21 - 30 of 104 results for: CEE

Part lecture, part seminar, and part workshop, this course is designed to prepare students to become leaders in creating a livable future. Students will engage critically with contemporary sustainability concepts, rating systems, and architecture case studies. A combination of creative and analytical exercises will develop students' abilities to integrate sustainability into the design process, including goal setting, selecting and evaluating sustainability strategies, and creating compelling sustainability narratives and diagrams.

Infrastructure is critical to the economy, global competitiveness and quality of life. Topics include transportation, social infrastructure, energy, water and communications sectors. Analysis of how projects are designed, constructed, operated, and maintained. Focus is on public works projects globally, alternative project delivery approaches and organizational strategies. Case studies include three real infrastructure megaprojects managed by the Instructor while in Industry. Nine integrated guest lecturers from Industry supplement specific functional areas of expertise. Student teams prepare competing design/build/finance/operate/maintain (DBFOM) proposals for a large infrastructure project.

The built environment enables access to economic and social mobility, however access to such systems is not uniform across communities. This creates infrastructure inequity. Climate change threatens to exacerbate existing inequities in interdependent infrastructure systems such as energy, transportation, air, and water/wastewater to name a few. The engineer of tomorrow must understand the inequities in the system and the policies that produced them in order to develop robust and innovative approaches to design and manage future systems. This course will introduce students to the prominent theories of equity and environmental justice with a focus on implementation for infrastructure. Students will learn the limitations of decontextualized technical engineering solutions and their impacts on society. Upon completion of the course, students will understand how to abstract and develop models that incorporate elements of equity and justice in civil engineering systems. This course is designed to prepare next generation engineers for careers in which they will participate in projects that directly affect historically marginalized communities.Who can take the course: It is going to be a graduate course, so students should have completed an engineering degree OR are in their final year of their degreePrerequisites: There are no pre-requisites, however familiarity with engineered systems is expected

The course is an introduction to Visual Machine Perception technology - and specifically Computer Vision and Machine Learning (CV-ML) - for the built environment. It will explore fundamentals in this technology both in research and products, in tight reference to design, construction, and operation/management. It will consider the current and potential impact of this technology on achieving sustainability goals, such as related to reuse, circularity, and performance-based lifecycle, as well as the organizational considerations behind development and adoption.

This course introduces practical applications of data analytics and machine learning from understanding sensor data to extracting information and decision making in the context of sensed physical systems. Many civil engineering applications involve complex physical systems, such as buildings, transportation, and infrastructure systems, which are integral to urban systems and human activities. Emerging data science techniques and rapidly growing data about these systems have enabled us to better understand them and make informed decisions. In this course, students will work with real-world data to learn about challenges in analyzing data, applications of statistical analysis and machine learning techniques using MATLAB, and limitations of the outcomes in domain-specific contexts. Topics include data visualization, noise cleansing, frequency domain analysis, forward and inverse modeling, feature extraction, machine learning, and error analysis. Prerequisites: CS106A, CME 100/ Math51, Stats110/101, or equivalent.

HVAC, lighting, and envelope systems for commercial and institutional buildings, with a focus on energy efficient design. Knowledge and skills required in the development of low-energy buildings that provide high quality environment for occupants.

An introduction to what causes the motions in the oceans. Topics include: the physical environment of the ocean; properties of sea water; atmosphere-ocean interactions; conservation of heat, salt, mass, and momentum, geostrophic flows, wind-driven circulation patterns; the Gulf Stream; equatorial dynamics and El Nino; and tides. By the end of the course, students will have physical intuition for why ocean currents look the way they do and a basic mathematical framework for quantifying the motions. Prerequisite: PHYSICS 41

Introduction to the physics governing the circulation of the atmosphere and ocean and their control on climate with emphasis on the large-scale ocean circulation. This course will give an overview of the structure and dynamics of the major ocean current systems that contribute to the meridional overturning circulation, the transport of heat, salt, and biogeochemical tracers, and the regulation of climate. Topics include the tropical ocean circulation, the wind-driven gyres and western boundary currents, the thermohaline circulation, the Antarctic Circumpolar Current, water mass formation, atmosphere-ocean coupling, and climate variability. Prerequisites: MATH 51 or CME100; and PHYSICS 41; and a course that introduces the equations of fluid motion (e.g. ESS 246A, ESS 148, or CEE 101B).

The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Note: The course will be taught in-person at the Hopkins Marine Station of Stanford University in Pacific Grove, Monterey Bay. Occasionally students my attend a few classes via Zoom. Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply.