PURPOSES : The tire-pavement interaction noise (TPIN) comprises four sources, among which the tire tread vibration noise (TTVN) and air pumping noise (APN) are known to be the most influential. However, when evaluating TPIN, the noise level is estimated based on the overall noise, because general noise measurement methods cannot separate TTVN and APN. Therefore, this study aims to develop a method to separate TTVN and APN in TPIN for quantitative assessment of pavement noise. METHODS : Based on the results of our literature review and frequency band noise data measured in our study, we identified the dominant frequency ranges for TTVN and APN. Additionally, we evaluated TTVN and APN across various pavement types. RESULTS : TTVN was found to be dominant in frequency bands below 800 Hz, while APN was dominant in frequency bands above 800 Hz. Additionally, regardless of the vehicle type, vehicle speed, or pavement type, APN exhibited higher levels compared to TTVN. This result shows that APN has a more significant impact on TPIN than TTVN. CONCLUSIONS : The separation method of TTVN and APN proposed in this study can be utilized to quantitatively assess the relationship between the primary noise sources in TPIN and the characteristics of pavement texture in future research. Furthermore, it is anticipated that characteristics of low TPIN and optimal texture conditions can be proposed to mitigate TPIN, thus contributing to the development of lownoise pavements.

PURPOSES : The purpose of this study is to identify the dynamic behavior of a cement concrete paving machine (paver) by measuring its response using accelerometers. This is because the dynamic behavior of pavers affects the quality of data from various applications of IoT sensors, such as laser, ultrasonic, optical sensors and so on. Therefore, it is believed that the understanding of dynamic behaviors can contribute to the effective use of various IoT sensors for the acquisition of real-time quality control data in pavement construction. METHODS : Dynamic signals are obtained using accelerometer sensors to identify the dynamic characteristics of paving machines. The main parameters for acquiring dynamic signals are the status of the machine’s operating or standby conditions, and available locations for attaching various IoT sensors. Time domain data are logged at a particular sampling speed using a low-pass filter, subsequently, they are converted to digital data, which are analyzed on three rectangular axes. In addition frequency analysis is conducted on the measured data for identifying the peak frequencies, via FFT (Fast-Fourier-Transform) using MATLAB. RESULTS : The magnitude of the x-directional vibration is higher than that of any other direction under the paver’s operating or standby condition. However, signals from the smoother beam show that the z-directional vibration is more significant in the operating status. It means that the primary vibration depends on the location. Furthermore, the peak frequencies are quite various depending on the status of a paver and its sensing location. CONCLUSIONS : The magnitude of machine vibration and peak frequencies at each status or location are identified from time- and frequency-domain data. When using IoT sensors for quality control or monitoring pavements in construction, the dynamic characteristics of a paver should be considered to mitigate the interference of signals from the paver body or its elements.

본 논문에서는 Euler-Bernoulli Beam(EB-beam) 및 신속 Fourier 변환을 이용하여 수치분석적 빔 모델 및 Co-rotational plane beam EDISON program(CR-beam)을 이용한 빔 모델의 가진주파수 변화에 따른 외팔보의 자유단 진동 연구를 수행하였다. 위의 두 빔 모델에서의 끝단에서는 진동이 시간이 지남에 따라 감소하다가 정상상태에 이르는 것을 확인하였다. 끝단에서 가진주파수가 증가함에 따라 구조적 감쇠에 의해 변위이 감소하는 경향을 보인다. 감쇠를 고려한 EB-beam과 CR-beam가 정상상태로 진입하는 경향이 비슷하나, 가진주파수는 정상상태가 나타나는 시간과 독립적임을 제시한다.

본 논문에서는 중고주파수 영역에서 진동하는 단순평판의 진동을 해석하기 위하여 파워흐름유한요소법을 적용하였다. 파워흐름해석법에서 주어지는 진동 에너지지배방정식의 해를 구하기 위한 수치해석 도구로써 유한요소법을 활용하였다. 이러한 파워흐름유한요소법을 적용하여 중고주파수 영역에서 진동하는 단순평판의 진동 변위와 진동인텐시티 분포를 구하였다. 또한 수치해 결과를 엄밀해와 유한요소법에 의한 근사해와 비교함으로써, 파워흐름유한요소법은 중고주파수 영역에서 진동 변위 및 진동 인텐시티를 예측하기 위하여 효과적으로 적용될 수 있음을 보였다.