The modulus of asphalt mixture is traditionally measured from the laboratory dynamic modulus test. However, different laboratory test methods often lead to obviously different test results. To evaluate the moduli of asphalt mixtures as constructed in field pavements, this study back-calculated the moduli of the pavement layers, based on measured strain data in Accelerated Pavement Testing (APT). Field tests were conducted to measure strains at different locations of the built pavement section at different temperatures and wheel motion speed. The loading frequencies of the asphalt layer subject to different motion speeds were calculated based on the duration of measured strain pulses. Subsequently, the relationship between the motion speed and the loading frequency was established. An finite element (FE) model of the pavement section was created, which was used to back-calculate the moduli of asphalt pavement layers using the measured strain data. Based on the back-calculation results at different loading conditions, the master curve of the field asphalt layer was determined. This master curve was further used to compare with that obtained from laboratory uniaxial compressive test. The relationship between the field and laboratory moduli was determined to be E_Laboratory= 1.298E_Field.

The asphalt-based pavement performance evaluation includes the derivation of the dynamic moduli and loading frequencies of pavement layers under various traffic- and climatic-induced loading conditions. The traffic-induced strain pulses and loading frequencies of commonly used (semi-rigid, flexible, and steel deck) asphalt pavements were experimentally determined by vehicular loading field tests with embedded strain gauges for different axle loads, motion speeds, and temperatures. It was found that the axle load values had no noticeable effect on the pavement loading frequency, which was mainly controlled by the vehicular motion speed. The transverse frequencies were found to be higher than longitudinal ones, while the distributions of loading frequencies by pavement depth differed for three pavements under study. The frequency values at temperatures over 35 °C exceeded those at lower temperatures, while in the temperature range from 4 to 31 °C, the motion speed vs. loading frequency relations for three pavements were nearly identical. The loading frequency f increased approximately linearly with the motion speed V, according to the unified fitting equation for three types of pavements under study, namely f = 0.127 × V. This unified equation was further proved valid to predict the dynamic modulus properties of field asphalt pavement layers. Moreover, several previous prediction models for loading frequency, including the Brown model, Ullidtz model, MEPDG procedure and Ulloa model, were compared to the results in this study. These previous models were found to overestimate the loading frequencies within the asphalt layer. The prediction errors of the Brown model and the Ullidtz model were pronounced. The loading frequencies calculated by the MEPDG procedure and the Ulloa model need to be modified by dividing 2.8 and 1.7, respectively.

Asphalt pavement is a multi-layer continuous structure with an interlayer bonding condition that can greatly influence its bearing capacity and must be monitored over the whole service length. Hence, proper maintenance and rehabilitation measures are taken and better feedback can be made for the design. Nevertheless, asphalt pavement interlayer bonding condition plays a hidden role and is difficult to quantify unless applying damage discovery techniques like drilling core process. The original structure is destroyed by the damage detection approaches, the results also suffer from low coverage rate and high dispersion, therefore, actual engineering demands are not satisfied. Here, some recent methods are briefly presented based on asphalt pavement interlayer bonding condition non-destructive diagnosis and their restrictions are discussed. Moreover, a new technique is provided with the merits of utilizing only mechanical simulation and overall superficial distresses condition evaluation index with high accuracy. This simulation procedure adopts bridging principles to build a connection between asphalt pavement superficial distresses and asphalt pavement micro mechanic parameters. It uses a structure behavior equation capable of identifying four typical deterioration modes and has specific physical meaning parameters to predict asphalt pavement overall superficial distresses. The established model allows the asphalt pavement interlayer bonding acting as a hypothetical input variable. The known input variables include easily obtainable design parameters of the asphalt pavement structure, historical superficial distresses, surface deflection, traffic load, and environmental factors, however, the output is the predicted asphalt pavement overall superficial distresses condition. The asphalt pavement interlayer bonding condition is defined by contrasting the theoretical asphalt pavement overall superficial distresses conditions between its complete bonding condition and discontinuous bonding condition to a certain degree. Examples in parts of the Jingha highway show that great discrepancies exist between theoretical asphalt pavement overall superficial distress in the condition of complete interlayer bonding and actual asphalt pavement overall superficial distress. The actual asphalt pavement sections in overall superficial conditions are deteriorated much faster than normal ones. By setting the asphalt pavement interlayer bonding continuations as 75% and 90% for K315-K316 section and K316-K317 section, respectively, the revised and predicted overall superficial distresses of the theoretical asphalt pavement are consistent with the real ones. The hypothetical values are then chosen as the diagnosed quantitative asphalt pavement interlayer bonding continuations and are proven to be correct through drilling core sample results. The analyses in other sections show that middle traffic volume asphalt pavement structure is quite weak when its interlayer bonding continuation is 70%, and under this value, its bearing capacity is almost equivalent to the situation when its interlayer bonding continuation is 0%; when its interlayer bonding continuation is 85% or more, its bearing capacity is almost equivalent to the situation where its interlayer bonding continuation is 100%. This proposed non-destructive diagnosis method for asphalt pavement interlayer bonding condition is effective and shows good application prospects in road engineering.

Asphalt concrete (AC) modulus reduction caused by repeated axle loading significantly affects pavement long-term performance; including when built on a semi-rigid layer. However, quantifying this effect is challenging. The primary objective of this paper was to monitor and evaluate modulus reduction and fatigue damage accumulation at various AC depths utilizing data obtained from two semi-rigid pavement sections. During loading, a non-destructive method, portable seismic pavement analyzer (PSPA), was used to predict the modulus ratio. PSPA test results show that the damage is nonlinear with respect to the loading passes. Also, depth and AC thickness can influence the development of damage. A developed model showed that it could predict the aforementioned nonlinear relationship. The model parameters can be used to identify the damage level at various AC depths. Unexpected compared with previous understanding, the damage in AC layers was found to increased first, then decreased, and finally increased with the depth. Since PSPA is cheap, portable, and easy to apply, this method to identify the damage level in AC layers is proven to be applicable and practical.

The temperature field of asphalt pavement with thick asphalt layer (>30 cm) was analyzed. Based on the cumulative effect of air temperature and solar radiation on pavement temperature, a regression analysis was conducted on the measured pavement temperature from four selected test sites and the obtained meteorological data. The prediction models for different sites were determined as a function of depth, average air temperature and total solar radiation. Then, the cause for the difference between the models at each site was analyzed, and the historical mean monthly air temperatures were incorporated into the model. The model can be applied to the pavement temperature prediction in different areas. The results show that the model has high applicability and high accuracy.

Temperature is one of the most important factors affecting functional as well as structural performance of asphalt pavements with thick asphalt layer (>30 cm). For a successful pavement design, it is vital to accurately predict the pavement temperatures at various depths. However, most previous researches focused on the temperature predictions for conventional asphalt pavements, of which the asphalt thickness is less than 30 cm. This suggests their proposed models are applicable in top layers, but may not be so effective for temperature predictions at deeper depths. As a result, the primary objective of this research was to develop a statistical model to predict temperatures at deep depths. Three test sites were selected, and they were instrumented with a number of sensors and a data logger to record the pavement temperature hourly. Also, all test sections can provide meteorological monitoring to collect hourly air temperatures and hourly total solar radiation. The recorded meteorological conditions were found to have cumulative effect on the measured pavement temperatures at various depths. On basis of their relationship, a statistical regression was performed, and the temperature prediction model was determined as a function of depth, average air temperature and total solar radiation calculated in the cumulative time. For an improvement in applicability, historical mean monthly air temperatures were also incorporated into the mode. The accuracy and applicability of the improved model were validated by applying it to additional sites for which the measured pavement temperatures and meteorological data were available. Also, by comparing with existing models, the developed model was testified to be more effective for asphalt pavements with thick asphalt layer, promising the model's potential use.

Rutting is closely related to the pavement temperature field. As a result, using the concept of rutting equivalent temperature to predict rutting distress is a common method. Different from previous studies, this study puts forward a calculation method of the rutting equivalent temperature for asphalt pavement at different depth, which is based upon the pre-established prediction models of asphalt temperature field and rutting distress. Meanwhile, the rutting equivalent temperature prediction model considering regional differences and asphalt material properties is initially established and tested. Compared with the existing rutting equivalent temperature prediction model, the proposed model reflects the influences of different depth, different regions and different types of asphalt materials. It is demonstrated that this model has better applicability.

The determination of a single temperature to stimulate permanent deformation is quite significant in pavement design and maintenance. The practical use of this single temperature to replace the seasonal temperature fluctuation throughout the whole year can apparently simplify the prediction of permanent deformation of an asphalt pavement. This single temperature is termed effective temperature (T_eff) and is defined as a simulated temperature to achieve a permanent deformation, which is accumulated due to the traffic loading and pavement temperature within an entire year. However, most previous studies on effective temperature focus on investigating the performance temperature for permanent deformation instead of simplifying the prediction of this distress. For this reason, the primary goal of this study was to develop a T_eff model and simplify the prediction of permanent deformation of asphalt pavement. The predicted distress and the T_eff results used to build the models and find the optimized coefficients were obtained by the rut depth prediction model, which was based on shear stress and widely applied in China. The proposed T_eff models incorporated degree-days and the earth temperature, which was represented by mean annual air temperature (MAAT). By incorporating those parameters, these temperature models predominantly considered the effect of high temperature on the permanent deformation and can be utilized to improve their prediction in various regions. In addition, the asphalt mixture properties and vehicle speeds were also incorporated in the models. By comparing with different models in previous studies, the proposed models were testified to be reasonable.