Martínez Ibernón Ana, Lliso Ferrando Josep, Gasch Isabel, Valcuende Manuel
Interuniversity Research Institute for Molecular Recognition and Technological Development (IDM), Universitat Politècnica de València, 46022 Valencia, Spain.
Department of Architectural Construction, Universitat Politècnica de València, 46022 Valencia, Spain.
Sensors (Basel). 2022 Sep 26;22(19):7279. doi: 10.3390/s22197279.
Reinforced concrete structures' (RCSs) ageing and early deterioration are some of the main challenges faced by the building sector today, and steel bar corrosion is one of the main problems. In this phenomenon, water and concrete's electric resistivity play a fundamental role. Therefore, developing sensor systems capable of estimating any variations in these parameters in real time and remotely would represent considerable progress in sustainably maintaining RCSs. Many types of sensors capable of estimating humidity variation and electrical resistivity in concrete currently exist, but the variability of these sensors' sensitivity can be extreme depending on several factors; for example, temperature or presence of ions and their incorporation into smart monitoring systems, which is difficult. As an alternative to today's sensors, this study centered on developing two estimation models by means of the response of a novel voltammetric stainless steel (SS) sensor. The estimation models were one of humidity variation and another of concrete's electric resistivity. These models were calibrated, fitted and validated. In the validation, both these models explained a percentage of variance over 80%.
钢筋混凝土结构（RCSs）的老化和早期劣化是当今建筑行业面临的一些主要挑战，而钢筋腐蚀是主要问题之一。在这种现象中，水和混凝土的电阻率起着至关重要的作用。因此，开发能够实时、远程估计这些参数任何变化的传感器系统，将在可持续维护钢筋混凝土结构方面取得重大进展。目前存在许多能够估计混凝土湿度变化和电阻率的传感器类型，但这些传感器的灵敏度变化可能极大，这取决于几个因素；例如，温度、离子的存在及其融入智能监测系统的难度。作为当今传感器的替代方案，本研究专注于通过新型伏安不锈钢（SS）传感器的响应开发两个估计模型。估计模型一个是湿度变化模型，另一个是混凝土电阻率模型。这些模型经过了校准、拟合和验证。在验证过程中，这两个模型解释的方差百分比均超过80%。
