Bridge condition monitoring using fixed moving principal component analysis

Abstract

Summary This paper proposes a data‐driven damage detection method based on fixed moving principal component analysis (FMPCA) to analyze structural dynamic responses and monitor the bridge operational condition and the damage occurrence. The damage indices based on principal components (PCs) and eigenvalues can be calculated continuously by applying a fixed moving window. The length of the moving window is determined by using a new criterion based on the convergent spectrum of cumulative contribution ratio. Numerical simulations and experimental tests in the laboratory on beam bridge models subjected to stochastic loads are conducted to investigate the accuracy and effectiveness of the proposed approach. Both simulation and experimental results indicate that using the FMPCA can well analyze the dynamic vibration data to detect damage or abnormal vibration behavior during the operational condition. It can be used to accurately monitor the time instant of damage occurrence, which is very important in long‐term monitoring of civil engineering structures. The proposed method is successfully applied to analyze the data recorded during an incident that a real large‐scale suspension bridge was slightly scraped by the mast of a sand ship, which further verifies the effectiveness and feasibility of this method in engineering applications. The results also indicate that the bridge was not damaged after the incident but presented a short time abnormal vibration behavior owing to the impact of the ship mast.

Durability control through design and health monitoring during construction

“…Even though several health monitoring procedures have been defined through several experiences during decades for in‐service structures (mainly bridges), 1–3 a conceptual model is needed for durability health monitoring of structural concrete elements during fabrication and based on the methodology proposed in NMX‐530‐ONNCCE 4 and other investigations 5 . This procedure is used in this investigation to discuss the achievement of durability through the design and health monitoring during construction.…”

Structural damage detection using principal component analysis of frequency response function data

“…In general, PC analysis (PCA) has been used for damage detection and localization in two ways. In the first way, PCA has a main role in damage detection and localization, whereas it was used in the second way as an initial step for other algorithms 28–33 . The former way was utilized in the following researches.…”

Development and application of random forest technique for element level structural damage quantification

“…PCA is widely used for conducting the data compression and extracting information from the high dimensional data. It is used in the field of SHM for performing the dimensionality reduction of the input dataset and for removing the uncertainties, for example, measurement noise and environmental effects 25 while retaining most of the information of the original data 26 . For the datasets measured for a long duration, the number of sampling points becomes significantly large, which increases the computational time for obtaining the covariance matrix and principal components.…”

“…These principal components are obtained by performing eigen‐decomposition of covariance matrix obtained from the centred matrix of the input datasets. The eigenvectors can be arranged in the descending order of the eigenvalues, and the number of components can be selected based on certain criteria on the singular values or energy of covariance 25,26 . Posenta et al 27 proposed using a moving window of a constant size, and the computation of covariance matrix was carried out inside the window.…”

“…Posenta et al 27 proposed using a moving window of a constant size, and the computation of covariance matrix was carried out inside the window. Nie et al 25 utilised fixed MPCA with a sliding fixed window, to detect the time instant of damage occurrence for real‐time SHM of bridge structures.…”