluating those materials. Unfortunately, the majority of tests used to determine the resilient modulus of materials are performed in the laboratory where the material either has been removed from the in-situ conditions or has been reconstituted. Soil samples which are removed from the ground using various techniques are at best moderately disturbed. Typically the testing of these samples is performed in a triaxial device equipped for repetitive axial loading. The strain used to calculate the resilient modulus is the recoverable portion of the deformation response. The fact that this response varies with state of stress is widely accepted, but the laboratory test results continue to be used for lack of a more useful and convenient method of determining resilient moduli (Yoder and Witczak, 1975). The purpose of this study is to develop a method for continuous, in-situ evaluation of the resilient modulus of subgrade material under a highway pavement using seismic waves. Although this technique is not mobile and the equipment is fully embedded in the soil under the pavement, it provides a more accurate means of evaluating resilient modulus. This approach can then be used as a benchmark with which to compare the laboratory results to improve design methods as well as our fundamental understanding of the behavior of pavement materials in the field.

Understanding the changes in the structural characteristics of paving layers with season and asphalt binders type is essential in predicting how well the pavement responds to traffic loads and thus how long it will last. This paper presents a small scale investigation on the seasonal variations in the structural characteristics of pavement layers constructed employing polymer modified asphalt binders. These variations are compared to those of an unmodified binder. Illustrated are the influences of asphalt binder type and seasonal temperature variations on: One) center deflection (D0) measured by the falling weight deflectometer (FWD), Two) in situ asphalt concrete modulus (EAC), Three) in situ base course modulus (Eb) and Four) in situ subgrade resilient modulus (MmR) Analysis of results for the two polymer modified sectins and the three unmodified onctrol section used in this study indicates that EAC is the parameter most affected by the change in temperature followed by D0 and Eb . The in situ asphalt concrete modulus (EAC) of the polymer modified sections has shown less sensitivity to temperature changes than other three control sections, especially at high temperature levels (35 0 C -45 0 C). Variations in Eb with temperature are believed to be associated indirectly with variations in EAC with temperature. Changes in EAC with temperature result in changes in stress levels imposed on the underlying layer that causes variations in Eb . Temperature adjustment factors for D0, EAC and Eb are provided for both polymer modified and unmodified sections. For the covering abstract of this conference see IRRD number 872978.

In this study, a flexible pavement system was instrumented using fiber-optic strain sensors (FOSS). The purpose of this study was to demonstrate the feasibility of a FOSS installation, monitor the long-term strains under repeated traffic loading, and compare the measured strains with the calculated ones from multi-layer elastic (MLE) analysis. MLE analysis was performed before and after FOSS installation to monitor strains during and after construction. Insitu strains during construction under the hot-mix asphalt (HMA) delivery truck, paver operations, and roller operations were compared to the results of theoretical MLE analysis. In addition, in-situ strains after construction under dump truck and falling weight deflectometer (FWD) loadings at multiple load levels were compared to the results of theoretical and in-situ MLE analysis. The in-situ strain under construction was at least 50 fold that obtained with MLE analysis. The FOSS were sensitive enough to collect strain measurements during construction at very high construction temperatures and moisture conditions. Further, the MLE analysis results were very close to the measured deflection under dump truck and FWD loadings. The results show that MLE analysis can be used to validate and calculate the strains in asphalt pavement sections. Long-term performance monitoring is continuing, and the study will be repeated after FOSS placement in new HMA pavement sections. Understanding the behavior of asphalt pavement under repeated traffic loads can result in an optimized design, thus reducing the rehabilitation costs associated with premature failures or the higher costs associated with conservative asphalt pavement designs. The in-situ strains can be used to calibrate mechanistic-empirical pavement design guide (MEPDG) performance models for local conditions so that measurements can better predict the life of pavement layers and the layers that will need replacement. The installation of FOSS at selected pavement sites that represent the typical pavement designs across the state would allow for the development of accurate statewide mechanistic-empirical performance models, which would lead to more cost-effective pavement rehabilitation decisions.