Enhancing performance evaluation of low-cost inclinometers for the long-term monitoring of buildings
The development of low-cost structural and environmental sensors has revolutionized monitoring practices across numerous fields, enabling cost-effective solutions for infrastructure and building health assessment. However, a critical challenge associated with these sensors is their long-term durabil...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo General de la Arquitectura Técnica de España (CGATE) |
| Repositorio: | RIARTE |
| OAI Identifier: | oai:www.riarte.es:20.500.12251/3753 |
| Acesso em linha: | http://hdl.handle.net/20.500.12251/3753 https://doi.org/10.1016/j.jobe.2024.109148 |
| Access Level: | acceso abierto |
| Palavra-chave: | Sensorización Evaluación continua de estructuras Monitorización estructural Estructuras metálicas Inteligencia Artificial Patologías - Construcción Mantenimiento preventivo 3305.21 Construcciones Metálicas 3311.02 Ingeniería de Control 3311.17 Equipos de Verificación 1203.04 Inteligencia Artificial 3310.04 Ingeniería de Mantenimiento 1203.25 Diseño de Sistemas Sensores |
| Resumo: | The development of low-cost structural and environmental sensors has revolutionized monitoring practices across numerous fields, enabling cost-effective solutions for infrastructure and building health assessment. However, a critical challenge associated with these sensors is their long-term durability and reliability. Surprisingly, despite the significant interest in these low-cost devices, the literature does not present any solutions for ensuring their long-term performance. To address this gap, this study proposes an innovative artificial intelligence-based approach for evaluating the long-term performance of low-cost inclinometers using a low-cost adaptable reliable anglemeter. This method automatically compares the inclinations of actual onsite measurements with predicted values under real environmental conditions. Over time, if the discrepancies between both measurements surpass a predefined statistical threshold, it may signal potential inaccuracies in the low-cost inclinometer, thereby suggesting the need for recalibration or presence of structural anomalies. The effectiveness and applicability of the proposed tool were demonstrated through a long-term study conducted on a real steel frame in Spain. |
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