Browsing by Author "Zhao, Kang"

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    Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method
    Li, Jianfeng; Liu, Huifang; Wang, Wentao; Zhao, Kang; Ye, Zhoujing; Wang, Linbing (MDPI, 2021-09-13)
    The wave velocity of acoustic emission (AE) can reflect the properties of materials, the types of AE sources and the propagation characteristics of AE in materials. At the same time, the wave velocity of AE is also an important parameter in source location calculation by the time-difference method. In this paper, a new AE wave velocity measurement method, the arbitrary wave (AW) method, is proposed and designed to measure the AE wave velocity of an asphalt mixture. This method is compared with the pencil lead break (PLB) method and the automatic sensor test (AST) method. Through comparison and analysis, as a new wave velocity measurement method of AE, the AW method shows the following advantages: A continuous AE signal with small attenuation, no crosstalk and a fixed waveform can be obtained by the AW method, which is more advantageous to distinguish the first arrival time of the acoustic wave and calculate the wave velocity of AE more accurately; the AE signal measured by the AW method has the characteristics of a high frequency and large amplitude, which is easy to distinguish from the noise signal with the characteristics of a low frequency and small amplitude; and the dispersion of the AE wave velocity measured by the AW method is smaller, which is more suitable for the measurement of the AE wave velocity of an asphalt mixture.
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    Development of a Novel Piezoelectric Sensing System for Pavement Dynamic Load Identification
    Zhao, Qian; Wang, Linbing; Zhao, Kang; Yang, Hailu (MDPI, 2019-10-28)
    In order to control the adverse effect of vehicles overloading infrastructure and traffic safety, weight-in-motion (WIM)-related research has drawn growing attention. To address the high cost of current piezoelectric sensors in installation and maintenance, a study on developing a low-cost piezoceramic sensing system is presented in this paper. The proposed system features distributed monitoring and integrated packaging, for calculating vehicle’s dynamic load and its wheel position. Results from the laboratory tests show that the total output of the sensing system increases linearly with the increase of the peak load when the loading amplitude is 5–25 kN (equivalent to the half-axis load of 20–100 kN); when the loading frequency is between 15 Hz and 19 Hz (equivalent to a speed of 17.8–23.2 km/h), the total output of the system fluctuates around a value of 1.305 V. Combined with finite-element simulation, the system can locate load lateral position with a resolution of 120 mm. Due to the protection packaging, the peak load transferred to the sensing units is approximately 4.36% of the applied peak load. The study indicates the proposed system can provide a promising low-cost, reliable and practical alternative for current WIM systems.
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    The Impact of Corporate Crisis on Stock Returns: An Event-driven Approach
    Song, Ziqian (Virginia Tech, 2020-08-25)
    Corporate crisis events such as cyber attacks, executive scandals, facility accidents, fraud, and product recalls can damage customer trust and firm reputation severely, which may lead to tremendous loss in sales and firm equity value. My research aims to integrate information available on the market to assist firms in tackling crisis events, and to provide insight for better decision making. We first study the impact of crisis events on firm performance. We build a hybrid deep learning model that utilizes information from financial news, social media, and historical stock prices to predict firm stock performance during firm crisis events. We develop new methodologies that can extract, select, and represent useful features from textual data. Our hybrid deep learning model achieves 68.8% prediction accuracy for firm stock movements. Furthermore, we explore the underlying mechanisms behind how stakeholders adopt and propagate event information on social media, as well as how this would impact firm stock movements during such events. We adopt an extended epidemiology model, SEIZ, to simulate the information propagation on social media during a crisis. The SEIZ model classifies people into four states (susceptible, exposed, infected, and skeptical). By modeling the propagation of firm-initiated information and user-initiated information on Twitter, we simulate the dynamic process of Twitter stakeholders transforming from one state to another. Based on the modeling results, we quantitatively measure how stakeholders adopt firm crisis information on Twitter over time. We then empirically evaluate the impact of different information adoption processes on firm stock performance. We observe that investors often react very positively when a higher portion of stakeholders adopt the firm-initiated information on Twitter, and negatively when a higher portion of stakeholders adopt user-initiated information. Additionally, we try to identify features that can indicate the firm stock movement during corporate events. We adopt Layer-wised Relevance Propagation (LRP) to extract language features that can be the predictive variables for stock surge and stock plunge. Based on our trained hybrid deep learning model, we generate relevance scores for language features in news titles and tweets, which can indicate the amount of contributions these features made to the final predictions of stock surge and plunge.