Falling weight deflectometer (FWD) is currently used by most highway agencies to determine the structural condition of the highway network. Utilizing the deflections measured by the FWD, the resilient moduli of layers in the flexible pavement is determined using backcalculation software packages. The moduli can be input into semi-empirical mechanistic equations to estimate the remaining life of the pavement system and aid in informing pavement engineers about timing of maintenance and rehabilitation needs. There have been concerns among practitioners and the research community about the adequacy of the resilient moduli determined by the backcalculation software. Some of the backcalculation models have been simplified and field verification may be needed. Field-measured stresses and strains may be used to quantify the reliability of the backcalculated moduli. The Virginia Smart Road, which has 12 different flexible pavement designs and was built and instrumented with pressure cells, strain gages, thermocouples, frost probes and moisture sensors. To validate the backcalculated moduli theoretically and through instrument response, this research was conducted with following objectives: 1) to determine the resilient moduli of the unbound granular materials on the Virginia Smart Road using small and large plates of the FWD; 2) to investigate the extent of spatial and temporal variability of the FWD deflections among pavement sections; 3) to develop a temperature correction model for the backcalculated HMA resilient moduli; 4) to define an appropriate backcalculation approach and compare the four widely used software approaches; and 5) to correlate backcalculated and laboratory measured moduli. In addition, the FWD measurements were used to establish a comparison between in-situ measured and computed stresses and strains in the pavement. The analytical approaches used are linear elastic, viscoelastic, and viscoelastic combined with nonlinearity. Results show that estimation of unbound granular materials moduli using surface deflections is more reliable when 457-mm-diameter loading plate is used. Analysis of deflections from different sensors showed evidence of spatial and temporal variability. The lowest coefficient of variation of deflections (7%) within sections occurred at low temperatures (2 to 6 °C), while the highest coefficient of variation (42%) occurred at temperatures between 35 to 40 °C. This resulted in the development of a deflection temperature correction model. The model was validated at different temperature ranges. A backcalculation procedure was defined to achieve good root mean square error using four selected software packages. This resulted in the selection of the most reliable software to perform moduli backcalculation. A correlation was established between the nonlinear models produced by backcalculation and laboratory testing of the granular 21-B material. However, for the HMA materials, difference in loading period between laboratory testing and FWD loading pulse could affect the results. The study found that when utilizing the backcalculated moduli, computed strains using viscoelastic modeling were comparable to in-situ measured values. Similarly, calculated stresses compared well with the field-measured stresses; especially at high temperatures. Mix properties, temperature of testing and loading were found to have an effect on the agreement between the measured and computed strains in the wearing surface. The study also recommended further validation of FWD measurements using embedded instruments to calibrate analytical models and further analysis of deflection data so that optimum number of testing points can be determined to limit amount of testing performed for determination of deflection variability. / Ph. D.

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<p dir="ltr">Resilient Modulus (M_R) is a fundamental parameter in the Mechanistic-Empirical Pavement Design Guide (MEPDG) that characterizes the stiffness of subgrade soils under repeated traffic loads. Traditionally, M_R determination involves direct laboratory testing, which can be labor-intensive, costly, and impractical for large-scale pavement projects or rehabilitation efforts. To address these challenges, the current research has explored non-destructive testing methods, such as Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR), as well as the use of predictive models to estimate M_R based on soil properties. This study aimed to enhance the understanding of M_R testing and improve predictive models, contributing to more reliable and efficient M_R estimation techniques.</p><p dir="ltr">The research involved an extensive experimental program, which began with the collection of subgrade soil samples from various road construction projects across Indiana. The collected soils were characterized through standard geotechnical tests, including gradation analysis, Atterberg limits, and compaction tests. Resilient modulus testing followed the AASHTO T 307 protocol, performed on both untreated and treated soil samples to simulate field conditions. Post-construction, the test sites were revisited to conduct FWD and GPR tests, ensuring a comprehensive dataset for correlating M_R with field test results. The use of GPR for pavement thickness estimation proved effective in identifying discrepancies between as-built and design thicknesses in both flexible and rigid pavements. For flexible pavements, a strong correlation was observed between laboratory M_R values and FWD backcalculated moduli, indicating that FWD testing can reliably estimate M_R for untreated subgrade soils.</p><p dir="ltr">The study also explored the use of machine learning algorithms, such as random forest and gradient boosting, to predict M_R based on soil properties, offering an alternative to traditional regression analysis. The research found that stress-independent models failed to yield statistically significant correlations between M_R and basic soil properties such as moisture content, dry density, and Atterberg limits. In contrast, stress-dependent models, particularly the Uzan and octahedral models, revealed weak dependencies on confinement and deviatoric stresses, leading to significant variability in M_R values across tested samples. The results highlight the limitations of current soil- and stress-based models, suggesting that while they may work well for specific cases, they cannot be generalized across a wide range of conditions.</p><p dir="ltr">An effort to compare soil performance during the different stages of resilient modulus testing and a numerical method that included a stress-dependent soil model confirmed the empirical finding of a weak dependency between M_Rand confinement and deviatoric stress. This was the case not only for the standard AASHTO T 307 protocol, but also for other protocols where the loading sequence was reversed compared to the standard test.</p><p dir="ltr">The research demonstrated the potential of machine learning for M_R prediction and the complexity of the soil behavior during resilient modulus testing. Thus, models to accurately predict M_R results should be able to follow the stress path that the soil is subjected to during the test.</p>

Este trabalho investiga um procedimento para retroanálise utilizando Redes Neurais Artificiais (RNAs). Nesta pesquisa foram utilizadas 35.472 bacias de deflexões hipotéticas, criadas pelo programa ELSYM5. A base de dados de treinamento das RNAs consistiu dessas bacias de deflexão e dos módulos e espessuras que as geraram. A camada de entrada das RNAs foi compostas da(s) espessura(s) da(s) camada(s) do pavimento, da bacia de deflexão (na simulação com a viga Benkelman, além desses parâmetros, incluiu-se o raio de curvatura (R)) e a camada de saída foi composta pelos módulos resilientes das camadas do pavimento. Esses dados serviram de entrada para o processo de aprendizagem, utilizando-se o simulador EasyNN 3.2, que se baseia em redes Multilayer Perceptron e no algoritmo de treinamento Backpropagation. Para o procedimento de retroanálise proposto foram implementadas seis RNAs: duas simulando o procedimento para pavimento de duas camadas (uma simulando o ensaio da viga Benkelman e a outra a do Falling Weight Deflectometer), duas para pavimento de três camadas (simulação com os mesmos aparelhos) e duas para pavimento de quatro camadas (simulando os ensaios descritos anteriormente). Mediante as regressões lineares entre os módulos reais (ELSYM5) e os previstos pela RNA, obtiveram-se coeficientes de determinação (R2) e erros médios relativos (EMR). Estes parâmetros demonstraram uma boa correlação linear entre os módulos reais (ELSYM5) e os previstos (RNA). Com os resultados obtidos, conclui-se que as RNAs são ferramentas potentes para serem utilizadas como procedimento de retroanálise para pavimentos flexíveis de duas, três e quatro camadas. / This paper investigates a backcalculation procedure using Artificial Neural Networks (ANNs). In the research 35,472 hypothetical deflection basins were used, created by the program ELSYM5. The ANNs training database consisted of these basins, and of the moduli and thickness used to generate them. The input layer of these ANNs was composed by thickness(es) of the pavement layer(s), the deflection basin (in the simulation with the Benkelman beam, beyond of those parameters, the curvature radius included (R)) and the output layer was composed by the resilient moduli of the layers of the pavement. Those data were used as output for the learning process, using the easyNN 3.2 simulator, which is based on Multilayer Perceptron and in the training algorithm Backpropagation. For the backcalculation procedure proposed six ANNs they were implemented: two simulating the procedure for pavement of two layers (a simulating the testing of the Benkelman beam and the other the one of Falling Weight Deflectometer), two for pavement of three layers (simulation with the same equipments) and two for pavement of for layers (simulating the testing described previously). The values founds throught linear regression between the real moduli (ELSYM5) and the predicted of ones for ANN, were obtained determination coefficients (R2) and relative average errors (EMR). These parameters demonstrated a good linear correlation between the real moduli (ELSYM5) and the predicted of ones (ANN). The conclusion .is that ANNs are potent tools for they be used in backcalculation procedures flexible pavements of two, three and four layers.

Este trabalho investiga um procedimento para retroanálise utilizando Redes Neurais Artificiais (RNAs). Nesta pesquisa foram utilizadas 35.472 bacias de deflexões hipotéticas, criadas pelo programa ELSYM5. A base de dados de treinamento das RNAs consistiu dessas bacias de deflexão e dos módulos e espessuras que as geraram. A camada de entrada das RNAs foi compostas da(s) espessura(s) da(s) camada(s) do pavimento, da bacia de deflexão (na simulação com a viga Benkelman, além desses parâmetros, incluiu-se o raio de curvatura (R)) e a camada de saída foi composta pelos módulos resilientes das camadas do pavimento. Esses dados serviram de entrada para o processo de aprendizagem, utilizando-se o simulador EasyNN 3.2, que se baseia em redes Multilayer Perceptron e no algoritmo de treinamento Backpropagation. Para o procedimento de retroanálise proposto foram implementadas seis RNAs: duas simulando o procedimento para pavimento de duas camadas (uma simulando o ensaio da viga Benkelman e a outra a do Falling Weight Deflectometer), duas para pavimento de três camadas (simulação com os mesmos aparelhos) e duas para pavimento de quatro camadas (simulando os ensaios descritos anteriormente). Mediante as regressões lineares entre os módulos reais (ELSYM5) e os previstos pela RNA, obtiveram-se coeficientes de determinação (R2) e erros médios relativos (EMR). Estes parâmetros demonstraram uma boa correlação linear entre os módulos reais (ELSYM5) e os previstos (RNA). Com os resultados obtidos, conclui-se que as RNAs são ferramentas potentes para serem utilizadas como procedimento de retroanálise para pavimentos flexíveis de duas, três e quatro camadas. / This paper investigates a backcalculation procedure using Artificial Neural Networks (ANNs). In the research 35,472 hypothetical deflection basins were used, created by the program ELSYM5. The ANNs training database consisted of these basins, and of the moduli and thickness used to generate them. The input layer of these ANNs was composed by thickness(es) of the pavement layer(s), the deflection basin (in the simulation with the Benkelman beam, beyond of those parameters, the curvature radius included (R)) and the output layer was composed by the resilient moduli of the layers of the pavement. Those data were used as output for the learning process, using the easyNN 3.2 simulator, which is based on Multilayer Perceptron and in the training algorithm Backpropagation. For the backcalculation procedure proposed six ANNs they were implemented: two simulating the procedure for pavement of two layers (a simulating the testing of the Benkelman beam and the other the one of Falling Weight Deflectometer), two for pavement of three layers (simulation with the same equipments) and two for pavement of for layers (simulating the testing described previously). The values founds throught linear regression between the real moduli (ELSYM5) and the predicted of ones for ANN, were obtained determination coefficients (R2) and relative average errors (EMR). These parameters demonstrated a good linear correlation between the real moduli (ELSYM5) and the predicted of ones (ANN). The conclusion .is that ANNs are potent tools for they be used in backcalculation procedures flexible pavements of two, three and four layers.

A utilização da tela de aço como reforço de pavimentos asfálticos no combate ao trincamento por reflexão é uma alternativa utilizada desde 1980 em países europeus. Uma revisão da literatura a respeito de sua utilização na reabilitação de pavimentos asfálticos, mostra que, a sua utilização acaba diminuindo o aparecimento de trincas nas camadas de revestimento, agindo como uma barreira contra a sua propagação, oferece resistência ao cisalhamento especialmente sob elevadas tensões e ainda, melhora a resistência à fadiga contribuindo para a longevidade do pavimento. Entretanto, o potencial de sua utilização tem sido pouco investigado em rodovias brasileiras. Neste contexto, insere-se esta pesquisa com o objetivo de avaliar o efeito do reforço gerado pela inserção da tela em revestimentos asfálticos para o uso em pavimentos flexíveis, com o propósito de tornar as estruturas rodoviárias menos onerosas com consequente aumento da sua vida útil. Para isso, foi realizada uma pesquisa visando à análise do seu desempenho em um trecho experimental localizado na rodovia SP-354, no Estado de São Paulo entre as cidades de Campo Limpo Paulista e Jarinu, fundamentado nas melhores práticas internacionais, recorrendo à observação em campo e laboratório, análise por meio de ensaios de módulo de resiliência e cálculos por retroanálise para verificação do efeito da inserção da tela, ainda propondo, uma metodologia de dimensionamento estrutural de reforço de pavimentos asfálticos considerando a faixa de valores de módulo de resiliência integrados com a tela e o fator de deflexão (K) para cálculo de espessura de reforço. Conclui-se que esta técnica de reforço tem potencial para prolongar a vida útil de revestimentos asfálticos em pavimentos flexíveis, com benefícios também para o desempenho da camada na fase pós-trincamento. Foram obtidos modelos que permitem dimensionar o revestimento asfáltico com o propósito de avaliar o efeito da tela de aço na zona tracionada da camada. Por fim, foi verificada através de um estudo de viabilidade técnica/econômica que a incorporação da tela de aço em pavimentos flexíveis é uma alternativa eficaz e de adequada viabilidade técnica e econômica. / The use of the steel mesh as reinforcement of asphalt pavements to combat reflective cracks by reflection is an alternative used since 1980 in European countries. A review of the literature on the use of the steel mesh in the rehabilitation of asphalt pavements shows that its use prevents the appearance of cracks acting as a barrier against its propagation, offers resistance to shearing especially under high tensions and also improves the resistance to fatigue contributing to the longevity of the pavement. However, the potential if its use has been little investigated in Brazilian highways. In this context this project is to develop a new technology for road construction and rehabilitation. The idea is to use steel mesh reinforcement in asphalt roads in order to make road structures more cost effective by improving the lifetime of new constructed roads and by developing an optimal rehabilitation method for existing roads. For this, a research was performed aiming at the analysis of its performance in an experimental section located on the highway SP-354, in the State of São Paulo between the cities of Campo Limpo Paulista and Jarinu, based on the best international practices, using observation of its behavior in the field and laboratory, analysis by means of resilient modulus tests and calculations by backcalculation to verify the effect of the insertion of the screen, still proposing, a methodology of asphalt pavement design considering the range of integrated resilience module values with the screen and the structural deflection reduction factor (K) admissible for reinforcement projects. It was concluded that this reinforcing technique has potential for improvements crack propagation post-cracking behavior and permanent deformation in the asphalt concrete surfacing layer, with a ten fold increase on fatigue life to be expected. A model was developed that may be employed for pavement design modifying a model based on the use of conventional fatigue laws for the asphalt concrete, if the grid is positioned at the tensile zone of the surfacing layer. Finally, it was verified through a technical / economic study that the incorporation of the steel mesh in flexible pavements is an effective alternative and of adequate technical and economic viability.

A utilização da tela de aço como reforço de pavimentos asfálticos no combate ao trincamento por reflexão é uma alternativa utilizada desde 1980 em países europeus. Uma revisão da literatura a respeito de sua utilização na reabilitação de pavimentos asfálticos, mostra que, a sua utilização acaba diminuindo o aparecimento de trincas nas camadas de revestimento, agindo como uma barreira contra a sua propagação, oferece resistência ao cisalhamento especialmente sob elevadas tensões e ainda, melhora a resistência à fadiga contribuindo para a longevidade do pavimento. Entretanto, o potencial de sua utilização tem sido pouco investigado em rodovias brasileiras. Neste contexto, insere-se esta pesquisa com o objetivo de avaliar o efeito do reforço gerado pela inserção da tela em revestimentos asfálticos para o uso em pavimentos flexíveis, com o propósito de tornar as estruturas rodoviárias menos onerosas com consequente aumento da sua vida útil. Para isso, foi realizada uma pesquisa visando à análise do seu desempenho em um trecho experimental localizado na rodovia SP-354, no Estado de São Paulo entre as cidades de Campo Limpo Paulista e Jarinu, fundamentado nas melhores práticas internacionais, recorrendo à observação em campo e laboratório, análise por meio de ensaios de módulo de resiliência e cálculos por retroanálise para verificação do efeito da inserção da tela, ainda propondo, uma metodologia de dimensionamento estrutural de reforço de pavimentos asfálticos considerando a faixa de valores de módulo de resiliência integrados com a tela e o fator de deflexão (K) para cálculo de espessura de reforço. Conclui-se que esta técnica de reforço tem potencial para prolongar a vida útil de revestimentos asfálticos em pavimentos flexíveis, com benefícios também para o desempenho da camada na fase pós-trincamento. Foram obtidos modelos que permitem dimensionar o revestimento asfáltico com o propósito de avaliar o efeito da tela de aço na zona tracionada da camada. Por fim, foi verificada através de um estudo de viabilidade técnica/econômica que a incorporação da tela de aço em pavimentos flexíveis é uma alternativa eficaz e de adequada viabilidade técnica e econômica. / The use of the steel mesh as reinforcement of asphalt pavements to combat reflective cracks by reflection is an alternative used since 1980 in European countries. A review of the literature on the use of the steel mesh in the rehabilitation of asphalt pavements shows that its use prevents the appearance of cracks acting as a barrier against its propagation, offers resistance to shearing especially under high tensions and also improves the resistance to fatigue contributing to the longevity of the pavement. However, the potential if its use has been little investigated in Brazilian highways. In this context this project is to develop a new technology for road construction and rehabilitation. The idea is to use steel mesh reinforcement in asphalt roads in order to make road structures more cost effective by improving the lifetime of new constructed roads and by developing an optimal rehabilitation method for existing roads. For this, a research was performed aiming at the analysis of its performance in an experimental section located on the highway SP-354, in the State of São Paulo between the cities of Campo Limpo Paulista and Jarinu, based on the best international practices, using observation of its behavior in the field and laboratory, analysis by means of resilient modulus tests and calculations by backcalculation to verify the effect of the insertion of the screen, still proposing, a methodology of asphalt pavement design considering the range of integrated resilience module values with the screen and the structural deflection reduction factor (K) admissible for reinforcement projects. It was concluded that this reinforcing technique has potential for improvements crack propagation post-cracking behavior and permanent deformation in the asphalt concrete surfacing layer, with a ten fold increase on fatigue life to be expected. A model was developed that may be employed for pavement design modifying a model based on the use of conventional fatigue laws for the asphalt concrete, if the grid is positioned at the tensile zone of the surfacing layer. Finally, it was verified through a technical / economic study that the incorporation of the steel mesh in flexible pavements is an effective alternative and of adequate technical and economic viability.

This thesis focused on using a detailed structural evaluation of seven (three flexible and four composite) high performance in-service pavements designated as high-priority routes to verify the applicability of the Mechanistic Empirical (M-E) models to high performance pavements in the Commonwealth of Virginia. The structural evaluation included: determination of layer thicknesses (from cores, GPR and historical data), pavement condition assessment based on visual survey, estimation of layer moduli from FWD analysis as well as material characterization. One of the main objectives of this study was to utilize the results from the backcalculated moduli in order to predict the performance of this group of pavement structures using the M-E Design Guide Software. This allowed a quick verification of the performance prediction models used by comparing their outcome with the current condition. The in-depth structural evaluation of the three flexible and four composite pavements showed that all the sites are structurally sound. The investigation also confirmed that the use of GPR to determine layer thicknesses and the comparison with a minimum number of cores is a helpful tool for pavement structural evaluation. Despite some difficulties performing the backcalculation analysis for complex structures, the obtained results were considered reasonable and were useful in estimating the current structural adequacy of the evaluated structures. The comparison of the measured distresses with those predicted by the M-E Design Guide software showed poor agreement. In general, the predicted distresses were higher than the distresses actually measured. However, there was not enough evidence to determine whether this is due to errors in the prediction models or software, or because of the use of defaults material properties, specially for the AC layers. It must be noted that although an in-depth field evaluation was performed, only Level 3 data was available for many of the input parameters. The results suggest that significant calibration and validation will be required before implementation of the M-E Design Guide. / Master of Science