## 4 Al and ML in Bridge Inspection
### Neural Network-Based Bridge Inspection Techniques
It was observed that several combination techniques rendered promising results, such as when data are generated by fusing multi-sensor information. Also, data processing achieved by Hilbert-Huang transform or wavelet packet transform (both damage-sensitive feature extraction methods) yielded good results. Emphasis was laid on using fog devices to minimise noise filtering and enhance data compression and fusion after seismic events. Hybrid approaches (physics- and data-based) were explored to overcome the bottlenecks of stand-alone modelling. Studies revealed that accuracy can improve if anomalous sensor data are combined with real-time monitoring. [PERSON] et al. [129] stated that conventional human detection methods and normal statistical actions must be more accurate for damage detection, besides being hefty and expensive. To overcome these, the MAE of various models was compared. The learning outcomes of the pre-developed networks improved along with the prediction error with the help of advancements made in ANN. Vibrational information extracted from models under testing was utilised to arrive at the results. [PERSON] et al. [130] used an algorithm based on k-means clustering to obtain anchor sizes corresponding to damages. Fixed focal length and shooting distance were maintained throughout the data acquisition phase to secure an explicit visual of damage size. The optimised solution developed by the authors was better than Faster R-CNN, improving mean average precision (mAP). Besides cracks, other types of damage, like spalling and exposed reinforcement, were also detected. It was applied to the Anzac Bridge in Christchurch to study various structural damages, and the results outmatched those of faster R-CNN with predefined anchor points. Cracks, spalling, and exposed reinforcement had an 84.56% average identification accuracy. [PERSON] et al. [131] deployed a 1D CNN neural network and ground penetrating radar (GPR) system to detect bridge decks. GPR data were obtained from human-generated damage on eight experimental samples. The efficiency of GPR was augmented using a few pre-extracted features. The type of CNN used was a one-dimensional type (1D) to discover concrete bridge decks. Laboratory-based GPR consisted of artificially created sub-surface defects and defect-free samples to classify the entire collection. A thoroughly investigative study deploying ML algorithms for SHM in bridges, paying special attention to reinforced concrete bridges, was carried out by [PERSON] et al. [132]. Many aspects, like design, construction, quality management, and bridge engineering work, were explored. The study supported the idea that ANN is best suited for classification and regression tasks. ML algorithms like SVM and Random Forest (RF) are easy to use and find the optimal parameters related to the problem because of their smaller number of hyperparameters. CNN is best suited for image information, processing, and establishing a connection between the dependent and independent variables of high dimensions. [PERSON] et al. [133] explored reinforced concrete bridges using vision and infrared thermography. The hybrid image combines images captured with RGB cameras with infrared camera data. The hybrid images were input into Faster R-CNN, which yielded details about the corrosion-induced damage of bridges on the surface and also sub-surface damage. Faster CNN led to automated corrosion detection. Practical application and sub-surface corrosion, often missed in single modalities, were easily pinpointed. [PERSON] et al. [134] input more than 3750 scanned images into a Faster R-CNN to identify structural faults in concrete bridges. The proposed Faster R-CNN attained better results in terms of accuracy when there were 0.3 anchors and a 0.7 ratio. [PERSON] et al. [135] performed bridge crack detection using a combination of Faster R-CNN and infrared thermography. By applying Inception V2 to execute feature extraction for Faster R-CNN, a model was developed that exhibited about 80.35% accuracy.