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.