A data management infrastructure for bridge monitoring

Abstract

This paper discusses a data management infrastructure framework for bridge monitoring applications. As sensor technologies mature and become economically affordable, their deployment for bridge monitoring will continue to grow. Data management becomes a critical issue not only for storing the sensor data but also for integrating with the bridge model to support other functions, such as management, maintenance and inspection. The focus of this study is on the effective data management of bridge information and sensor data, which is crucial to structural health monitoring and life cycle management of bridge structures. We review the state-of-the-art of bridge information modeling and sensor data management, and propose a data management framework for bridge monitoring based on NoSQL database technologies that have been shown useful in handling high volume, time-series data and to flexibly deal with unstructured data schema. Specifically, Apache Cassandra and Mongo DB are deployed for the prototype implementation of the framework. This paper describes the database design for an XML-based Bridge Information Modeling (BrIM) schema, and the representation of sensor data using Sensor Model Language (SensorML). The proposed prototype data management framework is validated using data collected from the Yeongjong Bridge in Incheon, Korea.

Novel platform for development, training, and validation of computer-assisted detection/diagnosis software

“…JavaScript runs both on the front end (browsers) and the back end (servers, via Node.js [10]), which was an important characteristic for achieving efficient image viewing, as discussed later. Other technologies we adopted included Nginx [11] (a lightweight web server used as a reverse proxy), MongoDB [12] (a NoSQL database management system that enables a flexible data structure [13,14]), and Docker [15] (operating system virtualization software, discussed in detail later).…”

A NoSQL data management infrastructure for bridge monitoring

“…The leaf document named sensordata collects a list of measured data over a certain time period along with the timestamp. Currently, the interface program is tuned to allow each document to store the sensor data measured over a period of one second (Jeong et al 2015a). For example, if the sampling rate of a sensor is 5Hz, then the measured data is discretized into buckets where each bucket has five consecutive data and is stored in a single sensordata document.…”

“…For example, if the sampling rate of a sensor is 5Hz, then the measured data is discretized into buckets where each bucket has five consecutive data and is stored in a single sensordata document. Since the upper limit of data size of a document is 16MB, this discretization strategy is required to prevent exceeding the maximum data size which can be caused by sensors that have high sampling rate (Jeong et al 2015a). Fig.…”

A NoSQL-based Data Management Infrastructure for Bridge Monitoring Database

“…On the other hand, MongoDB is selected for the on-site and local computers because of its schema-free features, rich aggregation, and efficient query for supporting real time analysis (such as Internet of Things (IoT) applications [21][22][23][24]). Details on the mapping and implementation of BrIM schemas on Cassandra and MongoDB database system have been discussed elsewhere [10]. This paper describes the basic data flows for the archiving of sensor data and the integration of the database system with external application modules.…”

“…The data needs to be stored, processed, interpreted and, desirably, integrated with lifecycle bridge management. While structural health monitoring research continues to develop and explore new sensor technologies, very few efforts have been devoted to investigate proper data management tools to efficiently store, manage and retrieve sensor data [8,9,10].…”