PURPOSES : For most local governments, including that of Gangwon-do, the establishment of an organized pavement management system is insufficient, resulting in problems such as inefficient distribution and use of maintenance budgets for deteriorated road pavements. In this study, we aimed to contribute to the establishment of a more reasonable road maintenance strategy by developing a model for predicting the annual international roughness index (IRI) change for national highway asphalt pavements in Gangwon-do based on big data analysis. METHODS : Data on independent and dependent variables used for model development were collected. The collected data were subjected to exploratory data analysis (EDA) and data preprocessing. Independent variable candidates were selected to reduce multicollinearity through correlation analysis and specific conditions. A final model was selected, and sensitivity analysis was performed. RESULTS : The final model that predicts annual IRI change uses independent variables such as annual temperature range, minimum temperature, freeze-thaw days, IRI, surface distress (SD), and freezing days. The sensitivity analysis confirmed that the annual IRI change was affected in the order of annual temperature range, minimum temperature, freeze-thaw days, IRI, SD, and freezing days. CONCLUSIONS : Road maintenance can be performed rationally by predicting future pavement conditions using the model developed in this study. The accuracy of the prediction model can be improved if additional data, such as material properties and pavement thickness, are obtained in future studies.

PURPOSES : The initial smoothness of concrete pavement surfaces must be secured to ensure better driving performance and user comfort. The roughness was measured after hardening the concrete pavement in Korea. When the initial roughness is poor, relatively large-scale repair works, such as milling or reconstruction must be performed. Hence, a method to measure the roughness of the concrete pavements in realtime during construction and immediately correct the abnormal roughness was developed in this study. METHODS : The profile of a concrete pavement section was measured at a construction site using sensors that were attached to the tinning equipment of the paver. The measured data included outliers and noise caused by the sensor and vibration of the paving equipment, respectively, which were further calibrated. Consequently, the calibrated data were input into the ProVAL program to calculate the roughness based on the international roughness index (IRI). Additionally, the profile of the section was re-measured using another method to verify the reliability of the calculated IRI. RESULTS : The profile data measured at the concrete pavement construction site were calibrated using methods, such as overlapped boxplot outlier removal and low-pass filtering. The outlier data from the global positioning system (GPS), which was installed to identify the construction distance, was also calibrated. The IRI was calculated using the ProVAL program by matching the measured profile and GPS data, and applying the moving average method. The calculated IRI was compared to that measured using another method, and the difference was within the tolerance. CONCLUSIONS : A method to measure the roughness of the concrete pavements in real time during construction was developed in this study. Hence, the performance of concrete pavements can be improved by enhancing the roughness of the pavement considerably using the aforementioned method.

PURPOSES : This study aims to develop and evaluate computer vision-based algorithms that classify the road roughness index (IRI) of road specimens with known IRIs. The presented study develops and compares classifier-based and deep learning-based models that can effectively determine pavement roughness grades. METHODS : A set road specimen was developed for various IRIs by generating road profiles with matching standard deviations. In addition, five distinct features from road images, including mean, peak-to-peak, standard variation, and mean absolute deviation, were extracted to develop a classifier-based model. From parametric studies, a support vector machine (SVM) was selected. To further demonstrate that the model is more applicable to real-world problems, with a non-integer road grade, a deep-learning model was developed. The algorithm was proposed by modifying the MNIST database, and the model input parameters were determined to achieve higher precision. RESULTS : The results of the proposed algorithms indicated the potential of using computer vision-based models for classifying road surface roughness. When SVM was adopted, near 100% precision was achieved for the training data, and 98% for the test data. Although the model indicated accurate results, the model was classified based on integer IRIs, which is less practical. Alternatively, a deep-learning model, which can be applied to a non-integer road grade, indicated an accuracy of over 85%. CONCLUSIONS : In this study, both the classifier-based, and deep-learning-based models indicated high precision for estimating road surface roughness grades. However, because the proposed algorithm has only been verified against the road model with fixed integers, optimization and verification of the proposed algorithm need to be performed for a real road condition.

PURPOSES : For large-scale construction, such as a concrete pavement, design and construction are not entirely consistent. If the inconsistency between design and construction is very large, construction quality is significantly degraded, affecting performance life span and driving comfort. The quality of pavement construction is managed according to standards. However, it is difficult to improve construction quality as the standard measures construction quality after construction is completed. Therefore, this study developed a system to measure the construction quality of concrete pavement in real-time and presented the corresponding standards. METHODS : A basic module for simultaneously measuring the width, thickness, and roughness of the concrete pavement was designed. Based on the measurement results of the distance measurement sensor, a calibration method is presented that can remove noise. The system process was developed to measure construction quality based on location and distance data, measured in real-time using GPSs and sensors. The field application experiment was conducted and the results were analyzed. RESULTS : The measurement module is properly designed to be used in concrete pavement construction sites. Noise was removed from the distance measurement sensor results according to the presented calibration method, leaving only the wave of pavement surface irregularities. As a result of applying the system process in the field application, a reasonable level of PRI was observed. CONCLUSIONS : In the past, the width, thickness, and roughness were measured after construction was completed and, if the standard was not met, construction quality control was performed via reconstruction or repair. Through this study, it is expected that the width, thickness, and roughness of the concrete pavement can be measured in real-time and, if the standard is not met, construction quality can be immediately controlled during construction to maintain high quality.

PURPOSES : The purpose of this study is to propose the certification process of International Roughness Index measuring device, i.e., a method for evaluating riding quality on road surfaces. METHODS : ROMDAS was selected as a reference device for verifying the accuracy of the IRI measuring device and the reliability of ROMDAS was checked through leveling in the laboratory and outdoors. To verify four different IRI measuring devices in Korea, the proper field test section was selected and IRI evaluation was conducted. A distance measuring instrument (DMI) - for verifying the accuracy of mileage - and IRI (as an index of roughness) were selected as the main evaluation parameters. For DMI verification, five repeated experiments were conducted for a 1 km section and, for IRI verification, speed variables of 40 km/h, 60 km/h, and 80 km/h were selected. Each device was tested at each speed 10 times. The accuracy of the measurement device was analyzed by comparing the measurement results with the verification criteria. RESULTS : As a result of the comparative experiment between the leveling and ROMDAS devices, the deviation of each measurement point value was within 1 mm and the R2 value was 0.8, demonstrating an excellent correlation. As a result of DMI verification, the tolerance of the three devices was found to be within 0.1 %; however, one device had a tolerance of 0.8 %, indicating that correction was necessary. For IRI evaluation, the average IRI value of the two reference devices was 2.02 m/km and the Minnesota standard was used as an analysis criterion. After the test, only one of the four devices was found to be effective across all speed ranges. Therefore, it was determined that additional sensor calibration is required to improve accuracy. CONCLUSIONS : In this study, the IRI device accuracy was evaluated through field tests. Hence, a new certification process for the IRI test device was proposed via four steps. To improve the accuracy of the IRI measurement, it is necessary to periodically verify the device. If this proposed certification process is applied, the accuracy of the IRI devices can be improved.

PURPOSES : In this study, the flatness of a sidewalk surface was evaluated by measuring the vibration acceleration experienced by wheelchair users passing the sidewalk using a walking measure. The objective of this study is to derive a linear equation for converting the vibration acceleration of a sidewalk surface measured using a walking measure into vibration acceleration of a wheelchair. METHODS : To this end, field measurements were obtained. Twenty sites with different sidewalk surface materials were selected to measure the vibration accelerations of the walking measure and wheelchair. A regression analysis was conducted with the measured root mean square value of the vibration acceleration, and a linear equation expressing the walking measure and wheelchair vibration acceleration was derived. RESULTS : The regression analysis was conducted by inputting the vibration acceleration of the wheelchair into the Y-axis and that of the walking measure into the X-axis. Thus, a linear equation between the walking measure and wheelchair acceleration could be derived. Further, linear equations could be derived and applied based on the situation using various standard walking measures and wheelchairs. These equations could be used to convert the vibration acceleration of the walking measure into that of the wheelchair. Substituting the converted vibration acceleration into the comfort level specified in ISO 2631-1 was expected to indicate the flatness of the sidewalk. CONCLUSIONS : This study confirmed that a linear equation for the two vibration accelerations could be derived by measuring the vibration accelerations of the walking measure and wheelchairs of 20 different sites. The vibration acceleration measured using the walking measure could be converted into that of the wheelchair using this linear equation. In addition, the flatness of the sidewalk could be evaluated by measuring the vibration acceleration of the walking measure using this linear equation.

PURPOSES: The purpose of this research is to analyze the characteristics of panels that affect the evaluating results of riding quality and to evaluate the appropriateness of roughness management criteria based on ride comfort satisfaction. METHODS: In order to analyze the influence of panel characteristics of riding quality, 33 panels, consisting of civilians and experts, were selected. Also, considering the roughness distribution of the expressway, 35 sections with MRI ranging from 1.17 m/km to 4.65 m/km were selected. Each panel boarded a passenger car and evaluated the riding quality with grades from 0 to 10, and assessed whether it was satisfied or not. After removing outlier results using a box plot technique, 964 results were analyzed. An ANOVA was conducted to evaluate the effects of panel expertise, age, driving experience, vehicle ownership, and gender on the evaluation results. In addition, by using the receiver operating characteristics (ROC) curve, the MRI value, which can most accurately evaluate the satisfaction with riding quality, was derived. Then, the compatibility of MRI was evaluated using AUC as a criterion to assess whether the riding quality was satisfactory. RESULTS: Only the age of the panel participants were found to have an effect on the riding quality satisfaction. It was found that satisfaction with riding quality and MRI are strongly correlated. The satisfaction rate of roughness management criteria on new (MRI 1.6 m/km) and maintenance (MRI 3.0 m/km) expressways were 95% and 53%, respectively. As a result of evaluating the roughness management criteria by using the ROC curve, it was found that the accuracy of satisfaction was the highest at MRI 3.1-3.2 m/km. In addition, the AUC of the MRI was about 0.8, indicating that the MRI was an appropriate index for evaluating the riding quality satisfaction. CONCLUSIONS: Based on the results, the distribution of the panels’age should be considered when panel rating is conducted. From the results of the ROC curve, MRI of 3.0 m/km, which is a criterion of roughness management on maintenance expressways, is considered as appropriate.

PURPOSES: This study aims to develop a rational procedure for estimating the pavement roughness index considering vehicle wandering. METHODS: The location analysis of the passing vehicle in the lane was performed by approximately 1.2 million vehicles for verification of the wandering distribution. According to verification result, the distribution follows the normal distribution pattern. The probability density function was estimated using each lane's wandering distribution model. Then the procedure for applying a weighted value into the lane profile was conducted using this function. RESULTS : The modified index, MRIw, with consideration towards applying the wandering weighted value application was computed then compared with MRI. It was found that the Coefficient of Variation for distribution of lateral roughness index in the lane was high in the case of a large difference between each index (i.e., MRIw and MRI) observed. CONCLUSIONS : This result confirms that the new procedure with consideration of the weight factor can successfully improve the lane representative characteristics of the roughness index.

본 논문에서는 최소단면 보수가 완료된 아스팔트 콘크리트 포장의 평탄성 평가를 위하여 경량 프로파일러를 이용하여 측정하였으며 평탄성 분석을 실시하였다. 측정 프로파일러의 정확도를 검토하기 위하여 2회 측정한 프로파일을 이용하여 반복성(repeatability) 검증을 수행하였다. Cross-Corelation 함수를 이용하여 측정치간의 일치를 검증하였다. 최소단면 보수가 이루어진 아스팔트 콘크리트 포장 부분과 기존 포장의 평탄성을 국제평탄성지수와 PrI 지수를 비교 분석하였으며, 최소단면 보수가 이루어진 차선의 평탄성이 대체적으로 좋지 않았다. 향후 최소단면 보수 지역의 평탄성 평가에 대한 기초자료로 사용할 수 있다고 판단된다.

평탄성은 도로 공용성을 확보하기 위한 중요한 인자이며, 줄눈콘크리트 포장에서 수명에 큰 영향을 미친다. 포장의 공용성을 평가하는 요소로는 크게 기능성 평가, 안정성 평가 및 구조적 성능 평가가 있다. 기능성 평가 가운데 가장 대표적인 평탄성은 유지보수를 위한 중요한 기준으로 이용되고 있으나 체계적인 연구를 통해 포장 설계시에 적용하기 위한 연구는 미흡한 실정이다. 따라서 신뢰성 있는 평탄성 모델의 개발이 필요하다. 본 연구에서는 국내 콘크리트포장의 노선별 총 연장 280.93km를 일정기준으로 나누어 238개의 섹션으로 선별하였다. 또한, 현재 국내 평탄성 모델에서 고려하고 있는 영향인자인 스폴링, 균열, 재령뿐만 아니라 동결지수, 연평균강수량, 기층조건, 교통량 등에 대한 검토가 이루어졌으며, 재령의 다양화를 위하여 추가적인 노선을 선별하여 평탄성에 영향을 미치는 인자들에 대한 분석이 이루어졌다. 분석결과는 스폴링(%), 균열(%), 재령(년), 기층이 평탄성에 영향을 미치는 것으로 나타났다. 이를 통해 평탄성과의 상관관계를 분석한 결과 신뢰성 있는 평탄성 모델이 도출되었으며, 평탄성를 설명할 수 있는 비율은 68.8%로 나타나 통계적으로 유의한 것으로 판단된다. 본 연구에서는 소입경 골재노출콘크리트포장의 시험시공을 실시하여 초기 공용성을 평가하였으며, 이를 통하여 적정 강도, 저소음 및 적정 미끄럼저항을 확보함을 확인하였다.