Francisco Rebelo

Today, everything is connected, including the exchange of data and the generation of new information. As a result, large amounts of data are being collected at an ever-increasing rate and in a variety of forms, a phenomenon now known as Big Data. Recent developments in information and communication technologies are driving the generation of significant amounts of data from multiple sources, namely sensors. In response to these technological advances and data challenges, this paper proposes a Big Data system architecture for paved road monitoring and implements part of this architecture on a section of road in Portugal as a case study. The challenge in the case study architecture is to collect and process sensor data in real time, at a rate of 500 records per second, producing 15 GBytes of data per day, using a real-time data stream for real-time monitoring and a batch data stream for deeper analysis. This allows users to obtain instant updates on road conditions such as the number of vehicles, loads, weather, and pavement temperatures on the road. They can monitor what is happening on the road in real time, receive alerts, and even gain insight into historical data, such as analysing the condition of structures or identifying traffic patterns.

The evolution of technological tools, namely affordable sensors for data collection, and the growing concerns about maintaining roads in adequate conditions have promoted the development of continuous pavement monitoring systems. This paper presents the installation and use of an innovative pavement monitoring system, which was developed to measure the effects of vehicle loads and temperature on the performance of a pavement structure. The sensors used are based on fibre Bragg grating optical technology, collecting data about the strains imposed in the pavement and the temperature at which those measurements are made. The site selection for the system’s installation and the essential installation details to ensure successful data collection are addressed. A calibration procedure was implemented by performing falling weight deflectometer tests and passing preweighed heavy vehicles over the sensors. In addition to validating the system installation, the results obtained in the calibration confirmed the importance of adequately choosing the distance between sensors. Differences of 50 mm in the position of the load may cause differences of about 20% to 25% in the resulting strains. These results confirmed the importance of increasing the sensor concentration in wheel paths. Furthermore, for loads between 25 kN and 65 kN, raising the temperature by 8 °C caused an increase of about 20% in the horizontal tensile strains measured in the pavement. In summary, it was possible to conclude that this innovative system is capable of capturing the effects of temperature and vehicle speed on the response of the pavement, which may be considered an advantage of this type of monitoring system when compared to those that are only used to determine the loads applied to the pavement or to characterise the type of vehicle.