Session

Session 1 - Fluid Viscous Dampers and Replaceable Structural Fuses: Improving Building Performance and Functional Recovery

Creating safe and functional buildings, both new and retrofit, is a critical task for society. The changing expectations in our industry, and for society in general, require better performing structures while constraining already tight resources. Improving community resilience by enhancing performance during an earthquake and functional recovery after is essential. Efficient use of resources (financial and material) also is a rising challenge that often is at odds with designing for resilience. The more we can adaptively reuse (retrofit) our existing infrastructure is one way to address these competing needs. Furthermore, considerations of performance and post-earthquake status of new structures helps us change the paradigm moving forward.

The challenge is that typical methods used today to design and construct buildings need to be disrupted. Designing, constructing, and retrofitting buildings to remain undamaged in major seismic events is not economically viable. Current codes allow substantial damage to occur in specific structural components with the aim of preventing collapse and preserving life; there is little consideration for the post-earthquake functionality. The impact of this approach was witnessed in the 2010/2011 Christchurch, New Zealand earthquakes, where the loss of life was limited, but over 1,400 structures in the central business district were demolished in the years following the earthquake. A significant contributor to this was that the cost of repairing the damaged structures was not economically viable. The U.S. uses similar design codes so the impact here is expected to be similar.

To do better, we must design and build differently. Fluid viscous dampers (FVDs) and replaceable fuses can disrupt and improve the industry if all involved stakeholders understand the advantages. The primary physical safety requirement of a building during a significant earthquake is the ability to dissipate the energy imparted to the structure due to the ground shaking. With typical structures, this means the beams, braces or shear walls incur damage while maintaining structural stability and integrity. Alternatively, energy dissipation can be provided by components that dissipate energy without damage or intentional yielding elements that can be easily repaired or replaced. Replaceable structural fuse mechanisms work off the basic principle that all the earthquake damage is concentrated into an easily accessed and replaced component. When designed correctly, the construction cost of a building with a replaceable fuse is the same or less than conventional construction. The post-event benefits are significant in terms of both repair costs and downtime duration. FVDs absorb seismic energy without damage to the device and limits damage to the structural systems by reducing deformations and accelerations throughout the structure. Utilizing FVDs can result in minor cost increases in the structural frame, but the reduced damage and downtime duration more than offset these initial costs. Coupling FVDs and replaceable fuses creates even more post-event advantages. Case studies using replaceable fuses and FVDs demonstrate successful projects for new construction and retrofit. Estimates of the post-earthquake response, expected repair costs, and predicted downtime duration are provided for each of the case studies.

Learning Objectives:

• Describe the ways replaceable fuses can be implemented for new construction and retrofit of existing construction
• Describe the applications and advantages of fluid viscous dampers in buildings and other structures for new construction and retrofit
• Identify the advantages of replaceable fuses and fluid viscous dampers to improve performance and functional recovery of buildings in seismic regions
• Quantify the impact of including fluid viscous dampers and replaceable fuses on initial construction cost, post-earthquake repair costs, and downtime estimates

Session 2 - Advancing Resilient Communities with AI: The NCSEA Foundation’s Strategic Plan for AI and Machine Learning Application in Seismic Evaluation

The NCSEA Artificial Intelligence in Structural Engineering initiative focuses on advancing the adoption of AI within the profession to enhance resilience, efficiency, and innovation. The initiative provides structural engineers with resources to explore AI-driven processes, including curated datasets, workflows, and tools for practical application. It emphasizes education, partnerships, and the development of industry-specific policies to integrate AI technologies effectively into design, analysis, and risk management tasks. By fostering collaboration with academia and industry leaders, the program aims to equip engineers with cutting-edge skills and insights, driving the future of structural engineering toward smarter, more sustainable solutions.

AI provides the structural engineering profession with new tools for advancing resilient communities. Natural disasters are very difficult to predict, and they often come with a long downtime for reoccupancy and casualty toll. Today, structural engineers design stronger and more resilient buildings thanks to the development of codes and guidelines. However, evaluation of existing building stock still is a difficult and time-consuming task.

In this presentation, we will give an example of how to do this efficiently with AI machine learning, a subset of artificial intelligence, which is the scientific study of algorithms and statistical models that enable computer systems to automatically learn and improve from experience without explicit programming. In the context of structural engineering, machine learning empowers engineers to leverage data-driven approaches to enhance design, analysis, and decision-making processes.

The session will focus on machine learning, discussing its intriguing history and emphasizing its significance in the field of built environment. Attendees will gain insights into the various applications of machine learning and its relevance in performing engineering and consultancy tasks for the built environment.

A key highlight of the presentation will be a compelling case study that investigates the feasibility of utilizing machine learning algorithms for a seismic evaluation of existing low-rise reinforced concrete buildings portfolio. This case study aims to address two fundamental questions: The first question to be explored is whether a machine learning algorithm, trained using meticulously selected datasets, can effectively forecast building performance levels and other key engineering demand parameters. The focus here is on the algorithm's ability to accurately predict these crucial aspects. The second question pertains to the algorithm's capability to provide instant predictions. In other words, can it swiftly generate quick forecasts without any significant computation or processing time?

In conclusion, this session offers a comprehensive exploration of the transformative potential of machine learning in addressing seismic risk assessment challenges within the built environment. By leveraging data-driven approaches, engineers and professionals in the AEC industry can enhance their understanding of structural performance and make more informed decisions to ensure the resilience and safety of existing building stock. Through insightful discussions and a compelling case study, attendees will gain valuable insights into the practical applications of machine learning and its profound implications for the future of structural engineering and consultancy practices.

Learning Objectives:

• Understand the role of AI in building resilience. Attendees will explore how artificial intelligence and machine learning can empower structural engineers to design and evaluate buildings that contribute to the resilience and safety of communities against natural disasters.
• Leverage machine learning for seismic risk assessment. Attendees will learn how machine learning can streamline the seismic evaluation of existing building stock, providing faster and more accurate insights to enhance community preparedness and recovery.
• Evaluate AI’s impact on community downtime and recovery. Attendees will examine how AI-driven predictions can minimize downtime for reoccupancy and improve recovery strategies, ensuring faster restoration of community functionality after seismic events.
• Apply data-driven approaches for resilient infrastructure. Attendees will gain insights into practical workflows and case studies that demonstrate how AI tools can be effectively integrated into structural engineering practices to support resilient and sustainable urban development.