The 12th international seminar of Port-city Universities LeagueThe 13 th International Conference of...

Port-city Universities League

The 13th International Seminar of Port-city Universities League “Green Port Development：Innovation and Practice”

Proceedings

ISSN 2434-4133

Shandong University, Qingdao, China October 15-16, 2019

Table of Contents

Shengchuan Zhao, Vanduy Tran, Dalian University of TechnologyA Study on Public’s Acceptance of Autonomous Vehicles: the Case of China

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Suresh, P.K, Sakthivel. S, Sundaravadivelu, R., Yerrangunta Abhilash,Shyamala, Ramanamurthy M.V, Indian Institute of Technology MadrasModeling and Monitoring of Groin Field Along West Coast of India

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Ayman Alghanmi, King Abdulaziz UniversityApplication of a Hybrid Decision-Making Approach to Enhance the OperationalSafety in Petroleum Transportation Systems

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Han Liu, Ning Ma, Xiechong Gu, Shanghai Jiao Tong UniversityEvaluation of the Controllability of a Ship Navigating in a Port Channel Based onHydrodynamic Force Analysis

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Mustarakh Gelfi, Hendra Achiari, Rahayau Sulistyorini, Ofyar Z. Tamin,Institut Teknologi SumateraScenario-Based Planning for Long-Term Masterplanning of Ports

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Youhei Takagi, Yasumi Kawamura, Takanori Hino, Yokohama NationalUniversityNumerical Analysis of Single Oil Tank Behavior Under Tsunami Inundation byUsing Fluid Structure Interaction

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Masuda Hiroyuki, Yokohama National UniversityMarine Environment Impact Assessment for Blue Economy

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Tai K, Tran, Tri M.L. Pham, Dai H. Nguyen, Phuoc. T.N. Le, Truong V. Vo,Hiep Hoang, Dat Q.H. Quang, Portcoast Consultant Corporation, VietnamThu A. Nguyen, Sy T. Do, Ho Chi Minh City University of TechnologyThe Application of 3D Laser-Scanning Technique for Developing As- Built BuildingInformation Modeling

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Dongwoo Jang, Gyewoon Choi, Youngsam Hwang, Incheon National University Suggestion of Optimal Water Circulation Systems in Songdo Waterfront Canal

Jang K. Kim, Jae-Sung Rhee, Youn-Jung Kim, Chang-Bum Jeong, Il-Nam Kim, Incheon National UniversityStable Carbon and Nitrogen Isotopic Characterization and Tracing Nutrient Sources of Ulva Blooms Around Jeju Coastal Areas

Ranganathan Sundaravadivelu, Barapatre Rohan Prakash, Dinesh Ganapathy, Phani Kumar SVS, Ramana Murthy MV, Indian Institute of Technology MadrasHydrodynamic Response of Cold Water Steel and HDPE Pipeline for Desalination Plant

Yerrangunta Abhilash, Sakthivel Sundaravadivelu, Suresh, P.K, Sundaravadivelu Ranganathan, Indian Institute of Technology Madras Numerical Study of Groin Field at Mandaikadu on the West Coast of India

Sevil Deniz Yakan, Istanbul Technical UniversityObserving the Water Quality in the Vicinity of Green Ports in Turkey

Qinghu Wang, Chonglei Wang, Jiameng Wu, Deyu Wang, Shanghai Jiao Tong UniversityTorsional Failure Characteristics of the Hull Girder with Large Deck Openings

Ichiro Araki, Yokohama National UniversityIntellectual Property and Trade-How will the China-U.S. “Trade War” affect the regime?

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The 13th International Conference of Port-city Universities League, PUL 2019/Oct. 15-16, 2019 / Shandong University

A Study on Public’s Acceptance of Autonomous Vehicles: the case of China

Shengchuan Zhao 1, Vanduy Tran 2 1,2 School of transportation and logistics, Dalian University of Technology

No.2 Linggong Road, Ganjingzi District, Dalian City, Liaoning Province, P.R.C., 116024

E-mail: 1 [email protected]; 2 [email protected]

Abstract: This study seeks to find the factors affecting the willingness to purchase emerging Autonomous

vehicles (AVs) in China. A Stated Preference (SP) survey was carried out to collect the data. The Mixed

Logit model was built to model the purchasing behavior toward AVs of individuals. The results showed

that AVs’ lanes and government subsidy would encourage the individuals’ purchasing decision. The price

also had a strong effect on the intention to purchase AVs. Several social-economic characteristics such as

age, income, car ownership, and driving experience have significantly effected on purchasing behavior of

AVs. We found that a majority of respondents who had experience with a traffic accident in the past years

are more likely to buy the AVs. Furthermore, the Mixed Logit model confirmed the existence of

unobserved heterogeneous across respondents in their response to market penetration and government

subsidy. Findings from this study can be useful to government, developers, and other stakeholders to

prepare for the launching and deployment of AVs in the future.

Keywords: Autonomous Vehicles (AVs), Public’s Acceptance, SP survey, Mixed Logit Model, China

Introduction The rapid urbanization has brought China many challenges, such as traffic congestion,

traffic safety, environmental pollution, and energy shortage. Reducing these negative consequences have become frequent demands of the whole society. The previous studies on Autonomous Vehicles (AVs) have stated that the promotion and application of AVs would reduce traffic accidents, improve road capacity, and reduce environmental pollution [1-3]. AVs or self-driving vehicles are regarded as the inevitable trend of the future of the

automobile industry and intelligent transportation system. In recent years, countries around the world have considered AVs as an essential strategy for future direction. On October 4, 2018, the United States issued the "Automated Vehicle 3.0: Preparing for the Future of Transportation 3.0" [4], which provides policy guarantee for the development of AVs. In May 2018, the European Commission clearly stated that the AVs would be produced and appeared on the expressway by 2030 [5]. At the same time, the Japanese government also released guidance to carry out superior design and policy support for the development of AVs. The Chinese government has also attached great importance to the industrialization of

AVs and has promoted the development of AVs strategic to the national level. In April

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2017, the Ministry of Industry and Information Technology, the National Development and Reform Commission, and the Ministry of Science and Technology jointly issued the medium-and long-term development plan for the car industry. The plan is meant to push forward the government’s strategy for increasing key technologies for intelligent networked vehicles and carry out demonstration and promotion of intelligent network vehicles. Mainly, the rate of new cars system (included: DA_driving assistance, PA_partial automation, and AC_conditional automation) will exceed 50% in 2020. Of these, the assembly rate of the networked driving assistant system reaches 10%, meeting the requirement of intelligent transportation city construction. By 2025, the assembly rate of DA, PA, and CA new cars will reach 80%. In January 2018, the “Autonomous Vehicle Readiness Index” released by KPMG argued that the maturity index of AVs includes four aspects: policy and legislation, technology and innovation, infrastructure, and social acceptance [6]. The popularization and application of AVs depend not only on their technical maturity,

but also on the public acceptance, and also on policies and legal system. However, there is only a few research that formulates the policy of promoting the application of AVs in China. AVs is an emerging product, understand the public acceptance would lead to a successful implementation. Therefore, this study was conducted to a better understanding of the public acceptance of AVs. More specifically, this study aims to investigate the factors affecting the customers’ intention to buy AVs by using the mixed logit model; and to formulate the policies needed to promote AVs in China.

Literature Review Previous studies have attempted to examine the advantages of AVs within the social

aspect. For example, the application of new technology can reduce some of the costs associated with driving, such as gasoline costs and insurance costs [7]. The AVs also recognized as that can reduce the number of crashes [8]. Other advantages of AVs include environmental friendliness, time savings, increased mobility, and comfort [9-11]. AVs’ preference is mostly predicted base on the stated preference survey (SP). Several

related factors have been demonstrated that effect on individual willingness to use AVs, such as trust, cost, travel time, and waiting time [12, 13]. For example, Shabanpour et al. [14] used the SP surveys to investigate the acceptance of Chicago citizens to AVs. Theresults showed that the respondents were more sensitive to the price of AVs and theincentive policy (e.g., the identification of accident liability). Zhang Junyi et al., [15]have conducted a study in Japan to investigate the willingness to purchase AVs. Byusing the mixed logit model, the factors influencing willingness to purchase L3, L4, andL5 vehicle were investigated. The results found that respondents who had experiencedtraffic accidents, and those who travel much longer were more interesting in AVs.Furthermore, Bansal et al., [2] found the character of potential customers of AVs,

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including high-income, men who are concerned about new technology, and people who have experienced with traffic accidents. In the other aspect, the public acceptance of AVs has been determined base on the

psychological factors and technology acceptance model (TAM). For example, Choi et al. [16] predicted the intention to use AVs by using TAM model. The results showed thatperceived usefulness and trust in technology are the critical factors for determining theintention of AVs. Panagiotopoulos et al. [17] also found that perceived usefulness,perceived ease of use, perceived trust, and social interaction all affected the intention touse AVs.

Overall, the literature indicates a small number of studies on AVs acceptance, and only a few publications have used the discrete choice model to model various aspects of AVs. Furthermore, the previous studies have not considered the influence of the AVs lane and the government subsidy on the purchase intention of customers. Therefore, in this study, the SP survey method and discrete choice theory were used to investigate the AVs acceptance. The factors affecting the public buying AVs were thoroughly analyzed.

Methodology Survey design Given that the autonomous vehicles are still in the testing stage and has not entered the

market, this study used the Stated Preference (SP) survey method to collect the data. Besides, to reduce the correlation between the AVs’ attribute variables in the questionnaire, this study used the orthogonal design method to design the scenario for the assumption part. The present study aim at understanding the public's acceptance and investigating the

factors that affect on customer towards AVs purchase. The questionnaire is divided into three-part: (1) Basic information: The necessary personal information considered in this study

included age, gender, occupational type, education background, driving experience, commuting mode, and traffic accidents experience. (2) Awareness and acceptance of AVs: This part asks respondents about their prospects,

expected social benefits, and willingness to use of AVs. Since AVs are a new concept, most of the respondents may not be aware of the definition of AVs at all levels. Therefore, a short video about AVs was also inserted into the questionnaire to defining all levels of the automobile in this study. (3) Stated preference exercise: This part is the essential part of the questionnaire, and

also the core part of the SP survey method. In this section, the participants would be presented three choice sets, each choice set describing several attributes of AVs. Four alternatives were considered in each scenario of choice set (see figure 1), including L0 (conventional car), L3 (conditional AV), L4 (highly AV), and L5 (fully AV) as defined in [18]. The AVs attributes include the setting rate of the particular lane for the AVs, the

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market penetration, the price of vehicles, and the government's subsidy for AVs. The attribute of carsharing and their levels are described in table 1.

Table 1. Specific variables of AVs and level value L3 L4 L5

Lane (%) 0, 10, 20 20, 30, 40 40, 50, 60

Market Penetration (%) 20, 30, 40 10, 20, 30 5, 10, 20

Price (10,000 RMB) 25, 27, 29 27, 30, 33 30, 35, 40

Subsidy（10,000 RMB） 1, 2, 3 2, 3.5, 5 3, 6, 9

Figure 1. Example of a choice task Model Specification As a common type of statistical model for the discrete choice situation, the

multinomial logit (MNL) model is a robust statistical tool to represent choice behavior. It predicts the systematic taste variation that is related to several observed characteristics of the decision-maker. However, the disadvantage of the MNL model is that the model is estimated under the assumption of the IIA problem. To avoid IIA experts and scholars forward to use mixed logit models, which examine the random variables in the utility function. The random variables would assume following several distributions such as normal, log-normal, and uniform. The mixed logit model takes into account the heterogeneity between consumers (decision-makers), and can also deal with the correlation between alternatives and panel data (in repeated selection scenarios, the same individual investigates the "scenario correlation" of selection time; it is usually assumed that a decision maker's preferences in multiple choices). Also, McFadden et al. [19] argued that the mixed logit model could initially estimate any model based on theutility maximization hypothesis.The utility function of the mixed logit model as following:

𝑈"# = 𝑉"# + 𝜂"# + Ɛ"# (1)

Where, 𝑉"# = 𝛽𝑋"#; β = the vectors of coefficients;𝑋"# = the vector of observed

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variables. The probability conditional in η of decision-maker n choosing alternative i is:

𝑃"#|𝑋, 𝜂 =/01(34567456Ɛ45)

34967496Ɛ4999:;

(2)

Where j = total number of alternatives. The unconditional probability of individual n choosing alternative i is the integral of the Equation (2):

𝑃"#|𝑋, = (𝑃"#|𝑋,7 𝜂)𝑓(𝜂)𝑑𝜂 (3)

Where 𝑓(𝜂) = probability density function of𝜂. The simulated likelihood estimation technique is adopted to solve Equation (3).

Data In order to improve the reliability and validity of the survey data, the survey process is

divided into two stages: pilot and formal survey. It should be noted that in order to avoid the possible errors in statistical questionnaires, offline surveys are conducted using an application on iPad. The pilot survey was conducted from March 11, 2019, to March 12, 2019, to test the rationality of the questionnaire and seek suggestions for improvement. In the first stage, 68 questionnaires were collected, 31 valid questionnaires, and 37 invalid questionnaires. The effective rate of the questionnaires was 46.27%. The formal survey was conducted from March 13, 2019, to April 6, 2019, a total of 819 questionnaires were distributed, obtained 592 valid questionnaires and 227 invalid questionnaires. The validity rate of the questionnaires was 72.28%. The respondents in this study were distributed in all provinces and cities of China, but there were no respondents from the Xinjiang, Tibet, Hong Kong, Macao, and Taiwan. The regional distribution of sample data is shown in Figure 2. Table 2 represents the socio-demographic characteristics of the sample. The percentage

of men was higher than women (54.05% vs. 45.95%). The age of respondents was mainly between 18 and 49 years old. Respondents’ education level of the sample was relatively high, 44.76% of the respondents with bachelor's degree, 22.30% of the respondents below bachelor's degree, and 32.94% of the respondents with master's degree or above. The monthly income level of the respondents was mainly below 20,000 yuan. The middle-income between 5000-10000 yuan accounted for 38.01% of the respondents. Among the respondents, 61.49 had own one car, 30.74% had no private car, and 7.77% had two or more cars. The proportion of respondents using public transport (bus, subway, etc) for daily commuting was higher than the proportion of respondents using a private car (46.47% vs 34.46%).

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Table 2. The statistical table of individual-attribute Attributes Category Percentage of Population

Gender Male 54.05%

Female 45.95%

Age 18-29 years 48.82%

30-49 years 32.43%

50-64 years 18.75%

Education level High school and under 22.30%

Undergraduate and above 77.70%

Income ≤5,000 RMB 33.45%

5,000 – 10,000 RMB 38.01%

10,000 – 20,000 RMB 19.93%

> 20,000 RMB 8.62%

Car ownership None 30.74%

1 61.49%

≥ 2 7.77%

Commuting mode Private car 34.46%

Public transportation 65.54%

Traffic accident experience No 71.28%

Yes 28.72%

Are you a professional

driver?

No 89.02%

Yes 10.98%

Do you like driving? No 47.47%

Yes 52.53%

Driving license None 15.54%

0-1 year 15.37%

1-5 years 46.96%

5 years 22.13%

Occupation Full-time 57.09%

Freelancer 10.98%

Student 27.70%

Retire 0.84%

Other 3.38%

Do you care about new

technologies and new

products?

No 27.36%

Yes 72.64%

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Figure 2. Regional distribution map of sample data

Model estimation results The result of the model (Table 3) shows that the coefficient associated with the rate of

the particular lane for AVs was positively significant, which means that the increase in the number of the lane for AVs would help to enhance the purchase intention of the respondents. The market penetration has also positively impact on intention to buy AVs of respondents. This shows that when respondents consider buying an AV, they must be blindly following the crowd. Moreover, the standard deviation of the variable is significant, indicating that this variable has heterogeneity on the willingness of respondents to purchase an AV. The coefficient of the parameter associated with the price was negative and significant. This result implied that the AVs manufacturer should reduce the manufacturing cost as far as possible under the premise of guaranteeing the performance. Otherwise, it is not conducive to the popularization and application of the automatic driving vehicle. As expected, the average value of government subsidies was significant and positive. At the same time, the standard deviation of the variable was also significant. This indicates that the government could give some subsidies to the respondents when they are expected to buy an AV. Regarding the individuals’ socio-economic characteristics, the results showed that

respondents with middle-older ages were more willing to purchase an AV than those aged between 18 and 29. The coefficient associated with education was negatively significant for only alternative L3, indicating that highly educated people are unlikely to use AVs. The variable associated with driving experience also affected on AV purchasing intention. The result showed that senior drivers (driving for more than five years) are unwilling to purchase AV. Furthermore, respondents who own car were also

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reluctant on willingness to buy the AVs. Respondents who used private cars for long trips were more willing to purchase L5 AV.

Also, respondents who had experienced with traffic accidents in the past tended to buy AVs. The results of parameter estimation showed that the respondents who did not observe the road conditions on their own were unwilling to purchase AVs. This indicates that the acceptance of driving cars will affect the intention to purchase AVs.

Table 3. The result of model estimation

Note: Significance: *** = p < 0.001; ** = p < 0.01; * = p < 0.05

Variables L3 L4 L5

Coef. (t-value) Coef. (t-value) Coef. (t-value) Constant 1.64 (1.92*) 0.63 (0.62) -2.60 (-1.24)Age groups (reference 18-29 years)

30- 49 years

2.22 (5.49***) 1.55 (3.55***) 1.44 (3.07***) 50-64 years

5.10 (4.65***) 4.47 (3.98***) 3.85 (3.37***)

Education 1= Undergraduate or above, 0 =Otherwise -0.75 (-1.72

*) -0.46 (-0.98) -0.81 (-1.62)

Income ( reference = 20000 RMB -0.03 (-0.04) 1.07 (1.69) 1.72 (2.51)

Driving License 0 - 1 year -0.38 (-0.88) -0.45 (-0.94) -0.75 (-1.44)1 – 5 years -0.00 (-0.00) 0.04 (0.10) 0.08 (0.17)> 5 years -1.31 (-2.59***) -1.80 (-3.15***) -2.07 (-3.42***)Driving likelihood

1= likely, 0 = Otherwise 0.05 (0.16) -0.42 (-1.40) -0.78 (-2.37**)

Car ownership 1= Owning at least one car, 0 = Otherwise -1.42 (-4.02

***) -1.29 (-3.43***) -2.12 (-5.26***)

Long trip with private car 1= yes, 0 = no 0.28 (0.79) 0.52 (1.34) 0.70 (1.71

*)

Traffic accident experience 1= Yes, 0 = No 0.72 (2.02

**) 0.86 (2.32**) 1.22 (3.16***)

Willingness to use AV (reference = unlikely)

Use as a public transport mode 2.96 (4.78***) 3.85 (5.37***) 7.88 (3.95***) Rental 4.34 (6.71***) 5.28 (7.08***) 9.53 (4.72***) Purchase 5.99 (8.06***) 7.41 (8.67***) 12.32 (5.92***) Observing road condition when using L5 (reference = no) Occasional -0.12 (-0.34) -0.37 (-1.01) -0.80 (-2.10**)Always -0.54 (-1.32) -0.65 (-1.49) -1.62 (-3.34***)Lane 2.01 (3.74***) Market penetration 2.38 (4.14***) Std. (Market penetration) 5.62 (11.39***) Price -0.16 (-10.33***)Government Subsidy 0.15 (4.80***)Std. ( Government Subsidy) 0.42 (8.85***)Observation : 1776 McFadden𝜌? = 0.2087

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Conclusions The promotion and application of AVs are expected to alleviate urban traffic

congestion and reduce traffic accidents. To make these objectives more scientific and reasonable, based on the results of this study, the author provides several suggestions for the AV’s manufacturers and the relevant government departments. (1) Formulate relevant laws and regulations for automatic driving vehicles are essential

keys for implementation AVs. According to the research results, respondents were more concerned about the identification standard of traffic accident liability for AVs. For different types of AV, respondents believe that the responsibility for traffic accidents is different. Therefore, different types of AV should be formulated separately for corresponding traffic accident liability standards. Furthermore, the finding of this study shows that the majority of respondents believe that driving an AV also requires a driver's license. Therefore, additional training courses are necessary to include in the driving qualification system.

(2) Setting up the special lanes for AVs to increase the willingness to use of potentialcustomers. (3) Reasonable price is another key factor to increase the AVs acceptance. Since AVs is

a highly technological application, the manufacturing cost may be quite high. The high price will definitely reduce the consumers' purchase intention. Therefore, the AVs manufacturer should reduce the manufacturing cost and reduce the selling price as far as possible under the premise of guaranteeing the performance of the AVs. Meanwhile, the government should also formulate laws and regulations to improve the sales supervision system of AVs.

(4) The government should formulate a reasonable subsidy standard for AVs toencourage the development of AVs system. The research results have shown that government subsidy had a strong impact on the willingness to purchase AVs. Therefore, a reasonable subsidy giving to consumers would speed up AVs popularization and application. Furthermore, the result showed that car owners are more likely to buy AVs than those without a private car. Therefore, the sale of AVs can be considered as a replacement for the conventional cars in the future. (5) AVs should be considered as public transport and sharing service in the first stage

adoption. This study found that respondents had a herd mentality when considering whether to purchase AVs. Therefore, the AVs will first be used as public transport and shared vehicles, which will help to increase the market share of AVs, thereby affecting consumers' purchase intention. (6) AVs’ manufacturers can organize the testing and training activities to enable

consumers to experience the convenience of automatic driving and the stability of driving skills. Increasing knowledge about AVs can enhance the purchasing intention of customers to AVs.

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Modeling and Monitoring of Groin Field Along West Coast of India

Suresh, P.K1 , Sakthivel.S1, Sundaravadivelu, R 1, Yerrangunta Abhilash1, Shyamala,D 1and Ramanamurthy, M.V 2

1 Department of Ocean Engineering IIT Madras, Chennai, 600036, India,

[email protected],[email protected]

2 National Institute of Ocean Technology, Chennai

Abstract: The coastline of Tamilnadu along west coast of India bordering the Arabian Sea is a thickly populated one. The south west monsoon wave action is severe along the coast creating heavy erosion

resulting in the loss of valuable lands, roads, worship places, and houses. Mandaikadu (80 23’ N, 770 32’E)

is one such affected coast located on the west coast of India. The place is famous as a pilgrimage centre. As a part of remedial measures, a Groin field is being constructed for arresting erosion. Under these

circumstances, the numerical study of the groin field becomes very important. The coast is sensitive to cross

shore and alongshore sediment transport. The bathymetry is modelled and near shore wave climate is obtained using model MIKE21. The cross-shore profile of the beach is predicted using LITPROF and the

shoreline evolution was predicted using LITLINE. The historical shoreline changes were evaluated using

imageries. The post project monitoring indicates that the beach formations were in match with predictions. The details and methodology are highlighted.

Keywords: coast; monsoon; wave; numerical; remedial; monitoring

Introduction The west coast of Tamilnadu bordering the Arabian Sea is a thickly populated one. Fishing is the main occupation of the hamlets. The coast experiences two monsoons namely southwest (SW) from June-September and northeast (NE) from October –December. The SW monsoon is severe along the coast creating heavy erosion resulting in the loss of valuable lands, roads, worship places and houses. Mandaikadu is one such affected coast on the west coast of India. The place is a famous piligimage centre.As a part of remedial measures, a Groin field is being constructed for arresting erosion. The present study deals with the effects of groins on the evolution of shoreline by adopting the suitable numerical models. The wave climate was assessed and wave transformation studies were carried out using Mike21 model. Details of protection measures The protection measures consists of a groin field with five groins. The commencement of the groin field is from the coast adjacent to Church. It consists of six groins G1,G2,G3,G4,G5 & G6 of length 65m,81m,90m,60m,63m & 60m respectively . The corresponding depth at which the above groins terminate are (-)2.0m, (-)3.0m, (-)3.0m, (-)2.0m, (-)2.0m and (-)2.0m. The spacing between the groins G1 to G6 are 100m, 150m,

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150m, 150m & 100m respectively with with a “T” head of 50m .The “T” head will be constructed after completing the groin field. A Groin field consisting of six groins of varying lengths (Table 1) and the proposed layout (Fig. 2) are given below. Shorter length groins were proposed as per the site conditions and bathymetry

Fig 1 Study area

Table 1 Groin details

Sl no Groyne Length (m) 1 G1 65 2 G2 81 3 G3 90 4 G4 60 5 G5 63 6 G6 60

Fig 2 Proposed groin field Wave climate

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A quantitative understanding of wave characteristics in the near shore is essential for the estimation of sediment transport and morphological changes along the coastal areas. Unfortunately measured or visually observed wave data is available only for locations of port. Hence, numerical models were resorted to for the simulation of wave climate. In the present study, the wave data is adopted from the wave climate generated (Suresh 2010) by numerical models. The two wave models that were adopted are Offshore Spectral Wave (OSW) or WAM model and Near Spectral Wave (NSW) models of MIKE21 developed by Danish Hydraulic Institute (DHI-2001), Denmark. The coast is influenced waves from south. Waves are predominantly from southeast, south and south west. The monthly average wave climate is as described in Suresh (2010) presented Rose diagram of wave climate is prepared for three seasons namely non monsoon NM(January-May), south west monsoon SW (June-September) and north east monsoon NE(October-December). The diagrams are furnished vide fig 3.

Fig 3 Wave climate

Parabolic Mild Slope (PMS) model

MIKE 21 PMS is linear refraction-diffraction model based on a parabolic approximation to the elliptic mild slope equation. The model takes into account the effect of refraction and shoaling due to the varying depth, diffraction along the perpendicular to predominant wave breaking. The model also takes into account the effect of frequency and directional spreading using linear superposition. The basic output data from the model are integral wave parameters such as the root mean square wave height, the peak wave period and the mean wave direction. Other output data that can be obtained from the model are radiation stresses and instantaneous surface elevations. The near shore bathymetry which was surveyed by public works department was digitized and discretized into 5 m by 5 m grid. The bathymetry data adopted for Mandaikadu coast was shown in Fig.4. The wave climate was applied at north-south boundaries of model. The solution parameters used was minimax model and the friction factor is specified using

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manning roughness of 0.002. The coast is mostly subjected to high energy waves during south-west monsoon. The near shore wave climate is obtained and is analysed for the changes in the wave climate in the vicinity of the groins. Later the groins were fitted with T head shown in the Fig.4. This T head is to minimise the onshore-offshore sediment transport. The PMS model is run with the bathymetry with T-groins also. The changes in the wave climate in the three models were analysed. Initially straight groins will be constructed in first phase and in the second phase the “T” head will be provided. The critical wave climate was observed from May to October.

Fig 4 Bathymetry with proposed “T” Groin

. It was seen that the wave height and wave energy were reduced in the lee-side due to the presence of groins. The wave height and wave energy due to the presence of T-head was found reduced. The changes in the wave heights were shown in Table 2

Table 2 Changes in Wave Height due to Groins

Month Without Groin (m) With T-Groin (m)

May 0.89 0.21

June 0.82 0.13

July 0.89 0.18

August 0.91 0.24

September 0.89 0.20

October 0.93 0.10

Modeling of shoreline changes

The beaches along the west coast experiences cross shore sediment transport during the

14

south west monsoon from June to September. The high monsoon waves will create a eroding profile and in the later part of monsoon the swell waves will reform the eroded profile resulting in the formation of berm profile. The LITPROF model of DHI (2001) was adopted and based on the wave data prediction of cross shore transport was made. It was observed that the wave direction during SW monsoons is normal to the coastline. As a result of this there was onshore-offshore sediment transport. So the profile changes were predicted during SW monsoons. It was seen that there would be rapid erosion of about 30 m, shown in Fig.5, during the SW monsoons the swell waves would cause accretion of about 20 m shown in Fig.5.3.

Fig 5 LITPROF results

During the months of October to April the longshore drift is dominant and these predictions were made using the LITLINE module of DHI (2001)It was seen that there was formation of beach on the east side and erosion on the west side of the groins. The results show that there was formation of beach about 70 meter which by passes the 1st groin. The formation extends up to 3rd groin and there would be reduced formation in between groin 3 and 4. After that there would be slight erosion on the lee side and formation on the east side of groins 5 and 6. The predicted shoreline is shown in Fig. 6.

Field Observations In order to assess the prediction detailed observations were carried out along the study area with respect to the formation of beaches and effect of groins. Satellite imageries were also analysed to get the historical changes .During the period from 2001-2014 it was assessed that about 35m of beachwidth was lost. An area of about 31000m2 was lost due to erosion. After the completion of groin field project , beach formation was high in between first thee groins and other groins have also resulted in beach formation process (Fig 7).The total area reclaimed as assessed from imageries is about 15000m2.

-20

0

20

1200 1100 1000 900 800 700 600 500 400 300 200 100 0

accretion erosion initial

15

Fig 6 LITLINE results The details of beach formations were observed from the time of construction and till completion. It was later seen that during field observation in March 2019, the beach was formed about 60 meters between groin 2 and 3. This can be seen in the photographs taken shown in Fig 8.

Fig 7 Assessment using imageries A ramp and road was under heavy threat of erosion during the initial construction period. With the completion of groin the ramp is now well protected and the place is now a recreation beach for public (Fig 9).

16

Fig 8 Field observations (2014-2019)

Fig 9 Protection created by groin

17

Conclusions The study of the coast was made in details to evolve suitable remedial measures to combat erosion during south west monsoon waves. It was assessed that during south west monsoon cross shore sediment transport dominates and alongshore sediment dominated during the other periods. The direction of alongshore transport is towards west. Groin field with short groins was proposed and implemented. The post project observations were made to assess the beach formations in between groins. The predictions on erosion and accretion profiles during monsoons were clearly visible. It was predicted that the effect of alongshore sediment transport will be dominant during non-monsoon. The effect was observed and the sediments almost by passed the first three groins after forming beach. The results show that there was formation of beach about 70 meter which by passes the 1st groin. The formation extends up to 3rd groin and there would be reduced formation in between groin 3 and 4. After that there would be slight formation on the east side of groins 5 and 6 which will increase with time. At present the beach formation areas are used by children to play sports which is one of the positive social impacts. The observations indicate that as per predictions beach formations were observed on eastern side of groins.

Acknowledgements

Authors acknowledge the services rendered by Er Cristunesakumar and ER Ratisan nair for collection of field data.

References Danish Hydraulic Institute DHI (2001) User Manual and Reference Guide for LITPACK and MIKE21.

P K Suresh (2010) “Near shore sediment dynamics along the coast of Tamilnadu” Phd thesis 2010 Department of Ocean Engineering IIT Madras 2010

18

Application of a Hybrid Decision-Making Approach to Enhance the Operational Safety in Petroleum Transportation Systems

Ayman Alghanmi

Ports and Maritime Transportation Department Faculty of Maritime Studies

King Abdulaziz University

Jeddah, Saudi Arabia [email protected]

Abstract: Petroleum Transportation Systems (PTSs) are vital in the flow of crude oil from production sites to end users. As these complex systems often operate in dynamic environments, safe operation of their key components, such as ports and transportation, is key for their success. This paper aims to determine the best

risk-control options available to PTSs in order to help ensure that PTSs operate at an optimal level. The

selection of the best risk-control options for operational safety in PTSs has been a widely debated subject among terminal managers, safety engineers, and various other representatives of petroleum transportation

and shipping businesses. This paper applies a mathematical model to identify and select the most

appropriate and most effective risk-control options for optimal operation by combining the Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method with the Analytical Hierarchy

Process (AHP). The resulting hybrid model is capable of assisting decision makers in selecting appropriate

options for enhancing the operational safety of PTSs. Finally, a case study is presented in this paper to demonstrate the proposed model.

Keywords: Techniques for Order Preference by Similarity to an Ideal Solution, Analytical Hierarchy Process, Maritime Risk, Maritime Transport, Petroleum Transportation

Introduction

Since the 19th century, the petroleum industry has become one of the fastest growing businesses. The total volume of petroleum production and movement has increased and is expected to continue increasing in the next years due to the critical role that this natural resource plays in world development (UNCTAD, 2017; OPEC, 2016; IEA, 2018). As a result, the safety operations of Petroleum Transportation Systems (PTSs) are continuously challenged.

Petroleum Transportation Systems (PTSs) play a critical role in the flow of crude oil within a Petroleum Supply Chain (PSC). The PTSs enable the movement of crude oil from point A to point B, via land or sea. Ports and transportation modes are the basic elements in a PTS. To ensure the smooth flow of the product within the system, tankers and pipelines are the two most commonly used transportation (Pootakham and Kumar, 2010; Herrán et al., 2010). While ports act as a connecting point between the

19

transportation modes, pipelines and tankers are used for inland and sea transportation respectively. Therefore, it is vital to control the hazards affecting the flow of crude oil within PTSs to ensure the overall safety and reliability of the systems.

This paper aims to ensure that PTSs operate at an optimal level by offering the best risk-control options. To achieve the objective of this chapter, a literature review of The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach and why the technique is used introduced in the following section. Followed by a step-by-step explanations of the evaluation process. This section identifies the hazard and then outlines the ranking process for the alternatives. A case study is presented in the fourth section to demonstrate the methodology proposed in this chapter. Finally, the conclusion is presented in the final section. The paper shows overall how this hybrid approach (TOPSIS-AHP) can provide a proper method to select a suitable risk-control option.

Risk Control

Risk may described as the chance of occurrence of undesired activities. In the case of petroleum transportation, the consequences of these activities are environmental damage. Oil spillage has the potential to have adverse effects on the surrounding environment, culture, and economic resources. The outcome of crude oil spillage might threaten not only the petroleum flow within the system but also human and natural life. In risk analysis, a higher risk reflects a higher probability and more severe consequences of the hazard event. Risk-control/mitigation is a major element in addressing a risk and is required in order to reduce the risks associated with high-risk hazards (Wang, 2001; Wang and Foinikis, 2001; Wang, 2002). Risk mitigation usually involves taking steps to reduce the effects of a risk. Mitigation can be accomplished through two major steps: reducing the probability of occurrence of the likely undesirable result and mitigating the consequences should the unwanted event occur anyway (Lassen, 2008). In other words, these two steps are introduced in order to reduce the frequency of an event or to prevent it from happening altogether. When reducing the consequences, preparation must be made to reduce the impact of the spill on humans and valuable resources. For example, fires should be avoided as well as human contact with the spilled substance (Menoni and Margottini, 2011). Various analysis techniques are usually performed for measuring and selecting the best solution among various strategies for mitigating the risk. The multi-criteria decision-making techniques are popular techniques for risk mitigation purposes.

The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS)

The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), was generated by Hwang and Yoon in 1981 (Peng et al., 2011; Chang et al., 2014). The developed approach is utilized to deal with Multi Attribute Decision Making (MADM)

20

problems. The technique built around the concept of distance from the Positive Ideal Solution (PIS) and the Negative Ideal Solution (NIS) for ranking the alternatives, where the preferred alternative has the shortest distance to the PIS and the farthest distance to the NIS (Ertuğrul and Karakaşoğlu, 2009; Behzadian et al., 2012). Both of the PIS and the NIS as a two statements in which in the PIS its increase the benefit criteria and reduces the cost criteria however the NIS is the opposite of this statement (i.e. increase the cost criteria and reduces the benefit criteria). TOPSIS has been extensively used due to the various advantages such as its ability to take in consideration the weight of each criterion in the evaluation process.

The TOPSIS technique is a powerful technique which has been extensively used for its capability in dealing with complex systems, which includes selecting the best alternative from several alternatives in a system. The technique has been widely applied in different fields due to its simplicity in calculation and the advantages it offers as a decision-making tool and mechanism for weighting the risk factors, such as in engineering (John et al., 2014; Krohling and Campanharo, 2011), healthcare (Büyüközkan and Çifçi, 2012), finance (Mardani et al., 2015; Ertuğrul and Karakaşoğlu, 2009) and management (Liao and Kao, 2011).

Methodology

For mitigating the hazards associated with the PTSs, TOPSIS and Analytic Hierarchy Process (AHP) is employed in this paper. TOPSIS was used to rank the solutions, whilst AHP was employed to identify the weight of each criterion. The flow chart for the evaluation procedure is presented in Figure 1.1.

Figure 1.1: The risk-control assessment model flow chart

The evaluation process contained four steps. The procedure began by identifying the

Step 1Identification of

the goal


the possible criteria and

alternatives for mitigating the risk


the best alternative by using TOPSIS

Step 4Sensitivity

analysis

21

most significant hazard affecting the safety of PTSs. It concluded by identifying the best alternative for eliminating and/or mitigating this hazard. Within this process, the AHP technique for determining the weight of the hierarchal criteria was used.

The steps for hazard controlling improvement are listed as follows:

• Identify the system’s most significant hazard.

• Identify the possible criteria and alternatives for mitigating the risk

• Identify the best alternative by using Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS)

• Sensitivity analysis

Step 1: Identify the Goal

Decision makers need to have a clear understanding of the most significant decision-making problem that affects the safety operation of the PTSs. The ultimate goal is to address the problem that has the greatest influence on the safe operation of PTSs. Therefore, to eliminate and/or mitigate that hazard, decision makers first have to identify the criteria and the solution required to ensure the safety of the PTSs.

Step 2: Identification of the Possible Criteria and Alternatives for Mitigating the Risk

To accomplish this step, firstly a set of criteria must be identified and satisfied to fulfil the goal. Secondly, a number of possible alternatives need to be identified for eliminating and/or mitigating the risk. At the end of the evaluation process, these identified alternatives were ranked by considering the collected data. Both the criteria and the alternatives were defined by conducting a brainstorming session and a literature review of previous related studies. The identified criteria and alternatives for the most significant hazard were discussed with operational experts to ensure the efficiency of the presented solutions. As a result, a list of criteria and alternatives for eliminating and/or mitigating the risk was produced (see Table 1.1).

Step 3: Identification of the Best Alternative by using TOPSIS

To rank the risk-control options and identify which alternative is the best solution for mitigating the hazards within this system, the TOPSIS technique was applied. The final result indicated the best alternative for mitigating the most significant hazard. To achieve the objective of this step, the following sub-steps, namely the steps of TOPSIS, were

22

conducted:

Step 1: Developing the TOPSIS decision matrix

To determine the best alternative for mitigating the risk, firstly a decision matrix had to be constructed (see Equation 1.1). This decision matrix was constructed considering the number of alternatives (Aa), criteria (Cc), and decision-makers (d). Equation 1.1 shows the decision matrix:

Dd =

A1A2⋮

Aa

C1 C2 … Cc

�

n11 n21 … n1cn21 n22 … n2c⋮ ⋮ ⋮ ⋮

na1 na2 … nac

� 𝑖𝑖 = 1,2,3,…,a; ,j = 1,2,3,…,c 1.1

where 𝑖𝑖=1,2,3,…,a and j=1,2,3,…,c represent the number of alternatives and criteria respectively. Moreover, nac presents the rate of the alternative Aa with respect to each criterion, which is identified by its average when more than one expert was involved.

Step 2: Normalising the TOPSIS decision matrix

This step aimed to change the attribute from its original formation (i.e. attribute dimensions) to non-dimensional attributes. This process was achieved by dividing the rating of each attribute nac by its average. Therefore, the normalised decision matrix Nac was calculated by using the following equation (Opricovic and Tzeng, 2004):

Nac =nac

�∑ nac2ai=1 𝑖𝑖 = 1,2,3,…,a; ,j = 1,2,3,…,c 1.2

Step 3: Determining the weight of each criterion by using the AHP technique

In this step, all the identified criteria were weighted by using a weighting technique to identify the importance of each criterion compared to another. The AHP approach was applied for weighting the criteria. The process started with a pairwise comparison technique for data collection and then progressed to measuring the consistency.

Step 4: Determining the weighted normalized decision matrix

The weighted normalized decision matrix was calculated by multiplying each normalized attribute by its weight as follows:

WNac = wj × Nac 𝑖𝑖 = 1,2,3,…,a; ,j = 1,2,3,…,c 1.3

Step 5: Determining the positive and negative ideal solution (PIS and NIS)

23

The ideal solution gathers together the ideal rates for all the considered criteria (Yoon and Hwang, 1995). Both the Positive Ideal Solution (PIS) and the Negative Ideal Solution are determined based on the results of the previous stage. The Following Equations (i.e. Equations 1.4 and 1.5) were used to determine the PIS ( WN+) and NIS (WN+) respectively:

WN+ = [WN1+, WN2+, WN3+, … , WNa+] = ��maxjWNij�j ∈ J��, ��minjWNij�j ∈ J′�� 1.4

WN− = [WN1−, WN2−, WN3−, … , WNa−] = ��maxjWNij�j ∈ J��, ��minjWNij�j ∈ J′�� 1.5

where J represents the benefit criteria, and J′ represents the cost criteria (Mahmoodzadeh et al., 2007).

Step 6: Determining the PIS and NIS distance separation measure (𝐃𝐃𝐢𝐢+ 𝐚𝐚𝐚𝐚𝐚𝐚 𝐃𝐃𝐢𝐢−)

This stage aims at identifying the separation distance between the involved alternatives. In order to reach this goal, all the weighted normalised decision matrix are measured with their PIS and NIS. The calculation formulae for calculating the Di+ and Di− are as follows (Yoon and Hwang, 1995):

Di+ = �∑ �WNac − WNj+�2c

j=1 𝑖𝑖 = 1,2,3,…,a

(6.6)

Di− = �∑ �WNac − WNj−�2c

j=1 𝑖𝑖 = 1,2,3,…,a 1.6

Step 7: Determining the relative closeness to the ideal solution

This step aimed to determine the alternative closeness to the ideal solution. The determining distance (i.e. step 5) previously was used to identify the closeness to the ideal solution (RCi+) by applying the following equation (Yoon and Hwang, 1995):

RCi+ =Di−

Di++Di

− 𝑖𝑖 = 1,2,3,…,a 1.7

The results were finally used to rank the alternatives, where the higher the value of RC, the higher the alternative for mitigating the risk (Yoon and Hwang, 1995).

Step 5: Sensitivity Analysis

24

To validate the model, the weighted results obtained from the AHP were slightly increased; this increase was performed on each criterion individually (John et al., 2014).

Case Study and Results Analysis

For the purpose of this research, a case study was carried out to demonstrate how the methodology can be employed to mitigate the evaluated hazards associated with PTSs. Based on the analysis procedure presented in Figure 1.1, the case study was conducted as follows.

Identification of the Goal

In Alghanmi et al., 2017 research, the hazards associated with PTSs were identified. The hazards within ports and transportation mode were evaluated by experts in their fields. All the experts have a great deal of experience in operations and are still actively working in their fields. This evaluation process indicated that procedural failure during ship/port interference has the highest risk within PTSs. Due to the present safety level of this hazard, a list of risk-control measures had to be identified to improve the safety practice for an optimal operation of this identified hazard.

Identification of Possible Criteria and Alternatives for Mitigating the Risk

This research aim to improve the safety operations regarding the hazard of procedural failure during ship/port interference. In accomplishing this goal, it was necessary to consider the many criteria that have an effect on the evaluation of the alternatives in order to identify which solution (alternative) would be the best one. Through conducting an extensive literature review (John et al., 2014; Vugrin et al., 2011; Hollnagel et al., 2007; Omer et al., 2012; Wang and Chang, 2007), the criteria and alternatives were identified. The criteria were categorised either as “Benefit” or “Cost” (see Tables 1.1 and 1.2).

Then the identified criteria and alternatives were discussed with petroleum ports operational experts. These consultation meetings took place in 2017, with twelve petroleum/refined products’ terminal managers, and scholars. With the help of experts, by using a brainstorming technique, the obtained results from the identified criteria and alternatives were validated.

Table 1.1: List of criteria with an explanation of each one

Level number Criteria Explanation Category

Level 1 (Criteria) Operating Safety (OS) Safety level offered by applying any of the alternatives

Benefit

25

Operating Costs (OC) Cost of applying any of the

alternatives Cost

Operating Time (OT) Cost attributed to period during which infrastructure is working

effectively

Cost

Operating Quality (OQ) Quality of operation from applying

any of the alternatives Benefit

Table 1.2: List of alternatives with an explanation of each one

Level number Alternatives Explanation

Level 2 (Alternatives)

Hiring qualified labour (A1) Raising the minimum qualifications a

new employee is required to have

before being hired

Hiring highly qualified

labour (A2)

Hiring only specialists who are competent and have over 7 years’

experience and multiple certifications

Labour training programme

(A3)

Implementing training programmes that new and current workers are

required to take to improve their

knowledge, skills and experience

Enhancing work force capacity (A4)

Increasing the number of workers involved in the operation

Requiring

loading/discharging terminal supervision officer (A5)

Posting an operator (port

representative) from the port side to represent the port on the ship during

ship-board operations to ensure the

safety of the loading and unloading operation

Intensive regulation for safety and security checks

(A6)

Requiring an intensive checklist

before, during, and after the operating

process to ensure the safety of the loading and unloading operation

26

Apply new equipment (A7) Renewing the equipment (Loading

Arm/SBM) involved in the

loading/unloading process

Regulate an intensive

maintenance program (A8)

Implementing an intensive

maintenance plan to ensure the safety

and quality operation of the equipment

Requiring visual operating signs (A9)

Implementing visual guides to

assist the workers during the

operation process

The PTSs assessment indicated that the most significant hazard was within the port transportation system, so twelve actively working experts from the port sector were recruited to participate in this study. Through using a brainstorming technique, the experts were invited to discuss whether the identified criteria and control options addressed in the literature review aligned with real-life decision-making regarding the hazard of procedural failure during ship/port interference. The experts were also asked to address whether other criteria or alternatives not revealed in the literature review existed in real-life practice and should be included in the study. The resulting list of criteria is presented in Table 1.1, and a list of alternatives is presented in Table 1.2 (the hierarchal striation of the identified criteria and alternatives in Tables 1.1 and 1.2 are presented in Figure 1.2).

Eliminating and/or Mitigating Human Accidental Performance Error during the Ship/Port

Interference

Operating Risk ReductionOperating Costs Operating TimeOperating Quality

Hiring Qualified Labour

Labour Training Program

Port Representative on

Ship

Number of Labour

Safety and Security Checks Maintenance Equipment

Hiring Highly Qualified Labour Operating Signs

27

Figure 1.2: The hierarchical structure for mitigating the hazard of procedural failure during ship/port interference

Identification of the Best Alternative by using TOPSIS

This step aimed to identify the best alternative in order to improve the safety level of the hazard of procedural failure during ship/port interference by applying the TOPSIS technique. Accordingly, the seven steps of TOPSIS were applied. However, before starting, a questionnaire was designed for petroleum port operation experts and its results were collected. Each expert was required to fill in each part by using a rating scale ranging from 0 to 10. Once the experts had completed the questionnaires, the researcher collected them and ranked the alternatives to improve the safety operations at petroleum ports.

After receiving the questionnaires from all the participants, the TOPSIS technique was applied to rank the alternatives. The alternatives were rated with respect to the criteria. Identifying the weight of each of the identified criteria influences the detection of the ideal risk control option for mitigating the risk. Therefore, a questionnaire was constructed and sent to experts in order to identify the weight of each criterion. After the experts had completed the questionnaires and the resulting responses had been reviewed by the researcher, AHP technique were performed to calculate the weight of each criterion.

The weight of the P(𝑂𝑂𝑆𝑆𝑂𝑂𝐶𝐶𝑂𝑂𝑇𝑇𝑂𝑂𝑄𝑄) is presented in Table 1.3.:

Table 1.3: AHP Weight Weight Rank

OS 0.3399 1

OC 0.2578 2

OT 0.1895 4

OQ 0.2128 3

This AHP step aimed to identify the weight of four criteria. Therefore, for detecting the Consistency Ratio (CR), the Random Index (RI) value was 0.89.

Based on the outputs of TOPSIS steps 1, 2, 3, 4, 5 and 6, the PIS and NIS distance separation were identified. This step took into consideration whether the criterion is a cost or a benefit criterion for identifying the maximum and minimum criterion functions. By applying TOPSIS steps, relative closeness to the ideal solution were identified as shown in Table 1.4.

28

Table 1.4: TOPSIS RC values

RC Rank

A1 0.5273 6

A2 0.5873 4

A3 0.6523 2

A4 0.3579 9

A5 0.5659 5

A6 0.9448 1

A7 0.6514 3

A8 0.3829 8

A9 0.4974 7

The identified RC of each alternative was used for ranking the alternatives starting from the best solution down to the worst solution: the higher the RC value, the higher the alternative for mitigating the risk. The result from Table 1.4 can be used by decision makers in order to mitigate procedural failure hazard during ship/port interference. As a result of this evaluation, with a value of 0 and rank of 1, the alternative A6 (Intensive regulation for safety and security checks) was identified as the best risk control option in order to improve the safety level of the identified hazard.

Sensitivity Analysis

To validate the sensitivity of the model, the weight of each criterion (i.e. Operating Safety (OS), Operating Cost (OC), Operating Time (OT), and Operating Quality (OQ)) was increased by 20% in this step. As a result, the increases of the criteria weight, which increased by 0.2, led to a change in the RC, as presented in Figure 1.3. For instance, the RC of the evaluated A9 has impacted due to increasing the criteria OC weight by 0.2 (from 0.4974 to 0.6638). Furthermore, the analysis revealed that the weight increment by, 20% has not affected the final ranking of the best alternatives for the investigated PTS hazard.

29

Figure 1.3: Sensitivity analysis output

Conclusion

The petroleum industry benefits from enhancing its safety level and eliminating the hazards associated with the system’s operation and thereby avoiding any unexpected disasters within its supply chain. This goal could be reached by identifying the mitigation solutions and applying multi-attribute decision-making tools. This study is one of the first studies that employed an AHP–TOPSIS technique within the petroleum industry in order to identify which control option is the best one for eliminating/mitigating the hazards associated with PTSs. Based on expert judgements, AHP was employed to identify the weight of each criterion, whilst TOPSIS was used to rank the solutions.

The proposed methodology was applied to mitigate the identified hazards within PTSs. The most significant hazard within the system was identified (Alghanmi et al., 2017), followed by the identification of a list of alternatives and a list of criteria in order to mitigate and/or eliminate this hazard. Using the RC, the solution with the higher RC value was ranked as the most important one and vice versa. Therefore, A6, which was ranked as 1, is a more effective option than the other risk-control options (i.e. A4 ranks 9) in enhancing PTS operations.

Finally, a sensitivity analysis was performed to validate the sensitivity of the developed model. Performing axiom 1 (increases the criteria weight by 0.2) on all the risk control

0

0.2

0.4

0.6

0.8

1

1.2

A1 A2 A3 A4 A5 A6 A7 A8 A9

OSOCOTOQ

30

options highlights that any change in the criteria weight affects the TOPSIS of PTS’ risk control option with an increase or a decrease as presented in Figure 1.3. In addition, the modal outputs highlighted that the weight increasing by, 20% will not influence the position of the most efficient risk control option for the investigated PTS hazard.

This work has presented a platform that can support decision makers in the petroleum transportation industry in dealing with problems. This model has presented control options for the system’s most significant hazards. The proposed methodology is not only suitable for enhancing the safety of PTSs. In fact, the methodology can be applied in any system in order to evaluate the alternatives of the tested system. The proposed method provides a decision-support system for enhancing the safety practices of the transportation system in general and particularly for the PTSs. References

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33

Evaluation of the controllability of a ship navigating in a port channel

based on hydrodynamic force analysis

Han Liu1,2,3, Ning Ma2,3, Xiechong Gu2,3

1 School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University

2 State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University

3 Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong

University

Shanghai, 200240, China

[email protected]

Abstract: The controllability of a ship when sailing in a channel to a port is an important issue for the

ship safety as well as the operation of a port. Serious ship-bank collisions may occur if the rudder

forces are exceeded by the forces disturbing the ship, e.g. strong winds and bank effects. In this paper

a methodology is presented for evaluating the controllability of a ship navigating in a restricted port

channel by means of a hydrodynamic force analysis. This method is applied to assess the

controllability of a 10000TEU container ship in straight channels. For estimating the components of

the force, use is made of the results of wind tunnel tests and captive model tests through planar motion

mechanism (PMM) in restricted waters. By comparing different initial ship-to-bank distances and

bottom configurations the influence of different ship operational parameters (such as speed),

environmental parameters (such as wind speed and direction), and channel characteristics (water depth

and bank slope) on this controllability can be evaluated.

Keywords: controllability of a ship, bank effect, wind load, structural and operational measures

1. Introduction

Manoeuvrability-related safety during approaching to and entering ports under strong

wind is critical for ships with large windage area. A statistical analysis given by Ventikos

et al. (2015) shows that among the accidents linked to manoeuvrability in adverse weather

conditions, 57.3% of all relevant accidents happened in ports. The mean wind speed

during the navigational accidents is 9.84m/s corresponding to Beaufort scale 5, which

indicates that the wind load is a non-negligible factor in contacts. In addition, a ship

following a course parallel to a bank will be subject to a lateral force and a yaw moment

that tends to turn the ship’s head off the nearest bank. This effect, which is called the bank

effect, is caused by an asymmetric flow due to the ship speed and the action of the

propeller (Eloot et al., 2007). For a considered manoeuvre in port channel or similar

confined water area, the wind force and the bank effect are important factors in the

manoeuvring problems.

Prolific research works have provided the empirical formulations of the bank

induced forces regressed from model tests (Norrbin, 1974; Ch’ng et al., 1993; Vantorre

34

et al., 2003). Moreover, advanced mathematical models have been developed for the

simulation of ship course keeping performance under wind loads (Yasukawa et al., 2012)

and in shallow water near channel wall (Sano et al., 2014). On the other hand, it is

impossible to keep the ship under control if the available control forces generated by the

rudder are exceeded by the forces disturbing the ship (e.g. bank effects, current) or

required to perform a given trajectory. A permanent exceeding of the control forces

inevitably results in an uncontrollable ship.

In order to evaluate the inherent safety of a considered manoeuvre, a methodology

can be used for comparing the available control forces with the forces to be counteracted.

This hydrodynamic force analysis will only take sway forces and yaw moments into

consideration. With respect to parameter variations, generally a large number of

parameters affecting a manoeuvre can be identified. In the case of a ship taking a straight

course parallel to the bank of a channel, the following non-exhaustive distinction can be

made: ship dependent characteristics such as draft, geometric dimensions and rudder area;

environmental parameters such as wind and current; channel characteristics such as bank

geometry and water depth; operational parameters such as propeller rate and ship speed.

In this paper, a 10000TEU container ship approaching to a port through a straight

channel is taken as an example to present the application of the proposed approach. The

first part introduces the mathematical architecture for estimating the controllability and

the models of the forces disturbing the ship including wind forces and bank induced forces.

Then the ship in this study as well as the aerodynamic and hydrodynamic coefficients is

presented. Finally, the evaluation of controllability considers the ship sailing a straight

course parallel to the channel with variations of water depth, bank slope and wind speed.

2. Architecture of the methodology

2.1 Basic Manoeuvring Motion Equations

The earth-fixed coordinate system O0-ξηζ and ship fixed coordinate system O-xyz are

right-handed coordinate systems with the positive ζ and z axis pointing into the page and

the origin O at the mid-ship point. The ship initially moves in the direction of the ξ axis

with speed, U0. Uw and θw denote the absolute value of the wind speed and wind direction.

Affected by the environmental forces, the ship’s velocities are generated as the vector [u,

v, r], and the heading angle ψ as well as the drift angle β appears. A rudder deflection δ

is required to maintain the ship’s direction. ybank is the distance between the ship and the

bank.

Yasukawa et al. (2013) assumed that the ship has enough engine output to maintain

a constant ship speed, and therefore, the effect of surge coupling on the directional

stability is negligible. The equations of motion for maneuvering in the ship’s fixed

coordinate system are written as

35

( ) ( )y x Gm m v m m ur mx r Y+ + + + =& & (1)

( ) ( )z z GI J r mx v ur N+ + + =& & (2)

where m is the ship mass and Iz is the yaw moment of inertia. mx, my and Jz are the added

mass and added moment of inertia for the surge, sway and yaw motion. Y and N represent

the lateral force and yaw moment acting on the ship, which include hydrodynamic inertia

terms and will be expressed by the following equations.

+H R A bank

H R A bank

Y Y YY Y

N N N N N

= +

=

+

+ + + (3)

Figure 1. Coordinate systems.

In Eqs. 3 and 4, YH and NH are the hydrodynamic forces acting on the ship. YR and

NR are the hydrodynamic forces generated by the given rudder deflection. YA and NA are

the aerodynamic force and moment due to wind, Ybank and Nbank are the hydrodynamic

forces caused by the bank effect.

The hydrodynamic lateral force and yaw moment acting on the ship are expressed

by the following polynomial equations 3 2

3 2

H v r vvv vvr

H v r vvv vvr

Y Y v Y r Y v Y v r

N N v N r N v N v r

= + + +

= + + + (4)

where Yv, Yr, Nv, Nr, etc. are the hydrodynamic derivatives on manoeuvring. Xδδ, Here the

authors follow the experience from the analysis of Sano et al. (2014) to neglect high-order

terms like rrrY and rrrN because their values are small.

For a ship in the state of motion equilibrium, which from the point of view of force

the the forces disturbing the ship are balanced by the control forces generated by rudders,

36

the terms dependent on the acceleration and the rotational speed disappears from Eqs. 1

and 2. The resultant equations for this situation are

30 +v vvv R A bankY Y YY v Y v= + + + (5)

30 v vvv R A bankN v N v N N N= + + + + (6)

2.2 Expressions for Force components

YR and NR are expressed using hydrodynamic derivatives as follows:

R

R

Y Y

N N

=

= (7)

where Yδ, Nδ, etc. are the rudder force derivatives.

The aerodynamic force and moment are based on Isherwood (1972)

( )

( )

2

2

1 2

1 2

A A Y A YA A

A A Y PP A NA A

Y A V C

N A L V C

=

= (8)

where

2 2 2

A A AV u v= + (9)

( )cosA w wu u U = + − (10)

( )sinA w wv v U = + − (11)

( )1tanA A Av u−= (12)

ρA is air density. AY is the lateral projected area exposed to wind, respectively. VA is the

relative wind velocity and θA is the relative wind direction angle. CYA and CNA are wind

force coefficients that are expressed as functions of θA.

For the estimation of the yawing moment induced by a bank, a regression model

described in (Ch’ng et al., 1993) has been applied. The lateral force Ybank and the yawing

moment Nbank are given as a function of ship-bank distance, water depth to draft ratio,

Froude number Fr and thrust coefficient CT:

5 3 3 7 3 9 32 2

2 2 2

13 3

1000

0.5

0.11 0.0006

B B r B B r

PP

B B T B T

Ya y y F a y A a y F A

U L

a y A y AC y A C

= + +

+ + +

(13)

37

2 2 2 2 2

8 3 3 13 3 14 3 16 32 3

2 2

1000

0.5

0.0009 0.0044

B B B B B r

PP

B T B B T

Nb y y A b y A b y A b y F A

U L

y AC y y AC

= + + +

+ −

(14)

where LPP denotes the ship length between perpendiculars and U denotes the ship speed.

yB, yB3 and A are defined as (see Fig. 2)

3

3 3

1 1 1 1;

2 2B B

p s p s

B By y

y y y y

= + = +

(15)

TA

h T=

−(16)

where B is the breadth and T is the draft. The regression coefficients (ai and bi, i=5, 7,

9…) in the formulae above are deduced from systematic model test programs and they

are functions dependent on the ship geometry parameter. Readers can refer to (Ch’ng et

al., 1993) for those functions.

Figure 2. Graphical interpretation of the symbols used in Ch’ng et al. (1993).

Since the wind force coefficients and the hydrodynamic derivatives are analysed

from model-scale measurements, the equations of motion (Eqs. 5 and 6) have been

transformed into the non-dimensional form in order to simulate the motion of the full-

scale ship.

30 +v vvv R A bankY Y YY v Y v = + + + (17)

30 v vvv R A bankN v N v N N N = + + + + (18)

The non-dimensional treatment is based on water density ρ, ship length between

perpendiculars LPP and speed U. Some examples are given as follows

38

2 2 3 2, , ,

0.5 0.5PP PP

u vu v

U U

Y NY N

L U L U = = = = (19)

By substituting the wind condition and ship’s lateral position into Eqs. (17) and (18),

the rudder angle δ0 and heading angle ψ0 can be solved. In this paper, the maximum rudder

angle that represents the full rudder capacity is set to 35°. Moreover, if the wind condition

(speed and wind direction) and the bank geometry are provided, the ship speed and the

ship-bank distance corresponding to δ0=35° are grouped as the marginal speed and ship-

bank distance for the ship’s controllability.

3. Approach to port through a straight channel

3.1 Target ship

A 10000TEU container ship which sails from Far East to the west coast of the United

States is studied in this case study. According to the official file of 10000TEU Container

Ship Vessel Trim and Stability Booklet, the paper chose a typical non-full loaded

condition marked 10T/TEU DEP. AT DESIGN DRAFT with 7453 containers overall and

each container weights 10 ton. The main particulars of the ship and shipping information

for the loaded condition are listed in Table 1. The service speed of the ship is VS=23.75kn.

The wind tunnel test on a scale 1/200 10000TEU model was carried out by Qiao et

al. (2017) in the Wind Tunnel of Shanghai Jiao Tong University. A conventional stacking

configuration was selected for constructing the on-deck form of the model ship. The

stacking configuration and the corresponding CYA and CNA varying with θA are shown in

Fig. 3.

Table 1. Main particulars of 10000TEU and shipping information of loaded condition 10T/TEU DEP. AT DESIGN DRAFT

Length btw. perpendiculars LPP 320.0m

Breadth B 48.2m

Mean draft T 12.94m

Displacement Δ 124337t

Block coefficient 0.602

Long. position of LCB xG 4.33m

Container number - in hold 4578TEU

Container number - on hatch cover and deck 2875TEU

Rudder area 72.0m2

39

Figure 3. Diagram of the stacking configuration and wind force coefficients

The hydrodynamic derivatives in Eqs. 5 and 6 were obtained through planar motion

mechanism tests carried out in the Circulating Water Channel (CWC) of Shanghai Jiao

Tong University (Liu et al. 2018). Fig.4 shows a sketch of the CWC. The dimensions of

the measuring section are 8.0 m × 3.0 m × 1.6 m and it can hold a model with the

maximum length of 3 m for testing. The selected model is scale 1/128.77. The tests

include oblique towing test and static rudder test. The water speed was set at 0.646 m/s,

corresponding to Froude number Fr= 0.131 (50% design speed at full-scale). The values

of the non-dimensional hydrodynamic coefficients are Y’v =-0.0033, N’v=-0.0041, Y’δ =-

0.0012 and N’δ=6.85E-4.

Figure 4. Sketch of the CWC in Shanghai Jiao Tong University.

3.2 Situation

The Shanghai International Shipping Center Yangshan deep water port is the world’s

largest port to handle container throughput with 40 million TEUs per year (People’s Daily,

July 26th, 2017). The heavy traffic in the port brings in a growing risk of ship-bank

contacts. Fig.5 adapted from Yang et al. (2012) shows the water area of Yangshan deep

water port. The narrowest section of the waterway marked as Section A is 1.05km wide,

0 45 90 135 180-0.08

-0.04

0.00

0.04

0.08

CN

A

(deg)0 45 90 135 180

0.0

0.2

0.4

0.6

0.8

1.0

CY

A

(deg)

40

and the width of the approach channel is 550m. Meteorological data (Yuan et al., 2002)

shows the recorded average and maximum wind speed in the port area are 7.7m/s and

29.1m/s, respectively. Fig. 6 shows the distribution of ship speeds when passing Section

A from the period July 2018 to May 2019. The mean value is 9.13kn.

In order to pass through the channel successfully, the rudder capacity of a specific

ship will have to counteract the influence of the bank effect and the wind force. The ship

is assumed to be sailing with a closer proximity to the right bank side, so the ship-bank

distance is represented by ys3 as marked in Fig. 2.

Figure 5. Water area of Yangshan deepwater port, adapted from Yang et al. (2012).

Figure 6. Distribution of ship speeds when passing Section A.

41

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