A data-based structural health monitoring approach for damage detection in steel bridges using experimental data

Abstract

There is a need for reliable structural health monitoring (SHM) systems that can detect local and global structural damage in existing steel bridges. In this paper, a data-based SHM approach for damage detection in steel bridges is presented. An extensive experimental study is performed to obtain data from a real bridge under different structural state conditions, where damage is introduced based on a comprehensive investigation of common types of steel bridge damage reported in the literature. An analysis approach that includes a setup with two sensor groups for capturing both the local and global responses of the bridge is considered. From this, an unsupervised machine learning algorithm is applied and compared with four supervised machine learning algorithms. An evaluation of the damage types that can best be detected is performed by utilizing the supervised machine learning algorithms. It is demonstrated that relevant structural damage in steel bridges can be found and that unsupervised machine learning can perform almost as well as supervised machine learning. As such, the results obtained from this study provide a major contribution towards establishing a methodology for damage detection that can be employed in SHM systems on existing steel bridges.

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

“…Arguably, the main limitation is related to the requirement of damage labels while training the proposed PDNN model. At present, this can be addressed by combining hybrid approaches and transfer learning techniques, as discussed in [55]. In a hybrid approach, the target bridge labels can be acquired from numerical simulations from Finite element model (FEM) of the bridge and then further combined with real measurements of the bridge for further damage assessment.…”

A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data

“…The bridge is used as a full-scale damage detection test structure. 37,38 Figure 3 shows an overview of the bridge structural system, which is composed of the two bridge walls, bridge deck, and lateral bracing. The bridge was used for an extensive experimental benchmark study carried out in 2020, where it was damaged in a number of damage scenarios while structural monitoring was performed.…”

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

“…To solve the problem of inclusive outliers in the training data, robust statistical methods are considered in a wide range of researches, including SHM [ 13 , 14 , 15 ], to provide unbiased estimates of mean and covariance parameters computed from a smaller subset of data whose behaviour is assumed to be close to the true population values. Alternatively, data normalization techniques such as principal component analysis [ 9 ], autoencoders [ 16 ] or cointegration [ 17 , 18 , 19 , 20 , 21 ], are adopted to project the data into a different space to remove or at least reduce the effect of environmental and operational changes. Although previous methods proved their effectiveness in creating a normal condition training set clear from the influence of external factors, they are unable to distinguish which of the outliers indicated in the data are “benign” and which are “malign”.…”