Automatic Code Generation of Data Visualization for Structural Health Monitoring

Authors

Keywords:

Structural health monitoring, model driven development, data visualization, domain specific languages

Abstract

Structural Health Monitoring (SHM) aims at detecting, localizing, and characterizing damages in civil, mechanical, and aerospatial structures, which are hardly detectable in visual inspections. The collection, analysis, and visualization of data captured by sensors installed on these structures can be strongly supported by modern techniques of Data Science. In particular, the visualization of these data provides valuable help to experts on structural health and decision makers on preventive and corrective maintenance. Unfortunately, existing systems of data visualization still demand those stakeholders for a high level of software programming skills to take full advantage of visual and interactive exploration of data that sensors capture and output. This work introduces a model-driven approach to develop data visualization in the domain of structural health monitoring, in particular, of bridges. This approach is based on the definition of a Domain Specific Language (DSL) that describes the main concepts of an infrastructure of sensors typically used in SHM, along with common graphics and visual alternatives of data visualization. This DSL is instantiated by a modeling language, composed of a metamodel, a visual representation of concepts, and a set of model-to-text transformation rules. In this way, non-programmers can implement their own data visualization from a graphical and intuitive design, by automatically generating the corresponding code. This approach was implemented in a modeling and code-generation tool, called Vis4bridge, whose usability and output were successfully evaluated through the development of tasks and case studies.

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Author Biographies

Braulio Quiero Hernández, Universidad de Concepción, Chile

Braulio Quiero es Ingeniero Civil Informático de la Universidad Católica de la Santísima Concepción, Chile (2017) y Magíster en Ciencias de la Computación de la Universidad de Concepción, Chile (2021). Su trabajo se orienta a la generación automática de código, interfaces humano-computador, y visualización de datos.

Gonzalo Rojas, Universidad de Concepción, Chile

Gonzalo Rojas es Profesor Asistente del Departamento de IngenieríaInformática y Ciencias de la Computación de la Universidad de Concepción, Chile, y Doctor en Ingeniería de Software por la Universidad Politécnica deValencia, España (2008). Su trabajo se orienta a arquitectura de software, sistemas de Big Data, visualización de datos y calidad de software.

References

C. R. Farrar and K. Worden, “An introduction to structural health monitoring,” CISM International Centre for Mechanical Sciences, Courses and Lectures, vol. 520, no. 1851, pp. 1–17, 2010.

J. Morales Valdez, L. Alvarez-Icaza, and J. A. Escobar, “Online identification system for damage location in building structures,” IEEE Latin America Transactions, vol. 17, no. 08, pp. 1283–1290, 2019.

C. Ledur, D. Griebler, I. Manssour, and L. G. Fernandes, “Towards a domain-specific language for geospatial data visualization maps with big data sets,” in 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp. 1–8, 2015.

S. F. Masri, L.-H. Sheng, J. P. Caffrey, R. L. Nigbor, M. Wahbeh, and A. M. Abdel-Ghaffar, “Application of a Web-enabled real-time structural health monitoring system for civil infrastructure systems,” Smart Materials and Structures, vol. 13, pp. 1269–1283, dec 2004.

S. L. Desjardins, N. A. Londoño, D. T. Lau, and H. Khoo, “Real-Time Data Processing, Analysis and Visualization for Structural Monitoring of the Confederation Bridge,” Advances in Structural Engineering, vol. 9, no. 1, pp. 141–157, 2006.

I. Khemapech, W. Sansrimahachai, and M. Toahchoodee, “A real-time Health Monitoring and warning system for bridge structures,” in TENCON 2016 Conference, pp. 3010–3013, IEEE, jun 2016.

M. A. Fabrício, F. H. Behrens, and D. Bianchini, “Monitoring of industrial electrical equipment using iot,” IEEE Latin America Transactions, vol. 18, no. 08, pp. 1425–1432, 2020.

K. Smeltzer and M. Erwig, “A domain-specific language for exploratory data visualization,” in Proceedings of the 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2018, (New York, NY, USA), p. 1–13, Association for Computing Machinery, 2018.

M. Brambilla and E. Umuhoza, “Model-driven development of user interfaces for IoT systems via domain-specific components and patterns,” in ICEIS 2017 - Proceedings of the 19th Int. Conf. on Enterprise Information Systems, vol. 2, pp. 246–253, SpringerOpen, dec 2017.

A. Van Deursen, P. Klint, and J. Visser, “Domain-specific languages: An annotated bibliography,” ACM Sigplan Notices, vol. 35, no. 6, pp. 26–36, 2000.

A. Rodrigues Da Silva, “Model-driven engineering: A survey supported by the unified conceptual model,” Computer Languages, Systems and Structures, vol. 43, pp. 139–155, 2015.

J. Nielsen, “10 usability heuristics for user interface design,” Nielsen Norman Group, vol. 1, no. 1, 1995.

OMG, “MOF Model to Text Transformation Language, v1.0,” Jan. 2008.

S. Brown, “The c4 model for visualising software architecture,” https://c4model.com/, 2018.

J. R. Lewis, “IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use,” International Journal of Human-Computer Interaction, vol. 7, no. 1, pp. 57–78, 1995.

Published

2022-04-27

How to Cite

Quiero Hernández, B., & Rojas, G. (2022). Automatic Code Generation of Data Visualization for Structural Health Monitoring. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5742