본 논문에서는 건설 현장 관리의 과제를 다루고 IoT 기술 활용을 위한 기술 적용에 대해 정리하였다. 도로 포장 장비의 유휴 시간을 모니터링하는 IoT 장치를 설계 및 구현하여 효율적인 장비 관리 시스템을 개발하는 것을 목표한다. 또한, 본 연구에서는 통신방식 선 정, 사용자 친화적인 플랫폼 설계, 데이터 수집 및 분석을 위한 진동센서 기반 IoT 디바이스 개발을 통한 실시간 관리에 중점을 두고 있다. 플랫폼을 통해 공사현황을 실시간으로 모니터링하고 장비 유휴시간을 관리해 효율성을 높일 수 있으며, IoT 디바이스는 90% 이 상의 데이터 정확도를 보장한다. 현장 테스트를 통해 장비 사용 추적 효과가 확인되어 보다 효율적인 건설 관리에 기여하고자 한다.

PURPOSES : In this study, a preliminary study on the optimal clustering techniques for the preprocessing of pavement management system (PMS) data was conducted using K-means and mean-shift techniques to improve the correlation between the dependent and independent variables of the pavement performance model. METHODS : The PMS data of Jeju Island was preprocessed using the K-means and mean-shift algorithms. In the case of the K-means method, the elbow method and silhouette score were used to determine the optimal number of clusters (K). Moreover, in the case of the mean-shift method, Scott’s rule of thumb and Silverman’s rule of thumb were used to determine the optimal cluster bandwidth. RESULTS : The optimal cluster sets were selected for the rut depth (RD), annual average daily traffic (AADT), and annual maximum temperature (AMT) for each clustering technique, and their similarities with the original data were investigated. Additionally, the correlation improvement between the dependent and independent variables were investigated by calculating the clustering score (CS). Consequently, the K-means method was selected as the optimal clustering technique for the preprocessing of PMS data. The K-means method improved the correlations of more variables with the dependent variable compared to the mean-shift method. The correlations of the variables related to high temperature—such as the annual temperature change, summer days, and heat wave days—were improved in the case wherein the AMT, a climate factor, was used as an independent variable in the K-means clustering method. CONCLUSIONS : The applicability of the clustering methods to preprocessing of PMS data was identified in this study. Improvements in the pavement performance prediction model developed using traditional statistical methods may be identified by developing a model using clustering techniques in a future study.

도로의 포장 상태의 노후화나 관리미흡으로 인하여 시민의 사유 재산 중 주요한 요소인 자동차 등의 손상이나 자동차 사고 로 이어질 수 있어 큰 사회적 비용이 발생할 뿐 아니라, 시민들의 불편과 불만을 초래할 수 있다. 최근 도로 포장의 경우 포트홀 발생 건수와 그에 따른 민원 및 소송 건수가 증가해 행정력 및 예산이 낭비되고 있으며, 서울시의 경우 포장도로 노후화 추이가 증가함에 따라 유 지 관리 비용 또한 증가하고 있다. SOC 시설물 안전성 강화에 대한 사회적 요구는 지속적으로 증가하고 있어 한정된 예산의 효율적 활용을 위한 첨단 유지관리기술 도입이 시급하다.

PURPOSES : The evaluation of the low-temperature performance of an asphalt mixture is crucial for mitigating transverse thermal cracking and preventing traffic accidents on expressways. Engineers in pavement agencies must identify and verify the pavement sections that require urgent management. In early 2000, the research division of the Korea Expressway Corporation developed a three-dimensional (3D) pavement condition monitoring profiler vehicle (3DPM) and an advanced infographic (AIG) highway pavement management system computer program. Owing to these efforts, the management of the entire expressway network has become more precise, effective, and efficient. However, current 3DPM and AIG technologies focus only on the pavement surface and not on the entire pavement layer. Over the years, along with monitoring, further strengthening and verification of the feasibility of current 3DPM and AIG technologies by performing extensive mechanical tests and data analyses have been recommended. METHODS : First, the pavement section that required urgent care was selected using the 3DPM and AIG approaches. Second, asphalt mixture cores were acquired from the specified section, and a low-temperature fracture test, semi- circular bending (SCB) test, was performed. The mechanical parameters, energy-release rate, and fracture toughness were computed and compared. RESULTS : As expected, the asphalt mixture cores acquired from the specified pavement section ( poor condition – bad section) exhibited negative fracture performances compared to the control section (good section). CONCLUSIONS : The current 3DPM and AIG approaches in KEC can successfully evaluate and analyze selected pavement conditions. However, more extensive experimental studies and mathematical analyses are required to further strengthen and upgrade current pavement analysis approaches.

PURPOSES : The purpose of this study was to improve the performance of concrete pavements by decreasing measurement deviations using an Internet of Things (IoT)-based air content measurement device. METHODS : We calculated the properties of concrete which varied according to the air content. For a low measurement deviation, the concrete pavement performed according to the design standard. To confirm the difference in the performance of the concrete pavement for various air contents, we verified the change in the relative dynamic modulus according to the number of freeze–thaw cycles for each value of the air content. In addition, we analyzed the number of durability cracks according to the freeze–thaw cycles in the field. RESULTS : We confirmed that IoT-based measurement equipment improved the performance of pavements without changing their mixing designs or specifications. We confirmed that the performance of concrete pavements changed even with variations in air content within the range of quality standards. Using IoT-based air content management, we confirmed the reduction in concrete pavement durability cracks without changing the mixing design. CONCLUSIONS : We confirmed that IoT-based air-content management improved pavement performance. The feasibility of extending this concept to manage other concrete properties such as the chloride content should be acknowledged. Future research will require laboratory tests to understand the variation in concrete properties with varying air contents and to consider diverse load conditions.

PURPOSES : The purpose of this study was to improve the performance of concrete pavements by measuring the unit-water content with an Internet-of-Things (IoT)-based unit-water content measurement device at an increased precision compared with that of existing measuring equipment. METHODS : We calculated the properties of concrete that varied according to variations in the unit-water content. To confirm the change in the performance of concrete pavements, we compared and analyzed the fatigue cracking rate and international roughness index of concrete pavements at the 20-year point of public use according to the changes in properties using the Korea Pavement Research Program(KPRP). RESULTS : We confirmed that IoT-based measurement equipment can improve the performance of pavements without changing their mixing designs or specifications. We confirmed that the performance of the concrete pavements changed significantly, even with unit-water content variations within the range of quality standards. According to IoT-based unit-water content management, we confirmed that the performance of the concrete pavement (fatigue cracking rate and international roughness index) improved without changing the mixing design. CONCLUSIONS : We confirmed that by using IoT-based unit-water content management, pavement performance can be improved. It is necessary to consider whether the application of this concept to other concrete property management items, such as the chloride content, is possible. Considering the changes in concrete properties according to the unit-water content based on laboratory tests and considerations of various load conditions will be necessary for future research.

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS: Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS: The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.