Damage detection in bridges based on patterns of dynamic amplification

Abstract

frequencies with temperature, noting a daily change of 4.7%, 6.6%, and 5.0%, for the first three modal frequencies of a bridge deck in New Mexico that varied temperature by about 22°C, and changes of 14-18% for the first four modal frequencies of the Z24 bridge in Switzerland during 10 months. For a reinforced concrete beam and slab, Liu et al 4 suggest that modal frequencies will decrease 0.12-0.33% per degree Celsius with respect to the modal frequencies at 0°C, being the variation of concrete elasticity modulus with temperature the main cause of these changes. There are two approaches towards separating effects due to operational conditions from effects due to damage: (a) input-output methods, where operational variables such as temperature, humidity, and/or traffic loading are measured together with modal properties to establish their relationship, that is, via the polynomial chaos expansion method proposed by Spiridonakos et al 5 and (b) output-only methods, where operational variables are treated as embedded variables that do not need to be measured, as their influence will be discarded via methods such as neural networks, factor analysis, 6 principal component analysis, 7,8 or kernel principal component analysis. 9 Rytter 10 classifies vibration-based damage identification approaches into four levels of increasing degree of complexity that establish: Whether damage occurred or not (Level 1), the position of damage (Level 2), its quantification (Level 3), and the remaining lifespan of the structure (Level 4). Fan and Qiao 11 carry out a thorough review of the available vibration-based damage detection methods. Although there is a group of model-based SHM methods that can reach Level 4 depending on the amount of available data and the complexity of the finite element (FE) model to be updated, they are time-consuming compared with data-driven SHM methods, which are typically Levels 1 or 2 and able to provide results in real time. There is a third group of methods that combines model-based and data-driven SHM, that is, the damage-locating vector and statistical fault isolation methods, as discussed by Limongelli et al, 12 who compare data-driven with model-based SHM using the measurements of the post-tensioned concrete two-cell box Z24 bridge girder.Cebon 13 argues that heavy vehicles induce vibrations in the bridge that can cause damage to the structure and consequently; they affect its lifespan and safety. A pavement-based weigh-in-motion (WIM) system placed prior to the bridge (i.e., bending plate or piezoelectric sensors embedded in the pavement) can be employed to identify and prevent overloading. Load monitoring is the basis for the Level 1 damage detection technique proposed by Cantero and González 14 that measures truck loads via two means: (a) a pavement-based WIM system and (b) a bridge weigh-inmotion (B-WIM) system (i.e., from strains measured on the bridge soffit based on the concept of influence lines). The principle of their technique is that the estimation of truck loads by both WIM and B-WIM sy...

Deep learning-based bridge damage identification approach inspired by internal force redistribution effects

“…Gaussian Mixture Model was adopted to analyze the different components of the strain response which is able to accurately portray their statistical characteristics. [39][40][41] Li et al 42 proposed a model using Gaussian mixture distribution to describe the index of the cable force ratio and achieved the accurate identification of the state of the inclined cable. This is also inspiring for the damage identification of main beams based on strain residuals.…”

Research on Bridge Structural Health Assessment Based on Finite Element Analysis

“…They are not used as correction parameters. Qianjiang Fourth Bridge is a newly built bridge [20]. It is assumed that the structural parameters of the main girders and pylons of cable-stayed bridges are uniform and do not change with the coordinate position.…”

A data‐driven approach for regional bridge condition assessment using inspection reports

“…Therefore, it is necessary to establish an appropriate bridge deterioration model to accurately predict the future condition of structures and optimize the regional maintenance strategies based on these predicted data 10–12 . Regular inspection is a periodic way (i.e., yearly) to provide detailed and accurate information of measured structures 13,14 . Bridge configurations can be extracted from the reports, as can the structural ratings and related maintenance actions that are also recorded in these documents 15 .…”