In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.
Accurate damage identification is of great significance to maintain timely and prevent structural failure. To accurately and quickly identify the structural damage, a novel two-stage approach based on convolutional neural networks (CNN) and an improved hunter–prey optimization algorithm (IHPO) is proposed. In the first stage, the cross-correlation-based damage localization index (CCBLI) is formulated using acceleration and is input into the CNN to locate structural damage. In the second stage, the IHPO algorithm is applied to optimize the objective function, and then the damage severity is quantified. A numerical model of the American Society of Civil Engineers (ASCE) benchmark frame structure and a test structure of a three-storey frame are adopted to verify the effectiveness of the proposed method. The results demonstrate that the proposed approach is effective in locating and quantifying structural damage precisely regardless of noise perturbations. In addition, the reliability of the proposed approach is evaluated using a comparison between it and approaches based on CNN or the IHPO algorithm alone. The comparison results indicate that in single and multiple damage events, the proposed two-stage damage identification approach outperforms the other two approaches on the accuracy, and the average consumption time is 20% less than the method using the IHPO algorithm alone. Therefore, this paper provides a guideline for the study of high-accuracy and quick damage identification using both data-based and model-based hybrid methods.
A novel two-stage method is proposed to properly identify the location and severity of damage in plate structures. In the first stage, a superposition of modal flexibility curvature (SMFC) is adopted to locate the damage accurately, and the identification index of modal flexibility matrix is improved. The low-order modal parameters are used and a new column matrix is formed based on the modal flexibility matrix before and after the structure is damaged. The difference of modal flexibility matrix is obtained, which is used as a damage identification index. Meanwhile, based on SMFC, a method of weakening the “vicinity effect” is proposed to eliminate the impact of the surrounding elements to the damaged elements when damage identification is carried out for the plate-type structure. In the second stage, the objective function based on the flexibility matrix is constructed, and according to the damage location identified in the first stage, the actual damage severity is determined by the enhanced whale optimization algorithm (EWOA). In addition, the effects of 3% and 10% noise on damage location and severity estimation are also studied. By taking a simply supported beam and a four-side simply supported plate as examples, the results show that the method can accurately estimate the damage location and quantify the damage severity without noise. When considering noise, the increase of noise level will not affect the assessment of damage location, but the error of quantifying damage severity will increase. In addition, damage identification of a steel-concrete composite bridge (I-40 Bridge) under four damage cases is carried out, and the results show that the modified method can evaluate the damage location and quantify 5%–92% of the damage severity.
The most commonly used method for sampling damage parameters from the posterior distribution is the Markov chain Monte Carlo (MCMC) method. The population MCMC method as one of the MCMC methods has been utilized for damage identification by some researchers recently. Nevertheless, for the conventional population MCMC methods, these sampling methods often require significant computational resources and tuning of a large number of algorithm parameters. Aiming at the problem of difficulty in selecting the proposal distribution and low computational efficiency in the conventional MCMC method, this paper proposed a simple population Metropolis–Hastings (SP-MH) algorithm for the damage identification, which is realized by exchanging information among chains in a relatively small population and using tuning-free strategy. Then, a numerical cantilever beam and an experimental frame are utilized to verify the effectiveness and feasibility of the proposed algorithm, it can be seen that the convergence rate of the SP-MH algorithm is faster than that of the Differential Evolution Monte Carlo (DE-MC) algorithm, and in a small population state, the SP-MH algorithm can still maintain convergence, saving plenty of computing time for damage identification. The results show that the SP-MH algorithm is feasible and accurate in practice damage identification, and the SP-MH algorithm performs better than the DE-MC algorithm. Compared with the DE-MC algorithm, the SP-MH algorithm is simple and convenient for damage identification due to its tuning-free strategy and relatively small population.
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