“PROION” is a research project, focused on the development of a platform for the continuous monitoring of high priority infrastructure (public infrastructure, dams, bridges, etc.) in the broader area of the Hellenic Supersite, named Enceladus. The project started on September 2020 and it was financially supported by the European Union and the Hellenic government. Monitoring is based on the combination of instrumental and remote sensing measurements in conjunction with soft computing algorithms to assess infrastructure stability. The results of the project are presented in the current study.
«Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EΔK-02396 Μultiparametric monitoring platform with micro-sensors of eNceladus hellenIc supersite)».
Structural health monitoring of civil infrastructures is a complex engineering problem that requires the use of advanced soft computing algorithms. The rapid advances in soft computing have been a step forward in the direction of infrastructure monitoring. The field of computer science has given promising results in monitoring systems when applied along with engineering technologies. In this framework, Artificial Intelligence (AI) Deep and Machine Learning applications, Neural Networks, Fuzzy Logic, Fuzzy Cognitive Maps (FCM), Genetics Algorithms and Hybrid systems are growing exponentially in the field of structural health monitoring, including structural recognition, change detection, crack detection, damage identification, damage quantification and damage prediction. Specifically, some of the above-mentioned more sophisticated infrastructure soft computing monitoring algorithms are utilized to generate strategies and processing pipelines towards structural building damage mapping and assessment. In this paper remote sensing data, acquired by Global Navigation Satellite System (GNSS), Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) sensors will be processed and a state-of-the-art unified platform imbedding Neural Networks, Fuzzy Cognitive Maps and Hybrid systems in the field of Structural Health Monitoring of civil structures is proposed.
Climate change constitutes a serious global challenge with consequences that are directly affecting infrastructure. Thus, there is a great need to develop reliable cost-effective systems, which integrate remote sensing data, in situ measurements and advanced methods for infrastructure monitoring. In this framework, the European Union and the Hellenic government are financially supporting a R&D project, named “PROIΟΝ”. The purpose of the project is the development of a platform for the continuous monitoring of high-priority infrastructures (public infrastructure, dams, bridges, etc.) which are located in a particularly active area in terms of tectonics and seismicity. Monitoring is based on the combination of instrumental and remote sensing measurements along with fuzzy logic networks methods and machine learning algorithms in order to generate an innovative decision-making and decision-support tool. Specifically, measurements derived from three-axis accelerometers, Global Navigation Satellite System (GNSS) receivers and Persistent Scatterer Interferometry are imported into the platform. The measurements will be validated using high-accuracy reference representations arising from data acquired by Terrestrial Laser Scanning (TLS) surveys and Unmanned Aerial Vehicles (UAV) campaigns and subsequently, deformation maps will be generated. Intelligent data analysis methods will contribute to making decisions on the current as well as the future state of the infrastructure. At this initial stage of the project, the proposed monitoring system is described in detail.
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