Abstract
The integration of discrete wavelet transform and independent component analysis (DWT-ICA) method can directly identify time-varying changes in linear structures. However, better metrics of structural seismic damage and future performance after an event are related to structural permanent and total plastic deformations. This study proposes a two-stage technique based on DWT-FastICA and improved multiparticle swarm coevolution optimization (IMPSCO) using a baseline nonlinear Bouc–Wen structural model to directly identify changes in stiffness caused by damage as well as plastic or permanent deflections. In the first stage, the measured structural dynamic responses are preprocessed firstly by DWT, and then the Fast ICA is used to extract the feature components that contain the damage information for the purpose of initially locating damage. In the second stage, the structural responses are divided at the identified damage instant into segments that are used to identify the time-varying physical parameters by using the IMPSCO, and the location and extent of damage can accordingly be identified accurately. The efficiency of the proposed method in identifying stiffness changes is assessed under different ground motions using a suite of two different ground acceleration records. Meanwhile, the effect of noise level and damage extent on the proposed method is also analyzed. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed approach identifies stiffness changes within 1.25% of true stiffness within 8.96 s; therefore, it can work in real time. Parameters are identified within 14% of the actual as-modeled value using noisy simulation-derived structural responses. This indicates that, in accordance with different demands, the proposed method can not only locate and quantify damage within a short time with a high precision but also has excellent noise tolerance, robustness, and practicality.
Highlights
Civil engineering structures are subjected to continuous structural deterioration caused by aging, low-cycle fatigue loads from smaller earthquakes, and daily environmental loading. erefore, the severity of deterioration needs to be monitored periodically in order to ensure structural integrity and safety [1]
Structural responses due to structural vibrations induced by external loading are on-time collected by structural health monitoring (SHM) systems; analyzing and processing collected structural response is a popular way to identify, locate, and quantify structural damage based on the principle that damage affects the mechanical properties of the structure, which will change structural dynamic properties [2, 3]
Due to the fact that few methods can detect structural response novelty and identify structural damage severity in nonlinear structures, this paper focuses on developing an approach that will capture a nonlinear behavior in time domain to detect structural response novelty firstly and utilize an optimization algorithm, with fitness function in time domain, to identify structural parameters and structural stiffness in less time. e first question that arises is how to characterize a nonlinear response as mentioned above. is has been addressed in a wide range of mechanical and civil engineering applications via signal processing methods like Hilbert–Huang transform-based [1, 17], Wavelet transform-based [7], neural network-based [18, 19], and independent component analysis-based methods [20]
Summary
Civil engineering structures are subjected to continuous structural deterioration caused by aging, low-cycle fatigue loads from smaller earthquakes, and daily environmental loading. erefore, the severity of deterioration needs to be monitored periodically in order to ensure structural integrity and safety [1]. If damage is detected in the early stage, maintenance works will be carried out timely with low cost. For this purpose, structural responses (e.g., accelerations) due to structural vibrations induced by external loading are on-time collected by structural health monitoring (SHM) systems; analyzing and processing collected structural response is a popular way to identify, locate, and quantify structural damage based on the principle that damage affects the mechanical properties of the structure (i.e., stiffness and damping), which will change structural dynamic properties (e.g., frequencies and model shapes) [2, 3]. Yang and Nagarajaiah [6] proposed a unsupervised blind source
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