## 7 Corrosion Control and Monitoring
In this work, four different tidal positions representing the high tidal zone, the middle tidal zone, the central zone between the middle tidal zone and low tidal zone, and the low tidal zone were assumed. The results show that the corrosion behavior of steel in immersion and wet stages is different. The work showed that the fastest corrosion rate is attributed to the longest wet time in a tidal cycle. In 2015, lab-based experimental corrosion rate monitoring of two steel rebars in wet conditions using a TFBG corrosion sensor was performed by [PERSON] et al. [125]. During the test, two steel rebars were immersed in a container filled with water containing NaCl. A TFBG sensor was attached to the rebar that was electrically connected to an anode DC power supply. The measurement was performed by comparing the spectral response of cladding resonances and the SRI change during the corrosion process. Corrosion monitoring in different material types using other methods was presented by [PERSON] et al. [126]. The crevicle corrosion monitoring relevance of a copper alloy marine structure based on electrochemical sensing using a boron-doped diamond electrode (BDD) was investigated. The electrochemical behavior on the BDD electrode was investigated in NaCl solution (equal to average seawater salinity). The proposed method is promising for monitoring the corrosion of copper alloys in marine environments. Ships are periodically situated in a dockyard and inspected by well-trained surveyors and class societies to assess their physical and structural performance. The expenses required for onboard inspection can be significant, so introducing new technological tools is a must. [PERSON] et al. [127], under the EU-FP7 project MINOAS in 2016, developed a micro-aerial vehicle (MAC) with different locomotion capabilities to collect images focusing on a remote area that the surveyor has difficulty accessing. A novel method for detecting coating breakdown/corrosion (CBC) on a complete ship hull was performed by classifying image pixels (color and texture information) using a three-layer feed-forward artificial neural network. The MAC comprises a flight management unit (FMU) for platform stabilization, an inertial measuring unit (IMU) as a height and distance sensor, embedded PC and control software, and a camera set. The detection method using this novel technology was reported to successfully classify images between coating breakdown and corrosion. In the following year, sensing technology using an electrochemical probe was used to monitor atmospheric corrosion by [PERSON] et al. [128]. Monitoring of the 60-day corrosion form in the extreme atmospheric conditions in the Zhoushan offshore environment was conducted by comparing the electrochemical noise (EN) data of two different steel plates. The EN data were analyzed by wavelet transform to identify corrosion and transform it into an energy distribution plot (EDP). It was discovered that noise resistance is well correlated with weight loss data. Furthermore, the concentration of salt as a critical indicator for diagnosing the corrosion occurrence of an offshore concrete structure was investigated by [PERSON] et al. [129]. In this work, a 48-h experimental test for salinity detection was performed using a low-cost salinity sensor with four sensor probes multiplexed into one system. The experimental findings indicate that the proposed sensor has higher sensitivity with a lower cost compared to previous work. Two years later, the application of artificial intelligence (AI) technology for corrosion monitoring in naval architecture and ocean engineering was investigated in depth by [PERSON] et al. [130]. Applying deep learning to ship corrosion monitoring reduces workforce and financial resources. The corrosion of a ship hull plate was detected and monitored using a convolutional neural network (CNN) model.