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 : Roller-compacted concrete pavement (RCCP) is a superstiff-consistency concrete pavement that exhibits excellent strength development owing to a hydration reaction and interlocking aggregates owing to the roller compaction. A zero-slump concrete mixture is generally used. Hence, it is important to control the consistency of the RCCP mixture to prevent the deterioration of the construction quality (such as material separation during paving). The workability of the RCCP is characterized by its consistency and controlled by the Vebe time, whereas a conventional concrete pavement is controlled based on the slump test. The consistency of the RCCP changes over time after concrete mixing owing to delivery, construction time delays, etc. Thus, it is necessary to use the optimum Vebe time to achieve the best construction quality. Therefore, this study aims to develop a Vebe time prediction model for efficiently controlling the consistency of RCCPs according to random time variations. METHODS : A Vebe time prediction model was developed using a multiple linear regression analysis. A dataset of 131 samples was used to develop the model. The collected data consisted of variables with large potential effects on the consistency of the RCCP, such as the water-cement ratio (W/C), sand/aggregate ratio (S/a), water content (ω), water content per unit volume (W), cement (C), fine aggregate (S), coarse aggregate (G), water reducing admixtrue (PNS), air-entraining admixture (AE), delay time (T), air temperature (TEM), and humidity (HUM). In the multiple linear regression analysis, the mentioned parameters were used as the independent variables, and the Vebe time was the dependent variable. The Vebe time prediction models were evaluated by considering the adjusted R2 and p-values. The selection of the model was based on the largest R2 value and an acceptable p-value (p<0.05). RESULTS : The Vebe time prediction model achieved an adjusted R2 value of 64.14% with a significance level (p-value) of less than 0.05. This shows that the predictive model is adequately described for the dependent variable, and that the model is suitable for Vebe time predictions. Moreover, the significance level of the independent variables is less than 0.05, indicating significant effects on the Vebe time (i.e., the dependent variable). CONCLUSIONS : The Vebe time prediction model developed in this study can be used to estimate Vebe times with an R2 of 63.33% between the measured and predicted values. The proposed Vebe time prediction model is expected to be effectively utilized for the quality control of RCCP mixtures. Moreover, it is expected to contribute to achieving good RCCP construction quality.