PURPOSES : Currently, the domestic construction industry is dominated by large-scale projects such as roads, ports, airports, and buildings. Construction on such projects is generally conducted simultaneously, but the process and quality management are led by a small number of responsible managers. In the case of road pavements, owing to rapid industrial development, economic growth, and the expansion of social overhead capital investment in the road construction industry, highways and general national roads have been constructed on a large scale. Therefore, this study aimed to improve and develop domestic concrete production and construction quality management by improving the reliability and transparency of production quality management and simplifying business processes. This was accomplished through the development of an Internet of Things (IoT)-based cement quality management system capable of automated design and build (D/B) construction and real-time monitoring. METHODS : The "IQ" system is a quality management system for enabling real-time monitoring of D/B quality at the time of concrete production and according to the designated age by utilizing quality test equipment developed with an LTE-Bluetooth function. It is possible to immediately identify and respond to quality problems through real-time monitoring, secure a reliable quality D/B because the quality test results cannot be arbitrarily manipulated, and to simplify the work process through the automatic D/B construction. In addition, improved quality control can be achieved through real-time information sharing and feedback system operations between contractors, managers, and personnel involved in construction. The quality control test items for developing the IQ system are the compression and flexural strengths, as these can be used to determine the design standard strength of pre-curing concretes (such as their slump and unit quantity) and the adequacy of the workability and durability, as well as the air volume to predict the durability, and the chloride content in the sections where reinforcement is used. CONCLUSIONS : This study identified difficulties and limitations in quality management according to the operation method in the domestic quality management systems, and in the real-time monitoring between managers and contractors. Thus, it was necessary to establish an improved systematic and reliable quality management system. The IQ system was developed to solve this problem.

PURPOSES : In this study, an existing speed-controlled Marshall stability tester was systemized as an Internet of Things(IoT) system. The Marshall stability test data were transmitted to the cloud in real-time, and an IoT optional-controlled board capable of additional load and displacement control was proposed. METHODS : The IoT systemization was built based on an improvement of an IoT height measuring system, the re-verification of standard samples for comparative analysis, and the development of a wireless IG-IoT board. The developed wireless Induk-GeoTS(IG)-IoT board was compared with existing commercial data logger using displacement- and load- calibration equipment. After the conformity of the developed wireless IoT board was established, a urethane standard sample was reproduced and verified using the recipe presented in a previous study to conduct a round-robin test. In addition, the adequacy of the speed, load, and displacement control tests for the optional-controlled characteristics was verified. the round-robin test for the Marshall stability and deformation strength and the comparative test of indirect tensile strength with the existing Marshall tester were performed using the re-verified standard sample. RESULTS : The improved two-point IoT height measurement system reduced the average relative error by 2.11% relative to the one-point measurement. From the re-verification results of the regenerated urethane standard sample, it was suitable with relative error of 3.65% in the loading elastic modulus and 4.07% in the unloading elastic modulus, compared to the existing standard sample. From the comparative analysis of the developed wireless IG-IoT board and existing commercial data logger, it was confirmed that the wireless IoT board could be reliably used, based on the average relative error of the wireless IoT board, 0.64% and that of the data logger, 3.79% in terms of the displacement(flow value) and an average relative error of 0.78% for the wireless IoT board and 0.79% for the data logger in terms of the load(stability). By analyzing the optional-controlled characteristics, it was found that the Marshall stability speed control conditions were satisfied based on the error results, with an average relative value of 2.96% under deformation strength test condition of 30mm/min, 3.23% under the indirect tensile strength test condition of 50mm/min, and 2.6% under the Marshall stability test condition of 50.8mm/min. It was also found that proper control characteristics were obtained, with an average relative error of 0.72% within the experimental load range in the load control conditions, and an average relative error of 2.4% in the experimental displacement range in the displacement control conditions. The results from the round-robin Marshall stability and deformation strength testing to verify the applicability of the IoT optional-controlled board show that the data were reliable based on the 3σ quality control method. In addition, by comparing the results of the indirect tensile strength tests, the usability of the wireless IG-IoT board was verified, with an average relative error of 0.96%. CONCLUSIONS : The IoT height measuring system was improved, and a wireless IG-IoT board that can transmit test data to a cloud platform was developed. The usability of the developed wireless IoT board was verified by round-robin testing using a re-verified urethane specimen. The IG-IoT optional-controlled board extends the verified wireless IG-IoT board, it was developed and validated for not only the existing speed control, but also for load, and displacement control.

The Army, Navy, and Air Force are currently making various efforts to apply defense IoT technology by reflecting the characteristics of each military. However, there are concerns that comprehensive collection, management, and analysis may be limited because each military has no overlapping system or guaranteed interoperability of data, and there are inefficient aspects in terms of using and investing a limited defense budget. In this study, evaluation items and priorities for pilot projects were selected by applying Delphi technique and AHP method to determine which projects are preferentially effective when commonly applied in terms of Army, Navy, and Air force. As a result of the analysis, the project basis and driving force, project feasibility, performance and ripple effect, redundancy and connection were identified in the order of IoT-based small detachment units and unmanned base construction, intelligent smart unit, smart ground ammunition depot, and smart training management system. According to the comprehensive results reflecting the weights of each evaluation item, the fact that small detachment units and unmanned base construction project are recognized as the first project to be applied to the Army, Navy, and Air Force is a valid approach to be effective while each military is burdened with personnel management because of the reduction of manpower. Through the research results, it is expected that the direction of application to the policy of the defense IoT project can be confirmed in terms of efficient use of limited budgets.

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.

PURPOSES : In a previous study, an error was detected in data pertaining to the direction and velocity of a roller. Hence, in this study, the correlation between these two variables and acceleration data is analyzed. Relevant algorithms are developed by adding variables to existing algorithms. METHODS : A tachometer and GPS are used to acquire the velocity, compaction direction of rollers, and number of compactions. Subsequently, data input to an accelerometer are compared and analyzed. RESULTS : Based on FFT analysis, it is discovered that the data are inaccurate when a forward reverse is performed. Using the GPS, the velocity of the roller is differentiated based on the number of pledges, and then added as a variable to the algorithm. Subsequently, it is evaluated and analyzed only with data during forward movement based on changes in the latitude and longitude. CONCLUSIONS : It is discovered that the acceleration data values from both the left and right rollers differ owing to their weight difference, as indicated by the asphalt gradient. Data changes based on asphalt gradients are analyzed using gyro sensors. If the correlation between the two sets of data is high, then the algorithm is advanced by introducing a cross spectrum after calibrating the acceleration value based on the gradient.

사회기반 시설물의 노후화에 대응해 이상 징후를 파악하고 유지보수를 위한 최적의 의사결정을 내리기 위해선 디지털 기반 SOC 시설물 유지관리 시스템의 개발이 필수적인데, 디지털 SOC 시스템은 장기간 구조물 계측을 위한 IoT 센서 시스템과 축적 데이터 처 리를 위한 클라우드 컴퓨팅 기술을 요구한다. 본 연구에서는 구조물의 다물리량을 장기간 측정할 수 있는 IoT센서와 클라우드 컴퓨팅 을 위한 서버 시스템을 개발하였다. 개발 IoT센서는 총 3축 가속도 및 3채널의 변형률 측정이 가능하고 24비트의 높은 해상도로 정밀 한 데이터 수집을 수행한다. 또한 저전력 LTE-CAT M1 통신을 통해 데이터를 실시간으로 서버에 전송하여 별도의 중계기가 필요 없 는 장점이 있다. 개발된 클라우드 서버는 센서로부터 다물리량 데이터를 수신하고 가속도, 변형률 기반 변위 융합 알고리즘을 내장하 여 센서에서의 연산 없이 고성능 연산을 수행한다. 제안 방법의 검증은 2개소의 실제 교량에서 변위계와의 계측 결과 비교, 장기간 운 영 테스트를 통해 이뤄졌다.