The Korean Pavement Design Guide (KPDG) was developed based on a mechanistic–empirical approach. This paper presents the permanent deformation model of asphalt mixtures in the KPDG. The permanent deformation model for various types of asphalt mixtures was developed by using the results of triaxial repeated loading tests. This model was calibrated with the accelerated pavement test (APT) and the Long-Term Pavement Performance (LTPP) database. Through the triaxial repeated loading tests, the permanent deformation model of asphalt mixtures was developed to address the effects of temperature and initial air voids. The model coefficients for the temperature and air voids obtained from the triaxial tests do not need to be calibrated. However, the model coefficients for the shift factor and number of load repetitions should be calibrated with APT data. This study found that the model requires a correction factor to consider the effects of total asphalt concrete (AC) layer thicknesses. The correction factor is useful for simulating the distribution of a real vertical plastic strain for different AC layer thicknesses. A shift factor was found by comparing the measured rut depth from the LTPP sections and predicted values from the KPDG program. A verification study showed that the final permanent deformation model developed in this study can predict the field rut depth of various AC pavements quite accurately.

Recently, the Korean Pavement Design Guide (KPDG) has been developed based on the mechanistic-empirical design principle as part of the Korea Pavement Research Program (KPRP). This paper presents the detailed information about the input parameters and pavement performance models used in the KPDG for new construction of asphalt pavements. Input parameters for pavement design such as traffi c, environment, and materials were character-Input parameters for pavement design such as traffic, environment, and materials were characterized by considering the domestic condition. The structural analysis program based on the layer elastic theory has been developed to calculate the critical pavement responses in asphalt pavements. The pavement performance models for fatigue cracking and rutting were developed and calibrated using various types of laboratory testing results and field monitoring data. The procedure for determining the critical evaluation points in calculating the pavement responses has been proposed to reduce the computational time of the design program. The concept of stiffness reduction of asphalt mixtures was also incorporated with the structural analysis program and pavement performance models for more realistic prediction of the pavement distresses.

Anomaly detection has recently gained enormous attention from the research community. It is widely applied in many industrial areas, such as information security, financing, banking, and insurance. The data in these fields can mainly be represented as time series data, the corollary being that time series anomaly detection plays an essential role in these applications. Therefore, many authors have tried to solve the problem of collective anomaly detection in time series. They have proposed several approaches, from classical methods such as Isolation Forests to modern deep learning networks such as Autoencoders. However, a comprehensive framework for handling this problem is still lacking. In this work, firstly, we propose using an Attention-based Bidirectional LSTM Autoencoder (Att-BiLSTM-AE) as an anomaly detection model. Furthermore, in the essential part of this paper, we developed a comprehensive unsupervised deep learning framework, udCATS, to solve the problem of detecting collective anomalies in time series. Our experiments show that the Att-BiLSTM-AE outperforms other detection models, and using it within the udCATS framework increases the detection accuracy.

No abstract

Currently, application of industrial waste or by-product in road construction industrials is a major interest by researchers, government officers and engineers. Coal ashes by-product from industrial parks negatively impact environment, costly in treatment, and require large ground for disposing areas. Therefore, this paper proposes on using the coal ash from furnace products of an industrial park in South of Vietnam to be incorporated into dense graded asphalt concrete using Nominal Maximum Aggregate Size 12.5mm. Laboratory performance tests including Marshall stability, indirect tensile strength, Cantabro loss, and dynamic fatigue test were conducted. The effects of coal ash contents in replacement of fine aggregate which is passing 4.75mm sieve from asphalt mixture into laboratory performance of mixture is also discussed in detail.