Damage Localization Of Medium-and Small-Span Beam Bridges Monitored Within One Cluster Under Operation Environment

The beam bridges monitored within one cluster refer to several medium-and small-span beam bridges located in highway or urban elevated corridor.The structural health monitoring data of bridge under operation environment are the comprehensive response of various coupling effects such as vehicle load,temperature load,shrinkage and creep.It is usually difficult to eliminate the influence of complex coupling factors on damage localization of bridge by using only structural monitoring data from a single bridge.Consequently,it is challenged to localize the structural damage in all bridges within one cluster under operating environment.To address this issue,the following three types of bridges within one cluster are taken as research objects in this study,i.e.,the beam bridges with same structure,the beam bridges with similar structure and the beam bridges without health monitoring data.Based on the characteristics that the beam bridges within one cluster bear similar environment and vehicle load,the damage localization methods of medium-and small-span beam bridges within one cluster under the operation environment was systematically studied by fusing the monitoring data from multiple bridges.The main contents of this study are described as following.Aiming at the issue of damage localization of beam bridges with same structure under time-varying environment,a probabilistic SDDLV method for localizing damage in same beam bridges monitored within one cluster is proposed.Fitst,the limitation of traditional SDDLV method for damage localization of a single bridge is discussed,and the damage localization vector based on the structural characteristics of beam bridges within one cluster is established.Then,considering the influence of time-varying temperature,a method for calculating structural stress field based on Gaussian mixture clustering and probabilistic finite element model is studied.Finally,the damage localization threshold based on the probability characteristics of damage localization index is calculated,and then by incorporating hypothesis testing and a cross-validation strategy,the structural damage of all monitored same bridges within one cluster is localized.Compared with the empirical damage localization threshold of the traditional SDDLV method,the proposed method completes the damage localization in the whole area of the superstructure of beam bridges by using only sparse measuring points under the time-varying environment.Based on the above research,to avoid modal parameter identification error,a damage localization method for same bridges within one cluster using the difference ratio of projected strain monitoring data is proposed from the perspective of strain time domain analysis.First,a damage diagnosis feature is established by using the difference ratio of projected strain monitoring data obtained from the key stress sections of same beam bridges monitored within one cluster.On this basis,the relationship between the statistical characteristics of the proposed damage diagnosis feature and the degree of structural similarity between two bridges are discussed in detail,and the applicable conditions of the algorithm is explored.Then,a damage localization index is presented by calculating the subspace angle between two damage features.Finally,combined with kernel density estimation and a cross-validation strategy,the proposed index is implemented to localize the damage in the key stress sections of all same bridges monitored within one cluster.Compared with the traditional damage localization methods for a single bridge,this method can eliminate the influence of operation environment on damage localization accuracy more effectively.Aiming at the issue of damage localization of beam bridges with similar structure under time-varying environment,a damage localization method for similar beam bridges monitored within one cluster is proposed based on a spatiotemporal correlation model of strain monitoring data.First,a deep learning architecture combining a convolutional neural network with a long short-term memory network is established,which can reveal the complex time-varying mapping relationship between the strain monitoring data for similar bridges within one cluster to obtain an accurate spatiotemporal correlation model.Then,a strain prediction framework is presented that uses the proposed spatiotemporal correlation model after training.On this basis,the predicted and measured strains can be utilized to calculate a damage localization index that is not affected by complex coupling factors.Finally,the proposed index is implemented to accurately localize damage in all similar bridges within one cluster.Aiming at the issue of damage fast localization of existing beam bridges with similar structure,a damage fast localization method is proposed for similar beam bridges monitored within one cluster without reference model.First,an area equation of the strain time-history curve is derived theoretically under a moving load for a simple beam bridge and a continuous beam bridge.Then,a damage localization index is established based on the area-ratio of the strain time-history curve by analyzing the constitutive characteristics of the area equation of the strain time-history curve at each measurement point.On this basis,the relationship between the normalized damage localization index and the damage of girder is discussed.Finally,damage fast localization for beam bridges within one cluster is realized under temporary closed traffic.The proposed method does not need to reference historical health data or a finite element model and can transform strain time-history curves with different amplitudes from different positions along bridges into a unified normalized index reflecting only structural stiffness changes,which is especially suitable for structural damage localization of long-distance bridges within one cluster.This study explored the idea of structural damage localization by integrating monitoring data from multiple bridges,which has good theoretical significance and practical value for improving the accuracy of damage localization of beam bridges under the operation environment and realizing the practical engineering application for damage localization of medium-and small-span beam bridges within one cluster.