Pavement performance of airport runway plays a key role in the process of aircraft landing and takeoff. To predict runway pavement performance in life cycle, a novel artificial neural network (ANN) has been developed since 2011 by Civil Aviation University of China (CAUC). Evaluation data of 33 runways were used to train, validate and optimize the ANN by a series of heavy weight deflectometer (HWD) tests. By estimating the simplified pavement damage index (ξ) in ANN, investigations on the influences of service life and air traffic on runway pavement performance were analysed. The outcomes from our analysis lead to a novel method of estimating and classifying runway pavement performance in entire service life. Besides ANN analysis, in situ experiments on runway pavement from 2014 to 2018 were also conducted to verify the predicted results. It demonstrates that ANN is an efficient and alternative tool in predicting runway pavement performance. The results show that the service quality of runway pavement decreases in a linear (non-linear) way for regional (hub) airport. In the process of runway pavement deterioration, service life plays a key role in all airports, while air traffic has the greatest contributions to the hub airport. Then runway pavement performance can be classified according to the simplified pavement damage index. In this way, the runway pavement performance in life cycle can be divided into four zones from excellent, good, poor to dangerous. It is possible to help airport agencies to make scientific decisions on runway pavement maintenance, rehabilitation and reconstruction.

In order to improve the dynamic prediction and management capabilities of soil field safety in airfield, a new artificial neural network (ANN) model was established based on the evaluation data of six years in an airport. By factors analysis of the actual soil field of the airport, natural density (ND), actual water content (AW), optimal water content (OW), rainfall condition (RC) and compaction condition (CC) were chosen as the input data, and hyperbolic tangent sigmoid function was set as the transfer function. The network was trained by 400 sets of data and validated for its accuracy by 100 sets of data selected randomly from the database. The prediction capability of achieved ANN model was analyzed by Nash-Sutcliffe efficiency coefficient (NSE) method. And engineering application had been done in another airport. The results show that soil field compactness can be effectively predicted by well-trained ANN model with R-Squared of 0.98 and NSE of 0.89. The outcomes of validation test in another airport prove that the errors of most sample zones are between -5% and 5%, with only one exception of 15%, with calculated NSE of 0.86, which satisfies the requirements of engineering application. By optimization of ANN model with factor analysis method, it indicates that ND and AW are the controlling factors of model compactness prediction, and the best ways of improving the safety of soil field in airport are soil gradation and drainage control strictly.

：In order to investigate the test method of airport pavement voids, a laboratory model test was conducted to achieve the time-history curve of acceleration response of airport pavement under impact loading. The characteristic value of the acceleration curve was obtained by the wavelet transform tool box of Matlab, by which the influence law of pavement voids on vibration signal was analyzed. By optimizing loading steps and screening input vectors, a pavement void prediction method based on back propagation neural network was established. To calibrate the prediction method, a runway field test was carried out by using heavy weight deflector, by which the reliability of back propagation neural network prediction was evaluated by plate coring. The results show that the loading steps, the input vector, training times, the training intensity and algorithm have significant influences on the prediction accuracy of the back propagation neural network. The pavement acceleration influenced by void could be used as the input vector of back propagation neural network, the prediction result of the back propagation neural network was proved by void observation of site coring.

Rapid freeze-thaw cycling experiment of concrete was conducted to investigate the influence of freeze-thaw cycles on transportation safety of airport rigid pavement (ARP). The dynamic elastic modulus and mass of concrete specimens were measured and analyzed by a finite element model, which resulted in a four-linear service life prediction model of ARP and a performance based evaluation method (PBEM) of transportation safety. The results showed that the finite element model could accurately simulate damage conditions of concrete specimens, and the four-linear service life prediction model could conservatively predict damage conditions and specify bearing capacity of ARP. Both of the above models were integrated into PBEM to determine pavement capacity under freeze-thaw conditions, and PBEM was verified to be an efficient tool for aviation agencies to evaluate transportation safety. An example of transportation evaluation that considered the increasing of traffic loads and effects of freeze-thaw was provided to illustrate the applications of PBEM. And an optimized airport management system was achieved by integrating PBEM into decision-making processes.

Maintaining desired subgrade performance is an effective way to reduce runway pavement deterioration. Due to lack of extensive field test data, life-cycle performance of runway subgrade has not been fully understood. In order to quantitatively estimate subgrade condition, a novel method of evaluating subgrade performance was developed and validated using the 726 sets of Heavy Weight Deflectometer (HWD) test data of ten runway sections. Statistical analysis demonstrates that the structural behaviour of rigid runway subgrade follows normal distribution in different service stages and can be efficiently evaluated by the subgrade performance index (ψ). The results of factor analysis show that Accumulated Air Traffic Volume (ATV) during service life is the major cause of spatial variations in subgrade condition. In the designed service period of runway, it validates that sea-reclaimed subgrade results in faster degradation in the initial stage of service life while thicker pavement exhibits better capability in protecting the subgrade soil in long-term view. Besides, the differences in applied loads and pavement thickness give rise to the subgrade performance variation in longitudinal direction. Meanwhile, the comparison between the main and the less trafficked test lines in transversal direction reveals that the aircraft impacts play a positive role in resisting the natural fatigue process. By the suggested method, subgrade performance of HWD test points can be categorized into 4 levels from “Excellent”, “Good”, “Fair” to “Poor” based on ψ value. It is helpful for airport agency to make scientific decisions on Maintenance and Rehabilitation (M&R) treatment by calculating the effective area of envelope (β) using the ratio of subgrade performance (η).