Early warning for abnormal strains of continuous bridges using a proposed temperature-strain mapping model

Bridge strain is an essential monitoring index for small to medium span bridges because abnormal strains could be attributed to bridge deterioration or damage. Thus, an early warning method for detecting abnormal strains is highly desired. In this study, a temperature-strain mapping model for predicting the temperature induced strains was proposed by fusing structural health monitoring data and deep learning neural networks. First, the sensitivity of temperature induced strains to structural damage was analyzed, and the thermal strain was chosen as a suitable indicator for bridge performance detection. Next, a high-precision prediction model was established to overcome the time-lag effect between the original temperature and temperature induced strain sequences. By comparing the model's prediction with the identification of measurements, an early warning method for detecting the abnormal temperature induced strains was proposed. The evaluation results demonstrated that the proposed early warning method was able to accurately detect the abnormal strains, even when the abnormal strain is smaller than the normal daily variations.