Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent damage. Environmental and operational loads cause nonstationarity in the strain data of urban girder bridges. Such nonstationary can mask damage reduce accuracy established model. To address this problem, a steady-state bridges proposed. First, observable effects such as ambient temperature, directional projection decoupling method proposed, which effect effectiveness proven using equations. Second, unobservable traffic load, k-means clustering steady state proposed; method, divide nonsteady states on data, established. Finally, verified actual bridge. The results show that proposed error caused by effects, improve modelling accuracy, provide useful information identification.