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ASCE Manuals and Reports on Engineering Practice 147 - Objective Resilience: Objective Processes, 2022

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ASCE Manuals and Reports on Engineering Practice 147 - Objective Resilience: Objective Processes, 2022

Book_5124_C000 [Go to Page]
Half Title
Title Page
Copyright Page
Contents
Blue Ribbon Panel (In Alphabetical Order)
Authors (In Alphabetical Order)
Preface
Introduction
Book_5124_C001 [Go to Page]
Chapter 1: Uncertainty Quantification in Objective Resilience [Go to Page]
1.1 Introduction
1.2 Monte Carlo Simulation
1.3 Spectral Representation Method
1.4 Recommended Practices and Examples [Go to Page]
1.4.1 �Simulation of Seismic Ground Motion Acceleration Time Histories
1.4.2 Simulation of Wind Velocity Time Histories
1.4.3 Simulation of Random Heterogeneous Soil Properties
References
Book_5124_C002 [Go to Page]
Chapter 2: Reliability and Resilience Modeling and Quantification [Go to Page]
2.1 Introduction [Go to Page]
2.1.1 Reliability Metrics of Non-Repairable Systems
2.1.2 Reliability Metrics of Repairable Systems
2.1.3 Concept of Resilience
2.2 Multi Hazards [Go to Page]
2.2.1 Natural Hazard
2.2.2 Man-Made Hazard
2.2.3 Multi Hazard
2.3 System Resilience [Go to Page]
2.3.1 Resilience Definition
2.3.2 Qualitative and Semi-Quantitative Framework of Resilience
2.3.3 Resilience Quantification
2.3.4 Proposed Resilience Quantification
2.4 System Recovery [Go to Page]
2.4.1 Stochastic Recovery Process
2.4.2 Recovery Sequence
2.4.3 Proposed Importance Measuress [Go to Page]
2.4.3.1 Importance Measures for Nonrepairable Systems. Reviewing current IMs for nonrepairable systems, we notice that these IMs do not adequately and effectively distinguish the importance of components under some scenarios. For example, in paralle
2.4.3.2 Importance Measures for Repairable Systems. Similar to Equation (2-28), we propose a weighted IM for repairable systems by adopting the concept of availability as given in thefollowing equation:
2.5 Illustrative Example [Go to Page]
2.5.1 System Resilience Quantification
2.5.2 Importance of the Units
2.5.3 Resilience and Importance Measure of Repairable Systems
2.6 Application of Resilience in Different Fields
2.7 Summary
2.8 Recommendations
Bibliography [Go to Page]
On Multihazard
On Resilience Quantification (Qualitative and Semiquantitative)
On Resilience Quantification (Quantitative)
On Resilience in Communities
On Resilience in Chemical Systems
On Resilience in Communication and IT Systems
On Resilience in Water and Waste Water Systems
On Resilience in Infrastructure Systems
On Resilience in Electronic and Power Systems
On Resilience in Supply Chain Systems
On Resilience in Transportation Systems
More Bibliography on Resilience (Unclassified)
References [Go to Page]
On IM
Book_5124_C003 [Go to Page]
Chapter 3: From Sustainability to Resilience: A Practical Guide to Envision [Go to Page]
3.1 Introduction
3.2 Resilience through Sustainability [Go to Page]
3.2.1 Risk Management: Addressing Uncertainties through Planning [Go to Page]
3.2.1.1 Brief Example. The Snow Creek Stream environment zone restoration project (Placer County, California) earned Envision Platinum in 2013 for the removal of infrastructure and site restoration to a wetland park (Figure 3-16). Snow Creek’s e
3.2.1.2 Brief Example. The Low-Level Road project (North Vancouver, Canada) earned Envision Platinum in 2015 (Figure 3-17). The project was designed to enhance rail and port operations as international trade continues to grow and address long-sta
3.2.2 Production and Manufacturing: Energy and Emissions [Go to Page]
3.2.2.1 Brief Example. The William Jack Hernandez Sport Fish Hatchery (Anchorage, Alaska) was the first project to earn an Envision award. The project earned Envision Gold in 2013 for the redevelopment of a brownfield site into the largest indoor sp
3.2.2.2 Brief Example. The Kunia Country Farms project (Honolulu, Hawaii) was awarded Envision Gold in 2016 (Figure 3-18). The farm is one of the largest commercial aquaponics systems and producers of leafy greens in the islands’ supermarkets and
3.2.3 Climate Change: Risk and Threat [Go to Page]
3.2.3.1 Brief Example. The West Park Equalization Facility (Nashville, Tennessee) earned Envision Platinum in 2016 (Figure 3-19). The project included expanding wastewater storage capacity and upgrades to a park’s recreational facilities (an equ
3.2.3.2 Brief Example. The Sun Valley Watershed Multi-Benefit Project (Los Angeles County, California) earned Envision Platinum in 2014 (Figure 3-20). The stormwater management project provides flood protection, improved watershed health, open sp
3.2.3.3 Brief Example. The 26th Ward Wastewater Treatment Plant Upgrade project (New York City, New York) earned Envision Silver in 2015. The purpose of the project was to provide primary treatment redundancy and uniform grit distribution at the pr
3.2.4 Service: Durability and Adaptability [Go to Page]
3.2.4.1 Brief Example. The Tucannon River Wind Farm project earned Envision Gold in 2015 (Figure 3-21). The wind farm includes 116 turbines atop 80 m tubular steel towers generating 267 MW. The project team established plans and resources necessar
3.2.4.2 Brief Example. The San Diego International Airport Green Build project (San Diego, California) earned Envision Platinum in 2016. The Green Build project was a major expansion of Terminal 2 with 10 new gates, additional aircraft parking, a d
3.2.5 Survival: Recovery and Synergies [Go to Page]
3.2.5.1 Brief Example. The Grand Bend Area Wastewater Treatment Facility (Ontario, Canada) earned Envision Platinum in 2015. This wastewater treatment plant’s increase in seasonal capacity included incorporating an extended aeration mechanical tre
3.2.6 Sustaining Innovation: System Engineering Approach [Go to Page]
3.2.6.1 Brief Example. The South Los Angeles Wetlands Park (Los Angeles, California) earned Envision Platinum in 2013 (Figure 3-22). The 10 acre park included a stormwater treatment wetland facility on a remediated brownfield and provided a green
3.2.6.2 Brief Example. The Nashville Government West Park Equalization Facility (Nashville, Tennessee) earned Envision Platinum in 2016. The interdepartmental project combined equalization storage for wastewater flows with improvements to the recre
3.3 Applications
3.4 Implementation [Go to Page]
3.4.1 Brief Example
3.4.2 Brief Example
3.4.3 Brief Example
3.4.4 �Applied Examples and Case Studies [Go to Page]
3.4.4.1 Example 1. There is an urgent need to develop a temporary roundabout traveling path around a flood-damaged bridge. Consider a typical soil-compaction operation on a 0.15 m layer to achieve the 80% optimum compaction rate according to specifi
3.4.4.2 Example 2. Energy consumption is a concern for a small town with numerous masonry buildings. Consider a typical residential building with masonry systems produced from lightweight expanded clay aggregates to address the concern (Assari an
3.5 Conclusions
3.6 Recommendations
Bibliography
References
Book_5124_C004 [Go to Page]
Chapter 4: Quantitative Models for Interdependent Functionality and Recovery of Critical Infrastructure Systems [Go to Page]
4.1 Introduction
4.2 Fundamental Concepts and Background [Go to Page]
4.2.1 Definitions of Resilience
4.2.2 Dependency and Interdependency
4.2.3 Popular Classifications of Dependency and Interdependency
4.3 �Contemporary Practical Models for Dependency and Interdependency [Go to Page]
4.3.1 Dependency Tables [Go to Page]
4.3.1.1 Qualitative Tables. In general, qualitative tables are derived from expert judgments based on historical events (Bigger et al. 2009, Tang et al. 2004). They are useful for decision-makers to gain a general assessment of dependencies in p
4.3.1.2 Quantitative Tables. Quantitative tables usually describe the existence, the strength, and the impact level of an interaction between two components/systems under a hazard scenario. In quantitative tables, dependencies and interdependencies
4.3.2 Interaction Rules [Go to Page]
4.3.2.1 Discrete-Event Simulations. Discrete-event simulations refer to models that represent complex dependencies as an ordered sequence of defined events through sequential and conditional logic as well as causal relations. For instance, discrete-
4.3.2.2 Agent-Based Models. Agent-based models for infrastructure were originally developed by Sandia National Laboratories in the 1990s to simulate individual decision-makers for investigating the economy in the United States (Barton et al. 2000
4.3.2.3 System Dynamics Approach. A family of techniques that can be effectively used to model infrastructure interdependencies, and in particular their evolution in time, is the system dynamics approach. This type of modeling was originally devel
4.3.2.4 Bayesian Network–Based Approach. System dynamics approaches have some inherent limitations in dealing with uncertainties, which can be overcome through the use of a Bayesian network (Phan et al. 2016). A Bayesian network can comprehensive
4.3.2.5 Constrained Optimization Simulation. As mentioned, several recovery dependencies arise in the form of constraints in the recovery phase. Constrained optimization models can simulate how decision-makers develop plans for retrofit and restor
4.3.2.6 Population Mobility Models. From the point of view of local governments, accurately predicting the population mobility and the aggregate demands and required commodities under different hazard scenarios are essential for developing efficie
4.3.2.7 Aggregate Supply and Demand Models. Aggregate supply and demand tools are typically used to assess total demand and supply interactions from the economic perspective. As shown in Figure 4-5, aggregate demand is the total quantity of outp
4.3.3 Other Approaches.
4.3.4 Comparison of Models for Dependency and Interdependency
4.4 �Application of the Interdependency Model in Community Resilience Assessment [Go to Page]
4.4.1 The PRAISys Platform
4.5 Discussion
4.6 Recommended Practice
4.7 Concluding Remarks
Acknowledgments
References
Book_5124_C005 [Go to Page]
Chapter 5: Machine Learning: The Role of Machines for Resilient Communities [Go to Page]
5.1 Introduction [Go to Page]
5.1.1 Resilience Definition
5.1.2 Machine Learning and Artificial Intelligence
5.1.3 Semantic Representation of Emergency
5.2 Model Identification [Go to Page]
5.2.1 The Problem of Data Integration
5.2.2 Predicting Natural and Man-Made Hazards
5.3 Emergency Detection [Go to Page]
5.3.1 Detecting and Managing Emergencies
5.3.2 Emergency Detection: Real World
5.3.3 Emergency Detection: Virtual World
5.3.4 Managing an Emergency
5.4 Solution Generation and Decision Making [Go to Page]
5.4.1 An Excursus on Artificial Intelligence
5.4.2 Resilience and the Role of Machine Learning
5.5 Discussion and Conclusions [Go to Page]
5.5.1 On Human–Computer Interaction
5.5.2 Complex Decision-Making under Emergency Conditions
5.6 Recommendations
Acknowledgments
References
Book_5124_C006 [Go to Page]
Chapter 6: Toward City Resilience: Complex Systems’ Resilience, Interdependence, and Simulation [Go to Page]
6.1 Extreme Events: Types, Causes, and Consequences [Go to Page]
6.1.1 Types of Extreme Events
6.1.2 Consequences of Extreme Events
6.1.3 Extreme Events and Power Outages
6.2 �System Resilience: Importance and Characteristics [Go to Page]
6.2.1 Importance of System Resilience
6.2.2 Characteristics of a Resilient System
6.3 The Concept of Resilience [Go to Page]
6.3.1 Defining Resilience
6.3.2 City Resilience
6.3.3 Resilience Research Trends and Challenges
6.4 Resilience, Risk, and Vulnerability
6.5 Resilience Quantification [Go to Page]
6.5.1 Simple Resilience Metrics
6.5.2 Four Dimensions of Resilience
6.5.3 Three Capabilities of Resilience
6.6 �Infrastructure Systems: Definitions and Interdependence [Go to Page]
6.6.1 Critical Infrastructure Systems and Resilience
6.6.2 Critical Infrastructure System Interdependence [Go to Page]
6.6.2.1 Importance of Interdependence. CISs are dependent on one another; thus, when a system fails, other systems can be affected by this failure. This dependence is simplified when the relationship among these systems is unidirectional, as in this
6.6.2.2 Types of Interdependence. Quantifying interdependence among CISs is highly complicated given the diversity of the relationships among these systems. Resource usage is the simplest type of CIS interdependence (Zimmerman et al. 2016). Other
6.6.2.3 Infrastructure Interdependence Research. Interdependence among CISs has been addressed by several research studies over the last couple of decades. Many of these studies have focused on the importance of interdependence among specific CISs
6.7 �Resilient Infrastructure System Modeling and Simulation [Go to Page]
6.7.1 System Simulation [Go to Page]
6.7.1.1 Complex Network Theory. The origins of the CNT can be traced to graph theory, which is a mathematical theory that aims at simplifying the behavior of systems through simulating their comprising components as either vertices or edges (Cariso
6.7.1.2 Multiagent Simulation. Another technique being currently used to simulate CISs is MAS. MAS is a bottom-up modeling approach that can represent the heterogeneity of system components together with their emergent behavior. The components of t
6.7.1.3 System Dynamics. Unlike MAS, SD is a centralized top-down approach that originates from control theory (Teknomo 2004). The main building block of SD is not agents but feedback loops of interactions. Unlike focusing on individual-level mod
6.7.1.4 Economic Theory. First proposed in 1973 by Wassily Leintief to model the interconnections among economic sectors, economic theory was further developed to represent interdependence among different types of systems, including infrastructure
6.7.1.5 Other Simulation Techniques
6.7.2 System Assessment and Control [Go to Page]
6.7.2.1 Empirical Methods. Empirical approaches utilize observations and experiences as evidence to draw conclusions and make inferences about a process or a system (University of Warwick 2018). Based on this, a framework was developed by (McDani
6.7.2.2 Optimization. A mixed-integer programming model is an optimization model, where all decision variables are assumed to have integer values. This type of programming model can be either deterministic or stochastic, based on the state of the i
6.7.2.3 Game Theory Game theory is a strategy science that determines the actions that players (i.e., people, companies, or systems) should take to secure the best outcomes for themselves through mathematical procedures (Dixit and Nalebuff n.d.).
6.8 Chapter Best Practices and Takeaways
References
Book_5124_C007 [Go to Page]
Chapter 7: Community Resilience Lifeline Systems Methodology [Go to Page]
7.1 Introduction
7.2 FEMA Resilience Framework
7.3 �Community Resilience Lifeline Systems Methodology [Go to Page]
7.3.1 Resilience Components
7.3.2 Community Lifelines Framework
7.4 Methodology Approach [Go to Page]
7.4.1 Methodology Inputs from the User
7.4.2 Methodology Equations
7.4.3 Advantages
7.4.4 Limitations
7.5 Examples
7.6 Conclusion
Acknowledgments
References
Book_5124_IDX [Go to Page]

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