Related Concept Videos
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
00:54Uncertainty: Confidence Intervals
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
01:12Modeling and Similitude
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
01:30Typical Model Studies
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Related Experiment Video
Updated: Jul 15, 2026
11:41
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
Assessing uncertainty in simulation-based maritime risk assessment.
Jason R W Merrick1, J Rene van Dorp, Varun Dinesh
1Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, USA. jmerrick@vcu.edu
Summary
This study introduces Bayesian simulation techniques to model uncertainty in maritime risk assessments. These methods confirm that proposed ferry expansions in San Francisco Bay are robust despite inherent uncertainties.
Area of Science:
- Maritime Transportation
- Risk Assessment
- Computational Science
Background:
- Simulation-based probabilistic risk assessment (PRA) is used in maritime transportation.
- Previous PRA studies lacked uncertainty quantification, hindering decision-making.
- Uncertainty in risk estimates impacts the assessment of specific risks and benefits.
Purpose of the Study:
- To demonstrate the application of Bayesian simulation techniques for propagating uncertainty in maritime risk assessments.
- To address the need for quantifying uncertainty in simulation models used for risk analysis.
- To assess the impact of uncertainty on the conclusions of maritime risk studies.
Main Methods:
- Utilized Bayesian simulation techniques to model and propagate uncertainty.
- Applied these techniques to a case study of proposed ferry service expansions in San Francisco Bay.
- Integrated uncertainty modeling within existing simulation frameworks for risk assessment.
Main Results:
- The study successfully demonstrated the use of Bayesian simulation for uncertainty propagation.
- Conclusions from the original San Francisco Bay ferry expansion study were found to be robust to uncertainties.
- The research provides a method for quantifying the impact of uncertainty in maritime risk analyses.
Conclusions:
- Bayesian simulation techniques offer a robust approach to modeling uncertainty in maritime risk assessment.
- The developed technique represents a state-of-the-art advancement in computational sciences for complex system analysis.
- This work paves the way for more reliable decision-making in maritime transportation by accounting for inherent uncertainties.