# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Methods under Varying Temperature Conditions
The feasibility of this method was verified in the application of I-40 bridge. [PERSON] and [PERSON] [195] adopted an autoassociative neural network (AANN) to isolate the damage features in the structure when the temperature changes. The inputs of the network are frequency and temperature, and the output is the predicted value of the normal frequency. According to the difference between the input and the output of AANN in each damage case, an RBF neural network was used to classify each case. [PERSON] et al. [196] replaced the conventional finite element in the FEM with a probabilistic finite element to consider the influence of ambient temperature on the bridge FEM. They proposed a damage location method based on probabilistic features and demonstrated the effectiveness of the method through numerical simulation. [PERSON] et al. [197] studied the change in dynamic characteristics of an RC slab bridge and a rigid frame bridge under the influence of environmental factors. Long-term monitoring results showed that temperature was the most important factor, which affected the natural frequency of the bridge. [PERSON] et al. [198] introduced a new label for the damage characteristics caused by ambient temperature, namely, characteristics of the narrow dimension (CND). When the damage characteristics were determined to conform to the CND index, a method was proposed to detect the bridge damage under the influence of the environment. Numerical results and field examples demonstrated the effectiveness of the proposed method. It is undeniable that temperature change is a crucial aspect among the environmental factors that affect BHM. However, research on other environmental factors is also essential in improving BHM. [PERSON] et al. [199] investigated the influence of the soil-structure interaction (SSI) and site response on the dynamic performance of continuous viaducts. They used FEM to detect and verify the contribution of SSI in altering the dynamic response of continuous viaducts. [PERSON] [200] studied the extent to which SSI and pier column inelasticity variations affect modal parameters under different horizontal seismic excitations, aiming to provide a higher fidelity FEM. During seismic excitation experiments conducted on pile foundations established in five soil profiles, modal frequencies demonstrated a sensitivity to the effects of SSI and pier column inelasticity, which was not reflected in the mode shapes. The natural frequency of structure is an important parameter in SDI, which can guide the detection and improvement of structure by reflecting its dynamic characteristics. In general, the natural frequency is related to the stiffness, mass, and boundary constraints of the structure. When the external environment affects these properties, the natural frequency of the structure changes accordingly, among which the influence of temperature is particularly significant, as shown in Figure 9. Therefore, in the process of SDI, it is necessary to distinguish between structural property changes caused by environmental variations and those caused by structural damage [201], so as to obtain more accurate identification results.