The Study of Risk Assessment Method for Ship Berthing Based on the “Human-Ship-Environment” Synergy

Abstract

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of “human-ship-environment” has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment’s results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method.

1. Introduction

Berthing risk has long been a core concern for maritime authorities, ports, shipowners, and shipping companies. With the prosperity of international trade, the number of ships has been steadily increasing, and the navigation environment of ports is becoming increasingly complex. Additionally, during the berthing process, ships may pass through aquaculture zones, fishing activity areas, and restricted waterways, further increasing the risk of collisions, groundings, reef strikes, and other accidents. This will result in casualties and property damage, and it will also have a negative impact on ports and governing authorities. Therefore, accurately assessing berthing risks is key to ensuring the safety of both ships and ports [1].

The core of establishing a scientific evaluation model lies in constructing a reasonable evaluation index system and objectively and accurately setting the indicator weights. During the berthing process of a ship, personnel operations play a crucial role. The safety and smoothness of the berthing process are determined by the operator’s skill level, management experience, and ability to respond. Operators need to be proficient in handling ship equipment, possess extensive practical experience, and be capable of making critical decisions in various complex berthing situations. For example, decisions such as how to adjust the berthing angle and direction and when to activate the rudder and thrusters are crucial. Good decision-making skills can help the ship make timely and correct adjustments, ensuring safe berthing. In addition, the condition of the vessel also significantly affects the berthing process. The performance of the ship’s control system directly impacts its maneuverability and flexibility. A well-functioning control system can help the ship operate flexibly in complex port environments, thereby reducing berthing risks. The condition of the ship and the equipment directly influence the safety and efficiency of the berthing operation. A ship in good condition and well-maintained equipment can reduce operational risks and improve the efficiency of the berthing process [2]. At the same time, environmental factors also have a significant impact on berthing operations. Water depth, wind, waves, and tides can affect the ship’s navigation and maneuvering, which require operators to adjust and respond according to actual conditions. Additionally, the terrain and facilities of the dock also influence berthing operations [3]. Therefore, during the ship berthing operation, the three key factors of ‘human,’ ‘ship,’ and ‘environment’ complement and integrate with each other, collectively influencing the safety of the berthing process.

This paper conducts an in-depth analysis of these three major influencing factors, decoupling ‘human,’ ‘ship,’ and ‘environment’ for separate examination, and establishes a ship berthing risk assessment methodology based on the collaborative integration of ‘human-ship-environment’. First, an analysis is conducted from the three aspects of ‘human,’ ‘ship,’ and ‘environment’ to extract the key factors influencing berthing operations, and a ‘human-ship-environment’ integrated risk assessment index system for ship berthing is established. Second, given that some risk indicators are difficult to quantify, this paper employs a combination of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods to assess ship berthing risks. The AHP method is used to determine the weights of the influencing factors, while the FCE method is applied for the final evaluation of berthing risks. Finally, the proposed method was applied to comprehensively evaluate a berthing operation involving two vessels, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, which validated the practicality and effectiveness of the assessment method.

2. Establishment of a Ship Berthing Risk Assessment Index System

Berthing is a crucial step in the process of docking a ship at a port or pier. It plays a vital role in ensuring the smooth operation of maritime transportation, improving port efficiency, and ensuring the safety of both personnel and ships.

During the ship berthing process, factors such as the crew’s operational skills, the ship’s own conditions, and environmental factors like wind, waves, and tides, as well as the port facilities, all significantly affect the reliability and safety of the berthing operation. These factors interact, dynamically overlap, and couple with each other, collectively influencing the entire berthing process. The crew’s operational skills play a crucial role in the safety and efficiency of berthing, while the condition of the ship’s equipment is directly related to the smooth execution of the berthing process. At the same time, environmental factors, such as weather and sea conditions, also significantly impact the safety of the berthing process. The combined effects of these factors form the main sources of risk in the berthing process, which requires precise analysis, proper management, and evaluation to ensure the safety of ship berthing.

To effectively assess and quantify the various risk factors in the ship berthing process, this paper first analyzes the three main aspects that influence berthing operations, namely “human, ship, and environment,” and establishes a comprehensive and systematic berthing risk assessment index system. This index system scientifically breaks down the human factors, ship factors, and environmental factors in the ship berthing process, allowing for a comprehensive assessment of the risk levels involved. It provides a more accurate evaluation and prediction of the potential risks and safety hazards during berthing. This system offers a scientific basis for shipping companies and port management authorities to take appropriate measures to reduce berthing risks and ensure the safety of ships and port facilities.

2.1. Human Operational Factors

“Human operational factors” refer to the impact of crew actions on the berthing process. As the main controllers of the ship, humans play a crucial role in its operation. During the ship berthing process, human operations are a key factor affecting the safety of the ship and the risk of accidents. To accurately assess the impact of human factors on the berthing process, this paper breaks down the human operational factors into several indicators, including safety operation compliance, lateral distance upon approach, berthing angle, distance to the berth, distance to other ships, braking distance, approach angle, and approach orientation.

(1) For safety operation compliance, safety operation compliance is mainly used to assess the extent to which ship operators adhere to safety management measures and operational regulations during berthing operations. By monitoring and evaluating this indicator, management can take effective measures to enhance the personnel’s safety awareness and operational compliance;

(2) For lateral distance upon approach, the lateral distance upon approach refers to the horizontal distance between the ship’s centerline and the dock when the ship is approaching the dock in parallel. During berthing operations, the lateral distance upon approach is one of the key parameters. It determines the accuracy of the ship’s final docking position and plays an important role in a berthing risk assessment;

(3) For berthing angle, the berthing angle refers to the relative angle of the ship as it finally approaches the dock or berth. This paper primarily studies the angle between the centerline of the ship’s bow and the direction of the berth during berthing;

(4) For distance to the berth, the distance to the berth refers to the safe distance that a ship should maintain from its intended berth. Throughout the entire berthing operation, understanding the real-time distance between the ship and the berth is crucial to ensuring a smooth and safe berthing process. This distance affects the potential risk of collisions and damage during berthing;

(5) For distance to other ships, the distance to other ships is a key factor in ensuring a safe berthing process. This distance refers to the space between the ship and other vessels that are already moored or operating while the ship is approaching, maneuvering, or docking at the berth. This indicator in berthing risk assessment is crucial for preventing collisions and ensuring the safety of ships, cargo, and personnel;

(6) For braking distance, the braking distance refers to the minimum distance between the ship and the berth’s edge when the ship transitions from full speed to a complete stop. Assessing the braking distance is crucial to ensuring that the ship can safely and smoothly complete the berthing operation, especially in restricted waters or crowded port environments;

(7) For approach angle, the approach angle refers to the angle at which the ship approaches the dock or berth, and it is an important consideration. A correct approach angle is crucial for ensuring the safety and efficiency of the berthing operation. This paper primarily studies the angle between the ship’s bow direction and the shoreline of the berth during berthing;

(8) For approach orientation, the approach orientation refers to the ship’s heading when approaching the berth, also known as the approach course or approach heading. A correct approach orientation is crucial for conducting a safe and efficient berthing operation, as it affects the ship’s final docking position and whether it may come into contact with the dock, other vessels, or obstacles. This paper primarily studies whether the ship’s orientation upon reaching the braking zone aligns with the planned berthing orientation [4].

2.2. Ship Factors

“Ship factors” refer to the characteristics or conditions of the ship itself that affect the safety and risk level during the berthing process. Ship factors are a critical aspect affecting the safe navigation of vessels and require continuous attention and management to ensure the safety of the berthing process. Shipowners typically manage these factors through inspections, maintenance, and upkeep, ensuring that the vessel possesses good characteristics during berthing and reducing the risk associated with the berthing process.

This paper breaks down the ship factors into the age of the ship, the loading condition, the condition of mooring lines and mooring equipment, the condition of berthing assistance equipment, and the condition of hull structure maintenance.

(1) For age of the ship, the age of the ship can lead to wear and aging of its structure and equipment, which may increase the risk of accidents or malfunctions during berthing;

(2) For loading condition, the loading condition refers to the ship’s loading status, including but not limited to the type, quantity, and distribution of cargo, as well as the ship’s stability and stress conditions. It can affect the ship’s maneuverability, stability, draft depth, and the safety risk assessment under different loading conditions;

(3) For the condition of mooring lines and mooring equipment, the condition of mooring lines and mooring equipment is used to assess the status and reliability of the mooring lines and equipment used during the berthing process. At the same time, this indicator provides an important basis for ship management and emergency planning, ensuring reliable operations under different environmental conditions;

(4) For the condition of berthing assistance equipment, the condition of berthing assistance equipment is used to assess the completeness and functional effectiveness of the auxiliary equipment relied upon during the berthing process. By regularly assessing and improving the relevant equipment, berthing risks can be effectively reduced, and overall operational safety can be enhanced;

(5) For the condition of hull structure maintenance, the condition of hull structure maintenance refers to whether the hull remains in good condition during regular use and maintenance. The maintenance condition of the hull structure directly affects the ship’s stability, resistance to wind and waves, and overall safety. This is particularly crucial during berthing, as it impacts the ship’s ability to withstand impacts and resist external forces.

2.3. Environmental Factors

The environmental factors in berthing risk refer to the natural environment and the operational limitations of port infrastructure that affect the safety and risk level during berthing operations. For example, strong winds, severe weather, high waves, and tides affect the stability of the vessel and increase the difficulty of operations. Dock conditions, such as ship traffic and water depth, along with infrastructure and operational limitations, can also increase the difficulty of berthing under adverse environmental factors. When conducting berthing operations, ship operators need to fully understand and consider the impact of environmental factors, and promptly take appropriate countermeasures to ensure the safe berthing of the ship under complex environmental conditions. This paper systematically analyzes the key environmental factors affecting berthing operations, breaking down the environmental factors into tides, wind and waves, dock vessel traffic, navigable water width at the dock, and navigable water depth at the dock.

(1) For tides, during the berthing process, changes in tides can cause variations in water depth, thereby affecting the ship’s berthing capability and strategy. Ship operators need to choose an appropriate berthing time and water depth conditions based on the tidal situation;

(2) For wind and waves, strong winds and high waves increase the risks during ship berthing operations. They can cause the ship to lose stability, increasing the risk of collisions and groundings. Ship operators need to adjust berthing strategies and operational methods based on wind and wave conditions [5];

(3) For dock vessel traffic, dock vessel traffic refers to the number and frequency of ships entering and leaving the port. High-density vessel traffic may lead to congestion and conflicts during the berthing process, increasing the risk of accidents. Ship operators need to schedule the appropriate berthing time based on vessel traffic conditions to ensure a safe berthing operation;

(4) For navigable water width at the dock, navigable water width at the dock refers to the width of the water area that is available for ship navigation at the port dock. During the berthing process, the ship needs sufficient space to enter and leave the berth while maintaining safe maneuverability. If the water width is insufficient, ship operators may face difficulties in maneuvering and increased risks of collisions or groundings;

(5) For navigable water depth at the dock, the navigable water depth at the dock refers to the water depth in the port dock area. The ship requires sufficient water depth to safely enter and leave the dock. If the water depth is insufficient, the ship may run aground or collide with the bottom, leading to a berthing accident. Ship operators need to be aware of the water depth conditions and ensure that the ship’s draft is suitable for the depth requirements of the dock.

Through systematic research and analysis, this paper has constructed a comprehensive “Human-Ship-Environment” synergy-based ship berthing risk assessment index system, as shown in Figure 1.

3. Construction of the Ship Berthing Risk Assessment Model

This paper comprehensively adopts the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) to construct a ship berthing risk assessment model. The combination of these two methods aims to more comprehensively and precisely assess the various factors affecting ship berthing safety, providing a scientific basis for developing strategies to reduce risks [6]. The analytic hierarchy process (AHP) serves to organize the problem structure systematically and incorporates both subjective and objective factors, thereby making the assessment process more structured. The fuzzy comprehensive evaluation (FCE) serves to handle uncertainty and ambiguity in the assessment process, thereby enhancing the accuracy and reliability of the evaluation. By integrating these two methods, it becomes possible to assess the risk level of the ship berthing process more scientifically and accurately.

3.1. Determination of Indicator Weights Using the Analytic Hierarchy Process (AHP)

The analytic hierarchy process (AHP) is an intuitive and flexible decision analysis method that combines qualitative and quantitative analysis. It breaks down complex decision problems into smaller components, allowing for separate analysis and comparison to determine the relative importance or weights of each factor [7]. This method is divided into three steps: constructing a judgment matrix, performing a consistency check, and determining the weight vector.

3.1.1. Constructing the Judgment Matrix

In the hierarchical structure, the relative importance of each indicator is assessed, and the results are represented by specific numerical values, forming the judgment matrix. The scale definitions for the judgment matrix are shown in

Table 1. Scale definition for judgment matrix B.

$\mathbf{Scale}{\mathit{B}}_{\mathit{i}\mathit{j}}$	$\mathbf{Degree}\mathbf{of}\mathbf{Importance}\mathbf{of}{\mathit{B}}_{\mathit{i}}$ $\mathbf{over}{\mathit{B}}_{\mathit{j}}$
1	$B_i$ and $B_j$ are equally important compared to each other
3	$B_i$ is slightly more important than $B_j$ compared to the latter
5	$B_i$ and $B_j$ are significantly more important than the former than the latter
7	$B_i$ and $B_j$ compared, the former is more strongly important than the latter
9	$B_i$ and $B_j$ are extremely important compared to $B_i$ and $B_j$
2, 4, 6, 8	Median of the above neighboring judgments
Inverse	Two elements compared; the latter is more important than the former on a scale of importance

.

Judgment matrix B is generally constructed based on the expert survey method or the questionnaire method, and matrix B is a positive reciprocal matrix.

$$B=\left(\begin{array}{cc}\begin{array}{cc}1& B_{12}\\ B_{21}& 1\end{array}& \begin{array}{cc}{B}_{13}& B_{14}\\ B_{23}& B_{24}\end{array}\\ \begin{array}{cc}{B}_{31}& B_{32}\\ B_{41}& B_{42}\end{array}& \begin{array}{cc}1& B_{34}\\ B_{43}& 1\end{array}\end{array}\right)$$

(1)

This paper evaluates the berthing risks of ships, with the primary indicators being ‘personnel operation factors, ship factors, and environmental factors.’ Experts use the Delphi method to score according to the B scale definition table in the judgment matrix. If ‘personnel operation factors’ are considered slightly more important than ‘environmental factors,’ $B_{13}$ is assigned a value of three, and based on the properties of the reciprocal matrix, $B_{31}$ is assigned a value of one-third. If the former is considered significantly more important than the latter, $B_{13}$ is assigned a value of five, and $B_{31}$ is assigned a value of one-fifth, according to the same matrix properties. Similarly, the comparison results for the other indicators are obtained.

After applying the ranking algorithm and normalization process described in Equations (2) to (4) to the rows and columns of the judgment matrix, the weight vector $W_i={\left[w_1,w_2,\dots ,w_n\right]}^T$ is obtained.

$$U_i={\displaystyle \prod }_{j=1}^n{B}_{ij}$$

(2)

$$V_{ij}=\sqrt[n]{U_i}$$

(3)

$$W_i={\displaystyle \frac{V_i}{{{\displaystyle \sum }}_i{V}_i}}$$

(4)

3.1.2. Consistency Check

To avoid misjudgments of importance in expert surveys, a consistency check is necessary. Based on the weight vector $W_i={\left[w_1,w_2,\dots ,w_n\right]}^T$ and using Equation (5), the maximum eigenvalue ${\lambda }_{max}$ is calculated.

$${\lambda }_{max}={\displaystyle \frac{1}{n}}{\displaystyle \sum }_{i=1}^n{\displaystyle \frac{{\left(BW\right)}_i}{W_i}}$$

(5)

${\left(BW\right)}_i$ represents the i-th component of vector BW, and n is the order of the matrix.

Calculate the consistency index (CI):

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(6)

Define the random consistency index (RI). The calculated average random consistency indices for orders 1 to 9 are shown in

Table 2. Average random consistency index.

n	1	2	3	4	5	6	7	8	9
RI	0.00	0.00	0.58	0.96	1.12	1.24	1.32	1.41	1.45

[8].

Calculate the consistency ratio (CR):

$$CR={\displaystyle \frac{CI}{\mathrm{RI}}}$$

(7)

When CR < 0.1, it indicates that the judgment matrix has satisfactory consistency and passes the test. Otherwise, re-evaluation or consistency adjustment is required.

3.1.3. Determine the Weight Vector

If the judgment matrix passes the consistency check, the weights of each indicator in matrix B are determined by the component values of vector $W_i$ [9]. The normalized eigenvector corresponding to the maximum eigenvalue is used as the weight vector for each indicator, meaning that each component of this eigenvector represents the relative importance of the corresponding indicator within the overall assessment system. Through normalization, the sum of all indicator weights is ensured to be one, allowing for the direct evaluation and comparison of the importance of different indicators.

3.2. Fuzzy Comprehensive Evaluation Model

Constructing a fuzzy comprehensive evaluation model is a method that applies fuzzy mathematics principles to the evaluation of complex systems, aiming to handle and quantify uncertain and ambiguous information. This model first requires scientifically constructing an evaluation index system for the evaluation object and, then, assigning corresponding weights based on the importance of each indicator. Next, the fuzzy relation matrix is used to convert the actual condition of each indicator into a membership degree. This process involves the design of the membership function, which is a key step in mapping the indicator values to the [0, 1] range to reflect their contribution to the evaluation levels [10]. By integrating these membership degrees and combining them with the weights of each indicator, a comprehensive evaluation of the overall performance of the evaluation object is ultimately formed. The fuzzy comprehensive evaluation process includes three aspects: determining the factor set and evaluation set, forming the membership matrix, and developing a two-level fuzzy comprehensive evaluation model.

3.2.1. Determining the Factor Set and Evaluation Set

This paper divides berthing risk into three main factors: “human operational factors,” “ship factors,” and “environmental factors,” along with their sub-factors. In the index system shown in Figure 1, ($D_1$, $D_2$, and $D_3$) forms the first layer of factor set D, and ($D_{11}$, $D_{12}$ … $D_{35}$) forms the second layer of the factor set. Referring to the evaluation grading method commonly used by domestic and international experts, the evaluation set (evaluation set E) for ship berthing risk is established with five levels: low, relatively low, average, relatively high, and high, i.e., E = ${\mathrm{e}}_{\mathrm{j}}$ = $\left({\mathrm{e}}_1,{\mathrm{e}}_2,{\mathrm{e}}_3,{\mathrm{e}}_4,{\mathrm{e}}_5\right)$ = {low, relatively low, average, relatively high, high}.

3.2.2. Forming the Membership Matrix

Determine the membership degree ($r_{ij}$) for single-factor evaluation and the membership vector ($R_i$), forming the membership matrix (R). Membership degree is the most important concept in fuzzy comprehensive evaluation. For the membership degree ($r_{ij}$), it represents the likelihood that multiple experts make an (${\mathrm{e}}_{\mathrm{j}}$) evaluation of an evaluation object in terms of factor (${\mathrm{f}}_{\mathrm{i}}$) [11]. The membership vector is

$$R_i=(r_{i1},r_{i2}, \text{…},r_{im}),i=1,2, \text{…},n,{\displaystyle \sum }_{j=1}^m{r}_{ij}=1$$

(8)

The membership matrix is

$$R={\left(R_1,R_2,\dots ,R_n\right)}^T=\left(r_{ij}\right)$$

(9)

According to Equation (10), using this calculation method, the comprehensive evaluation vector (A) is computed.

$$A=W·R$$

(10)

where (W = $W_1,W_2$, … $W_n$) represents the weight distribution of each individual indicator, and after calculating it with the membership matrix (R) according to Equation (10), a fuzzy comprehensive evaluation of each indicator is obtained, resulting in the first-level fuzzy comprehensive evaluation model.

3.2.3. Second-Level Fuzzy Comprehensive Evaluation Model

The fuzzy comprehensive evaluation method for second-level and lower-level indicators is similar. First, an evaluation set for the second-level indicators is established. In this paper, the evaluation set for second-level berthing risk indicators is set to three levels or two levels, namely low, average, high or low, high, i.e., (E = ${\mathrm{e}}_{\mathrm{j}}$ = $\left({\mathrm{e}}_1,{\mathrm{e}}_2,{\mathrm{e}}_3\right)$ = {low, average, high}) or (E = ${\mathrm{e}}_{\mathrm{j}}$ = $\left({\mathrm{e}}_1,{\mathrm{e}}_2\right)$ = {low, high}).

Second, similar to the method for first-level indicators, the membership degree for second-level indicators is determined, which can be obtained based on the membership function.

The quantifiable secondary indicators in the fuzzy comprehensive evaluation model in this paper can obtain their membership degrees according to the membership function. For fuzzy indicators, experts need to judge the membership level of the evaluation object with respect to the corresponding evaluation indicators and, thereby, derive the frequency of the evaluation object’s level concerning the corresponding indicators. For example, for the secondary indicator ‘condition of berthing assistance equipment’ in this paper, experts can define the indicator based on the actual situation. If it is ‘there is some assistance equipment, but it is not adequate,’ referring to the relative assessment grade, they can determine that the risk level is ‘moderate.’ Below are the membership functions for each indicator.

3.3. Membership Functions of Each Indicator

3.3.1. Human Operational Factors

(1)

Safety operation compliance

The safety operation compliance is represented by levels of low, relatively low, average, relatively high, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 3. Safety operation compliance evaluation score.

Assessment		Definition
Level	Score
High	60	3 or more safety violations
Relatively high	65	3 safety violations
Average	75	2 safety violations
Relatively low	85	1 safety violation
Low	95	No safety violations

. The table data were obtained through the expert survey method.

(2)

Lateral distance upon approach

The lateral distance upon approach indicator is represented by (d). For small ships, the lateral distance is generally chosen to be 1.5 to 2.0 times the ship’s width, while for medium and large ships with tug assistance, it is typically set at 2.0 to 2.5 times the ship’s maximum width. The membership function ($\mathsf{\mu }\left(d\right)$) for the lateral distance upon approach is:

When small ships are berthing,

$$\mathsf{\mu }\left(d\right)=\left\{\begin{array}{c}{\mathrm{e}}^{-10{\left(d-1.5\mathrm{B}\right)}^2/\mathrm{k}}d<1.5\mathrm{B}\\ 100 1.5\mathrm{B}\le d\le 2\mathrm{B}\\ {\mathrm{e}}^{-10{\left(d-2\mathrm{B}\right)}^2/\mathrm{k}}d>2\mathrm{B}\end{array}\right.$$

(11)

When medium and large ships are berthing with tug assistance,

$$\mathsf{\mu }\left(d\right)=\left\{\begin{array}{c}{\mathrm{e}}^{-10{\left(d-2\mathrm{B}\right)}^2/\mathrm{k}}d<2\mathrm{B}\\ 100 2\mathrm{B}\le d\le 2.5\mathrm{B}\\ {\mathrm{e}}^{-10{\left(d-2.5\mathrm{B}\right)}^2/\mathrm{k}}d>2.5\mathrm{B}\end{array}\right.$$

(12)

When ultra-large ships are berthing with tug assistance,

$$\mathsf{\mu }\left(d\right)={\mathrm{e}}^{-10{\left(d-2.5\mathrm{B}\right)}^2/\mathrm{k}}$$

(13)

In the formula, (B) represents the ship’s maximum width, and (k) is the membership degree parameter.

(3)

Berthing angle

The berthing angle is represented by ($\beta$). During the berthing phase, the ship’s bow centerline should be parallel to the berth’s shoreline. The membership function ($\mathsf{\mu }\left(\beta \right)$) for the berthing angle is

$$\mathsf{\mu }\left(\beta \right)={\mathrm{e}}^{-{\left(\beta -{\mathsf{\beta }}_{\mathrm{s}}\right)}^2/\mathrm{k}},0\le \beta \le 90$$

(14)

In the formula, (k) is the membership degree parameter, and (${\mathsf{\beta }}_{\mathrm{s}}$) is the standard value for the berthing angle.

(4)

Distance to the berth

The distance to the berth is represented by ($S_a$), with a final distance of zero being considered optimal. The membership function ($\mathsf{\mu }\left(S_a\right)$) for the distance to the berth is:

$$\mathsf{\mu }\left(S_a\right)={\mathrm{e}}^{-{\left(S_a-{\mathrm{S}}_{\mathrm{as}}\right)}^2/\mathrm{k}}{S}_a\ge 0$$

(15)

In the formula, (${\mathrm{S}}_{\mathrm{as}}$) represents the standard value for the distance to the berth.

(5)

Distance to other ships

The distance to other ships is represented by ($S_b$). The distance to other vessels is an important factor in the assessment of berthing risks. If the distance is too close, collisions may occur, leading to danger or even disasters. The membership function ($\mathsf{\mu }\left(S_b\right)$) for the distance to other ships is:

$$\mathsf{\mu }\left(S_b\right)=\left\{\begin{array}{c}100,S_b\le 0.2\mathrm{L}\\ {\mathrm{e}}^{-{\left(S_b/0.2\mathrm{L}-1\right)}^2/\mathrm{k}},S_b>0.2\mathrm{L}\end{array}\right.$$

(16)

In the formula, (L) represents the ship’s l.o.a.

(6)

Braking distance

The braking distance is represented by ($D_0$), and it is usually taken as three to five times the ship’s length. ($\mathsf{\mu }\left(D_0\right)$) is the membership function for the braking distance.

$$\mathsf{\mu }\left(D_0\right)=\left\{\begin{array}{c}{\mathrm{e}}^{-{\left(D_0/\left(3\mathrm{L}\right)-1\right)}^2/\mathrm{k}}0<D_0<3\mathrm{L}\\ 100 3\mathrm{L}\le D_0\le 5\mathrm{L}\\ {\mathrm{e}}^{-{\left(D_0/\left(5\mathrm{L}\right)-1\right)}^2/\mathrm{k}}{D}_0>5\mathrm{L}\end{array}\right.$$

(17)

In the formula, (L) represents the ship’s l.o.a.

(7)

Approach angle

The approach angle is represented by ($\alpha$), and it is usually taken as 15° to 30°. ($\mathsf{\mu }\left(\alpha \right)$) is the membership function for the approach angle.

$$\mathsf{\mu }\left(\alpha \right)=\left\{\begin{array}{c}{\mathrm{e}}^{-{\left(\alpha -15\right)}^2/\mathrm{k}}0°<\alpha <15°\\ 100 15°\le \alpha \le 30°\\ {\mathrm{e}}^{-{\left(\alpha -30\right)}^2/\mathrm{k}}30°<\alpha \le 90°\end{array}\right.$$

(18)

(8)

Approach orientation

The approach orientation indicator is represented by low and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 4. Approach orientation evaluation score.

Assessment		Definition
Level	Score
High	60	Ship’s orientation is different from the planned orientation at berth
Low	90	The ship’s bearing is the same as the planned bearing at the time of berthing

.

3.3.2. Ship Factors

(1)

Age of the ship

The age of the ship indicator is represented by low, relatively low, average, relatively high, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 5. Age of the ship evaluation score.

Assessment		Definition
Level	Score
High	60	>20 years
Relatively high	65	16~20 years
Average	75	11~15 years
Relatively low	85	6~10 years
Low	95	0~5 years

.

(2)

Loading condition

The loading condition indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 6. Loading condition evaluation score.

Assessment		Definition
Level	Score
High	60	overloading $\ge$ 5%
Average	75	overloading < 5%
Low	90	no overloading

.

(3)

The condition of the mooring lines and mooring equipment

The condition of the mooring lines and mooring equipment indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 7. The condition of mooring lines and mooring equipment evaluation score.

Assessment		Definition
Level	Score
High	60	The mooring lines and mooring equipment lack maintenance, have insufficient strength, poor wear resistance, and show obvious signs of aging or wear
Average	75	The mooring lines and mooring equipment have undergone basic maintenance, with strength and wear resistance meeting minimum requirements, but there are slight signs of wear, indicating limited service life
Low	90	The mooring lines and mooring equipment are well-maintained, with high strength, excellent wear resistance, and long service life. The equipment shows no signs of wear or fatigue under various load and friction conditions

.

(4)

The condition of berthing assistance equipment

The condition of the berthing assistance equipment indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 8. The condition of berthing assistance equipment evaluation score.

Assessment		Definition
Level	Score
High	60	Lack of automated berthing assistance equipment or existing malfunctions
Average	75	There is some assistance equipment, but it is not fully adequate, and the level of ship automation is moderate
Low	90	All necessary berthing assistance equipment is complete, well-configured, and functioning properly, with a high level of automation, effectively supporting berthing operations

.

(5)

The condition of hull structure maintenance

The condition of the hull structure maintenance indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 9. The condition of hull structure maintenance evaluation score.

Assessment		Definition
Level	Score
High	60	The hull shows signs of corrosion, cracks, or deformation, which may affect berthing safety. Maintenance records are missing, or the hull has long lacked proper upkeep
Average	75	The hull has minor corrosion or localized cracks, but they have not affected the overall structural stability. Although maintenance records are incomplete, some repairs and maintenance have been carried out
Low	90	The hull structure is intact, with no significant corrosion, cracks, or deformation. Maintenance records are complete and compliant with regulations, and regular maintenance and inspections are conducted. The structure remains stable under external forces

.

3.3.3. Environmental Factors

(1)

Tides

The tides indicator is represented by low and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 10. Tides evaluation score.

Assessment		Definition
Level	Score
High	60	High tidal influence
Average	75	Light impact
Low	90	No tidal influence

.

(2)

Wind and waves

The wind and wave indicator is represented by low, relatively low, average, relatively high, and high to indicate the quality of the result. This paper selects the following five conditions for definition, with membership values assigned to each outcome. The details are shown in

Table 11. Wind and waves evaluation score.

Assessment		Definition
Level	Score
High	60	Force 6 wind, 3.0 kn current
Relatively high	65	Force 5 wind, 3.0 kn current
Average	75	Force 6 wind, 2.5 kn current
Relatively low	85	Force 5 wind, 2.5 kn current
Low	95	Winds below force 5, flow velocity less than 2.5 kn

.

(3)

Dock vessel traffic

The dock vessel traffic indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 12. Dock vessel traffic evaluation score.

Assessment		Definition
Level	Score
High	60	Higher concentration of ships
Average	75	General
Low	90	Ships are sparser

.

(4)

Navigable water width at the dock

The navigable water width at the dock indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 13. Navigable water width at the dock evaluation score.

Assessment		Definition
Level	Score
High	60	Less than 2 times the width of the ship
Average	75	Equal to 2 times the width of the ship
Low	90	Greater than 2 times the width of the ship

.

(5)

Navigable water depth at the dock

The navigable water depth at the dock indicator is represented by low, average, and high to indicate the quality of the result, with membership values assigned to each outcome. The details are shown in

Table 14. Navigable water depth at the dock evaluation score.

Assessment		Definition
Level	Score
High	60	draught < 5 m, abundance of water depth < 0.4 m 5 m $\le$ draught < 7 m, abundance of water depth < 0.5 m 7 m $\le$ draught < 9.7 m, abundance of water depth < 0.7 m 9.7 m $\le$ draught < 10.5 m, abundance of water depth < 0.8 m draught $\ge$ 10.5 m, abundance of water depth < 1 m
Average	75	draught < 5 m, abundance of water depth = 0.4 m 5 m $\le$ draught < 7 m, abundance of water depth = 0.5 m 7 m $\le$ draught < 9.7 m, abundance of water depth = 0.7 m 9.7 m $\le$ draught < 10.5 m, abundance of water depth = 0.8 m draught $\ge$ 10.5 m, abundance of water depth = 1 m
Low	90	draught < 5 m, abundance of water depth > 0.4 m 5 m $\le$ draught < 7 m, abundance of water depth > 0.5 m 7 m $\le$ draught < 9.7 m, abundance of water depth > 0.7 m 9.7 m $\le$ draught < 10.5 m, abundance of water depth > 0.8 m draught $\ge$ 10.5 m, abundance of water depth > 1 m

.

For indicators such as ‘the condition of mooring lines and mooring equipment’ and ‘the condition of berthing assistance equipment’ that cannot be quantified, expert evaluation methods can be used. Multiple experts are asked to judge the classification level of each evaluation object ($H_k$) regarding indicator ($N_i$), and the number of experts who agree that ($H_k$) belongs to classification level ($M_j$) for indicator ($N_i$) is recorded. This gives the frequency distribution of ($H_k$)’s classification level regarding ($N_i$). This frequency distribution can then be regarded as the membership distribution ($r_{i1}\left(k\right)$, $r_{i2}\left(k\right)$, dots, $r_{im}\left(k\right)$) of ($H_k$) regarding indicator ($N_i$) for classification level ($M_j$), where (i = 1, 2, dots, n).

3.4. Comprehensive Evaluation

The result of the fuzzy comprehensive evaluation of ship berthing risks is a set of real vectors, which can typically be evaluated based on the principle of maximum membership degree, thereby identifying the final attribute of the evaluated object. For the evaluation object in this paper, the calculation will proceed gradually from the second level to the highest level to obtain the final score. The comprehensive evaluation vector (membership degree) of the evaluated object is obtained by multiplying the weight set with the evaluation scores of each object. Taking second-level evaluation as an example, the second-level indicators are synthesized with their weights and membership degrees to form the membership degree of the first-level indicators.

When i = 1, the evaluation of ‘safety operation compliance,’ ‘lateral distance upon approach,’ ‘berthing angle,’ ‘distance to berth,’ ‘distance to other ships,’ ‘braking distance,’ ‘approach angle,’ and ‘approach orientation’ forms the comprehensive evaluation vector ($A_1$) for human operational factors, where ($W_1$) represents the weights of the second-level indicators for human operational factors [12,13].

Based on the principle of maximum membership degree, the result’s membership degree is determined. Following the above steps, the fuzzy comprehensive evaluation values are calculated sequentially, and the fuzzy comprehensive evaluation matrix for other indicators is constructed [14,15].

When i = 2, the evaluation of ‘age of the ship,’ ‘loading condition,’ ‘the condition of mooring lines and mooring equipment,’ ‘the condition of berthing assistance equipment,’ and ‘the condition of hull structure maintenance’ forms the comprehensive evaluation vector ($A_2$) for the ship factors, where ($W_2$) represents the weights of the second-level indicators for the ship factors.

When i = 3, the evaluation of ‘tides,’ ‘wind and waves,’ ‘dock vessel traffic,’ ‘navigable water width at the dock,’ and ‘navigable water depth at the dock’ forms the comprehensive evaluation vector ($A_3$) for external environmental factors, where ($W_3$) represents the weights of the second-level indicators for ship factors.

$$A_i=W_i·R_i(i=1,2,3)$$

(19)

Similarly, the first-level indicator evaluation is performed, constructing the fuzzy comprehensive evaluation matrix for the target level. This involves synthesizing the weights and membership degrees of the first-level indicators to form the comprehensive evaluation vector (A) for the target level. At this point, (W) represents the weights of the second-level indicators for ship factors.

$$A=W·\left(\begin{array}{c}{A}_1\\ A_2\\ A_3\end{array}\right)$$

(20)

4. Case Study

An evaluation of the actual berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field was conducted to verify the validity of the ship berthing risk assessment method proposed in this paper.

4.1. Test Plan

The case study involves two ships of different types. One is the KCS ship type, and the other is the S-175 large passenger ship type. The ship parameters are shown in

Table 15. Ship type parameter table.

Type	L.o.a (m)	Ship’s Breadth (m)	Draught (m)	Speed (Knot)	Design Displacement (m3)
KCS vessel type	230.0	32.2	10.0	24	51,020.0
S-175 large passenger vessel type	175.0	28.0	7.0	22	50,000.0

. The hulls are primarily made of steel and corrosion-resistant materials that are capable of withstanding complex marine environments and heavy load conditions. The ships are equipped with propulsion systems, such as propellers and rudder systems, as well as navigation systems including GPS, radar, and sonar, ensuring accurate real-time monitoring and data collection of the hull.

The experiment was conducted at the Qingdao Intelligent Ship Testing Field, selecting a berth that meets the experimental environment requirements for the berthing risk assessment test. To ensure the authenticity and reliability of the data collected during the experiment, sensors and instruments were calibrated before the test. The ship’s hull sailed from the sea towards the dock, and once it reached a distance of more than five times the ship’s length from the target berth, the ship berthing risk test commenced.

4.2. Weight Determination

The weight of the evaluation indicators is a crucial component in the assessment model. Weights reflect the relative importance of each indicator within the evaluation system, helping decision-makers better understand and utilize the evaluation results. To ensure the credibility and validity of the evaluation results, a group of 25 researchers was invited to form the evaluation team. Questionnaires were distributed to the experts, and the Delphi method was used to analyze the correlation of each indicator and calculate the weights. The first-level indicator scoring table is shown in

Table 16. First-level indicator scoring table.

Indicators	Human Operational Factors	Ship Factors	Environmental Factors	${\mathit{U}}_{\mathit{i}}$	${\mathit{V}}_{\mathit{i}}$	${\mathit{W}}_{\mathit{i}}$
Human operational factors	1	2	3	6	1.817	0.540
Ship factors	1/2	1	2	1	1	0.297
Environmental factors	1/3	1/2	1	1/6	0.550	0.163
$\sum$	$\sum =3.367$					1

.

As in this case study evaluating ship berthing risks, the first-level indicators are ‘human operational factors, ship factors, and environmental factors.’ Based on the first-level indicator scoring table (Table 16), the judgment matrix is obtained as follows:

$$\mathrm{B}=\left(\begin{array}{ccc}1& 2& 3\\ 1/2& 1& 2\\ 1/3& 1/2& 1\end{array}\right)$$

(21)

Using Equations (5) to (7), the following results can be obtained: ${\mathsf{\lambda }}_{\max }$ = 3.0092, CI = 0.00460, RI = 0.58, and CR = 0.0079 < 0.1. Thus, the above matrix (B) passes the consistency check, and the normalized weight vector corresponding to (${\mathsf{\lambda }}_{\max }$ = 3.0092) is (${\mathrm{W}}_1=\left(0.540,0.297,0.163\right)$). Similarly, using the above method, the second-level indicator weight vectors can be obtained. The ship berthing risk assessment weight table is shown in

Table 17. Ship Berthing Risk Assessment Weight Table.

Target Level	Level 1 Indicators	Weights	Level 2 Indicators	Weights
Ship berthing risk assessment	Human operational factors ${\mathrm{D}}_1$	0.540	Safety operation compliance ${\mathrm{D}}_{11}$	0.237
			Lateral distance upon approach ${\mathrm{D}}_{12}$	0.112
			Berthing angle ${\mathrm{D}}_{13}$	0.315
			Distance to the berth ${\mathrm{D}}_{14}$	0.039
			Distance to other ships ${\mathrm{D}}_{15}$	0.077
			Braking distance ${\mathrm{D}}_{16}$	0.172
			Approach angle ${\mathrm{D}}_{17}$	0.028
			Approach orientation ${\mathrm{D}}_{18}$	0.02
	Ship factors ${\mathrm{D}}_2$	0.297	Age of the ship ${\mathrm{D}}_{21}$	0.096
			Loading condition ${\mathrm{D}}_{22}$	0.162
			The condition of mooring lines and mooring equipment ${\mathrm{D}}_{23}$	0.432
			The condition of berthing assistance equipment ${\mathrm{D}}_{24}$	0.263
			The condition of hull structure maintenance ${\mathrm{D}}_{25}$	0.047
	Environmental factors ${\mathrm{D}}_3$	0.163	Tides ${\mathrm{D}}_{31}$	0.061
			Wind and waves ${\mathrm{D}}_{32}$	0.169
			Dock vessel traffic ${\mathrm{D}}_{33}$	0.284
			Navigable water width at the dock ${\mathrm{D}}_{34}$	0.079
			Navigable water depth at the dock ${\mathrm{D}}_{35}$	0.407

.

4.3. Fuzzy Factor Evaluation

In the ship berthing risk assessment model, due to the involvement of subjective judgment, uncertainty in natural conditions, and complex interactive environments for the five indicators, namely ’the condition of mooring lines and mooring equipment,’ ‘the condition of berthing assistance equipment,’ ‘the condition of hull structure maintenance,’ ‘tides,’ and ‘dock vessel traffic’, it is difficult to fully characterize them with precise data. Therefore, for these five second-level fuzzy indicators, after obtaining the indicator weights using the analytic hierarchy process (AHP), the fuzzy comprehensive evaluation (FCE) method is used to evaluate the ship berthing risk. Twenty-five experts were invited to judge the membership levels of these five evaluation objects based on the corresponding evaluation indicators, and the frequencies of the levels for these five evaluation objects were obtained accordingly.

Table 18. Comprehensive fuzzy indicator evaluation table for KCS ship type.

W	M
	Low (90)	Average (75)	High (60)
Ship factors
The condition of mooring lines and mooring equipment (0.432)	0.36	0.56	0.08
The condition of berthing assistance equipment (0.263)	0.64	0.32	0.04
The condition of hull structure maintenance (0.047)	0.32	0.60	0.08
Environmental factors
Tides (0.061)	0.80	0.20	0
Dock vessel traffic (0.284)	0.36	0.56	0.08

below shows the partial fuzzy indicator comprehensive evaluation table for the KCS ship type (where (M) represents the membership level score, (W) represents the indicator weight, and (R(k)) represents the relationship between the indicator and the level. The levels are divided into three categories: high, average, and low.

Then, using Equation (22), the comprehensive evaluation value ($V_k$) is calculated.

$$V_k={\displaystyle \sum }_{j=1}^n{M}_j·R_j\left(k\right)$$

(22)

Based on the comprehensive evaluation value ($V_k$), the specific evaluation score can be obtained by referring to the evaluation score table. Similarly, a partial fuzzy indicator comprehensive evaluation can be conducted for the S-175 ship type.

4.4. Test Results

The test data from the two ships mentioned above were selected for validation. The test data collected include lateral distance upon approach, distance to berth, distance to other ships, braking distance, approach angle, and approach orientation, among other data, with other necessary data calculable. Based on the constructed membership functions and evaluation scores for each indicator, the membership degrees and evaluation scores were obtained for each indicator, completing the ship berthing risk assessment. The assessment results are shown in

Table 19. Assessment results table.

Level 2 Indicators	Results of the Assessment of KCS-Type Ships		Results of the Assessment of the S-175 Ships
	Score	Risk Level	Score	Risk Level
Safety operation compliance (0.237)	85	relatively low	95	low
Lateral distance upon approach (0.112)	90.19	—	88.17	—
Berthing angle (0.315)	98.34	—	97.14	—
Distance to the berth (0.039)	96.24	—	98.15	—
Distance to other ships (0.077)	98.97	—	96.54	—
Braking distance (0.172)	97.44	—	95.13	—
Approach angle (0.028)	99.89	—	99.76	—
Approach orientation (0.02)	90	low	90	low
Age of the ship (0.096)	85	relatively low	95	low
Loading condition (0.162)	90	low	90	low
The condition of mooring lines and mooring equipment (0.432)	79.2	average	87	low
The condition of berthing assistance equipment (0.263)	84	low	79.2	average
The condition of hull structure maintenance (0.047)	78.6	average	79.2	average
Tides (0.061)	87	low	84	low
Wind and waves (0.169)	75	average	85	relatively low
Dock vessel traffic (0.284)	79.2	average	78.6	average
Navigable water width at the dock (0.079)	75	average	75	average
Navigable water depth at the dock (0.407)	75	average	75	average
Synthesized assessment	87.86		89.66

.

For the KCS ship type test, the comprehensive evaluation score was 87.86, determining that the ship berthing risk is at a ‘relatively low’ risk level. For the S-175 ship type test, the comprehensive evaluation score was 89.66, also determining that the ship berthing risk is at a ‘relatively low’ risk level. The evaluation results are consistent with the findings from expert surveys and interviews.

To validate the effectiveness of the method proposed in this paper, a hypothetical experiment using a KCS-type ship is conducted. In order to form a clear contrast between the results of the hypothetical experiment and the actual experiment, the weights of each indicator in the hypothetical experiment are kept consistent with those in the aforementioned experiment. Only the experimental data for the indicators ‘safety operation compliance’ and ‘approach angle’ are modified to reflect high-risk conditions (the original data can be found in the Supplementary Materials). After the modification, the membership levels of these two evaluation indicators are reassessed, and the frequency of the evaluation object’s level concerning these indicators, as well as the comprehensive evaluation results, are obtained accordingly. The results of the hypothetical experiment are shown in

Table 20. Assessment results table (high risk level).

Level 2 Indicators	Results of the Assessment of KCS-Type Ships		Evaluation Results of the Hypothetical Experiment for KCS-Type Ship
	Score	Risk Level	Score	Risk Level
Safety operation compliance (0.237)	85	relatively low	65	relatively high
Lateral distance upon approach (0.112)	90.19	—	90.19	—
Berthing angle (0.315)	98.34	—	64.17	—
Distance to the berth (0.039)	96.24	—	96.24	—
Distance to other ships (0.077)	98.97	—	98.97	—
Braking distance (0.172)	97.44	—	97.44	—
Approach angle (0.028)	99.89	—	99.89	—
Approach orientation (0.02)	90	low	90	low
Age of the ship (0.096)	85	relatively low	85	relatively low
Loading condition (0.162)	90	low	90	low
The condition of mooring lines and mooring equipment (0.432)	79.2	average	79.2	average
The condition of berthing assistance equipment (0.263)	84	low	84	low
The condition of hull structure maintenance (0.047)	78.6	average	78.6	average
Tides (0.061)	87	low	87	low
Wind and waves (0.169)	75	average	75	average
Dock vessel traffic (0.284)	79.2	average	79.2	average
Navigable water width at the dock (0.079)	75	average	75	average
Navigable water depth at the dock (0.407)	75	average	75	average
Synthesized assessment	87.86		79.47

.

Explanation: In the hypothetical experiments of KCS-Type ship, the experimental data collected for the indicators of “safe operation compliance” and “berthing angle” were modified, and the results obtained after the modification were 65 and 64.17, respectively. The risk level of “safe operation compliance” is “relatively high”, and the overall evaluation result is 79.47.

It can be observed that, in the hypothetical experiment for the KCS-type ship, the comprehensive evaluation score is 79.47, determining that the ship’s berthing risk is at a ‘moderate’ risk level, having risen by one risk level. This proves that the modifications to the ‘safety operation compliance’ and ‘approach angle’ indicators have a significant impact on the evaluation results. It also reflects that, during the process of determining the weights using the AHP method, experts have subjectively considered the ‘safety operation compliance’ and ‘approach angle’ indicators to be the most important factors influencing ship berthing. Therefore, the method proposed in this paper can objectively reflect the key factors influencing berthing risk, verifying the rationality of the ‘human-ship-environment’ integrated berthing risk evaluation indicator system. It can also reflect the risk level of the berthing process based on actual data from the operation, contributing to reducing berthing risks and improving safety for port and ship managers.

5. Conclusions

This paper thoroughly discusses the three key factors influencing ship berthing, namely human, ship, and environment. Through the study and analysis of these factors, a berthing risk assessment system based on the ‘Human-Ship-Environment’ synergy was established. First, through the decoupled analysis of human, ship, and environmental factors, key berthing influence factors were identified, and a ‘Human-Ship-Environment’ synergy-based ship berthing risk assessment index system was established.

Second, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) were combined to assess ship berthing risks, addressing the issues of ambiguity and uncertainty in the evaluation of various indicators. Finally, an evaluation experiment was conducted on a berthing operation involving two ships, namely a KCS-type ship and an S-175 large passenger ship, at the Qingdao Intelligent Ship Testing Field. A comparison was also made with the hypothetical experiment for the KCS-type ship, verifying the rationality of the ship berthing risk evaluation method proposed in this paper.

The research results indicate that human operational factors and their second-level indicators, namely ‘berthing angle’ and ‘safety operation compliance,’ have a relatively large weight. These should be prioritized during actual operations to reduce ship berthing risks.