AI-enabled indirect bridge strain sensing using field acceleration data

Life-cycle performance assessment of bridges is crucial for decisions pertaining to functionality, maintenance, and rehabilitation while accounting for inherent epistemic and aleatoric uncertainties stemming from noise or structural degradation. Since fatigue from repeated cyclic loads is a prominent source of performance degradation in bridges, a continuous and efficient method for structural monitoring is necessary. In fatigue assessment, engineers rely on strain response, which can be challenging to collect due to the labor-intensive and costly deployment of strain gauges that are not conveniently reusable. This paper proposes an indirect sensing approach that converts acceleration signals to strain signals, enabling a convenient and robust paradigm for a continuous, and accurate bridge fatigue assessment. A combination of convolutional neural networks and transformers are used in this work for estimating strain signals from acceleration measurements. The efficacy of the proposed framework is demonstrated through data collected from the Gene Hartzell Memorial Bridge in Pennsylvania, USA. Furthermore, physical insights have been drawn from the results that reinforce the rationale behind the proposed artificial neural network architecture. This novel framework for indirect sensing can be readily employed for strain estimation from acceleration measurements of the bridges, upon adequate training, which will contribute to bridge condition and life-cycle assessment.