PURPOSES : The objectives of this study are to evaluate the condition of concrete bridge decks using the multi-channel ground penetrating radar (GPR) testing and compare the value of its dielectric constant value with actual concrete condition. METHODS : The reflection coefficient method was used to measure the dielectric properties of concrete bridge decks. Air-coupled step-frequency GPR testing was used to measure the time taken for reflection from the interfaces between the layers. Specimens of the asphalt mixture and concrete bridge-deck were collected by field coring. GPR testing was conducted on two bridges with different concrete bridge deck conditions on national highways. After the GPR tests, the actual conditions of the concrete bridge deck were investigated using specimen coring. RESULTS : GPR testing indicated that the dielectric constants of concrete bridge decks in good condition ranged from 8 to 10, whereas those corresponding to poor condition ranged from 4 to 6. The results of GPR testing can determine the actual condition and degree of distress of concrete bridge decks determined from the specimen coring data. Therefore, GPR testing is appropriate for nondestructively evaluating the condition of a concrete bridge deck. CONCLUSIONS : The analysis results of the dielectric constants of the concrete bridge deck obtained from multichannel GPR testing were consistent with the actual bridge deck conditions. In the near future, an additional verification process for this approach under different bridge conditions will be required to improve its precision and ensure reliability.

In factory automation, efforts are being made to increase productivity while maintaining high-quality products. In this study, a CNN network structure was designed to quickly and accurately recognize a cigarette located in the opposite direction or a cigarette with a loose end in an automated facility rotating at high speed for cigarette production. Tobacco inspection requires a simple network structure and fast processing time and performance. The proposed network has an excellent accuracy of 96.33% and a short processing time of 0.527 msec, showing excellent performance in learning time and performance compared to other CNN networks, confirming its practicality. In addition, it was confirmed that efficient learning is possible by increasing a small number of image data through a rotation conversion method.

PURPOSES : The objective of this study is to develop the data driven pavement condition index by considering the traffic and climatic characteristics in Incheon city. METHODS : The Incheon pavement condition index (IPCI) was proposed using the weighted sum concept with standardization and coefficient of variation for measured pavement performance data, such as crack rate, rut depth, and International Roughness Index (IRI). A correlation study between the National Highway Pavement Condition Index (NHPCI) and Seoul Pavement Condition Index (SPI) was conducted to validate the accuracy of the IPCI. RESULTS : The equation for determining the IPCI was developed using standardization and the coefficient of variation for the crack rate, rut depth, and IRI collected in the field. It was found from the statistical analysis that the weight factors of the IPCI for the crack rate were twice as high as those for the rut depth and IRI. It was also observed that IPCI had a close correlation with the NHPCI and SPI, albeit with some degree of scattering. This correlation study between the NHPCI and SPI indicates that the existing pavement condition index does not consider the asymmetry of the original measured data. CONCLUSIONS : The proposed pavement condition provides an index value that considers the characteristics of the original raw data measured in the field. The developed pavement condition index is extensively used to determine the timing and method of pavement repair, and to establish pavement maintenance and rehabilitation strategies in Incheon.

Future autonomous vehicles need to recognize the ego lanes required for lane change and the side left and right lanes differently. Therefore, multi-lane recognition is needed. In this study, using the YOLO network, mainly used for object recognition, the proposed method recognizes the ego, left and right side lanes as different objects and identifies the correct lanes. As a result of the performance evaluation on the TuSimple test data, the proposed method recognized the ego lanes and the left and right side lanes differently. It showed very stable lane recognition results. And by detecting lanes that do not exist in the ground truth of TuSimple data, the proposed method is very robust in lanes detection. Nevertheless, studies related to learning data reinforcement in which lanes are located in the center or at the left and right edges of the image and accurate network learning for lanes are needed.

In this study, the multi-lane detection problem is expressed as a CNN-based regression problem, and the lane boundary coordinates are selected as outputs. In addition, we described lanes as fifth-order polynomials and distinguished the ego lane and the side lanes so that we could make the prediction lanes accurately. By eliminating the network branch arrangement and the lane boundary coordinate vector outside the image proposed by Chougule’s method, it was possible to eradicate meaningless data learning in CNN and increase the fast training and performance speed. And we confirmed that the average prediction error was small in the performance evaluation even though the proposed method compared with Chougule’s method under harsher conditions. In addition, even in a specific image with many errors, the predicted lanes did not deviate significantly, meaningful results were derived, and we confirmed robust performance.

We need data such as the number of lanes for lane change on the road as well as environmental and object recognition of the road for the autonomous vehicle of the future. This study proposed an algorithm that recognizes the left and right lanes and the center lane while driving differently from the black box image taken from a car. In general, deep learning does not recognize lanes individually but recognizes all lanes as only one lane. Therefore, using YOLO's object recognition function, the left and right lanes and the center lane were detected as different lanes, and a heuristic method was applied to recognize multi-lanes as more correct lanes. As a result of the performance evaluation, we confirmed that the proposed method detects the lane more accurately than Fast R-CNN and only YOLOv2.

PURPOSES : High concentrations of particulate matter (PM) are emitted or generated from vehicle emissions in urban roads with dense transient populations. To reduce the effect of PM emission on bus stop users at roadsides, a plan to reduce PM emitted from the roadside must be devised. In this study, an atmospheric environment at a roadside is simulated in a large-scale environment chamber, and a test for reducing PM around the bus stop is conducted by installing a bus stop adapted to a PM reduction system. METHODS : Exhaust gas is injected into the experimental and reference chambers using diesel and gasoline vehicles for roadside airquality simulations. The two vehicles are operated in an idle state without an acceleration operation to emit exhaust gas uniformly, and the initial conditions are achieved by injecting car emissions for approximately 40 min. The initial condition is set to 1 ppm of NOx concentration in the environment chamber. Between the two environment chambers, a bus stop adapted to the PM reduction system is installed in the experimental chamber to conduct a PM reduction experiment pertaining to the air quality around the roadside. The experimental progress is set as the start time of the experiment based on the time at which the initial conditions are achieved; simultaneously, the PM reduction system in the experimental chamber is operated. After the simulation is commenced, the PM concentration, which changes over time, is measured using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) without additional injection of car emissions or pollutants. The HR-ToF-AMS measures the chemical composition of non-refractory PM1.0 (NR-PM1.0) in real time. RESULTS : The NR-PM1.0 compound (organic aerosol (OA), NO3 -, SO4 2-) increases by 160% compared with the simulated initial concentration up to T90min in both environmental chambers; this is speculated to be due to secondary formation. The reference chamber indicates a slight decrease or a steady-state after T90min, whereas the experimental chamber indicates a gradually decrease as the experiment progresses. The bus stop adapted to the PM reduction system reduces the amount of black carbon in the experimental chamber by 37% at 200 min. This implies that the PM emitted from the roadside is filtered via the PM reduction system installed at the bus stop, and cleaner air quality can be provided to passengers. CONCLUSIONS : The PM reduction system evaluated in this study can be detached from and attached to the outdoor billboard of a bus stop. Since it adopts air filtration technology that uses a high-efficiency particulate air filter, it can be maintained and managed easily. In addition, it can provide an atmospheric environment with reduced PM emission to passengers as well as provide a better air-quality condition to passengers waiting for public transportation near roadsides.

PURPOSES : In this study, we analyzed the characteristics of nitrogen oxide and fine particulate matter concentration for boarding positions at the bus stop of an exclusive bus lane, using a correlation analysis and a generalized linear model. METHODS : To analyze the air pollution characteristics for boarding positions at the bus stop, data on nitrogen oxide, fine particulate matter concentration, relative humidity, temperature, wind speed, solar radiation, and bus traffic volume were acquired. Using the collected data, a correlation analysis for nitrogen oxide and fine particulate matter was carried out for each boarding position. Additionally, the prediction models for each pollutant were estimated using a generalized linear model, to analyze their characteristics. RESULTS : Correlation analysis revealed that relative humidity and bus volume were positively correlated with both nitrogen oxide and fine particulate matter concentrations in all boarding positions, whereas temperature, wind speed, and solar radiation were negatively correlated. Based on the estimated models from the generalized linear model, the nitrogen oxide concentration at the first measurement point was found to be affected by relative humidity, temperature, and bus volume, whereas at the second measurement point, it was found to be affected by relative humidity, temperature, and solar radiation. Additionally, all factors were significant for fine particulate matter concentration at both boarding positions. CONCLUSIONS : The analytical results indicated that the characteristics of nitrogen oxide and fine particulate matter concentration at the bus stop of an exclusive bus lane varied significantly depending on the boarding positions. Particularly, it was found that the correlation between solar radiation, and nitrogen oxide and fine particulate matter was different because of the conversion of nitrogen oxide to fine particulate matter.

PURPOSES : The objective of this study is to analyze the uniform diffusion mechanism of precursor gas species, and the effect of NOx reduction technology in a full-scale particulate matter testing facility, using computational fluid dynamics (CFD). METHODS : A full-scale environment chamber was constructed to evaluate the effects of particulate matter reduction technologies on the road. CFD analysis was conducted to simulate the road environment conditions in the chamber, and investigate the effect of the NOx removal panel. The time required to reach the NOx concentration to target value in the fluid field was determined at a given inflow velocity, inlet direction, and initial inflow concentration. The effect of the NOx removal panel, and solar energy on the reduction characteristics of the NOx concentration in the environment chamber was analyzed. RESULTS : The inflow velocity was determined to be the major factor affecting the time required to reach a uniform target NOx concentration in the environment chamber. The inlet location in the transverse direction requires additional time to approach the uniform target concentration, than the longitudinal direction at the same inflow velocity. Based on the CFD analysis in the 1ppm concentration condition of the chamber, a two-fold increase in the NOx removal panel efficiency can reduce the time to target concentration by approximately 50%. It is also observed that a 20% increase in solar energy can decrease the time to target concentration by 4%–12% depending on the panel efficiency. CONCLUSIONS : This study proved that a full-scale environment chamber can be effectively utilized to evaluate the particulate matter reduction technologies applied in road facilities

PURPOSES : The purpose of this study was to analyze the effect of reducing nitrogen oxide concentration in a photocatalyst (titanium dioxide) using statistical methods such as the Anderson-Darling test. METHODS : To compare and analyze the effect of reducing the nitrogen oxide concentrations in titanium dioxide, titanium dioxide was applied to the public road, and data acquisition in terms of nitrogen oxide concentration was conducted from roads with/without applying titanium dioxide (test section and reference section, respectively). Then, the probabilities of occurrence of nitrogen oxide concentrations in the test and reference sections were estimated and compared using the Anderson-Darling test. RESULTS : According to the comparison and analysis of probabilities in the nitrogen oxide concentration of the test and reference sections, the probabilities of nitrogen oxide concentration on December 4th were estimated as ‘High’ (17.5%, 37.9%), ‘Moderate’ (30.5%, 40.8%), and ‘Low’ (52.0%, 21.3%), respectively, and on December 5th, as ‘High’ (20.6%, 39.1%), ‘Moderate’ (26.2%, 33.0%), and ‘Low’ (53.2%, 27.9%), respectively. In addition, the probabilities of nitrogen oxide concentration in the test and reference sections were analyzed on December 6th as ‘High’ (16.5%, 36.8%), ‘Moderate’ (27.9%, 38.5%), and ‘Low’ (55.6%, 24.8%), respectively. CONCLUSIONS : Based on the results of this study, in the test section with application of titanium dioxide, the nitrogen oxide concentration was found to have a low probability, and in the reference section, the nitrogen oxide concentration was found to be higher than that in the test section. Therefore, it can be concluded that titanium dioxide applied to road facilities has a nitrogen oxide reduction effect.

PURPOSES : This study analyzes the service life of the repair methods of jointed plain concrete pavement (JPCP) on expressways in Korea using PMS data. METHODS : The Korea Expressway Corporation PMS data acquired from five major expressways in Korea were used for the analysis. The service lives of the repair methods were considered for two different cases: 1) the previous repair methods had been completely rerepaired by another or the same method due to their damage, and 2) the current repair methods were still in use. RESULTS : The service lives of D/G and section repair were shown to be at least 30 % and 50 % shorter than expected, respectively. Joint sealing and crack sealing exhibited a service life similar to that expected. The Mill-and-Asphalt-overlay method showed an approximately 30 % longer service life; this might be because some damage to the asphalt overlay is typically neglected until subsequent maintenance and repair. When multiple repairs were applied in series for an identical pavement section, the service life of repairs on previously damaged secti ons become even shorter compared to their first application. CONCLUSIONS : It was found that the analyzed service life of most important repair methods did not reach the expected service life, and that the service life of the same repair method becomes shorter as applied to the previously repaired concrete pavement sections. These shorter service lives should be seriously considered in future JPCP repair strategy development.

PURPOSES : This study analyzes the characteristics of nitrogen oxide concentration by applying titanium dioxide to existing roads in urban areas, using correlation analysis and a generalized linear model. METHODS : To analyze the characteristics of nitrogen oxide concentration with/without applying titanium dioxide to the urban road segment, data acquisition was conducted for nitrogen oxide concentration, weather information, and traffic information, etc., and a correlation analysis was conducted for each factor, with/without applying titanium dioxide to the roads. In addition, nitrogen oxide concentration generation models with/without the application of titanium dioxide to the roads were estimated using a generalized linear model. RESULTS : The results demonstrate that relative humidity and temperature were found to be slightly correlated with the nitrogen oxide concentration, both with and without the application of titanium dioxide to the roads; however, wind speed, solar radiation, and traffic volume were found to have somewhat low correlation according to the results of a correlation analysis. Moreover, relative humidity, temperature, solar radiation, and traffic volume were significant when titanium dioxide was applied to the roads, based on the estimated model from a generalized linear model, and the wind speed, solar radiation, and traffic volume were significant for the absence of titanium dioxide on the roads. CONCLUSIONS : Analytical results indicated that the characteristics of nitrogen oxide concentration vary depending on the application of titanium dioxide to the roads. In particular, when titanium dioxide was applied to the roads, the relative humidity and temperature were analyzed; according to both analyses, i.e., correlation analysis and a generalized linear model, the nitrogen oxide concentration was affected.

PURPOSES : The objective of this study is to determine the characteristics of nitrogen oxide (NOx) concentration in an exclusive median bus lane station. The hourly variations of NOx concentration and the effect of traffic volume on NOx concentration were analyzed using NOx measurement data and the number of vehicles at the station. METHODS : Data were collected using the chemiluminescence method for NOx concentration. Atmospheric information and traffic volume of buses were collected from the Korea Meteorological Administration and Bus Information System, respectively. RESULTS : As a result, the NO2 concentration in the test section was found to have a strong correlation with those in the atmospheric measurement station located near the test-bed. In addition, the average NOx concentrations in the test section were significantly higher than those of the other monitoring stations due to bus emissions. The average NOx concentration in the exit section was higher than that in the entry section owing to the vehicle’s frequent stops and fuel consumption due to acceleration. During the measurement period, the average NOx concentration was measured as approximately 33 % higher in the exit section than in the entry section. In addition, the NOx concentration at the bus station was found to increase as the bus dwell time increased, rather than the number of bus passages. CONCLUSIONS : This study provides clear characteristics of the NOx correlations with traffic information in an exclusive median bus lane station. It was shown that the NOx concentration at the bus station increased as the number of passes and bus dwell time increased. According to the coefficient of determination, the dwell time is more closely correlated to the NOx concentration at the bus station than the number of bus passes, indicating that it is a better parameter for predicting NOx concentration at bus stations.

PURPOSES : In this study, the characteristics of fine particulate matter (PM2.5) concentrations under different weather conditions of different types of bus stops, such as enclosed-type and open-type bus stops, were analyzed using statistical methods. METHODS : Data was collected inside and outside an enclosed bus stop on sunny and rainy days to compare and analyze the characteristics of fine particulate matter concentration in the target bus stop. The probability distributions were estimated for each data point using the Anderson–Darling test. Based on the estimated probability distributions, probability density functions were computed, and the values were used to estimate and compare probability for each air quality index inside and outside the bus stop under different weather conditions RESULTS : For the results of descriptive statistics, the average concentrations of fine particulate matter inside and outside the bus stop were 42.296 and 35.482 μg/m3 on a sunny day and 40.831 and 39.321 μg/m3 on a rainy day, respectively. Results of the statistical method, obtained using the Anderson-Darling test, indicate that the probability of the air quality index inside the bus stop reaching high concentrations on a sunny day was "high" or "very high," compared to that outside the bus stop. However, on rainy days, the differences in fine particulate matter concentrations inside and outside the bus stops were difficult to identify based on statistical evidence. CONCLUSIONS : It was found that the open-type bus stop had an advantage of preventing fine particulate matter effects on sunny days, compared to the enclosed-type bus stops. Furthermore, there were slight differences in fine particulate matter concentrations inside and outside the bus stop on a rainy day because of atmospheric flow and stormwater.