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ENCE 302 Probability and Statistics for Civil and Environmental Engineers
Statistics is the science of data. Civil Engineers must often make decisions based on incomplete, variable or uncertain information. In addition, modern methods of design and analysis need to account for variability in natural, engineered and human systems. After successful completion of this class, a student should have facility and familiarity with established basic techniques for managing data, modeling variability and uncertainty, communicating about data and decisions, and supporting or defending a decision or judgement based on uncertain or incomplete data.
ENCE 425 Decision Analysis for Engineering
Probability basics, subjective probability, using data, introduction to decision analysis, elements of decision problems, structuring decisions, making choices, sensitivity analysis, creativity and decision-making, Monte Carlo simulation, value of information, risk-based decision making and multi-criteria ranking.
ENCE 615 Structural Reliability
Probability and statistics. Fundamentals of uncertainty analysis. Fundamentals of structural reliability. Reliability-based design. Simulation and variance reduction techniques. Fuzzy sets and applications.
ENCE 620 Risk Analysis for Engineering
Sources of hazards, definition of risk, system analysis, functional modeling and analysis techniques, probabilistic risk assessment procedure, risk methods, risk acceptance, assessment of failure likelihood, consequence assessment, risk benefit assessment, uncertainty surces and types, modeling uncertainty, risk analysis and decision making under uncertainty, collection of data, expert-opinion elicitation, human-machine interface and human factors engineering.
ENCE 627 Risk Assessment and Decision Analysis for Project Management
Introduction to identifying, analyzing, assessing, and managing risks inherent to engineering projects. Includes: probability modeling, choice and value theory, schedule and cost risk, risk mitigation and transfer, and contract considerations of project risk. Examples are drawn from construction, software development, systems integration, and other large engineering projects; and cover probability basics, subjective probability, statistical data analysis, introduction to decision theory, Monte Carlo simulation, value of information, and risk-based decision making.
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