# Techniques of Safety and Environmental Risk and Reliability Modelling for Sustainable Inland Water Transportation System

Authors:
O. O.Sulaiman, School of Ocean Engineering, University Malaysia Terengganu, Kuala Terengganu, Malaysia. Corresponding author: o.sulaiman@umt.edu.my
A.S.A. Kader, Marine Technology Centre, Department of Marine Technology, Faculty of Marine Aeronautic and Automotive Engineering, University Technology Malaysia, 81310 Skudai, Johor, Malaysia

Editorial notes:
According to a qualified peer reviewer, the findings of the research presented in this article are applicable also on the sustainable inland water transportation system of the river Danube and other navigable rivers of the Danube basin.

## Introduction

Vessel collision risks in waterways are of huge consequence. Collision accident scenarios carry heavy consequence, thus their occurrence is infrequent. These accidents represent a risk because they expose vessel owners and operators, as well as the public, to the possibility of losses such as vessel and cargo damage, injuries and loss of life, environmental damage, and obstruction of waterways. They can also lead to instantaneous and point form release of harmful substances to water, air, soil and long time ecological impact (Eftratios Nikolaidis, 2005). Environmental problems and need for system reliability call for innovative methods and tools to assess and analyse extreme operational, accidental and catastrophic scenarios as well as accounting for the human element, and integrate these into a design environments part of design objectives. Risk and reliability – based design entails the systematic integration of risk analysis in the design process targeting system risk prevention, reduction that meet high – level goals and leave allowance for integrated components of the system includes environmental conditions that will facilitate and support a holistic approach for reliable and sustainable waterways and require trade-offs and advance decision-making leading to optimal design solutions (Wallace R. Blischke, 2000).

This paper discusses modelling of waterways collision risk. The paper presents the model of waterways collision risk and associated rate of occurrence and consequence. Relation with other variable risk factors like operator skill, vessel characteristics, traffic characteristics, topographic and environmental difficulty of the transit are taken into consideration (Wallace , 2000, John, 2000). Accident frequency and consequence are determined stochastically. Waterway variable and parameters are compared. The paper hopes to contribute to decision support for development and regulation of inland water transportation.

## Safety and Environmental Risk for IWT

Total risk approaches enable appropriate trade off for advanced sustainable decision making. Integrated risk based system design requires the availability of tools to predict the safety, performance and system components Waterway accident falls under scenario of collision, fire and explosion, flooding, grounding. Other causal factors could fall under Loss of propulsion, Loss of navigation system, Loss of mooring function and Loss of other accident from the ship or waterways. Risk work process targets the following (IMO, 1993):

1. Cause of risk and risk assessment; this involves system description, identifying the risks associated with the system, assessing them and organising them in degree or matrix. IWT risk can be as a result of the following: Root cause, immediate cause, Situation causal factor, Organization causal factor
2. Risk analysis and reduction process; this involves analytic work through deterministic and probabilistic method that strengthen reliability in system.
3. Reduction process that targets initial risk reduction at design stage; risk reduction after design in operation and separate analysis for residual risk for uncertainty as well as human reliability factor.

## Technique of Risk Analysis

Past engineering work has involved dealing with accident issues in a reactive manner. System failure and unbearable environmental problems call for new proactive ways that account for equity requirement for human, technology and environment interaction. The whole risk – assessment and analysis process starts with system description, functionality and regulatory determination and this is followed by analysis of:

1. Fact gathering for understanding of contribution factor
2. Fact analysis of check consistency of accident history
3. Conclusion drawing about causation and contributing factor
4. Countermeasures and recommendations for prevention of accident

The risk process begins with definition of risk which stands for the measure of the frequency and severity of consequence of an unwanted event (damage, energy, oil spill). Risk is defined as product of probability of event occurrence and its consequence.
Most risk based methods define risk as:

Risk (R) = Probability (Pa) X Consequence (Ca)  Eq. 1

Or, in a more elaborate expression, risk can be defined as:

Risk = Threat x Vulnerability x {direct (short-term) consequences + (broad) Consequences} Eq. 2

Frequency at which potential undesirable event occurs is expressed as events per unit time, often per year. The frequency can be determined from historical data. In risk analysis, serenity and probability of adverse consequence hazard are dealt with through systematic process that quantitatively measure perceived risk and value of system using input from all concerned waterway users and experts. Risk can also be expressed as:

Risk = Hazard x Exposure Eq. 3

Hazard is anything that can cause harm (e.g. chemicals, electricity, Natural disasters), while exposure is an estimate on probability that certain toxicity will be realised. Severity may be measured by No. of people affected, monetary loss, equipment downtime and area affected by nature of credible accident. Risk management is the evaluation of alternative risk reduction measures and the implementation of those that appear cost – effective where:
Zero discharge or negative damage = Zero risk Eq. 4

The risk and reliability model subsystem focusses on the following identified four risks assessment and analysis application areas that cover hybrid use of techniques ranging from qualitative to qualitative analysis (Wang, 2000):

1. Failure Modes Identification Qualitative Approaches
2. Index Prioritisation Approaches
3. Portfolio Risk – Assessment Approaches, and
4. Detailed Quantitative Risk – Assessment Approaches.

Effective risk assessments and analysis MODELLING required three elements highlighted in the relation below.

Risk modelling = Framework + Models + Process Eq. 5

Reliability – based verification and validation of system in risk analysis should be followed with creation of database and identification of novel technologies required for implementation of sustainable system.

SERM addresses risks over a entire life of the complex system like IWT system where the risks are high or the potential for risk reduction is slim. SERM addresses quantitatively, accident frequency and consequence of IWT. Other risk and reliability components include human reliability assessment which is recommended to be carried out separately as part of integrated risk process. Other waterways and vessel requirement factors that are considered in SERM model are: Construction; Towing operations and abandonment of ship; Installation; Hook-up and commissioning; Development and major modifications. Integrated risk-based methods combined various techniques as required in a process. Table 1 shows available risk-based design for techniques. This can be applied for each level of risk. Each level can be complimented by applying causal analysis (system linkage), expert analysis (expert rating), and organisational analysis (Community participation) in the risk process. From Figure 1, the method used is risk analysis that involves frequency analysis where the system is modelled with a hybrid of deterministic, probabilistic and stochastic process (Sulaiman, 2012).

Table 1: Risk based design techniques

Allowance should be made to introduce new issues defining the boundary in the port from time to time. The choice of appropriate types of risk tool required for the model depend on the objectives, criteria and parameters that are to be used. Various forms of risk presentation may be used. Risk to life is often expressed in two complementary forms. The risk experienced by an individual person and societal risk, the risk experienced by the whole group of people exposed to the hazard (damage or oil spill).

## System Level Collisions Risk Modelling

Collisions in waterways are considered low-frequency and high-consequence events that have associative uncertainty and characteristics of a dynamic and complex physical system. This makes risk and reliability analysis modest methods to deal with uncertainties that come with complex systems. Employment of hybrid deterministic, probabilistic and stochastic method can help integrate parametric elements of the system required for system-behaviour analysis. The mode example of Langat River, the Langat River where aspect of frequency, consequence model is used to represent total risk estimation of the system in the river. Couple with analyses of frequency of occurrence , potential accident causal factor and effect of occurrence is modelling of risk contributing factor of subsystem analysis which is performed using software to model fault tree and event tree (Brebbia et. al., 2000, DnV.2001). Figure 1 shows vessel-requirement parameters for the Langat River waterways vessel.

Figure 1: Langat vessel particulars

Other causes of accident in the river can be as a result of causes from external sources like small craft, under which there are cause of cause, cause from other uncertainty including human error that may attract separate subsystem analysis which are excluded in this analysis.

Figure 3 shows waterway system input and output block representation. P (collision) = P (propulsion failure) + P (loss of navigation failure) + P (Loss of vessel motion) +P (Uncertainty, system complexity) Eq. 6

### Traffic frequency estimation modelling

Equation 2 shows first principle deterministic equation for the physical model of the whole system.

Figure 2: Risk and reliability system analyses system

#### Analysis of present traffic situation

Table 2 shows risk acceptability criteria.

$\rho = \frac{N_m}{v.r.W} \quad \textrm{Ship/}^{m^2}$ Eq.6

Table 2: Accident frequency risk acceptability criteria

### Damage estimation modelling

$\textrm{Initial kinetic energy of ship = E = 1/2}^{\frac{M}{1000}V^2.K} \textrm{(MJ)}$
Eq 7
Where: E = impact energy (MJ), M= vessel mass (tonnes), V= vessel speed
K factor assignment of 1.1 for head on collision (powered) impacts, and 1.4 for for broadside (drifting)

Consequence impact acceptability can be deduced as shown in Table 6. Severity number can be assigned depending on modality of risk analysis and findings (DnV, 2001, David, 1996).

Table 3: Consequence acceptability criteria

RPN = S x O X D = 7X4X8 = Eq 8

RPN calculation of propulsion failure is the highest.

Advantage derived from channel-improvement work like channel widening, deepening and straightening could be quantified into sustainability equity for determination of risk cost control option for Langat River sustainable navigation and transportation.

## Result presentation

The result can be compared with risk-acceptability criteria for the offshore and maritime industry. The acceptability matrix is on ALARP principle. Figure 3 shows the result of correlation of combined graphs for accident variables of frequency analysis with changing channel variables (V, W, Nm, and B). For accident per 10,000 years, speed and number of ships is critical. Maximum speed of 4 knots is considered best for the channel. Following precision theory, impact is more likely for average excess speed of 12 knots at angle around 100 to 150 degree.

Figure 3: Combined graphs for frequency analysis with changing channel variables

Figure 4 shows accident is intolerable for vessel of 12 knots. The amount of energy is unacceptable from vessel of 20,000T and speed of 40 knot.

Figure 4: Correlation between accident energy, volume of collapse and mass

The consequence could further be broken down into effect for ship, human safety, oil spill and ecology.

## ALARP principal, risk-acceptability criteria and risk control option

The analysis is followed by influence diagram risk-control-option and sustainability balancing of cost-benefit towards recommendation for decision support for efficiency, reliability and effectiveness. Historical observation reveals that the collisions in Langat River have significantly increased. The ALARP (As Low As Reasonable Possible) graph show that the risk is low, but this is conservatively unacceptable for a channel with less traffic density. The ALARP work is followed by risk-acceptability criteria whose analysis is followed with risk cost-control option (RCO) using cost of averting fatality index (ICAF). Advantage derived from channel use and improvement work like channel widening, deepening and straightening could be quantified into sustainability equity for determination of RCO for efficient and sustainable transportation on Langat River. Figure 13 shows typical risk matrix diagram to match ALARP region.

Table 4: Risk-priority matrix, L = low risk; M moderate risk; H = high risk; V =very high risk

Risk-acceptability criteria established in many industries and regulations are for limit definition of the risk. Risk is never acceptable, but the activity implying the risk may be acceptable due to benefits safety, fatality, injury, individual and but societal risk, environment and economy. The rationality may be debated, societal risk criteria are used by increasing number of regulators. The consequence analysis is followed by an influence diagram that is based on ALARP principle. ALARP here has two main components (frequency Vs consequence) of risk whose combined plot against each other is a measure of overall risk.

Figure: 5: Accident energy Vs Accident Frequency at Changing beam of ship

## Conclusion

System-level damage estimation modelling under risk of consequence and its importance in risk-analysis process using hybrid of first-level approximation and stochastic method associated with risks has been modelled. Frequency and consequence graph has been generated and risk components variables have been simulated to facilitate choice of decision support for sustainable waterways transportation system design. Total risk for Langat River has been model from SERM. Accident collision per year has been determined, large barges have higher accident rates than small ships. Variables that affect accident rates have been simulated for necessary limit acceptability purpose for the channel. Accident rate has increased compared to previous year, a situation that required attention for solution. Damage estimation does have some degree of linear relationship with the risks to people and the environment. Risk measurement can further be reliable by determining risk-control option through sustainability balance between environmental, economy and safety. Quality of operator’s, real-time environmental information about environmental conditions, including currents, tide levels, and winds in transit can be useful in sustainability balance work for waterways developments. It is important to use caution when comparing accident rates across ports and over time because of differences in reporting criteria.

## References

Brebbia C.A, Olivella. J. Gallor. W. (2000). The safety of ship movement in port water area.
Maritime Engineering and port. WIT press, pg 80-89.

DnV. (2001). Marine Risk Assessment. Her majesty stationary office. United Kingdom.

David Vose. 1996. Risk Analysis – A Quantitative Guide. John Wiley & Sons, INC. Canada pp. 67-87.

IMO. (1993). Regulation on ship subdivision and damage stability of cargo ship, above 100m.

Resolution a 684(17). Sales number IMO 871E. International Maritime Organization, London.

O.O. Sulaiman, Safety and Environmental Risk Model for Inland water Transportation, (2012), ISBN (978-3-8465-0828-2) Lambert Academy publishing, Germany

Wallace R. Blischke. (2000). Reliability modeling, prediction, and optimization. pg 119-123.