**1. Introduction**

Wind energy, in particular, o ffshore wind power, has been recognized as one of the highest growing and the most important future renewable energy source [1,2]. Due to severe environmental conditions—such as severe storms, typhoons, ocean currents, and waves—o ffshore wind farms face more challenges in the issues of structure safety. Scour around the foundation of structure leads to the excavation of sediment deposits, reducing the safe capacity of the structure [3,4].

The scour mechanism of o ffshore wind turbine foundation caused by waves and currents is quite similar to bridge scour. From the studies of the past 30 years, flooding and foundation scouring was the primary cause of 600 bridges failed or collapse in the United States [5–14]. Average damage repair on highways cost of flooding in the United States is estimated to be \$50 million per year [11]. A scour monitoring and early warning system must be developed for evaluating the safety of structures. Moreover, conducting timely reinforcement and maintenance processes in response to seabed topographical changes induced by current erosion and scour processes around these structures

is also needed. Information obtained from scour monitoring systems can help engineers to design relatively safe and cost-e ffective o ffshore wind farms.

The phenomenon of pier scour is extremely complex because of the combined e ffects of the vortex system involving time-dependent flow patterns and sediment transport mechanisms. Scour processes around structures have received considerable attention over the past decades. Numerous studies have explored the mechanisms of hydraulic scour around foundations and have presented several formulas for scour depth estimation around piers. Most studies on scour have applied experimental flumes and mainly focused on the application of empirical regression equations for estimating the maximum scour depth. However, field data are limited because of the di fficulties associated with long-term measurement processes. Without su fficient field measurement data, such empirical equations may not be su fficiently accurate for field applications. In general, when a steady current encounters a cylindrical vertical foundation, the flow rate increases around the periphery of the foundation, producing a complex hydraulic flow such as a bow wave, a downflow in front of the pier, a horseshoe vortex, and a highly turbulent wake in the downstream region of the foundation. These combined e ffects of hydraulic scour lead to the erosion of sediments from the foundation in all directions and reduce the loading capacity of the foundation, thereby compromising the safety of the supported structure [5–17]. Uncertainties regarding the maximum scour depth around o ffshore wind turbines lead to complications in their design and risks in their operation. Several methods have been proposed for estimating or monitoring the maximum scour depth around structures. A real-time scour monitoring system can improve the safety of structures and a fford cost-e ffective operations by preventing premature or unnecessary maintenance [17].

In general, the di fficulty associated with developing measuring instruments with data acquisition systems is ensuring their durability in monitoring large-scale hydraulic and transportation structures under severe conditions. The Keulegan–Carpenter number (KC) was applied to realize the foundation uncertainties of marine wind farm structures scour [18]. There were many works focused on the vibration-based approaches to monitoring the structural health status of the wind turbine [19–21]. For example, a distributed-spring foundation model to estimate the variation of natural frequencies and provide a strategy for addressing scour-induced damage around monopile foundations has used [22]. Full-scale o ffshore wind turbines with tripod structure were analyzed using real structural features and three-dimensional (3D) finite element models; the results show that scouring has a slight e ffect on natural frequency data [23]. Another study employed nonlinear springs to simulate the interaction between the foundation of a wind turbine and soil subjected to di fferent wind, wave, and current loads—reflecting operational conditions—to determine the e ffects of scour on sti ffness properties. The results revealed that scour considerably altered the eigen frequency of the structure compared with that of an o ffshore monopile wind turbine with scour protection [24,25]. Furthermore, a study applied 3D computational fluid dynamics (CFD) to develop a numerical model to examine the seabed boundary-layer flow around monopile and hexagonal gravity-based foundations of o ffshore wind turbines; the flow was examined to determine the formation of horseshoe vortices and flow structures to estimate potential scour for engineering designs. The results showed that the horseshoe vortex size for the hexagonal gravity-based foundation was smaller than that for the monopile foundation [26–30]. Another study also proposed a wireless network monitoring system connected to an array of small capacitive sensor probes installed around a foundation for observing scour and sediment deposition processes; this system is similar to a sonar scanning approach [31].

The foundation of an o ffshore wind turbine constitutes approximately 35% of the installation cost of such a turbine [32]. Construction sites for o ffshore wind farms are typically surveyed using di fferent investigation approaches or hydraulic models such as bathymetry, seismometry, and side-scan sonar techniques before and after the main construction phase. However, as mentioned, the seabed topography changes constantly because of sea currents. Because uncertainties regarding seabed erosion and scour constitute a major risk for o ffshore wind farm development, the design and operation of offshore wind turbines should mainly focus on addressing the uncertainty regarding the maximum

scour depth around the foundations of such turbines. In order to protect against the erosion of the offshore wind turbine foundation, rock dump is usually laid to prevent removal of the sediment base. However, edge scour still continues to occur despite the foundation protective devices installed [33]. With the advancement of artificial intelligence (AI) technology, machine learning (ML) and deep learning (DL) will have a better contribution to o ffshore wind turbine condition monitoring [34–49]. Artificial intelligence (AI) is basically an algorithm for regression analysis of existing big data rules which include machine learning, deep learning, genetic algorithm, neural network, and fuzzy. Generally, the multilayer perceptron (MLP) neural network is commonly used as an AI model prediction. Feature extraction from the multiple linear regression (MLR) and multivariate nonlinear regression (MNLR) properties of supervised and unsupervised learning need to compare with existing empirical equations. To accurately predict the scouring process by means of inductive modeling, the AI modeling process still requires a large amount of data as training, test, and vilified dataset to analyze the sensitivity of the model. Once the scour depth can be measured, empirical formulas for measuring scour processes can be developed. Most of the current formulas are based on laboratory-based research models, engineering design assessments, and measurement experience after in situ scouring. However, due to the lack of reliable and durable instrumentation technology, scour data from real-time monitoring systems is still insu fficient.

An on-site scour monitoring system using visible light communication (VLC) for o ffshore wind turbine is proposed in this paper. Specifically, the monitoring system consists of arrays of small VLC modules attached directly to a pile structure and use the topology of the underwater optical wireless sensor network to enable remote data acquisition. Experiments conducted in flumes have revealed that the system was highly sensitive and accurate to monitor seabed scour processes. The proposed robust sensory monitoring system has considered for further on-site applications and as an indicator to improve the empirical scour formulas for sustainable maintenance in the life cycle of o ffshore structures.

#### **2. Underwater VLC Turbidity and Velocity Characteristics**

VLC, a novel free-space optical wireless communication technology, entails the combination of white and colored light-emitting diodes (LEDs) to utilize visible light (375–780 nm) as a transmission medium.

VLC is becoming an alternative choice for wireless technology because of its low operating cost, low maintenance cost, long-term service stability, broad bandwidth, and ubiquitous infrastructure support. Numerous studies have been conducted in both industry and academia to develop and commercialize VLC systems. Particularly, underwater wireless communication is of grea<sup>t</sup> interest to the marine industry and scientific society [50]. With the rapid development of solid-state lighting and semiconductor technology, VLC modules equipped with LEDs as light sources are expected to be mass produced at low cost. This technology has potential for use in a wide range of both indoor and outdoor applications for free communication services. Indoor VLC for an optical wireless communication system using LED lights was firstly proposed in 2004 with its high brightness, reliability, lower power consumption, and long lifetime advantages [50].

Measuring water turbidity has been widely developed over the past few decades. Theoretically, water turbidity was measured based on absorption, attenuation, and scattering e ffects by using spectrometers or photometric devices. For example, the acoustic Doppler velocimeter (ADV) measured the flowing velocity [51] while the optical laser Doppler velocimeter (LDV) [52] estimated the Doppler frequency shift (DFS) of coherent sound or light caused by the particle concentration in water. However, because of its size and inconvenient implementation, ADV and LDV are not suitable for measuring seabed turbidity and scouring. For o ffshore wind turbine foundation scour monitoring, the attenuation and absorption characters of the VLC measurement system in seawater, particularly the turbidity of the scouring suspension particles, need to be studied first.

Typically, Beer's law (also known as Beer–Lambert law) is a well-known optical law and commonly applied to derive the relationship in between absorption coe fficient, optical path length, and the media concentration in spectroscopy from a continuous wave [53,54].

$$\ln(\mathcal{D}) = h\_c \ e^{-c(\lambda)D} \tag{1}$$

where c (λ) = a(λ) + b(λ) is the cumulative attenuation coe fficient of the medium, a(λ) and b(λ) denote the absorption coe fficient and scattering coe fficient, respectively. Typically, λ stands for the light wavelength, D is the communication distance, h(D) is the output or detected intensity, *hc* is the input intensity.

VLC system implemented for underwater turbidity and scour laboratory demonstration in this paper, a nearby 2 cm distance of transmitter and receiver are arranged for less multiple scattering e ffects and avoided long distance channel attenuation loss. However, there are higher power optical lasers and higher intensity LEDs for long distance optical wireless communications (OWCs) which have less length intensity dispersion and improved the channel scattering e ffects [55]. A VLC system in the order of 100–200 m, and up to 300 m has been used to transmit data in the water environment [56,57]. However, due to the properties of oceanic turbulence such as the suspended particles, salinity, and temperature, the line-of-sight path attenuation loss is estimated by the radiative transfer equation (RTE). The vector RTE, implies energy conservation of a light wave traversing a scattering medium, is calculated by

$$\left[\frac{1}{\varepsilon}\frac{\partial}{\partial t} + a \cdot \nabla\right] \psi(t, \rho, \alpha) = \int\_{4\pi} \, \xi(\rho, a, a') \psi(\rho, a, a') da' - \kappa(\lambda) \psi(t, \rho, a) + \Phi(t, \rho, a) \tag{2}$$

Herein, α is the direction vector while ρ is the position vector. ∇ presents the divergence operator with respect to ρ, ψ is the irradiance, Φ is the internal source radiance, and ξ is the volume scattering function.

Experimental results obtained in our previous study demonstrated the feasibility of the VLC modules for executing both water turbidity and water flow velocity measurements [58]. To prevent the effects of ambient light, the proposed system applies a sinusoidal signal to modulate the VLC module. Figure 1 describes both the sinusoidal signal from the VLC transmitter and the interference signals from ambient light sources, obtained on the receiver side. Normally, VLC modules operate at a frequency of a few megahertz. The frequency of ambient light intensity changes is slower than that of the designed sinusoidal signal, bandpass filters implemented on the VLC receiver. Therefore, the VLC can receive the in-band sinusoidal signal and eliminate the interference signals of out-of-band frequencies; thus, the problems associated with ambient light interference are prevented in the proposed system.

**Figure 1.** Flowchart of signal processing for water flow velocity/turbidity measurement.
