Results for CEE 100

Managing the life cycle of buildings from the owner, designer, and contractor perspectives emphasizing sustainability goals; methods to define, communicate, coordinate, and manage multidisciplinary project objectives including scope, quality, life cycle cost and value, schedule, safety, energy, and social concerns; roles, responsibilities, and risks for project participants; virtual design and construction methods for product, organization, and process modeling; lifecycle assessment methods; individual writing assignment related to a real world project. Fulfills WIM requirement for CEE majors. Co-taught with ARCH 542/741 Managing Sustainable Design and Decarbonization Projects at Howard University.

Physical properties of fluids and their effect on flow behavior; equations of motion for incompressible ideal flow, including the special case of hydrostatics; continuity, energy, and momentum principles; control volume analysis; laminar and turbulent flows; internal and external flows in specific engineering applications including pipes and open channels; elements of boundary-layer theory. Laboratory exercises to illustrate key principles. Prerequisites: E14, Physics 41, Math 51, or CME 100.

Physical properties of fluids and their effect on flow behavior; equations of motion for incompressible ideal flow, including the special case of hydrostatics; continuity, energy, and momentum principles; control volume analysis; laminar and turbulent flows; internal and external flows in specific engineering applications including pipes and open channels; elements of boundary-layer theory. Prerequisites: E14, Physics 41, Math 51, or CME 100.

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.

This course discusses elements of a transition to 100% clean, renewable energy in the electricity, transportation, heating/cooling, and industrial sectors for towns, cities, states, countries, and companies. It examines wind, solar, geothermal, hydroelectric, tidal, and wave characteristics and resources; electricity, heat, cold and hydrogen storage; transmission and distribution; matching power demand with supply on the grid: efficiency; replacing fossil with electric appliances and machines in the buildings and industry; energy, health, and climate costs and savings; land requirements; feedbacks of renewables to the atmosphere; and 100% clean, renewable energy roadmaps to guide transitions.

(Formerly GEOLSCI 230) Lectures and laboratory experiments. Properties of rocks and geomaterials and how they relate to chemo-mechanical processes in crustal settings, reservoirs, and man-made materials. Focus is on properties such as porosity, permeability, acoustic wave velocity, and electrical resistivity. Students may investigate a scientific problem to support their own research (4 units). Prerequisites: Physics 41 (or equivalent) and CME 100. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).

Methods to manage the physical production of construction projects; design, analysis, and optimization of the fabricate-assemble process including performance metrics. Project management techniques and production system design including: push versus pull methods; master scheduling and look-ahead scheduling; scope, cost, and schedule control; earned value analysis; critical path method; location-based scheduling; 4D modeling; workflow; trade coordination; methods to understand uncertainty and reduce process variability; and supply chain systems including made-to-stock, engineered-to-order, and made-to-order. Prerequisite: 100 or consent of instructor.

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.

This course discusses elements of a transition to 100% clean, renewable energy in the electricity, transportation, heating/cooling, and industrial sectors for towns, cities, states, countries, and companies. It examines wind, solar, geothermal, hydroelectric, tidal, and wave characteristics and resources; electricity, heat, cold and hydrogen storage; transmission and distribution; matching power demand with supply on the grid: efficiency; replacing fossil with electric appliances and machines in the buildings and industry; energy, health, and climate costs and savings; land requirements; feedbacks of renewables to the atmosphere; and 100% clean, renewable energy roadmaps to guide transitions.

Pension funds, sovereign wealth funds, endowments, foundations, and other beneficial institutional investors control more than $100 trillion in investable assets. These funds may be beneficial in nature, they exist to secure a promised social benefit, but they are also the base of our modern capitalist system. They are responsible for funding industries that Stanford students will seek to work in (or with), such as venture capital, hedge funds, private equity, or other professional investors. As such, if we are to unlock financial capital to fund meaningful solutions to societies problems, such as climate change, these investors must allocate capital to these projects. But few people understand why these organizations exist, how they operate or invest, and what the implications of their decisions are for society and the planet. This course aims to equip the Stanford community with a deep understanding of beneficial institutional investors. The course will be given in a seminar format and be broken down into three modules: 1) Why beneficial investors exist; 2) How they invest their capital; and 3) How their investing affects the sustainability of modern capitalism.

Virtual Design and Construction (VDC) starts by understanding the client's objectives for building performance and the translation of these objectives into measurable project and production objectives. Based on a culture of proactive and constructive engagement, three mutually supportive strategies are essential to achieve these objectives: (1) the knowledge of the many disciplines contributing to the design and construction of a buildable, usable, operable, and sustainable building needs to be orchestrated concurrently, (2) the information supporting the project team must be integrated and be accessible seamlessly, and (3) the workflow carried out by the project team must enable the creation of integrated knowledge and information and lead to decisions that stick. This course will teach all the essential elements of VDC. This is an online course. Prerequisite: 100 or consent of instructor. Recommended: CEE 240, CEE 241.

The course delves into designing for gradient spaces. Gradient spaces are physical spaces that blend from the 100% physical (real reality) to the 100% digital (virtual reality) and anything in between. Through a series of lectures, discussions, and hands-on projects, students will explore what it means to design in such spaces on three core levels; from design thinking, to architectural design, and technological design (e.g., mixed reality applications). It will consider the above from the perspective of occupants of these spaces that have different physical and digital needs and preferences. The goal of the course is to create cross-disciplinary interactions to connect physical space design and digital application design, towards the creation of physical and digital experiences that are suitable and centered to users. While students will be exposed to all three levels of design, they are expected to focus only on one for their final project.