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 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.