Harmful nitrogen oxides (NO_X) are produced by vehicles, factories, mines, and power plants. In fact, over one million tons of NO_X are emitted into the atmosphere every year, making it the most prevalent air pollutant. Approximately 45% of the emitted NO_X in Korea is associated with the transportation sector. In this paper, the application of a new TiO₂ photocatalyst on the asphalt roads to remove combustion-produced NO_X was studied. In an effort to overcome the known constructability, adhesion, cost, and dispersion problems associated with TiO₂ photocatalysts, the liquid polyurethane (PUD) was added with TiO₂ to form a mixture later known as liquid PUD-TiO₂. Laboratory and field tests were conducted to determine the optimum amount of photocatalyst to be used and the performance of asphalt pavement coated with PUD-TiO₂ in terms of indirect tensile strength, water susceptibility, and rutting resistance. Additionally, the performance of PUD-TiO₂ under different humidity, wind speed, and temperature conditions was also evaluated. The results showed that the application of PUD-TiO₂ photocatalyst on the asphalt pavements road reduces the NO_X available on the surface of the road. The PUD-TiO₂ also was found to have no effects on the performance of asphalt pavement. Meanwhile, under different weather conditions, the reaction between the photocatalyst and NO_X is mainly affected by the humidity.

This study focuses in the impact of vehicle speed, vehicle type, and transverse rumble strip (TRS) design on the sound pressure level perceived by drivers when they traverse a TRS. TRSs are commonly installed on approaches to toll plazas and at intersections, etc. The ability of a TRS to capture a driver’s attention is directly related to the magnitude of the sound and vibration. However, the traffic noise generated by TRS creates a problem for adjacent residences and businesses near the roadway. Thus, the aim of this study is to quantify the level of traffic noise and verify the effects of TRS shapes. The research team measured the exterior noise, inner noise, and vibration generated by three types of vehicles (sedan, minivan, and truck) traveling over four types of transverse rumble strip applications (Types A, B, C, and D) with speed range between 40 and 100 km/h. In general, the increase in the exterior noise generated was greater at 100 km/h than at 40 km/h and lower for a sedan than for a...

This study presents a machine learning model for predicting representative surface distresses (crack, durability, patching, joint spall) in concrete pavements, focusing on South Korean examples. It thoroughly analyzes specific distress types using time series data to understand their development over time, aiming to surpass traditional regression methods in forecasting pavement conditions. The research fills a gap by applying machine learning algorithms to detailed long-term data, enhancing the accuracy of distress progression predictions, which is crucial for efficient pavement management. A notable aspect of this study is the use of particle filtering, recognized for its effective resampling in analyzing time series data. To validate predictions, we compared the results from particle filtering with those from traditional regression models, long short-term memory (LSTM) networks, and Deep Neural Networks (DNNs). The accuracy varied significantly, with differences ranging from 3.32% to 23.64%, indicating particle filtering’s suitability for time-series-based pavement condition predictions. These findings are especially relevant in the context of current image-based machine learning and AI research in pavement distress detection and prediction. This research offers a comprehensive reference that is especially valuable due to the lack of studies using long-term usage data, thereby making a significant contribution to pavement management research and practice.

South Korea is implementing various policies to address the aging of infrastructures and improve road infrastructure management. Moreover, numerous research projects aiming at the development of necessary technologies for the proper implementation of these policies are underway. This study specifically aims to overcome existing problems in bridge pavement maintenance, such as the inaccuracy of future condition predictions and the selection of incorrect evaluation indicators. Our goal is to provide a new approach for the improved management of the bridge pavement management system (BPMS). To address the issues of accuracy in future condition prediction and evaluation indicator selection within the existing maintenance system, we utilized particle filtering, a Kalman filter method among machine learning techniques. This method allows for the prediction of future conditions, based on the nonlinearly collected bridge pavement conditions within BPMS. Furthermore, we proposed a systematic bridge pavement management strategy. This strategy utilizes traffic volume (ESALs; equivalent single axle loadings), a factor that can influence the future condition of bridge pavement, in correlation with the future condition predicted through particle filtering within BPMS.