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Yutao Liu 1
Probability Analysis of Damage to Offshore Pipeline by Ship Factors 1
2
Yutao Liu 3
Ph.d. Candidate 4
School of Naval Architecture, Ocean and Civil Engineering 5
Shanghai Jiaotong University 6
800 Dongchuan Road, Shanghai, P.R. China 7
TEL: 021- 54748155, FAX: 8621-62933163 8
Email: [email protected] 9
10
11
Hao HU ∗ 12 School of Naval Architecture, Ocean and Civil Engineering 13
Shanghai Jiaotong University 14
800 Dongchuan Road, Shanghai, P. R. China 15
TEL: 8621-62933091, FAX: 8621-62933163 16
E-mail: [email protected] 17
18
19
Di Zhang 20
School of Naval Architecture, Ocean and Civil Engineering 21
Shanghai Jiaotong University 22
800 Dongchuan Road, Shanghai, P. R. China 23
TEL: 86-13472652951, FAX: 8621-62933163 24
E-mail: [email protected] 25
26
Submitted to the Transportation Research Board 92th Annual Meeting 27
for Presentation and Publication 28
Submission date: July 31, 2011 29
30
Word Count: 4247(text) + 3250(13 figures) = 7497 words 31 32
∗ Corresponding Author
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 2
ABSTRACT 33
The transport of hydrocarbons by offshore pipeline is threatened by the rapid expansion of pipe 34
networks and the increasing frequency of maritime activities. Risk management is thus necessary to 35
manage and prevent ship-related hazardous events that may damage offshore pipelines. Probability 36
analysis is the key to assessing the risk associated with ship operations on offshore pipelines, and 37
decision making in managing that risk. Bayesian Network (BN) models are proposed in this paper to 38
determine the probability of anchor damage and trawling damage to subsea pipelines. The BN 39
models are developed by integrating directed acyclic graphs, and three computational methods 40
(Boolean operation, standard and historical statistical analysis, and fuzzy set theory) to elicit 41
marginal probability tables and conditional probability tables. A case study illustrates the utilization 42
of two BN-related functions – probability prediction and probability updating – to determine final 43
probabilities of damage to a subsea pipeline. The results of the analysis support risk ranking and risk 44
reducing decisions associated with maritime operations in the area of offshore pipelines. 45
46
KEY WORDS 47
Offshore Pipeline; Ship Factor; Bayesian Network; Damage Analysis; Probability Analysis 48
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 3
INTRODUCTION 49
The transportation of hydrocarbons by subsea pipelines is highly efficient and convenient, while 50
requiring minimal cost. As a result, offshore pipelines are gradually becoming the primary mode of 51
transportation of oil and gas at sea. China currently has 3,000 kilometers of installed offshore 52
pipelines, and is planning to increase that length threefold in the next decade. Some hazards 53
associated with operating offshore pipelines include leakage, rupture and even bursts that result in 54
interruption to transportation and production of hydrocarbons, require clean-up operations, and can 55
cause catastrophic health, environment and safety accidents (1, 2). Consequently, maintaining the 56
integrity of offshore pipeline transportation networks is of vital importance to a nation’s economy 57
and peoples’ lives. Historical data (3) illustrate that a large number of accidents to offshore pipelines 58
were caused by impact, ship anchoring and corrosion, as shown in Fig.1. 59
60
FIGURE 1 Accident breakdown of offshore steel pipeline. 61
Obviously, the majority of anchoring and impact accidents are associated with passing 62
ships, which can be categorized as damage to offshore pipeline by “ship factors”. With the rapid 63
extension of offshore pipeline networks and the increasing frequency of maritime activities, it can 64
be reasonably expected that accidents to offshore pipelines by passing ships will become more 65
frequent. Therefore, risk assessment for ship factor hazards is necessary, and the results from such 66
assessments could support risk ranking and then serve as the main basis to judiciously divide 67
resources for inspection, maintenance and protection among different pipeline networks, pipeline 68
segments, or related assets. 69
In addition to the scoring-type algorithm method (4), many qualitative or 70
semi-quantitative methods, such as the analytic hierarchy process (AHP), fuzzy logic, and neural 71
networks, have been utilized in risk assessment models for onshore and offshore pipelines (5-9). 72
These models give relative values of the assessment results, which could support risk ranking but 73
fail in judging whether the risk is acceptable to the local community. Therefore, current research 74
ranges from qualitative schemes to quantitative probabilistic systems. Also, physical models in 75
association with probabilistic methodology are utilized to analyze failures of offshore pipelines 76
caused by accidental external loads in research and incorporated into standards (10-13). These 77
models offer absolute assessment results but require complex analytical procedures. In addition, the 78
parameters used in these models are not frequently updated when new data become available for 79
statistical analysis of the operation period of offshore pipelines. Fault tree (FT) analysis has also 80
been shown to be an effective method in probabilistic failure analysis and has been employed in 81
21%
30%26%
7%
6%5%
1%1% 1% 1% 1%
AnchoringImpactCorrosionStructuralMaterialNatural HazardConstructionMaintenanceHuman errorOperation problemsOther
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 4
pipeline engineering (14, 15); however, FT analysis is best limited to modeling simple static 82
systems, with complex systems being modeled through other procedures. 83
Bayesian network (BN) has a much more flexible structure than FT, and can fit a broad 84
range of accident scenarios. This paper presents a BN model for analyzing the probability of 85
damage to offshore pipelines by ship factors. The remainder of this paper is structured as follows: 86
Section 2 describe offshore pipelines and the hazards of anchor damage and trawling damage. Next, 87
the establishment of BN models is presented in Section 3. Section 4 introduces the case study of the 88
offshore pipeline from Pinghu Oil-Gas Field to Shanghai. Finally, conclusion and comparison of 89
BN and FT are provided in Section 5. 90
91
DESCRIPTION OF OFFSHORE PIPELINE AND ACCIDENT SCENARIO 92
As shown in Fig.2, the section of offshore pipelines considered in this paper is the middle portion of 93
a subsea pipeline, viz., the section of pipeline away from both the platform and the shoreline 94
(e.g., >500 m from the platform and >300m from the coastline). This middle section of pipeline is 95
minimally affected by offshore platform operations and onshore activities. 96
97 FIGURE 2 Illustration of a typical offshore pipeline. 98
Anchor and trawling accidents (i.e., “ship factor”) to offshore pipelines occur frequently 99
and, furthermore, previous studies (3) show that the frequency of containment loss caused by anchor 100
and trawling accidents were 37% (19/52) and 44% (23/52) respectively. Therefore, this paper is 101
limited to modeling anchor and trawling accidents. 102
In order to establish a complete model for probability analysis, the accident scenarios 103
may consider the following factors. 104
Passing ship: engineering ship (supply boat, crane ship, etc.); transport ship (tanker, 105
commercial ship, cruise ship, etc.); fishing vessel 106
Hazardous activities: emergency anchoring (anchor weight, anchor shape); bottom trawling 107
(type of fishing net, depth of casting net, draw force) 108
Offshore pipeline characteristics: type (steel pipeline, flexible or umbilical); water depth; 109
embedment depth; diameter, wall thickness, coating thickness 110
Possible consequence to pipeline: impact damage, hooking damage 111
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 5
For this paper, the energy (in kilojoules) that a “ship factor” accident imposes onto a 112
subsea pipeline is the hazard to be analyzed, and “damage” is the release of hydrocarbon from a 113
distressed pipeline exposed to hazard. As pipeline characteristics (e.g., diameter, thickness, and 114
material properties) vary significantly among pipelines and, therefore, energy was selected to define 115
damage to facilitate application of the method to a wide range of situations. 116
117
ESTABLISHMENT OF BN MODELS 118
Bayesian Network 119
The representation of BN is a directed acyclic graph, in which the nodes represent variables, and 120
arcs signify direct causal relationships between the linked nodes. If a node doesn’t have any parents 121
(i.e. root node), the node contains a marginal probability table which expresses the prior probability. 122
Otherwise, the node contains a conditional probability table that specifies how strongly the linked 123
nodes influence each other (16). 124
A BN methodology is used either to predict the probability of unknown variables or to 125
update the probability of known variables. The processes of probability prediction and probability 126
reasoning are all based on Bayes’ theorem. In the predictive analysis, conditional probabilities of the 127
form | are calculated, indicating the occurrence probability of a 128
particular accident given the occurrence or non-occurrence of a certain primary event. According to 129
the conditional independence and the chain rule, BN finally represents the joint probability 130
distribution of variables , … , included in the network as 131
| 1
where are the parents of in the BN, and reflects the properties of the BN. In the 132
updating analysis, those of the form | are evaluated, showing the 133
posterior probability of a particular event given new information, called evidence E. The evidence is 134
usually operational data including occurrence or non-occurrence of the accident or primary events 135
(17): 136
|, ,
∑ , 2
137
Directed Acyclic Graphs for Anchor Damage and Trawling Damage 138
As shown in Fig.3, anchor damage is defined as damage to an offshore pipeline by anchor impact, 139
where the anchor impact occurs when a ship passes above an offshore pipeline, the ship’s engine 140
fails and an anchor is deployed under emergency conditions. Two primary factors affecting impact 141
energy are 1) the water depth which greatly influences the impact probability of the anchor, and 2) a 142
pipeline’s coating which often reduces the severity of the impact (18). Both impact probability and 143
impact energy are integrated to determine anchor damage to a pipeline. 144
As shown in Fig.4, trawling damage to offshore pipelines is mainly caused by fishing 145
nets hooking on to a pipeline. Fishing boats cast nets to perform bottom trawling, and the nets 146
become caught on subsea pipelines. The nets entangled on the pipeline are dragged by the fishing 147
boats resulting on large tensile loads being placed on the pipeline and causing damage to the 148
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 6
pipeline. Entanglement of a fishing net on a pipeline is a function of net-casting depth, water depth, 149
and the pipeline coating (which mitigates the attachment of nets to a pipeline) (19). 150
151 FIGURE 3 Directed acyclic graph of anchor damage. 152
153
FIGURE 4 Directed acyclic graph of trawling damage. 154
155
Elicitation of Marginal Probability Table (MPT) and Conditional Probability Table (CPT) 156
The elicitation of MPT and CPT is complex due to the large amount of judgments required to fully 157
quantify these relationships in a BN model. They are elicited in the following three ways in this 158
study. 159
Table 1 illustrates the conversion of Boolean operations (disjunction and conjunction 160
) into CPTs, which is applied only to the events are considered binary (with two states: 0 and 1). 161
This conversion process has been introduced in some researches about the comparison between 162
fault tree and BN (20, 21). 163
Historical data and standards (12, 19) offer an alternative method to educe MPT and 164
CPT. One example is the marginal probability of engine. According to an investigative report (22), 165
the failure rate of engines is 2E-05 per hour. As engines have rapidly improved and their reliability 166
increased we choose 2E-06 as a conservative estimate of engine failure rate as a ship passes over an 167
offshore pipeline thereby requiring an anchor to deployed under emergency conditions. In addition, 168
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 7
Table 2 shows the example of the CPT of anchor damage, which refers to the proposed damage 169
classification used for bare steel pipes given by DNV-RP-F107 (13). 170
TABLE 1 CPTs of Ship Passing and Anchor 171
Boolean operation
engineering ship passing no transport ship passing no passing no fishing vessel passing no passing no passing no passing no ship passing
(passing = 1, no = 0)1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1
Boolean operation
ship passing passing no engine fail work fail work anchor
(anchor = 1, no = 0)1 0 0 0 0 1 1 1
TABLE 2 CPT of Anchor Damage 172
impact energy no
(0 kJ) low
(< 2.5 kJ) medium
(2.5 – 10 kJ)high
(10 – 20 kJ) tremendous
(>20 kJ) impact impact no impact no impact no impact no impact no
Dam
age no 1 1 0 1 0 1 0 1 0 1
minor 0 0 1 0 0 0 0 0 0 0 moderate 0 0 0 0 0.5 0 0.25 0 0 0 major 0 0 0 0 0.5 0 0.75 0 1 0
Minor damage: Damage neither requiring repair, nor resulting in any release of hydrocarbons. Moderate damage: Damage requiring repair, but not leading to release of hydrocarbons. Major damage: Damage leading to release of hydrocarbons, water, etc.
Finally, expert judgment is another important way of eliciting MPT and CPT. However, 173
experts often prefer to linguistic judgment than probabilistic description, like “safe” and “unsafe”. 174
The integration of fuzzy set theory can help domain experts to elicit MPT and CPT in an efficient 175
manner. For example, in this paper we define the buried depth of the offshore pipeline using three 176
fuzzy numbers, , defined over universe of discourse where each subset represents an depth grade; 177
, , , as illustrated in Fig.6. According to experts’ opinion, 178
the triangular membership function for each fuzzy number is represented by the 179
following set of equations, 180 0, 0 ,
,, ,
, , ,
,, ,
, , ,
0, ,
3
In this example, it can be seen that for a pipeline with buried depth 0.75 meters the 181 membership values are 0.75, and 0.25 and zero for . The fuzzy set 182
representing the buried depth can be written as a MPT, as shown in Fig. 5. 183
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 8
184
FIGURE 5 MPT of buried depth educing by fuzzy set theory. 185
Water depth can similarly be modeled as a fuzzy set, and buried depth and water depth 186
are modeled as fuzzy sets to complete the evaluation of anchor (Fig. 3) and trawling (Fig. 4) 187
damage. 188
The elicitation of CPT could also be carried out from expertise by integrating fuzzy set 189
theory with the AHP method. This method has been described extensively by (21), and is beyond 190
the scope of this paper. 191
192
CASE STUDY 193
Offshore Pipeline from Pinghu Oil-Gas Field to Shanghai 194
An offshore pipeline with a length of 386.2 km transports natural gas from Pinghu Oil-Gas Field to 195
Shanghai. The pipeline is divided into 6 segments in order to get evaluation results with significant 196
difference. The essential data to create the BN model are listed in the Table 3. As shown in Fig.6, the 197
1st and 2nd segments of the pipeline are located in the Zhoushan fishing ground, where fishing 198
vessels appear frequently. A national coastal shipping line with busy transport ships runs through the 199
3rd segment, and the 5th and 6th segments are mainly threatened by engineering ships servicing the 200
Pinghu Oil-Gas field. In addition, the offshore pipeline is set on the continental shelf of the East 201
China Sea, and thus water depth gradually increases from west to east. Finally, among all the 202
pipeline sections, only the 6th one enjoys a covering of submarine soil with a depth of about one 203
meter. 204
TABLE 3 Basic Data of the Offshore Pipeline 205
Pipeline segment
length (km)
water depth (m)
buried depth (m)
frequency of passing ship (per year) engineering transport fishing
1 25.2 0-10 0 50 50 1500 2 75.2 10-20 0 50 150 1000 3 70 30-50 0 50 2000 500 4 105.6 50-80 0 100 300 300 5 48.5 80-100 0 700 150 100 6 61.7 100-110 1 1000 100 50
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 9
206 FIGURE 6 Offshore pipeline from Pinghu Oil-Gas Field to Shanghai. 207
208
Probability Prediction for the Pipeline Segments 209
The procedure for estimating the probability of anchor damage, , is described by the equation: 210
· 4
In the Eq. (4), (1, …, n) means the type of ship. is the frequency of the type of ships 211
passing by per year. And calculated by the BN model reflects the probability of anchor damage 212
once an -type ship passing through. In this paper, we only consider engineering ship, transport 213
ship and fishing vessel. The probability of trawling damage, , is described by the equation: 214 · 5
where is the frequency of fishing vessels running through the offshore pipeline per year, and 215
expresses the probability of trawling damage by one fishing vessel. 216
In this paper, the BN model is analyzed using HUGIN 7.6 (23). By Eqs. (4) and (5), we 217
could obtain probability prediction results of anchor damage and trawling damage, as shown in 218
Table 4. Both types of damage are classified by the damage degrees of minor damage, moderate 219
damage and major damage, which have been briefly introduced in the Table 2. 220
TABLE 4 Probability Prediction Results of Anchor Damage and Trawling Damage 221
Pipeline segment
anchor damage trawling damage minor moderate major minor moderate major
1 0.00E+00 3.58E-04 1.59E-03 1.50E-01 4.37E-02 1.79E-03 2 0.00E+00 2.01E-04 9.03E-04 4.38E-02 1.11E-02 4.52E-04 3 0.00E+00 2.80E-04 1.36E-03 5.00E-02 1.46E-02 5.95E-04 4 0.00E+00 6.80E-05 3.05E-04 1.07E-03 2.66E-04 1.08E-05 5 0.00E+00 7.44E-05 2.77E-04 4.71E-05 1.17E-05 4.76E-07 6 1.83E-04 8.25E-05 1.09E-04 9.80E-08 2.43E-08 9.90E-10
222
Analyses of Main Causes 223
The main causes of accident scenarios could be analyzed by the probability updating process, as 224
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 10
introduced in the section 2. Here the example of anchor damage to the 1th pipeline segment is 225
utilized to illustrate the analysis process. After inputting the MPTs elicited from the Table 3 (prior 226
probabilities of primary events), then given major anchor damage occurs (top event occurs), the 227
updating of the marginal probabilities of primary events ( | ) could be 228
calculated by HUGIN 7.6. As shown in Table 5, it can be concluded that “engine fail”, “buried depth 229
is shallow”, “fishing vessel passes” and “water depth is shallow” generally have the greatest 230
contribution to the occurrence of the top event, i.e., they are the main causes of the “major anchor 231
damage” accident scenario. 232
TABLE 5 Prior and Posterior Probabilities of Major Anchor Damage to the 1th Pipeline 233
Segment 234
Primary event (per hour) Prior Posterior Primary event Prior Posteriorengineering ship passes 0.0057 0.0249 buried depth is shallow 1 1 transport ship passes 0.0057 0.0268 buried depth is medium 0 0 fishing vessel passes 0.1712 0.9485 water depth is shallow 0.7 0.8033 engine fails 2.0E-05 1 water depth is medium 0.3 0.1967 235
Risk Ranking and Risk Reducing Measures 236
In order to compare the damage probability and the risk of the relevant hazards, an individual 237
ranking from 1 (very low probability) to 5 (very high probability) is proposed (24), as shown in 238
Table 6. Note, however, that the limits given in the Table 6 may be adjusted to comply with case 239
specific requirements. Then the probability analysis results of the offshore pipeline (Table 4) could 240
be ranked. For example, Table 4 shows the probability of major anchor damage to the 1st segment 241
is 1.59E-03, which intervenes between 1E-03 and 1E-02. As a result, we consider it is high 242
according to the ranking in Table 6. Then the probability ranking for major damage and 243
minor/moderate damage are provided, with the analysis results of the main causes for several risky 244
pipeline segments (see Table 7). 245
Balance between safety and cost requires using different strategies for different damage: 246
the occurrence of major damage should be eliminated, while the minor and moderate damage 247
should be reduced as low as reasonably practicable (ALARP). Therefore, the following risk 248
mitigation measures are proposed to address the results presented in Table 7. 249
Risk reducing measures (reduce probability or reduce damage degree) must be taken for the 1st 250
and 3rd segments, so as to practically eliminate major damage to these segments during the 251
pipeline’s lifetime. 252
Risk reducing measures (introduce safe distance or safety areas, introduce extra chaser tug or 253
anchor chain buoys, etc.) are better suited for the 1st segment. As a result, the high probability 254
of major damage and the very high probability of minor/moderate trawling damage could be 255
both decreased by these measures. 256
Since it is impractical to control the frequency of passing ships or the emergency anchoring of 257
transport ships, measures to reduce the damage intensity, such as increasing a pipeline’s 258
concrete coating are most appropriate for the 3rd segment. 259
Furthermore, to evaluate the economic effects of any risk reducing measures, a cost-benefit 260
calculation should be performed. It will be a combination of engineering principles and sound 261
business practices based on economic theory (25), and the combination of BN model with 262
cost-benefit value (CBV) is the major purport of our ongoing research. 263
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 11
TABLE 6 Damage Probability Ranking for One Pipeline 264
Ranking Description Probability (per year) 1 (very low) So low probability that event considered negligible. <1E-05 2 (low) Event rarely expected to occur. 1E-04>1E-05 3 (medium) Event individually not expected to happen, but when
summarized over a large number of pipelines have the credibility to happen once a year.
1E-03>1E-04
4 (high) Event individually may be expected to occur during the lifetime of the pipeline.
1E-02>1E-03
5 (very high) Event individually may be expected to occur more than over during lifetime.
>1E-02
TABLE 7 Probability Ranking and Main Causes analysis for the Offshore Pipeline 265
Segment major damage minor/moderate damage main causes of risky segments anchor trawling anchor trawling
1 high (1) high (2) medium very high (1) “buried depth is shallow”, “fishing
vessel passes”, “water depth is shallow”
(2) “fishing vessel passes”, “buried depth is
shallow”, “water depth is shallow”
(3) “buried depth is shallow”, “transport
ship passes”, “water depth is shallow”
2 medium medium medium very high 3 high (3) medium medium very high 4 medium low low medium 5 medium very low low low 6 medium very low low very low 266
CONCLUSION 267
This paper analyzed the probability of damage to offshore pipelines by passing ships using Bayesian 268
Network. Firstly, anchor damage and trawling damage are identified as the main accident types to be 269
described and analyzed. This is then followed by the creation of BN models. Three methods 270
(Boolean operation, standard and historical statistic, fuzzy set theory) were used to elicit marginal 271
probability table and conditional probability table. Subsequently, the Pinghu-Shanghai offshore 272
pipeline is evaluated by BN models. Two important functions of BN – probability prediction and 273
probability updating - were used to analyze damage probability and find the main cause of damage, 274
respectively. Finally, the evaluation results were combined to support risk ranking and risk 275
reduction measures. 276
The risk assessment framework presented in this paper is of use to oil and gas operators 277
so that the risk of anchor and trawling damage can be effectively managed. The quantification of 278
risk allows an engineered design of pipelines and adjustment of maritime operations so that risk is 279
controlled. The largest concern of operators, related to offshore pipelines, is the disruption of 280
hydrocarbon delivery to the evacuation point. Anchor and trawling damage has previously caused 281
interruptions in hydrocarbon deliveries, and operators could use the BN model to quantify damage 282
frequency, consequences, mitigation measures, and cost of mitigation to achieve a specified risk of 283
trawling and anchor damage. By doing so, the expected loss of hydrocarbon and expected costs of 284
construction (depending on acceptable risk level to the operator) can be determined for establishing 285
budgets for design, construction and installation, and also for operations and maintenances. 286
287
288
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 12
REFERENCES 289
1. Liu, Y. T., H. Hu, and Y. B. Song. Pipeline Integrity Management System Based on Dynamic 290
Risk Assessment. Journal of Shanghai Jiaotong University, Vol.45, No.5, 2011, pp. 687-690. 291
2. Liu Y T and H. Hu. Functional Modeling for an Integrated Pipeline Integrity Management 292
System Using IDEF0 Approach. Proceedings of the 1st International Conference on 293
Transportation Information and Safety, 2011. 294
3. The Update of the Loss of Containment Data for Offshore Pipeline. Publication PARLOC 295
2001. Mott MacDonald Ltd., Offshore Operators Association, U.K. and the Institute of 296
Petroleum, U.K., 2003. 297
4. Muhlbauer, W. K. Pipeline Risk Assessment Manual (Third Edition).Gulf Publishing Co., 298
Houston, TX, U.S., 2003. 299
5. Dey, P. K., S. O. Ogunlana, and S. Naksuksakul. Risk-based maintenance model for offshore 300
oil and gas pipelines: a case study. Journal of Quality in Maintenance Engineering, Vol. 10, 301
No. 3, 2004, pp.169-183. 302
6. Rajani, B.B., Y. Kleiner, R. Sadiq. Translation of pipe inspection results into condition rating 303
using fuzzy synthetic valuation technique. Journal of Water Supply Research and Technology: 304
Aqua, Vol. 55, No.1, pp.11-24. 305
7. Markowski, A. S., M. S. Mannan. Fuzzy Logic for Piping Risk Assessment (pfLOPA). 306
Journal of Loss Prevention in the Process Industries, Vol. 22, 2009, pp.912-927. 307
8. Singh, M., T. Markset. A Methodology for Risk-Based Inspection Planning of Oil and Gas 308
Pipes Based on Fuzzy Logic Framework. Engineering Failure Analysis, Vol. 16, 2009, pp. 309
2098-2113. 310
9. Doremami, N., A. Afshar, A. D. Mohammadi. Hierarchical Risk Assessment in Gas Pipelines 311
Based on Fuzzy Aggregation. Proceedings of 2nd International Conference on Reliability, 312
Safety & Hazard, 2010. 313
10. Mozzola A.. A Probabilistic Methodology for the Assessment of Safety from Dropped Loads 314
in Offshore Engineering. Risk Analysis, Vol. 20, No. 3, 2000, 327-337. 315
11. Yan S. W., and Y. H. Tian. Analysis of Pipeline Damage to Impact Load by Dropped Objects. 316
Transactions of Tianjin University, Vol. 12, 2006, pp. 138-141. 317
12. Risk Assessment of Pipelines Protection. Publication DNV-RP-F107. Det Norske Veritas 318
(DNV), Norway, 2010. 319
13. Bai, Y., and Q. Bai. Subsea Pipelines and Risers. Elsevier Science Ltd., 2005. 320
14. Dong, Y. H., and D. T. Yu. Estimation of Failure Probability of oil and gas transmission 321
pipelines by fuzzy fault tree analysis. Journal of Loss Prevention in the Process Industries, 322
Vol. 18, 2005, pp.83–88. 323
15. Wang Q., and J. P. Zhao. Fuzzy fault tree analysis method on submarine pipelines fault by the 324
third-party damage. Nature Gas Industry, Vol. 28, No. 3, 2008, pp.109-111. 325
16. J. T. Francisco, and H. Manfred. Learning a Causal Model from Household Survey Data by 326
Using a Bayesian Belief Network. In Journal of Transportation Research Board: 327
Transportation Research Record , No. 1836, Transportation Research Board of the 328
National Academies, Washington, D.C., 2003, pp. 29-36. 329
17. Ni D. H., and J. D. Leonard Ⅱ. Markov Chain Monte Carlo Multiple Imputation Using 330
Bayesian Networks for Incomplete Intelligent Transportation Systems Data. In Transportation 331
Research Record: Journal of the Transportation Research Board, No.1935, 2005, pp. 57-67. 332
TRB 2013 Annual Meeting Paper revised from original submittal.
Yutao Liu 13
18. Bang, S., R. J. Taylor, J. Yu, H. T. Kim. Analysis of anchor mooring lines in cohesive seafloor. 333
In Journal of Transportation Research Board: Transportation Research Record, No. 1526, 334
1996, pp. 47-56. 335
19. Inference between Trawl Gear and pipelines. Publication DNV-RP-F111. Det Norske 336
Veritas, Norway, 2006. 337
20. Khakzad, N., F. Khan, and P. Amyotte. Safety analysis in Process Facilities: Comparison of 338
Fault Tree and Bayesian network approaches. Reliability Engineering and System Safety, Vol. 339
96, 2011, pp.925-932. 340
21. Wang, Y. F., M. Xie, K. M. Ng, Habibullah M S. Probability analysis of offshore fire by 341
incorporating human and organizational factor. Ocean Engineering. Vol. 38, 2011, pp. 342
2042-2055. 343
22. Ship-Modu Collision Frequency, Technica, London, 1987. 344
23. HUGIN Expert software version 7.6, www.hugin.com, 2012. 345
24. Sun, D. J., L. Elefteriadou. Research and implementation of lane-changing model based on 346
driver behavior, In Journal of Transportation Research Board: Transportation Research 347
Record 2161, 2010, pp. 1-10. 348
25. Perrin, J., Jr., and R. Dwivedi. Need for Culvert Asset Management. In Journal of 349
Transportation Research Board: Transportation Research Record, No. 1957, 2006, pp. 350
8-15. 351
TRB 2013 Annual Meeting Paper revised from original submittal.