# Fast Real-Time Data Process Analysis Based on NoSQL for IoT Pavement Quality Management Platform
###### Abstract  
The quality of road pavements is highly impacted by environmental variables, such as temperature, humidity, and weather; and construction-related variables, such as material quality and time. In this paper, an advanced data collection and analysis system based on big data/cloud was proposed for the use of IoT location-based smart platforms for pavement quality big data at road pavement sites. For the big data platform, a relational database management system (RDBMS) for a general alphanumeric data-based infrastructure for IoT-based systems was designed based on distributed/parallel processing to enable rapid and big data collection and analysis. The structure was established based on a NoSQL-based database to enable real-time high-speed collection and analysis, and the big data platform was developed as a data collection and visualization infrastructure. When the big data system was studied using data analysis methods, the proposed system demonstrated improvements in data collection performance and analysis speed, indicating that analysis results could be derived in real time. Specifically, the data collection processing (create) speed of the NoSQL-based system (0.405 ms) was significantly higher than that of the compared existing system (21.146 ms). Real-time processing capacity was also verified based on quality big data generated on actual road pavements, and the proposed system was proven suitable for the real-time monitoring (the data collection processing) of road pavement quality big data. road pavement; IoT; big data; database; data analysis +
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at various levels outside surfaces or inside packaging [1]. Smart construction technologies use data analysis techniques, which necessitate data collection and big data construction using Internet of Things (IoT) sensors [2; 3]. These technologies are being researched for the automation (or semi-automation) of raw material management and process management in civil engineering and construction sites, which are currently performed manually. One important objective of these endeavors is to enable efficient process, quality, and risk management, while reducing unnecessary tasks and manpower. Combined with machine learning analysis, smart construction technologies enable various services such as image recognition, predictive analysis, determination of the quality of raw materials, and determination of appropriate construction timing and deployed equipment. For these purposes, pre-emptive data collection and big data/cloud construction using IoT are required. If the data generated throughout the construction cycle from raw material production are digitalized and the events that occur in construction sites can be analyzed based not only on structured data but also on unstructured data such as drawings, the management of construction quality and even transparency will be facilitated. Most IoT-based smart construction systems currently used in construction management are applied to processing, major work situations between processes, positioning of equipment or workers, and safety management based on the locations of equipment and workers. IoT location-based platforms have also been introduced to the construction industry for processes such as transportation and indoor navigation. Meanwhile, the construction of digital twin models for expansion to smart cities and their application to smart construction are being researched [4], whereas other research studies have proposed the use of dynamic quality monitoring systems (DQMS). For example, a DQMS for asphalt concrete pavements has been established based on the BeiDou Navigation Satellite System, intelligent sensing, IoT, and 5G technologies [5]. This system allows key technical indicators to be collected and transmitted throughout the entire asphalt mixture process flow, which includes manufacture in the mixing plant, transport in vehicles, paving, and compaction.