Guoguang Liu’s research while affiliated with Civil Aviation University of China and other places

Subject to the coupling effect of climate and aircraft loads, different diseases of runway in cold regions have frequently occurred after long-term operation. To understand the causes of such diseases, finite element simulation and laboratory-scaled model tests were conducted. The results indicate that as the environment temperature decreased to −60°C, the landing impact load of aircraft could be magnified 1.7 times. Thus, the controlled depth for the required reinforcement construction quality of runway subgrade in cold areas should be increased to 3 m. The fundamental frequency of subgrade (f) was found to be sensitive to the coupling effect than the amplitude of the acceleration (amax) and of the acceleration energy spectrum function (PSDmax). This can supplement the dynamic difference indicated by amax and PSDmax. The Boltzmann model was successfully applied to the vibration analysis of runway subgrade (R2 > 0.9). The findings can facilitate the management and maintenance decisions for runways.

Non-destructive detection of pavement diseases on airport runways is of great importance for airport maintenance and ensuring the normal use of airport runways. In this study, a full coverage scan of an airport pavement was conducted using the MALA three-dimensional ground penetrating radar (GPR). Based on complex signal analysis technology, the Hilbert transform were used to comprehensively interpret the anomalous features of the scanned images at specific disease location. The theoretical void volume of pavement diseases was calculated using MATLAB image processing and Origin software. The analysis showed that the main airport pavement diseases were loose layers and small holes, which were more comprehensively revealed by three-dimensional radar images. The three instantaneous parameter spectrums were used to verify and optimize the initial results obtained with the Reflexw post-processing software. The theoretical void volume was calculated using the pixel equivalent method. The void value of typical diseases was 49.38 m³, which was approximately 2.97 times of the calculated value with the rSlicer post-processing technique. This proposed technique can provide a reference for the exact recognition of disease types and the corresponding calculation of grouting reinforcements while overcoming the shortcomings of the single parameter method with multi-parameter analysis.

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 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.