Spring 2019

ASPIRE is a quarterly magazine published by PCI in cooperation with the associations of the National Concrete Bridge Council. The editorial content focuses on the latest technology and key issues in the Concrete Bridge Industry.

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P E R S P E C T I V E 10 | ASPIRE Spring 2019 Quantitative Assessment of Resilience and Sustainability S u s t a i n a b i l i t y i s a w o rd t h a t i s ubiquitous, yet often vague. At the Massachusetts Institute of Technology Concrete Sustainability Hub (CSHub), we have a concise mantra: "We want our world to last." This saying helps us clarify what we mean by sustainability with regard to the structures we build, including bridges. Such structures will remain sustainable only if civil engineers are farsighted. Many aspects of a structure, including its future economic impact and the environmental conequences of construction, repairs, or replacement, affect its sustainability. Our research finds that quantitative assessment of these factors can lead to alternatives that improve a structure's sustainability (see news/lca-research-brief-quantifying- h a z a r d - l i f e - c y c l e - c o s t f o r m o r e information). Sustainability analyses are usually conducted by considering a structure's typical loading conditions throughout i t s l i f e . A n a d d i t i o n a l i m p o r t a n t consideration is the structure's response to atypical loading conditions, such as those caused by natural or human-made disasters. A structure's response to such conditions is a measure of resilience that is vital to understanding sustainability, particularly in regions where climate- related events are becoming increasingly frequent and destructive. Resilience, an important element of sustainability, can be quantifiably measured in two stages, response and recovery (Fig. 1). First, we assess the system's immediate response to an event. For example, let's imagine that a city experiences a hurricane. Before the storm arrives, the city operates at peak performance, as indicated by parameters like economic productivity, transportation efficiency, and so on. As the storm ensues, the city's performance will drop because o f e c o n o m i c i n a c t i v i t y, d i s a b l e d infrastructure, and other damages. The extent of the drop in performance is the first measure of resilience—a smaller reduction in performance indicates greater resilience. The second measure of resilience is the time to recovery to a performance level that may be lower than, the same as, or higher than the performance level before the disaster. A rapid and strong recovery signifies effective resilience. A recent example of resilience can be seen in the aftermath of Alaska's N o v e m b e r 2 0 1 8 7 . 0 - m a g n i t u d e earthquake. While the photos of crumbled highways were dramatic, the actual devastation was not extensive. Authorities reported no fatalities and no collapsed buildings, and seriously damaged roadways were repaired w i t h i n d a y s . 1 , 2 P u n d i t s t h e re f o re declared the state's response to the disaster a success. A l a s k a h a s n o t a l w a y s e n j o y e d such resilience. In 1964, the state experienced the largest earthquake in U.S. history. This 9.2-magnitude quake shattered roads and bridges, destroyed neighborhoods, disrupted livelihoods, and killed an estimated 139 people. 3 In response to the earthquake, authorities established more rigorous building codes intended to protect buildings from future seismic activity. Alaska's successful response to the 2018 earthquake is due, in large part, to the changes implemented in building and bridge design codes. 1 Alaska's rapid recovery demonstrates how efforts to improve resilience can deliver a return on investment (ROI). If stakeholders remain unaware of the ROI of resilience, success stories like that of Alaska in 2018 will occur less frequently. Calculating this ROI is therefore a key aspect of CSHub's work, and, to do it, a probabilistic hazard repair estimation model has been developed. The crux of this model is the probability curve, which allows us to analyze the uncertainties of disaster and predict outcomes. Using gover nment and other scientific data, we can generate a probability curve that estimates the likelihood of a hazard in a given location. We refer to this as a hazard curve (Fig. 2). The hazard curve's counterpart in resilience analyses is the fragility curve (Fig. 3), which estimates the damage a hazard will inflict on a structure. To generate fragility curves, we employ a technique inspired by molecular dynamics. We model the integrity of a structure's components like we would the bonds of atoms. This methodology allows us to efficiently estimate the effects of hazard-induced loads on these components. What makes this technique unique is its versatility and precision. Whereas conventional models of building fragility approximate by Dr. Jeremy Gregory, Massachusetts Institute of Technology Figure 1. Resilience has two parts—initial response to a damaging event (the drop in performance) and recovery time. All Figures: Dr. Jeremy Gregory.

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