9-10 September 2019, Greenwich, London

ADVANCES IN SIMULATION OF POST-EARTHQUAKE RECOVERY FOR PERFORMANCE-BASED ENGINEERING AND

RESILIENCE

Jack W. BAKER1

Abstract: State of the art performance-based earthquake engineering (PBEE) procedures such as FEMA P-58 generally treat buildings as “islands,” with respect to modeling regional impacts and post-earthquake recovery. Some research, and the REDi resilience framework, provide additional insights regarding regional impacts. But direct quantitative assessment of regional impacts and recovery remains a distant goal in general. This paper presents an overview of recent research to advance direct simulation of these effects within a PBEE framework. Specifically, work to scale FEMA P-58 single-building assessments to a regional scale is demonstrated. Then, refinements to include the impact of damaged roads and neighboring buildings on repair and recovery timelines is presented. Finally, a coupled assessment of economic and physical impacts is illustrated, in order to account for private-sector decision-making and regional industrial capacity constraints on recovery. Collectively, these developments move us closer to having regional-scale performance assessments that can incorporate a broader range of factors in forecasts, and thus can support a broader range of decision-making to increase community resilience.

Introduction Earthquake engineering practice in the United States has traditionally been explicitly focused on life safety. Thus, our methods of analyzing buildings and predicting performance have traditionally been focused on structural safety and stability. An important professional trend in the past two decades has been the development of performance-based earthquake engineering (PBEE, e.g., Moehle and Deierlein 2004), which has focused on predicting seismic performance through the additional dimensions of repair costs and repair times. With increasing access to these other performance metrics, the profession has more rationally designed high-performance buildings, and an increasing number of sophisticated building owners have begun to specify that they want “better than code” buildings (e.g., Haselton et al. 2018). A related trend, which extends beyond the earthquake field, is the desire for societal resilience to extreme events (e.g., Bruneau et al. 2003, McDaniels et al. 2008, McAllister 2016, NIST 2015)

As these ideas have grown more mainstream, and achieving these goals has grown more practical, there is significant inertia in the United States to consider resilience and “functional recovery” within building codes and design standards (EERI 2019). For example, the United States Federal Government, though the recent National Earthquake Hazards Reduction Program Reauthorization (Senate Bill 1768, 2018) mandated that the Federal Emergency Management Agency and the National Institute of Standards and Technology “convene a committee of experts… to assess and recommend options for improving the built environment to reflect performance goals stated in terms of post-earthquake reoccupancy and functional recovery time.” Similarly, California State Legislature is currently considering a bill (Assembly Bill 393 2019) to “consider whether a `functional recovery’ standard is warranted for all or some building occupancy classifications, … and investigate the practical means of implementing that standard.” At the local level, San Francisco issued a Tall Buildings Safety Strategy (ATC 2019) that recommends the establishment of recovery-based seismic design standards and the development of a comprehensive recovery plan for the financial district and adjacent neighborhoods.

There are a number of resilience-trajectory-altering activities underway or under consideration in some California cities. San Francisco's resilience-related activities include improvements to housing (San Francisco 2013), water systems (Knight 2010) and transportation networks (Lee and Otellini 2016). The City of Los Angeles resilience plan (2015) includes water infrastructure upgrades and private mandates and incentives to enhance performance of private commercial facilities.

The above examples illustrate the clear momentum towards a broader set of objectives in the way that we design and build buildings and infrastructure. Resilience (recovery at the community level) and functional 1 Professor, Stanford University, Stanford, USA, bakerjw@stanford.edu

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recovery (recovery at the single-building level) will have broad societal benefits. But new tools and techniques are needed to forecast these metrics, and to quantify how changes in our designs will impact our resilience and recovery.

With the above trends as motivation, the following section reviews a few selected research and technology transfer developments which contribute towards a deeper understanding of functional recovery, and thus facilitate the ultimate adoption of functional recovery standards. The discussion focuses on efforts in which the author has been a participant, but does refer also to a few of the many important contributions by others.

Community-level recovery simulation A challenge in resilience and functional-recovery goals is that the considerations in achieving these goals are regional in scale, unlike the building-level issues associated with achieving safety. Resilient communities and functional buildings require operating infrastructure systems, and the repair of damage requires safe surrounding neighborhoods, availability of needed resources, construction capacity, and many other considerations that go beyond the boundary of a single building.

Neighborhood-level recovery simulation. Functionality of a building after an earthquake depends upon the building’s neighborhood being safe and accessible. One notable consideration that influences this situation is the presence of cordons in heavily-impacted neighborhoods (Change et al. 2014). This was a significant factor influencing the recovery of the Christchurch Central Business District after the February 2011 earthquake there, and is a concern for many cities with urban cores. With this motivation, Hulsey et al. (2018) propose a framework to incorporate cordoning in the simulation of building recovery trajectories, as illustrated in Figure 1.

Figure 1. Community recovery analysis steps (from Hulsey et al. 2018).

In addition to the risk of direct damage to a building, the analysis approach also considers the possibility that surrounding buildings may be damaged and necessitate the cordoning of the building (Figure 2). This approach requires parallel performance-based assessments of all buildings within the study area of interest, but this is increasingly feasible due to newly available software tools discussed below. The results from this type of analysis can support planning by cities and emergency responders as to how they may manage future cordons, and can quantify how a particular cordon policy would impact the closure of businesses in an area and the potential safety risks due to further aftershock-related damage.

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Figure 2. Illustration of an urban area, with a damaged tall building, and highlighting indicating a

hypothetical area that may be cordoned in response to that damage (from Hulsey et al. 2018).

Coupled economic/physical assessment of buildings A second behavioral factor that influences resilience of a community following an earthquake is owner decision-making regarding damaged buildings. While the time to physically repair a building constrains recovery, in many situations the actual time to repair and re-open a building is much longer than the repair time, due to “impeding factors” and owner decisions (Comerio 2006, Arup 2013).

One example of a source of delay is when owners choose to demolish a building that in principle could have been repaired. This was observed, for example, following the 2011 Christchurch earthquake, where 56% of reinforced concrete buildings in the central business district with less than a 30% damage ratio were demolished, and further 10% were still vacant pending decision at the time of the study (Kim et al. 2017). For the case of commercial property owners, this decision-making is likely to be driven by the profits produced by each alternative decision. In a depressed property market, or a situation where the owner has insurance, it may be more profitable to demolish a property than to repair it, even if the latter would lead to it re-opening more quickly. This decision-making can be modeled within a performance-based engineering framework, by using the FEMA P-58 (FEMA 2012) procedure to simulate damage, repair costs, and repair time, and then to perform a net present value calculation for each decision alternative, coven additional economic information such as property rental rates and the owner’s discount rate (Markhvida and Baker 2018). Figure 3 shows example results from a net present value assessment that a commercial owner of a damaged building would perform, considering two alternatives (repair it, or demolish and redevelop it). The alternative with the larger net present value would be more desirable, so this type of analysis can reveal the conditions under which each alternative would likely be chosen. The results provide a greater understanding of potential recovery trajectories, and can also help cities understand how they might incentivize more rapid re-opening of commercial properties following an earthquake.

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Figure 3. Example results showing the sensitivity of Net Present Value for (a) repair, and (b)

redevelopment of an eight-story reinforced concerete commercial office building built in 2003. The building loss ratio, impeding factors, and a number of economic factors were varied, in order to

understand factors most influencing the building value. Red bars indicate a decrease in input parameter and blue bars, an increase. Parameter ranges used in these calculations are shown to the left and right of

each bar, and baseline parameter values are shown in the middle (from Markhvida and Baker 2018).

Household-level recovery simulation Another way in which economic considerations interact with physical damage is at the household level. Repair of damage provides one constraint upon the restoration of physical assets such as housing. But homeowners will be further constrained in their consumption of repair services, depending upon their savings and insurance, and on their ability to otherwise fund repairs from their income. Figure 4 illustrates one such model for this issue (Markhvida et al. 2019). The plot schematically illustrates the consumption of goods and services by a single household over time, following an earthquake that causes damage. Consumption may be reduced due to the loss of employment, the loss of housing services due to damage (necessitating payment for alternative housing), and redeployment of funds that are now needed for reconstruction. Savings or insurance may offset some consumption loss, and the reconstruction of housing may be slowed in order to limit consumption loss (assuming a household will not drop below poverty-level consumption in order to speed reconstruction). By simulating damage at a regional scale, and then simulating the consumption trajectories of all households in the region, we can understand several metrics indicating the resilience of the region.

Figure 4. Model for household consumption changes following an earthquake (from Markhvida et al.

2019).

This modeling approach is valuable in understanding how recovery may be impeded by household financial issues. It also helps quantify the differential impacts of disasters on poorer households: because they lack the savings and income to weather this loss of consumption, they experience much greater disruption than a wealthy household that suffers the same dollar amount of direct physical damage. This phenomenon, well

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understood qualitatively (e.g., Peacock et al 2014, Donner 2008, Masozeraa et al. 2007), can be better managed through policy if it can be predicted.

Another important implication of this modeling is that it allows the comparison of a broad range of resilience-oriented policies. Building codes, insurance, and social safety nets can all be evaluated in this framework because they all influence the outcomes for households measured by this this approach. To illustrate, Figure 5 shows changes in loss metrics for a study of San Francisco Bay Area earthquake losses, under three hypothetical resilience policies. Losses from a Hayward Fault earthquake were computed, and the present state of the region was used as a baseline. Then a building vulnerability policy (require retrofit of vulnerable housing), an insurance policy (facilitate greater property insurance penetration) and a social policy (enhance unemployment insurance benefits) were considered. Changes in asset losses are shown in the left figure, and well-being losses (a metric that quantifies the impact of the Figure 4 features on household well building) is shown on the right. We see that building retrofits and property insurance can reduce the costs of building damage to households, while unemployment insurance has no impact on that metric.

In the right portion of Figure 5, however, we see that all three policies impact well-being. Retrofits reduce damage and the resulting consumption loss. Insurance provides financial support that mitigates loss of consumption. And unemployment insurance can smooth the loss of consumption resulting from employment losses. Further, we see that relative to the left figure, the retrofit policy has greater effect than the insurance policy: while from an economic perspective insurance can mitigate the costs of reconstruction, households benefit more from simply avoiding damage and all of the recovery impacts it causes.

The specifics of the case study, and the details of the considered policies, are not of primary importance here. The important insight is that, through models like this, we are able to bridge between physical and socio-economic impacts of earthquakes. And through this bridging, we are able to support decision-making by groups that are likely to evaluate a range of resilience policies (i.e., not only engineering policies). Assuming that the case study results are accurate enough to be informative, and assuming that the costs of these hypothetical policies could be measured, results like Figure 5 would be highly relevant to stakeholders in government organizations and other institutions.

Figure 5. Effect of resilience policies on predicted change housing asset losses (left) and well-being

losses (right). A status quo loss is shown at a 0% change, an enhanced policy is shown in heavy shading, and a reduced policy is shown in transparent shading (from Markhvida et al. 2019).

Enabling software tools The above analysis frameworks allow insights about a variety of metrics related to resilience and recovery, and so support decision-making about building designs and other policies to speed post-earthquake recovery. But they also depend upon a great number of input data and calculations, and so can be infeasible if not enabled by supporting models and software. A few key enabling tools are mentioned here.

A foundational analysis is the FEMA P-58 assessment of repair costs and repair times (FEMA 2012a), as the above frameworks all utilize these metrics as baseline values to be modified when considering additional factors. While the original FEMA project provided a software tool called PACT (FEMA 2012b), it was a proof of concept more than a production-level tool suitable for commercial use or analyses of portfolios of buildings. As a commercial option, the Seismic Performance Prediction Tool (SP3, Cook et

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al, 2018, www.hbrisk.com) has proven popular in the past few years for both commercial and research assessments of building performance. As a more academically focused tool, the Pelicun software produced by SimCenter (Zsarnoczay 2019, https://simcenter.designsafe-ci.org) is newly available, and should facilitate even broader research applications due to its open-source nature. As this assessment procedure becomes more widely understood, and these tools become more widely used, repair cost and repair time assessment will likely become much more common in the coming years, similar to the way in which nonlinear structural analysis has grown greatly in popularity in the past decade.

The above calculations are mostly regional in nature as well, and regional analysis requires an additional set of seismic risk assessment tools. To quantify ground shaking throughout a region, for the purpose of then performing damage predictions, tools such as OpenSHA (Field et al. 2003) provide both computational workflows and necessary input data. For regional damage assessment, OpenQuake (Pagani et al. 2014) and rWHALE (Elhaddad et al. 2019) provide helpful workflows. These tools open up regional assessment to a much broader community than the specialized researchers and private companies that traditionally had access to these computations.

Conclusions This paper briefly introduced selected developments taking place in the United States to encourage or mandate resilience and functional recovery in the construction of the built environment. With this as motivation, several research developments in support of this goal were discussed. Specific examples were a model to simulate the recovery of urban cores, considering the possibility that cordons could be placed around damaged tall buildings, a model to simulate owner decision-making regarding demolition of damaged buildings, and a model to quantify impacts to households of housing damage and employment loss after an earthquake.

There are several common threads among these three developments. First, they all consider both physical damage to buildings, and organizational models to predict how institutions, companies and households will behave in response to that damage. Second, the physical damage calculations rely on performance-based engineering assessments (and specifically, FEMA P-58 assessments) in order to predict the repair costs and repair times of buildings; that procedure is important (compared a traditional portfolio loss assessment procedure based on broad building classes) because specific building properties can be linked to resulting damage, and so the resilience impacts of building design changes can be quantified. Third, they all require an understanding of factors outside of an individual buildings’ envelope. Resilience is ultimately a community property rather than a property of a physical building, and so the boundary of the analysis must expand beyond that of a more traditional building safety assessment. Fortunately, data sets and software tools are rapidly developing to make these more ambitious assessments feasible.

Ultimately, development of procedures to improve resilience and functional recovery requires more than just research, though fundamental research is still necessary to better understand and forecast the performance metrics of interest. Additionally, practical software tools are needed to facilitate analysis, standards of practice are needed to ensure consistency and transparency of analysis approaches, and education of analysts and stakeholders is required so that human capital exists to achieve these goals. On all of these fronts, we have seen significant developments in the past decade, and all signs indicate that the coming decade will be one of exciting further progress.

Acknowledgements This paper depends strongly on the far more complete research work of Maryia Markhvida and Anne Hulsey, with additional advising by Greg Deierlein. Their fundamental contributions to the work described above are gratefully acknowledged. Portions of this work were funded by the Pacific Earthquake Engineering Research Center, the UPS Foundation, and the Google Research Awards Program. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.

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