Education

CEE 292.
Planning, Design, and Management of Civil Engineering Systems. Introduction to the formulation and solution of civil engineering problems. Major topics are: engineering economy, mathematical modeling, and optimization. Techniques, including classical optimization, linear and nonlinear programming, network theory, ciritical path methods, simulation, decision theory, and dynamic programming are applied with the aid of personal computers to a variety of civil engineering problems. Prerequisite: Mathematics 130, and credit or concurrent registration in Mathematics 225. 3 hours.

CEE 339.
Environmental Systems Analysis I. Examination of principles of environmental engineering design: applications to mathematical methods, including single and multi-objective programming, to environmental systems; economic analysis, including benefit-cost; and management strategies. Prerequisite: Civil and Environmental Engineering 292; and Civil and Environmental Engineering 342 or 349. 3 hours or 3/4 unit.

CEE 398BSM.
Environmental Risk Assesment and Management. Risk assessment and management are becoming increasingly important in the environmental industry. In this course, risk assessment methods are introduced and issues associated with managing risk are discussed. The course is taught in a case study format, focusing on a variety of environmental case studies such as air pollution, climate change, drinking water, hazardous waste storage, transport and disposal, and Superfund remediation. A group term project on assessing and managing risk associated with an environmental case study is required. Prerequisite: Civil and Environmental Engineering 293; or STAT 100. 3 hours or 3/4 unit.

CEE 439.
Risk and Uncertainty in Environmental and Water Resources Decision Making. Exploration of the fundamental concepts of uncertainty, risk, and reliability as applied to environmental and water resources systems. Decision trees, Bayes' theorem, the value of perfect and imperfect information, chance constraints, Markov and Monte Carlo modeling, geostatistics, kriging, unconditional and conditional simulation, genetic algorithms, neural networks, and a review of relevant portions of basic probability and statistical theory. Many techniques are applied to a real-world environmental decision making problem initially developed in CEE 339.

CEE 498 OS.    
Optimization Methods for Engineering Design. Optimization models have been shown to be useful tools for aiding engineering design in many fields. This course will focus on methods for applying nonlinear optimization to engineering design, with a practical, applications-oriented perspective. Topics of discussion will include: strengths and weaknesses of different approaches; handling constraints and multiple objectives; setting optimization control parameters; and strategies for overcoming computational barriers, including multiscale, domain decomposition, parallel, and hybrid approaches. The course is intended to serve students from all areas of engineering and does not assume prior knowledge in any particular application area. Students will complete a project applying one of the methods to a problem in their own field.