# Artificial Intelligence (AI) Applied in Civil Engineering
## 2 Contributions
### Applications of Convolutional Neural Networks  
In earthquake engineering, the analysis of site seismic amplification characteristics is one of the most important tasks of seismic safety evaluation. [PERSON] et al. [18] propose a new prediction method for the amplification characteristics of local sites, using a CNN combined with real-time seismic signals. The CNN is used to establish the relationship between the amplification factors of local sites and eight parameters, while the training and testing samples are generated through observed and geological data. The results show that the CNN method can provide a powerful tool for predicting the amplification factors of local sites both for recorded and unrecorded positions. [PERSON] et al. [19] propose a measurement method of bridge vibration by unmanned aerial vehicles (UAVs) combined with convolutional neural networks (CNNs) and the Kanade-Lucas-Tomasi (KLT) optical-flow method. The KLT optical-flow method is used to track the target points on the structure and the background reference points in the video to obtain the coordinates of these points on each frame, while the characteristic relationship between the reference points and the target points is learned by a CNN according to the coordinates of the reference points and the target points. The objective is to correct the displacement time-history curves of target points containing the false displacement caused by the UAV's egomotion. Based on the features of cracks, [PERSON] et al. [20] propose the concept of a crack key point as a method for crack characterization and establish a model of image crack detection based on the reference anchor points method, named KP-CraNet. The accuracy of the model recognition is controllable and can meet both the pixel-level requirements and the efficiency needs of engineering. The results show that the method can improve crack detection quality and has a strong generalization ability. [PERSON] et al. [21] propose an architecture for segmenting cracks in facades with Deep Learning (DL) that includes an image pre-processing step. The authors also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout. Blockage of culverts by transported debris materials is the salient contributor to originating urban flash floods, with conventional hydraulic modeling having no success in addressing the problem. [PERSON] et al. [22] explore a new dimension to investigate the issue by proposing the use of intelligent video analytics (IVA) algorithms for extracting blockage-related information. Their research aims to automate the process of manual visual blockage classification of culverts from a maintenance perspective by remotely applying DL models. On the other hand, [PERSON] and [PERSON] [23] use transfer learning on three advanced NNs, ResNet, MobileNet, and EfficientNet, and apply techniques for damage classification and damaged object detection to a post-hurricane image dataset comprised of damaged buildings from the coastal region of the southeastern USA. The dataset includes 1000 images for the classification model with a binary classification structure containing classes of floods and non-floods and 800 images for the object detection model with four damaged object classes, i.e., damaged roof, damaged wall, flood damage, and structural damage. [PERSON] et al. [24] aim at the long-term (24-72 h ahead) prediction of wind power with a mean absolute percentage error of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DL networks. In their experiment, they perform TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. [PERSON] et al. [25] propose a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained bidirectional encoder representation from transformers (BERT).