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INSTRUCTOR: 

• Yujie Mao
• Yan Shi, Ph.D
• Ana Tijerina Esquino

This techsession will only award PDHs for completion.

Purpose and Background

Impacts of Energy Transformation on Coal Rail Transportation: Estimates and Projections for the Period 2005-2050 (10 minutes)

More than 70% of U.S. coal is produced by five states, but coal is consumed all over the country primarily for electricity generation. In 2021, about 69% of coal shipments were delivered to final destinations by rail. Coal shipments contributed 11% of total revenue and 27% of gross tonnage for Class I railroads. However, increased concerns about climate impacts and enactment of clean energy policies and regulations have led to the decline of coal production and transportation. The Energy Information Administration projected that the share of coal in U.S. electricity generation would decrease from 23% in 2021 to 10% in 2050. This study investigates the potential impacts of transformations in energy policies and practices on Class I railroads due to the projected decreased coal demand. The impacts are measured by three metrics: gross revenue, network efficiency, and system redundancy. Current impacts for the period 2005-2021 are evaluated using the weighted network analysis method and Waybill data. Future impacts for the period 2022-2050 are analyzed considering six energy scenarios, including the most likely pathway suggested by the Energy Information Administration, and five shared socioeconomic pathways (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) adopted by the sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC). The results can inform strategic planning for rail operations and facility management in a changing climate.

Risk Tolerance and Attitudes in the Economics of Electric Power and Gas Utilities: Case of Wildfire for Community Resilience (30 minutes)

This presentation introduces an innovative approach to quantifying and managing risks associated with electric power and gas utilities. The complex interplay of safety, service reliability, affordability, and financial well-being is addressed in the context of varying stakeholder risk tolerance and attitudes. The proposed method offers a structured approach to set risk tolerance and attitude preferences, which partition the loss range into practical regions, such as operational, critical, and catastrophic. The approach employs loss distributions and considers the incorporation of risk tolerance based on decision-maker objectives or existing policies. Implications of imposing risk tolerance policies on investment planning decisions are discussed, providing flexibility to account for stakeholder preferences in a systematic manner.

To assess the benefits of mitigation measures while considering risk attitude, the presentation computes dollar-equivalent expected losses under risk neutrality, then adjusts them to higher levels based on willingness-to-pay amounts for risk transfer. The ratios of these premiums to expected loss values estimate the extent of risk averseness. This method is versatile and applicable to various risk types, including earthquakes, wildfires, and gas leaks. It accommodates individual and societal risk preferences, reflecting the dynamic and evolving nature of risk attitudes. The presentation also explores using formal expert-option elicitation methods to calibrate model parameters and discusses its potential for developing guidance documents and manuals of practice in the field of utility risk management economics. This comprehensive approach addresses the complex landscape of utilities, risk management, and stakeholder interests.

Machine Learning assisted Network Resilience Design (15 minutes)

Network concepts are fundamental for modeling various critical infrastructure systems, such as transportation, power transmission, and telecommunications networks. These systems are vulnerable to disruptive events, which can lead to reduced functionality or even system failure. Resilience engineering is increasingly important to enhance a network's ability to recover swiftly and provide the desired performance levels following such disruptions. This presentation focuses on proactive analysis and network design to optimize network topology for improved resilience – a concept known as network resilience-based design.

However, solving this problem is computationally expensive as it requires searching through numerous topology structures and conducting multiple resilience analyses under different network topologies, which can become unaffordable for large-scale networks. To address this issue, a new graph neural network that incorporates response flow characteristics to capture additional network features is proposed. This neural network is designed to deal with complex problems with high dimensions and nonlinear characterization, and it is integrated into an adaptive framework that combines it with a probabilistic solution discovery algorithm to solve network resilience design problems accurately and efficiently.

Building Equity into Infrastructure through Transformational Social Change (30 minutes)

The Civil Engineering and Heavy Infrastructure industries have begun to reassess the impact their work has on the communities and end users we serve. This shift in framing is evident throughout the profession and has resulted in updates to the American Society of Civil Engineers (ASCE) code of ethics, targeted grant support from U.S. Federal administrations, and multiple articles and think pieces published in a variety of publications. This session is aimed at introducing ASCE INSPIRE conference participants to the importance of considering equity and social outcomes in infrastructure projects at all stages of the planning, design and construction process, to leave a positive legacy of sustainable, resilient, and inclusive communities.

The presentation is split into three segments: an acknowledgement of learning from the past; a brief overview of current changes in the civil engineering landscape as indicative of the direction the industry is heading; and an in-depth look to the future and what transformational social change in the industry can look like. The third and main segment will focus on examples and tools to drive change towards people-focused development that goes beyond minimizing impacts on communities, to delivering long-term benefits and leaving a lasting social, economic, cultural, or environmental legacy. This will include highlighting case studies covering a range of different infrastructure projects and sectors which have centered on social outcomes from the outset, responding more holistically to community and end user needs around equality, accessibility, and inclusivity, with lessons learned and applications to other projects offered.

Learning outcomes and session benefits:

Upon completion of this course, you will be able to:

• Explain the impact of transformations in energy policies and practices on Class I railroads in the United States, with a focus on the projected decrease in coal demand for electricity generation.
• Examine the potential consequences for Class I railroads using three key metrics: gross revenue, network efficiency, and system redundancy, shedding light on how changes in the energy landscape may affect their operations.
• Identify the historical impacts on railroads for the period 2005-2021 through the weighted network analysis method and Waybill data, providing insights into the industry's recent experiences.
• Describe the future impacts on Class I railroads from 2022 to 2050 under various energy scenarios.
• Explain the approach to quantify and manage risks in electric power and gas utilities, considering the interplay of safety, reliability, affordability, and financial well-being.
• Describe a structured method to set risk tolerance and attitude preferences.
• Define how loss distributions and risk tolerance based on decision-maker objectives or policies are incorporated to inform investment planning decisions.
• Summarize the assessment of benefits of mitigation measures by computing dollar-equivalent expected losses.
• Explain the critical role of network concepts in modeling various infrastructure systems and their performance to disruptive events.
• Describe the significance of resilience engineering in ensuring rapid recovery and maintaining desired performance levels in the face of disruptions.
• Discuss the concept of network resilience-based design and its efficient approach to optimizing network topology for enhanced resilience.
• Summarize how the machine learning techniques are applied to develop a network resilience-based design model.
• Describe the relationship between infrastructure and equity
• Identify what common historical infrastructure practices have caused inequitable effects on society
• Summarize how federal goals are attempting to incorporate equity objectives to federally funded projects
• Discuss actionable tools and practices to better the social impact of your projects.

Assessment of Learning Outcomes

Learning outcomes are assessed by responding to the post-session survey. If the course is taken On-Demand, there will also be a 10-question multiple choice post-test.

Who Should Attend?

• Energy Engineers
• Resilience Engineers
• Environmental Engineers
• Machine Learning Specialists
• Infrastructure Planners
• Utility Risk Analyst
• Energy Economists
• Railroad Executives and Operation Manager
• Energy Policy Analysts
• Students

How to Earn your PDHs

This course is worth 1.5 PDH. To receive your certificate of completion, you will need to attend the live session and/or watch the recording(s) and complete the post-session survey. If the course is taken OnDemand, there will also be a 10-question multiple choice post-test.