# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 3 Review of DL and ML Safety Applications
### Shm
#### 3.1.2 Vibration-Based Damage Detection  
Although a pixel-level representation of structural defects is beneficial for SHM, it can only identify the damage level on the structure surface and is not competent to infer the performance of internal structural components which may have been deteriorated in advance [69]. Vibration data is the main type of source of data utilized in SHM. Technically, any structural damage will change the stiffness and mass distributions of the structure and lead to differences in the natural frequencies and mode shapes [70; 71]. Hence, vibration-based SHM methods have the potential to detect internal structural damages by analyzing the abnormal data acquired from the sensors (e.g., accelerometers). The previous research of vibration-based SHM mainly focused on setting up a real physical model to imitate the status of a real structure. Basically, this model-driven method employs mathematical modeling and physical laws to represent the monitored structure [72]. Hence, the level and location of the damage can be determined accurately by analyzing and solving the model. Nevertheless, it is challenging to build and solve such a complicated model when the complexity of the monitored structure increases and the environmental factors are considered. Currently, model-driven methods have been progressively replaced by data-driven methods [73; 74]. The most critical drawback of the model-driven approach is that modeling usually requires expertise and is time-consuming. Unlike the model-driven method, the data-driven method can identify the anomaly data directly by measuring the data collected from the sensors. Most of the data-driven method is based on the ML paradigm [75]. As the appropriate sensors' layouts can improve the efficiency and accuracy of data collection and transmission, ML algorithms, such as a genetic algorithm (GA) [76], have also been used for the determination of optimal sensor layouts. However, when applying vibration-based SHM methods in practice, the natural frequencies of the structure are easily affected by environmental factors (e.g., temperature) [77; 78]. For example, if a structure has some small-scale damages, the changes in the natural frequency of the structure would possibly be suppressed by those environmental variables. Some scholars have conducted several analyses on the evolution of structural properties and their relationship with changes in environmental parameters [79; 80]. Among them, the monitoring of the Z24 bridge is emblematic for addressing this issue [81]. Although significant efforts have been made in this regard, it requires comprehensive expertise and is time-consuming [82]. Currently, DL methods can potentially remedy the issue, as they can fully utilize the sensor data by automatically extracting the data features. Therefore, without expertise, even a delicate anomaly can be perceived. Recently, [PERSON] et al. [83] presented a 1D CNN-based algorithm along with autoencoder data compression to identify anomaly data in a long-span suspension bridge. The results showed that the developed algorithm could achieve a precision of 97.53%. Similarly, [PERSON] et al. [84] presented a 1D CNN-based method to detect the structural damages on a steel frame by using wireless sensor data. [PERSON] and [PERSON] [85] introduced a novel CNN-based approach which could detect and locate the damage in a large-scale structure. [PERSON] et al. [86] trained a deep CNN by feeding it unrefined sensor data and applied it for identifying the damage of a simulated beam structure. The results revealed that the trained CNN could detect structural damage with high accuracy, even if the test data were noisy. [PERSON] et al. [2] proposed a CNN to detect structural stiffness and mass changes. The developed CNN achieved good results on both the in-lab structure and the in-service bridge. [PERSON] et al. [87] used a DL-based method for steel fatigue assessment.