Data Interpretation in Structural Health Monitoring: Toward a Universal Language
Structural Health Monitoring (SHM) relies on the effective communication between sensors and diagnostic systems, yet data interpretation remains inconsistent and subjective. This paper introduces a novel perspective, viewing data as a form of language with its own syntax, semantics, and pragmatics....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3054 |
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| author | Magda Ruiz Óscar Gualdrón José A. Peral Mondaza Luis Eduardo Mujica Delgado |
| author_facet | Magda Ruiz Óscar Gualdrón José A. Peral Mondaza Luis Eduardo Mujica Delgado |
| author_sort | Magda Ruiz |
| collection | DOAJ |
| description | Structural Health Monitoring (SHM) relies on the effective communication between sensors and diagnostic systems, yet data interpretation remains inconsistent and subjective. This paper introduces a novel perspective, viewing data as a form of language with its own syntax, semantics, and pragmatics. By adopting this linguistic framework, the study emphasizes the need for standardized “grammars” in data collection, processing, and analysis to reduce ambiguity and enhance diagnostic reliability. Using case studies from SHM, the paper illustrates how subjective decisions in variable selection, cluster labels, preprocessing, and modeling introduce biases that affect the outcomes. The findings highlight the potential of context-aware algorithms and integrated data sources to mitigate these biases. This conceptual approach has broader implications for data science, suggesting a universal “language of data” that fosters consistency and collaboration across disciplines. By recognizing the constructed nature of data, this work offers a path toward more accurate, efficient, and reliable structural diagnostics, advancing both SHM practices and data interpretation methodologies. |
| format | Article |
| id | doaj-art-480c70d837e5443a98000c3c5fc940b3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-480c70d837e5443a98000c3c5fc940b32025-08-20T02:33:55ZengMDPI AGSensors1424-82202025-05-012510305410.3390/s25103054Data Interpretation in Structural Health Monitoring: Toward a Universal LanguageMagda Ruiz0Óscar Gualdrón1José A. Peral Mondaza2Luis Eduardo Mujica Delgado3Departament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Carrer Eduard Maristany, 6-12, San Adrià de Besòs, 08930 Barcelona, SpainGo Advice and Consulting, Calle 99 11b-66, Bogotá 110221, ColombiaDepartament de Promoció Econòmica, Ajuntament de Sant Andreu de la Barca, Escoles Velles, Carrer Ctra. de Barcelona, 1, Sant Andreu de la Barca, 08740 Barcelona, SpainDepartament de Matemàtiques, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Carrer Eduard Maristany, 6-12, San Adrià de Besòs, 08930 Barcelona, SpainStructural Health Monitoring (SHM) relies on the effective communication between sensors and diagnostic systems, yet data interpretation remains inconsistent and subjective. This paper introduces a novel perspective, viewing data as a form of language with its own syntax, semantics, and pragmatics. By adopting this linguistic framework, the study emphasizes the need for standardized “grammars” in data collection, processing, and analysis to reduce ambiguity and enhance diagnostic reliability. Using case studies from SHM, the paper illustrates how subjective decisions in variable selection, cluster labels, preprocessing, and modeling introduce biases that affect the outcomes. The findings highlight the potential of context-aware algorithms and integrated data sources to mitigate these biases. This conceptual approach has broader implications for data science, suggesting a universal “language of data” that fosters consistency and collaboration across disciplines. By recognizing the constructed nature of data, this work offers a path toward more accurate, efficient, and reliable structural diagnostics, advancing both SHM practices and data interpretation methodologies.https://www.mdpi.com/1424-8220/25/10/3054Structural Health Monitoring (SHM)monitoringdiagnosisdiagnostic reliabilityAI and machine learning in SHMdata interpretation |
| spellingShingle | Magda Ruiz Óscar Gualdrón José A. Peral Mondaza Luis Eduardo Mujica Delgado Data Interpretation in Structural Health Monitoring: Toward a Universal Language Sensors Structural Health Monitoring (SHM) monitoring diagnosis diagnostic reliability AI and machine learning in SHM data interpretation |
| title | Data Interpretation in Structural Health Monitoring: Toward a Universal Language |
| title_full | Data Interpretation in Structural Health Monitoring: Toward a Universal Language |
| title_fullStr | Data Interpretation in Structural Health Monitoring: Toward a Universal Language |
| title_full_unstemmed | Data Interpretation in Structural Health Monitoring: Toward a Universal Language |
| title_short | Data Interpretation in Structural Health Monitoring: Toward a Universal Language |
| title_sort | data interpretation in structural health monitoring toward a universal language |
| topic | Structural Health Monitoring (SHM) monitoring diagnosis diagnostic reliability AI and machine learning in SHM data interpretation |
| url | https://www.mdpi.com/1424-8220/25/10/3054 |
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