Predictive earthquake damage modeling for natural gas distribution infrastructure

The Pacific Gas and Electric Company (PG&E) operates and maintains 48,000 miles of natural gas pipeline, serving over 4.3 million customer accounts. Along with water, electric power, and transportation services, these lifelines serve critical functions throughout multiple communities. Considering PG&E provides services in both densely populated and seismically active areas, the organization has invested extensively in modeling technology to help estimate resource needs and develop resiliency plans in the event of an earthquake. This thesis aimed to develop a damage prediction model to improve emergency response time and restoration efficiency. The machine-learning based model built upon currently used predictive algorithms, while adding features necessary to account for distribution branch lines and above-ground meter sets. Research and analysis showed factors beyond ground-motion prediction equations could be used to estimate pipeline damage and were consequently included.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Link, Steven B.

Advisor dc:contributor.advisor

• Georgia Perakis and Saurabh Amin.

• Sloan School of Management.
• Mechanical Engineering.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122832

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122832