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Application of Semantic and Big-Data Technologies to Structural Monitoring

Large civil engineering structures, such as hydro electrical power plants, bridges, and airports, play an important role in today’s society, providing it with multiple services and capabilities. Therefore, failures in those structures can produce catastrophic events with wide-spread impact on human life, the environment, and on the economy. Reducing the risk of such failures requires proactively acting, which implies being able to continuously assess their safety, threats and vulnerabilities.

For example, structural assessment depends on accurate information about the concerned structure, which noways can be supported by updated information provided by sensors and other means to monitor dynamics. Structural monitoring relies thus on using a set of sensors to periodically acquire data as a means of measuring properties of a structure (e.g. displacement, extension). Originally, data acquisition was carried out manually using mechanical or electrical devices, which produced a discrete and limited set of values that could then be correlated with long-term behaviour series. Later, the development of electronic data loggers and programmable controllers capable of automatically acquiring and transmitting data, led to the deployment of permanent monitoring systems that generate large volumes of data. This data is fundamental to understand the dynamics of a structure, to detect deviations to its expected behaviour, and, to act accordingly in case of need.

Structural monitoring manages multiple types of different sensors deployed on a structure, which can vary in number from a few dozens to thousands. Thus, sensors must be efficiently characterized due to their diversity and quantity. Moreover, structural monitoring not only deals with the operation of sensors in terms of data acquisition, but must also consider dependencies between sensors, and the impact of supporting processes, such as maintenance, replacement, and calibration. Finally, considering the expected long lifetime of these infrastructures, the long-term preservation of data and the definition of assessment methods and techniques that can deal with multiple generations of technology are other relevant challenges.

However, critical infrastructures also can be monitored in a wider perspective, considering not only their intrinsic information, but also other contextual information, making it possible to design and apply more global risk management processes (considering for example threats due to extreme weather conditions, accidents, human events, etc.).


  • Modeling of critical infrastructures as information entities (including of sensors descriptions and data)
  • Long-term preservation of information related with critical infrastructures
  • Methods and techniques for safety assessment of critical infrastructures, based on long-term acquired data
  • Methods and techniques for multiple view risk management of critical infrastructures


Semantic technologies

Semantic technology represents computational techniques that can used to address some of the problems behind risk management, safety assessment and structural monitoring, as they enable:

  1. the computational representation of complex systems made of multiple sensor types and corresponding sensor data;
  2. the integration of multiple data sources;
  3. the analysis of heterogeneous data sets; and
  4. the maintenance of heterogeneous models and data

We have been exploring the application of semantic technology (including graph databases and ontologies) to analyze sensors performance, sensor dependencies, as well as determining their role in structural monitoring. We have, for example, applied these solutions to the analysis of a scenario provided by the Portuguese National Laboratory for Civil Engineering (LNEC). The application uses ontologies to represent deployed sensors, and logical reasoning to analyze correlations and dependencies between sensors, and to check their compliance against a set of requirements.

Big-data technologies

Structural monitoring can be classified as a “big data” problem due to

  1. the volume of data, including the absolute volume of data, and the implicit volume of data that derives from data correlation and dependencies,
  2. the velocity required to capture the data and process it within a reasonable time interval, and
  3. the variety of sensors and sensor types and corresponding data that needs to be

managed and integrated.

Big-data techniques provide the means to perform structural monitoring while handling the volume, velocity and variety of the underlying data. In particular, structural monitoring benefits from exploiting big-data storage (e.g. Cassandra) and processing (e.g. Spark/RSpark and Hadoop/rHadoop).

Related Projects

  • TIMBUS, Digital Preservation for Timeless Business Processes and Services, FP7 project.
  • ELIXIR, European life-sciences Infrastructure for biological Information, Portuguese Node.
  • E-ARK, European Archival Records and Knowledge Preservation, FP7 project.
  • DataStorm, FCT project.

Related Publications

  • Artur Caetano, João Pombinho, Gonçalo Antunes, José Granjo , José Borbinha, Miguel Mira da Silva: Representation and analysis of enterprise models with semantic techniques: an application to ArchiMate, e3value and Business Model Canvas. Knowledge and Information Systems Journal, Springer, 2016.
  • Gonçalo Antunes, Artur Caetano, José Borbinha: An Application of Semantic Techniques to the Analysis of Enterprise Architecture Models. 49th Hawaii International Conference on System Sciences (HICSS). Hawaii, USA. 2016.
  • Gonçalo Antunes, José Barateiro, Artur Caetano, José Borbinha: Analysis of Federated Enterprise Architecture Models. 23rd European Conference on Information Systems (ECIS). Münster, Germany. 2015.
  • Gonçalo Antunes, Marzieh Bakhshandeh, Rudolf Mayer, José Borbinha, Artur Caetano: Using Ontologies for Enterprise Architecture Integration and Analysis. Complex Systems Informatics and Modeling Quarterly, No 1, pages 11-23 (DOI: 107250/csimq2014-1-01). 2014.
  • Filipe Ferreira, Ricardo Vieira, José Borbinha: The value of risk management for data management in science and engineering. In Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '14). IEEE Press, Piscataway, NJ, USA, 439-440. 2014
  • Ricardo Vieira, Filipe Ferreira, José Barateiro, José Borbinha: Data Management with Risk Management in Engineering and Science Projects. New Review of Information Networking, Vol. 19, Iss. 2, 2014

Check more publications here.


  • José Barateiro, LNEC, Portugal (email: jbarateiro [at] lnec.pt)
  • Gonçalo Antunes, INESC-ID, Portugal (email: goncalo.antunes [at] inesc-id.pt)
  • Artur Caetano, INESC-ID, University of Lisbon, Portugal (email: artur.caetano [at] tecnico.ulisboa.pt)
  • José Borbinha, INESC-ID, University of Lisbon, Portugal (email: jose.borbinha [at] tecnico.ulisboa.pt)