Quantifying the extent of local damage of a 60-year-old prestressed concrete bridge: A hybrid SHM approach

Abstract

The increasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest developments in the sensors field and computational power, real-scale SHM systems’ deployment has become logistically and economically feasible. However, it is still challenging to perform a quantitative evaluation of the structural condition based on measured data. The paper addresses recent efforts to associate measured observations with an identification of local stiffness reduction as a global parameter for damage onset and growth. It proposes a hybrid methodology for model updating and damage identification. The proposed methodology is built on data feature extraction using the principal component analysis (PCA), finite element (FE) simulation, and Monte Carlo simulation to quantify the extent of local damage of a 60-year-old prestressed concrete bridge. The methodology allows a sensor-specific quantification of the local stiffness reduction and makes it possible to focus succeeding bridge inspection, recalculation, and repair works on these areas. Even more, the monitoring in combination with the FE model and proposed methodology provides continuous information on developing stiffness reduction and the acuteness of rehabilitation measures.

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

“…The essence of damage identification is a pattern identification problem that examines the characteristics of multi-point data relative to the initial state. [31][32][33] Whether the damage occurs or not can be expressed as the change in the mapping relationship between sensor data under the concept of internal force redistribution. 34,35 Therefore, based on the historical data, this paper firstly constructs dataset 0 of the intact structure to build the relationship model of all the sensors using the Transformer model.…”

“…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.…”

Bridge Performance Prediction Based on a Novel SHM-Data Assimilation Approach considering Cyclicity

“…In recent years, diverse forecasting methods have been developed for predicting the dynamic and static structural responses of bridges, which can generally be categorized into model-based methods and data-driven methods [10]. Te model-based models often necessitate a prior knowledge of structural features and emphasize the relationship between external excitation and structural responses [11]. For example, Wu et al [12] proposed an efective method for dynamically predicting long-gauge strains in high-speed railway bridges using fber Bragg grating (FBG) sensors, based on the fnite element approach.…”

Structural damage quantification in shear buildings using mode shape slope ratio

“…Cheng et al 29 used output-only signals to establish relationships of damage severity with low-frequency components and showed its applicability on simple multi-degree-of-freedom (DoF) spring-mass system. A very recent study conducted by Sakiyama et al 30 focuses on data feature extraction using the principal component analysis, finite element simulation and Monte Carlo simulation to estimate the extent of local damage of a very old concrete bridge.…”