Development of a new Ni voltammetric sensor for hardened concrete conditions estimate

[EN] Developing efficient monitoring systems to control reinforced concrete structures (RCS) is still an open research line in the building sector. Thus, in this work was proposed the novelty use of Ni voltammetric sensor to control the concrete conditions by means of PCA model. The efficiency of vo...

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Detalles Bibliográficos
Autores: Martínez-Ibernón, Ana|||0000-0003-2136-5650, Gasch, Isabel|||0000-0001-7036-4481, Lliso-Ferrando, Josep Ramon|||0000-0002-2457-9024, Valcuende Payá, Manuel Octavio|||0000-0002-9967-1554
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/205373
Acceso en línea:https://riunet.upv.es/handle/10251/205373
Access Level:acceso abierto
Palabra clave:Voltammetric sensors
Monitoring systems
Concrete durability
PCA models
CONSTRUCCIONES ARQUITECTONICAS
MECANICA DE LOS MEDIOS CONTINUOS Y TEORIA DE ESTRUCTURAS
Descripción
Sumario:[EN] Developing efficient monitoring systems to control reinforced concrete structures (RCS) is still an open research line in the building sector. Thus, in this work was proposed the novelty use of Ni voltammetric sensor to control the concrete conditions by means of PCA model. The efficiency of voltammetric sensors are verified in other sectors like food or wastewater treatment, where the sensors are used in liquid media, in the study was intended verify the high potential use of this sensors in porous materials such as concrete. With this purpouse the sensor response was characterized in three different concretes (w/c = 0.6, w/c = 0.5 and w/c = 0.4) and three different concrete conditions (water satured conditions, presence of chlorides and concrete carbonation). Then, was developed a PCA model, where was verified the capability of the sensor to classify the concrete state. The validation of the model pointed an acceptance range between 78.3% and 95.4% (with a 95% confidence index).