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16 Jun

A Critical Look at the Numerical Modeling of Reinforced Concrete Structures for Extreme Events

A Critical Look at the Numerical Modeling of Reinforced Concrete Structures for Extreme Events

By Maha Kenawy, PhD, University of Nevada, Reno

The early 2000s witnessed the rise of performance-based earthquake engineering, and with it came the emphasis on understanding ‘extreme’ structural performance; i.e., how structures behave at very large deformation demands, and how rapidly they lose their ability to carry loads when subjected to extreme events. This fundamental change to the structural design philosophy was followed by continuing refinements and guidelines on how to assess expected structural performance under extreme loads (For example, FEMA P-58 (Applied Technology Council, 2018)). Today, estimating the expected collapse capacity of structural systems and components is a common practice for engineers and researchers. At the base of determining the collapse capacity of a structure is the fundamental assumption that numerical structural models are capable of simulating the deterioration of structural components and materials. Many challenges stand in the way of having such models.

Structural engineers often seek to utilize computationally inexpensive models. Naturally, computational efficiency is the result of creating a set of idealizations to simplify the way that the model works, without significantly compromising its performance. When the issue at hand is predicting the behavior of structural components subjected to extreme loading conditions (such as those of earthquakes), the ability to simulate anticipated structural damage is a critical component of our modeling tools. In this context, the structural engineering modeling philosophy lends itself to two major types of modeling tools; each comes with their own compromises. The first type is concentrated or lumped plasticity models, which idealize structural components as elastic elements with nonlinear hinges at their ends. This type of model typically possesses superior numerical performance; however, it requires extensive calibrations based on experimental tests of structural components (which makes it difficult to generalize its applicability), and suffers from other performance limitations that come from the very nature of its idealizations. Despite its limitations, this type of model remains the most common numerical tool today for assessing the collapse capacity of reinforced concrete (RC) structures.

The other common modeling approach offers more granularity – and therefore accuracy – to predicting the performance of structural components. The so-called fiber-discretized (FD) component model is a type of distributed-plasticity frame finite element (FE) approach that relies upon convenient and limited calibrations based on the fundamental behavior of materials (in this case, concrete and steel), which make the model easy to generalize to various applications. Because this type of model is more sophisticated than the former in terms of its predictive capability and versatility, it quickly gained much popularity as a superior tool for assessing structural performance.

Figure 1 – Schematic description of mesh bias in fiber-discretized frame finite elements. On the left, damage to a RC column in the 2016 Kaikoura, New Zealand earthquake is pictured (photo from EERI photo gallery by Dmytro Dizhur and Marta Giaretton). Simulation of damage as constitutive softening illustrates the dependence of the member deterioration on the size of the numerical model elements. The mesh bias appears at the global level (load-displacement behavior) of a column loaded with axial and lateral loads, as well as the local level (strain or curvature distribution along the member)

In the past few decades, however, the rising demand for predicting the deterioration of structural components has exposed the extent of the shortcomings associated with the FD element modeling approach. To break down the fundamental issue with FD models, one has to go back to the underlying modeling of progressive damage (or cracking) in quasi-brittle materials as constitutive “softening” or negative stiffness, which became popular in the 1970s (a large number of publications by Zdeněk Bažant discusses the subject in due depth – in addition to his unique anecdotal account in Bažant, 2002). The advent of computerized FE analysis exposed the major limitation of constitutive softening: mesh bias. In other words, the numerical solution to a physical problem describing material damage depends on the characteristic size of the FE model, leading to non-objective, and therefore unphysical, model predictions. This issue is schematically described in figure 1.

The 1980s were ripe with several approaches proposed by researchers to overcome mesh bias in constitutive softening problems. Such approaches were not widely adopted in structural frame models until the 2000s. Some structural engineering researchers proposed simple approaches to amend the current FD modeling tools (which suffer from the same mesh bias issues); others created fundamentally new FE formulations that suspend the traditional idea that individual finite elements are independent, and introduce some continuity between neighboring elements in a numerical model. This family of approaches is known as the nonlocal models (I review many of these approaches in Kenawy 2018). My former research group at the University of California, Davis, led by Amit Kanvinde and Sashi Kunnath, proposed new structural component and constitutive formulations that advance the latter approach for both reinforced concrete and steel structures.

Figure 2 – Results of the verification study of the proposed nonlocal model by Kenawy et al. (2018). In a pushover analysis of a RC column, the conventional fiber-discretized frame model (left) fails to converge to a unique solution. The novel nonlocal model (right) overcomes mesh bias and rapidly converges to a unique solution.

The frame FE formulation and concrete constitutive model I developed as a doctoral student overcome the mesh bias of FD models (as shown in figure 2), without imposing too many restrictions that would limit the applicability of the model (Kenawy et al. 2018; Kenawy et al. 2020). The major drawback of the proposed framework is an inevitable added computational expense to our numerical structural models. The model also requires additional assumptions regarding the extent of damage in a structural member (referred to as a characteristic length in a continuum, and more commonly approximated as a plastic hinge length by structural engineers). My doctoral work also proposes a rather simple approach to characterize this characteristic length for a wide range of structural problems. Figure 3 shows representative blind predictions of the behavior of RC columns along with their observed behavior in laboratory experiments. These plots are part of a large validation study which suggests that the proposed framework and underlying assumptions are capable of simulating the observed degradation of RC components due to crushing of the concrete material. Much work remains to extend this modeling approach to simulate different types of structural components (structural walls, for example) and failure modes, and to assess its ability to simulate the performance of entire structures (for which experimental test data are rather scarce). Nonetheless, the findings of my doctoral work, along with those of a few recent studies by structural engineering researchers, bring the promise of more sophisticated and reliable modeling approaches for assessing the performance of RC structures subjected to extreme loads, and utilizing these novel methods to improve structural design provisions.

Figure 3 – Representative results of the validation study of the nonlocal model. The simulated cyclic lateral load vs. drift response of several RC columns by the nonlocal model is compared against the observed component behavior in laboratory experiments. The experimental datasets were obtained from the PEER column database

Selected References

Applied technology Council (2018). FEMA P-58: Seismic Performance Assessment of Buildings. Federal Emergency Management Agency.

Bažant, Z. P. (2002). Reminiscences on four decades of struggle and progress in softening damage and size effect. Concr. J.(Japan Concr. Inst.), 40, 16-28.

Kenawy, M. (2018). Nonlocal Computational Framework for Simulating Extreme Limit States in Reinforced Concrete Structures. Ph.D. Dissertation, University of California, Davis.

Kenawy, M., Kunnath, S., Kolwankar, S., & Kanvinde, A. (2018). Fiber-based nonlocal formulation for simulating softening in reinforced concrete beam-columns. Journal of Structural Engineering, 144(12), 04018217.

Kenawy, M., Kunnath, S., Kolwankar, S., & Kanvinde, A. (2020). Concrete Uniaxial Nonlocal Damage-Plasticity Model for Simulating Post-Peak Response of Reinforced Concrete Beam-Columns under Cyclic Loading. Journal of Structural Engineering, 146(5), 04020052.



Benchmarking of FEMA P-58 Expected Seismic Losses to Observed Loss Data from the 1994 Northridge Earthquake

By Dustin Cook, P.E.

Recent developments in seismic risk analysis and performance-based design have opened doors to statistically rigorous and systematic frameworks for the quantification of seismic risk to buildings. One such method was recently formalized by the Applied Technology Council and is known as FEMA P-58: Seismic Performance Assessment of Buildings (FEMA, 2018c). FEMA P-58 provides a probabilistic approach to assess the seismic risk of individual buildings. Through FEMA P-58, seismic risk is quantified in terms of performance metrics that are valuable to engineers, building owners, and the public, such as economic losses, potential casualties, and disaster recovery time, facilitating decisions among design or mitigation alternatives. One of the pressing challenges of new and evolving seismic risk assessment methods is the need to validate, confirm, and/or verify the outcome of these assessments to support their broader use.

Records and estimations of damage and loss from the 1994 Northridge earthquake in southern California provide a unique opportunity to benchmark new performance-based risk assessment methodologies, such as FEMA P-58, to empirical data, especially for buildings of wood frame construction. To evaluate the results obtained through a FEMA P-58 assessment, this study compares the hindcast losses using the FEMA P-58 methodology, in terms of repair costs of buildings, with observed losses during the 1994 Northridge Earthquake. This article summarizes the methods and findings of recent a study performed jointly by the University of Colorado Boulder and the Haselton Baker Risk Group.

Figure 1 – Highway damage during the 1994 Northridge Earthquake. Photo from David Butow/Corbis/Getty Images.

Unprecedented levels of damage were observed during the Northridge Earthquake, with total estimated losses ranging between $40 and $44 billion (1994 USD), making it the costliest earthquake in U.S. history (Eguchi, 1998). Other sources estimate an additional $8 billion in indirect losses from business interruption, lost tax revenue, vacated housing, and defaults on Small Business Administration loans (Petak & Elahi, 2001). Widespread damage was observed in many different types of buildings across a large region. Wood frame structures make up a majority of the building stock in the US, and represent around 96% of the buildings in Los Angeles County, accounting for around 85% of the value of the building stock at the time of the Northridge Earthquake. Damage to wood frame structures was extensive due to the large number of buildings that were affected. In total, over 340,000 insurance claims were submitted for damage to residential structures, mostly from damage to nonstructural components (Eguchi, 1998). Considering both insured and uninsured damage, losses from wood frame residential structures are estimated to represent about half of the total building losses from the earthquake (Petak & Elahi, 2001).

FEMA P-58 only quantifies direct losses to the building (i.e. structural and nonstructural components), and does not represent losses to contents, detached structures, and loss of use. Therefore, adjustments are made to the estimated total direct losses to derive a best estimate of direct loss from building damage, to compare against FEMA P-58 losses.  In the Northridge Earthquake, direct building loss represented about 67% of the total loss from insurance claims (Eguchi, 1998, Petak & Elahi, 2001). We propose the reduction factor to go from reported loss to losses comparable to FEMA P-58 is likely to be somewhere between 60% and 75%, resulting in a range of estimated loss from $25 billion to $32 billion by multiplying the average reported estimated direct loss of $40 to $44 billion (Petak & Elahi, 2001) with this ratio range.

To quantify FEMA P-58 expected loss from the Northridge earthquake, this study uses the Haselton Baker Risk Group’s FEMA P-58 Risk Model (SP3-RiskModel) and the USGS Northridge ShakeMap to perform a scenario assessment on a set of 2.6 million buildings affected by the event. The SP3-RiskModel uses embedded algorithms to generate structural properties, building configurations, and building components from a simplified set of building inputs based on typical building configurations and inventories, engineering analysis, and expert judgment. This tool helps expedite the FEMA P-58 analyses for large building inventories with limited building information, such as the building data available in tax assessor databases, and can be used to run millions of performance models in a batch setting. This study uses a proprietary inventory database based on tax assessors and insurance data that contains building inventories of the greater Los Angeles region. Losses from the assessment are aggregated for all buildings within the ShakeMap region and compared with observed data.

Table 1 – Breakdown of FEMA P-58 predicted loss for each type of building where quantitative loss data is available. Dollar values represent 1994 USD.

Type of Building Number of Buildings Total Value FEMA P-58 Predicted Loss          Estimated Observed Loss
All Buildings 2,586,638 $722 billion $31 billion $25 to $32 billion
Residential 2,405,382 $508 billion $16 billion $12 to $16 billion
Tilt-Ups 39,677 $52 billion $5 billion > $1 billion


Figure 2 – 1994 Northridge Earthquake mean losses from FEMA P-58 for part of Los Angeles County.

The total hindcast losses from the FEMA P-58 assessment of the Northridge scenario came out to $31 billion due to building damage, which is on the higher end, but well within the range of observed loss. Mean loss results presented here represent the average cost, in 1994 USD, to repair structural and nonstructural components in the building. The total hindcast loss from residential buildings also compare well with observed losses. This comparison indicates that probabilistic methods such as FEMA P-58 can provide accurate predictions of post-earthquake economic losses.  However, results from probabilistic methods are heavily dependent upon modeling decisions. If factors that affect the response of structures at low levels of shaking are not properly accounted for, such as effective damping and stiffness, predictions of loss can change significantly. Outcomes of alternative modeling decisions are discussed in the full report. For more information on the complete findings of the study, please contact the Haselton Baker Risk Group at (530) 531-0295 or

Dustin Cook is a Ph.D. Candidate in the Department of Civil, Architectural, and Environment Engineering at the University of Colorado Boulder and is a member of the Haselton Baker Risk Group. Dustin holds a M.S. from University of California, Los Angeles and is a licensed Professional Engineer in the state of California. You can reach him by email at


What might have been: counterfactual thinking in risk analysis

What might have been: counterfactual thinking in risk analysis

By Yolanda C. Lin, Ph.D.

After an earthquake, earthquake engineers and scientists work tirelessly to understand exactly the mechanisms of what happened, what was damaged, who was affected, how we can best move forward. In the process, we typically strive to identify what valuable lessons-learned can be harnessed from the event, so that a future, similar event may be safer and less impactful for the next community.

Are we restricted to only learning from our past disasters? This has been the central question driving my work this past year as a Research Fellow at Nanyang Technological University’s Disaster Analytics for Society Lab (DASL), led by Dr. David Lallemant. Driving our work is the fact that every event that has transpired is driven at least in some part by randomness, and how those hazard events transpire through damage, impact, and recovery are subject to these random realizations. For every event that has happened, there exists a whole family of possible and similar-probability events that didn’t happen, only by chance. And yet, there are lessons to be learned in these as well. Even events that already seem like a disaster could actually be considered a near-miss, when compared to the full range of outcomes that could have transpired. This is counterfactual thinking: reimagining the past somehow different than it actually was. We specifically are interested in downward counterfactual thinking, where the outcomes are worse than in the actual past event. According to counterfactual scholars in psychology research, we are already experts at counterfactual thinking, but we’re not wired to think in a downward way — we usually want to reimagine things for the better, and need some extra scaffolding to think in the negative direction.


Figure 1: Conceptual diagram of applying counterfactual changes Δ1 and Δ2 to a past event (represented by the grey swan) in a 2-parameter model space to reach a downward counterfactual event with worse consequences (represented by the black swan). Contour lines represent levels of consequences.

Take, for example, the 1971 San Fernando earthquake. At M6.7, it was a surprisingly large event at that point in history, and the consequences of this event included the death of over 60 people, and damages valued at near half a billion dollars. Though already a costly disaster in many ways, this event could have been even more devastating. Exactly a year before in 1970, the Van Norman dam was nearly full at 6.5 billion gallons of water, but in 1971 it was just half full at 3.6 billion gallons of water. According to a study from the University of California, Los Angeles, the 1971 earthquake damaged the top 30 feet of the dam, and if this earthquake had occurred a year earlier, the dam may have failed and consequently claimed between 71,600 and 123,400 lives (Ayyaswamy 1974, Reich 1996). Instead, California has had a relatively safe record for direct deaths due to earthquakes in the last 60 years of about 200 lives lost (Woo 2017).

This August, we hosted the Counterfactual Black Swan Workshop at NTU in Singapore, with twenty-eight participants from six countries across three continents, to build momentum around this concept and establish a new community of practice centered on counterfactual thinking in risk analysis, bridging multiple disciplines and industries, such as earthquake engineering, insurance, human geography, and risk communication, spanning multiple geographic contexts (read more about the workshop here).

In one session of the workshop, we tested an activity of “Counterfactual Round Tables,” which is designed to guide participants through the process of building a counterfactual event, based on a real past event. The challenge is to find ways to make something from the past even worse — possibly even a so-called Black Swan, a low-probability, high-impact, and surprising event. Starting with one participant, the “event expert” describes a past event, using supporting materials (maps, figures, etc) as needed. Other participants are invited to ask clarifying questions, and finally all participants engage in counterfactual thinking to devise ways in which this event could have been worse.

We provided prompt cards (Figure 2) around the table to help guide downward counterfactual thinking. Participants then discussed their various counterfactual ideas and grouped their thoughts, written on post-it notes, onto a poster board in order to build a narrative around one or more counterfactual versions of their event. This process was then repeated for all participants at the table, with one past event per participant. The activity had a facilitator at each table, and there were three participants per table. The resulting counterfactual black swans were then shared during an informal coffee break poster session following the activity, and afterwards we also invited general impressions and feedback to share with the rest of the group. 

Figure 2. Example categories for counterfactual changes. These were printed and used as prompts to stimulate downward counterfactual changes to past events.

Many participants at our workshop voiced surprise at the difficulty in pushing back against the automatic “upward” counterfactual thinking, wanting instead to engage in finding ways that the situation could have been better. They needed to consciously remind themselves (and be reminded by the moderator and fellow participants) to think downwards instead, which points to the need for a facilitated group exercise around this process.

Ultimately, this exercise can jumpstart our imagination to understand any lucky near-misses that can be addressed in the future, provide the basis for a guided search through a high-dimensional model space, explore multi-hazard scenarios that are currently difficult to model computationally, and provide a structured framework for gathering  expert-opinions for a scenario in planning and preparedness purposes.

Next time you are gathered with two or three of your favorite earthquake engineering colleagues and experts, I invite you to grab some post-it notes and try this activity with a past event of your choice. Full directions for the activity are available here, and printable prompt cards are available upon request. Let me know what you come up with!

This work was supported by the National Research Foundation Singapore and the Earth Observatory of Singapore.

Yolanda C. Lin is a Research Fellow in the Disaster Analytics for Society Lab at Nanyang Technological University in Singapore. Yolanda holds a Ph.D. in Civil Engineering from Cornell University, a M.S. from University of Colorado Boulder, and an A.B./B.E. from Dartmouth College. You can reach her by email at or on twitter @DisasterYolanda.

4 Dec

Learning from Earthquakes: Returning to New Zealand

Learning from Earthquakes: Returning to New Zealand to observe long-term recovery efforts after the 2010-2011 Canterbury earthquake sequence and 2016 Mw 7.8 Kaikoura earthquake

By Christine Z. Beyzaei, Ph.D., P.E.

Post-earthquake reconnaissance typically, and by necessity, focuses on documenting observations immediately following an earthquake. Subsequent efforts may involve follow-up visits to the affected area, but these are often performed as part of individual research projects investigating specific observations from the event and may be focused primarily within a specific discipline (e.g., geotechnical, structural, social sciences). Documenting the long-term recovery process represents a unique opportunity to learn not only from the impacts of the event itself, but also from the response and recovery of an entire affected community. That is after all the ultimate goal in pursuing reconnaissance and research – to enable communities that can better withstand an earthquake event and recover quickly when one does occur.

The EERI Learning from Earthquakes (LFE) Travel Study Program provides the opportunity for young professionals to visit areas previously impacted by earthquakes and observe the long-term recovery efforts and resiliency measures implemented in the years following the earthquake event. The 2019 LFE Travel Study program brought a group of 25 young professionals to New Zealand, to observe recovery following the 2010-2011 Canterbury earthquake sequence and the 2016 Mw 7.8 Kaikoura earthquake. The program was co-hosted by EERI and QuakeCoRe (a NZ Crown Research Institute), with participants from around the world comprising a diverse, multidisciplinary group. At the outset, participants were sorted into sub-groups representing components of community: Built Environment, Natural Environment, and Social/Economic Environment. Our goal was to consider our observations in the context of these community components, thinking beyond our technical disciplines to broader, interdisciplinary, and community-oriented applications.

Figure 1. 2019 LFE Travel Study Program participants and organizers (Image source: Laura Whitehurst, Holmes Consulting).


Figure 2. Modified from “Integrated & Holistic Recovery”, Focus on Recovery: A Holistic Framework for Recovery in New Zealand (Ministry of Civil Defence & Emergency Management 2005).

The program began in the South Island, in Christchurch and Kaikoura, and continued throughout the Canterbury and Marlborough regions. We visited damage sites yet to be repaired and rebuild sites demonstrating innovation and a community dedication to building back better. We met with engineers, government officials, emergency responders, business owners, health care professionals, and others, all of whom shared their time and experiences to transfer knowledge on what they’d learned in the 8-9 years following the Canterbury earthquake sequence, and the three years following the Kaikoura earthquake. We ended the program in Wellington, focusing on recovery following the Kaikoura event and preparedness for future events. A common theme throughout the program was that for community recovery to truly take effect, multiple sectors must work together and there must be clear and open communication with the community throughout the process.

Figure 3. Overview map showing locations of Christchurch, Kaikoura, and Wellington. Inset shows country of New Zealand (Image source: Google Earth).

At the completion of the program, each group prepared a report summarizing our observations (URL). Highlights from the program are presented in the figures below.

Figure 4. Red Zone to Green Space: Severe liquefaction and lateral spreading along the Avon River in Christchurch caused pervasive damage throughout the adjacent neighborhoods, resulting in the area being designated as the “Red Zone.” Homes throughout the Red Zone have since been demolished, leaving open green space. The future use of this area is still under debate, with potential plans including permanent designation as a recreational green space. Base map source: Land Information New Zealand – Canterbury Maps ( Inset photo source: Christine Beyzaei, from 2014, taken in the eastern Red Zone neighborhood of Bexley.

Embracing the concept of “building back better,” repair and construction efforts at Ohau Point have worked to incorporate considerations of both the natural environment and tourism needs. This includes construction of a sea wall with a seal passageway, a new pullover and lookout area with parking, and improved rockfall protection along the coast. Photos taken by author.

Figure 5. Structural systems: seismic repair and retrofit at the University of Canterbury campus in Christchurch. Viscous dampers installed above office space. Tour led by Didier Pettinga (Holmes Consulting). Photos taken by author.
Figure 6. Landslide dam formed in 2016 Kaikoura earthquake (known as the “Leader Dam”). Photos and lidar scanning from 2019 field exercise led by Michael Olson (Oregon State University). Equipment provided by the NHERI RAPID Facility. Observation and documentation have been ongoing since the 2016 post-earthquake reconnaissance. Photos taken by author, lidar imaging provided by Michael Olson
Figure 7. Fault scarp along the Leader Fault generated by rupture during the 2016 Kaikoura earthquake. Photo taken in May 2019 during Kaikoura Earthquake Ruptures fault walk led by Tabitha Bushell (University of Canterbury). Vertical offset of 3 meters measured during 2016 post-earthquake reconnaissance. The Kaikoura earthquake resulted from complex ruptures across multiple faults. There are ongoing research efforts to map and characterize the faults involved in the event. Photo taken by author.
Figure 8. Kaikoura Recovery: The 2016 Kaikoura earthquake underscored that the natural environment and the tourism industry are vital to the city of Kaikoura and the well-being of the community. Damage along the coast isolated communities and exposed vulnerabilities in existing infrastructure systems. Embracing the concept of “building back better,” repair and construction efforts at Ohau Point have worked to incorporate considerations of both the natural environment and tourism needs. This includes construction of a sea wall with a seal passageway, a new pullover and lookout area with parking, and improved rockfall protection along the coast. Photos taken by author.

It was an exceptional experience to work with such a diverse group and learn from others’ perspectives and experiences. On a personal note, the 2019 program also marked 5 years since my initial visit to New Zealand in 2014 for my doctoral research. In 2014 and 2016 I worked in Christchurch at the University of Canterbury, investigating observations from the Canterbury earthquake sequence and during that time saw the recovery as it unfolded. Returning with the LFE program gave me a greater appreciation for the aspects beyond geotechnical engineering, and the opportunity to see how it all fits together. A sincere thanks to EERI, QuakeCoRE, and the people who generously shared their time and their communities.

Christine Z. Beyzaei is a Senior Engineer in the Civil Engineering Practice at Exponent in Oakland, where she specializes in geotechnical engineering. Christine holds a Ph.D. and M.Sc. from the University of California, Berkeley and a B.S. from George Washington University.
13 Sep

Cushing, Oklahoma: What’s Happened to the historic downtown

Cushing, Oklahoma: What’s happened to the historic downtown in the years following the 07 November 2016 M5.0 Earthquake?

By Ezra Jampole, Ph.D., P.E.

On 07 November 2016 (01:44:25 UTC) a M5.0 earthquake devastated the historic downtown of Cushing, Oklahoma. Within a week of the earthquake, EERI sent a reconnaissance team to Cushing to document the damage to the built environment and implement a business resilience survey in the historic downtown, shown in Figure 1. The team documented their observations and findings in a report, which addressed seismicity, geotechnical and ground motion effects, performance of buildings/lifelines, nonstructural components, emergency response, and social/economic impacts. Summarizing the observed building performance, unreinforced masonry (URM) buildings in the historic downtown (built circa 1900) sustained significant damage, including out-of-plumbness, partial collapses, and extensive façade damage. This pattern of damage to URM buildings has been observed following numerous earthquakes around the world. At the time of initial reconnaissance, 41 businesses in the historic downtown were surveyed: 59% of were open, 15% were closed, and 27% had either relocated, were seasonally closed, or had already closed prior to the earthquake. Damage to light frame homes and other non-URM structures was relatively minor, and there were minor disruptions to lifelines. Buildings in the business corridor, mostly constructed in the last thirty years, sustained little to no damage.

Figure 1. Cushing, Oklahoma (adapted from Google).

More than two years after the earthquake, I returned to Cushing’s historic downtown to observe how the community had rebuilt following the earthquake. One of the most common damage conditions observed by the EERI reconnaissance team following the earthquake was spalled brick from URM buildings, which effectively closed the streets in the historic downtown for several weeks after the earthquake. Figure 2 shows a building on N Cleveland Avenue that shed bricks onto the sidewalk (left), and the brick façade has since been replaced by metal sheeting (right).

(a) November 2016 (b) December 2018

Figure 2. Brick façade repair.

Numerous damaged buildings have been demolished. The outer brick wythe of the Lion’s Club URM building, positioned at the end of a block of URM buildings on W. Broadway St., collapsed onto the street during the earthquake (Figure 3a). Additionally, the building had a permanent drift of at least 1% away from the adjacent building. In March 2017, the front of the building reportedly collapsed. The building has since been demolished, and metal siding panels now line the façade at the new end of the block (Figure 3b).

(a) November 2016, (adapted from EERI reconnaissance report) (b) December 2018, from NW

Figure 3. Lion’s Club Building demolished.

Several additional URM buildings with businesses along W. Broadway St. that sustained earthquake damage were demolished after the earthquake, as shown in Figure 4. An additional building was demolished along E Moses St. (Figure 5), however, there did not appear to be an operational business here prior to the earthquake.

(a) November 2016, from NE (b) December 2018, from NW

Figure 4. Buildings demolished in the historic downtown along W. Broadway St. at N. Cleveland Ave.

(a) July 2013 (Google) (b) December 2018

Figure 5. Building on E Moses Street demolished after the earthquake.


Repairs have not been completed on numerous buildings. Figure 6a shows stone masonry that was dislodged during the earthquake, and two years after the earthquake is sitting on the fire escape below. Figure 6b shows a building that experienced an extensive out-of-plane exterior brick wall failure during the earthquake. Tarps now cover the openings in the façade, and bricks appear to have been gathered at the site, but reconstruction has not yet taken place. These buildings are representative of many damaged buildings in the historic downtown for which repairs have not been completed.

(a)December 2018, spalled masonry unrepaired (b) December 2018, out-of-plane masonry wall failure unrepaired.

Figure 6. Unrepaired damage conditions.

Figure 7 shows the Cimarron tower, the tallest building in Cushing, a concrete frame building with unreinforced clay brick and terra cotta infill masonry. At the time of EERI’s initial reconnaissance visit, the terra cotta cornice on the parapet at the roof level was being removed because of concerns that the masonry units were loose and posed a falling hazard. Two years after the earthquake, the ornamentation has not been replaced.


(a)November 2016, from SE (b) December 2018, from SW

Figure 7. Cimarron Tower cornice removed and not replaced.

The built environment in Cushing’s historic downtown has not recovered, as evidenced by the numerous demolished and unrepaired buildings. However, it is clear that the town was already developing in areas away from the historic downtown prior to the 2016 earthquake, with most new commercial construction along the highway corridor. There appears to be less incentive to invest in rebuilding and reoccupying the older, damaged areas of town. Communities with older districts in disaster-prone areas may grapple with similar issues in the future, and should consider mitigating the risk to their vulnerable buildings.

Ezra Jampole is a Senior Engineer in the Buildings and Structures Practice at Exponent in New York City, where he investigates structural engineering failures. Ezra holds a Ph.D. and M.Sc. from Stanford University and a B.S. from Northeastern University.
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