The instruments of destruction that created these piles occupy most of the lab. Towering over my head as I walked in was a 65-foot-tall steel piston that can deliver up to 4 million pounds of compression to whatever structure or material is unlucky enough to be in its grip. Want to simulate traffic load on a bridge support, or the pressures that a skyscraper might deliver to its foundation? This machine can help.
Behind the mega piston, I could see that day’s main experiment. Lab technicians had built a life-sized frame for a single-story building in the middle of the warehouse-sized lab space. Attached to the frame were huge hydraulic motors that looked a bit like pared-down robot arms that were braced between the building and a strong concrete wall. These motors were controlled by researchers in a room packed with computers. With the press of a button, the engineers could deliver small, precise earthquakes to the building—or bone-rattlingly big ones. Sensors on the structure would measure every deformation and shake propagated through it.
Takhirov, who bounced around the control room taking obvious delight in the powerful machines working outside, has always lived with the threat of earthquakes. His birthplace in Uzbekistan is known for its massive quakes, as is the San Francisco Bay Area, where he’s spent much of his adult life. Though he began his career as a mechanical engineer studying wave dynamics, over time he left theory behind and got interested in real-world applications. The day I visited, the researchers were deforming one wall of their building with the motors. The process was slow, involving tiny shifts in the slightly crushed structure, and a great deal of muttering from graduate students about the waveforms we could see undulating across several computer monitors.
In this image from the Earthquake Simulation Lab at the University of California, Berkeley, researchers were experimenting with the performance of steel-frame buildings reinforced with a variety of braces. They tested several braced frames and simulated earthquakes with large hydraulic actuators (the black objects with cylindrical bodies on the right side). Each actuator was capable of applying up to 1.5 million pounds of force. (illustration credit ill.12)
There are two ways to simulate earthquakes. Researchers can use a shaking table, which is exactly what it sounds like. They build a structure on top of a platform that can be shaken from underneath, creating an earthquake, so they can watch what happens and learn from it. The second way is what Takhirov’s colleagues and students were doing. They used their giant actuators to imitate how earthquake forces would deform the building, but they were doing it in slow motion. There was none of the violent motion you would see in an earthquake, but those robot arms carried the same force as a quake would. “Essentially we do this so we can look at each step,” Takhirov said. Using their computers, the researchers can also create a “hybrid simulation” that combines a mathematical model of a building with the physical object they’re manipulating in the lab.
The experiment that I was watching with the one-story building turned out to be a model of a two-story building—the second story existed only in software. We know enough about earthquake engineering at this point that we can actually extrapolate how a second story might behave based on what the first story does when it is slowly crushed by giant motors. Hybrid simulations make it easier for engineers to calculate how city buildings might respond in a quake, even if they aren’t able to build an entire 50-story building and wiggle it.
This particular hybrid simulation would ultimately reveal what happens to a multistory building during a quake if the second floor had been “isolated,” or built with a damper—usually a layer of flexible material—between it and the first story. Isolation stops the quake’s motion from propagating through a building unchecked, preventing it from swaying, torquing, and crumbling. Usually isolators are built into the bases of buildings, but the experiments I saw would demonstrate whether isolation units could be helpful between stories, too. If the isolator prevented significant damage in that simulated second story, these researchers would move on to the next phase of their work—getting their engineering discovery implemented in the real world.
How to Prevent Death with Engineering
What Takhirov and his colleagues learn in the Earthquake Simulator Lab gets translated into the building code, a set of safety rules that constrain how structures are built. These codes exist all over the world, varying slightly from region to region. When engineers like Takhirov make a new discovery about earthquake engineering, their next step is to petition to change the rules that govern city development. “I can conduct several tests, and then I can approach the coding committee with my results and say, ‘I should change things here,’ ” Takhirov said.
Failure to update building codes is a major reason so many lives are lost in cities during disasters. Takhirov visited Haiti soon after the series of quakes that nearly leveled the capital, Port-au-Prince. He and his team documented the damage, using conventional cameras as well as sophisticated laser-imaging devices that produced 3-D representations of the shattered city. A lot of the damage could have been prevented with better engineering. They found buildings that never would have collapsed if they’d used simple reinforcements. Unfortunately, however, the local building code lagged behind recent discoveries. But some buildings weren’t up to the local code, either—mostly because builders couldn’t afford the reinforcements and structural planning required. The more earthquake-proof a building is, the more expensive it gets. That’s why Takhirov tries to be pragmatic about earthquake engineering. When builders have to cut corners, they should always prioritize human safety over a building’s durability. “Sometimes it’s more cost effective to have a building that will be damaged but not collapse,” Takhirov explained. “That way people can escape, even if you have major damage.”
His thoughts turned to what Bay Area residents call the Big One, or the next massive earthquake that could hit the region at pretty much any time. “We must be aware that the Big One will be strong, but I have some confidence that it’s going to be okay, and a minimal number of lives will be lost from collapsed buildings,” Takhirov said. Still, he wasn’t sanguine. “Unfortunately, the Big One is going to happen no matter what,” he said. And then, like a true engineer, he began imagining the discoveries such an event would yield. “When it happens, we will deploy all our cameras, and that will be our next big project.”
Other engineers are more fatalistic than Takhirov about how many lives they can save. One state north of Takhirov’s earthquake-simulation lab, on a hillside in the middle of Oregon’s Willamette National Forest, a U.S. Geological Survey (USGS) engineer named Richard Iverson has created hundreds of landslides to learn more about how these often deadly disasters start. He does his work at the USGS “debris-flow flume,” which is pretty much what it sounds like. It’s an outdoor laboratory that consists of a massive enclosed slide, adorned with cameras and embedded with sensors that measure everything from pressure to sheer force while fast-moving globs of mud, rocks, and water rush down the slope. When I spoke to him, he’d just finished a series of experiments where he and his colleagues sent debris flying into mud dams at the base of the flume. They were imitating a common and deadly scenario, where a mudslide temporarily dams a canyon, water builds up behind it, and then homes below are destroyed when the whole mess breaks open in a terrifying flood. After each experiment, Iverson feeds the data he’s gathered into predictive models, or computer programs that forecast disasters based on current conditions. Already, he said, he and his colleagues had learned more about flood warning signs after mudslides.
Research at the flume has led to an extremely sophisticated warning system on Mount Rainier in Washington. Several communities on the mountain suffer from periodic landslides due to water runoff, but Iverson and his colleagues were able to plot where these slides were most likely to start. They set up a warning system, a network of sensors that get tripped when a landslide’s characteristic ground vibration begins. When that happens, an alarm system is immediately set off and residents below g
et 30 to 45 minutes’ warning so that they can escape an event that often ends in death. Engineers at the flume also tested specialized wire nets that now lie like spiderwebs across the hillsides and cliffs above many highways in California, preventing small landslides from spilling onto cars or blocking the road.
Still, Iverson said, he feels like people in the United States don’t think enough about natural disasters when building cities and towns. “Some places really do make use of our predictions for guiding future development,” he replied. “But frankly, in the United States, with our history of zoning laws and development, it doesn’t get taken into great account.” He said that the big problem is that a lot of risky areas, such as the flood-prone Los Angeles canyons, were built up before anyone knew about the danger of mud slides. “You don’t always get to change much, so you do what you can.”
Ideally, Iverson said, he and his team would have enough resources to get detailed topographical maps of any part of Earth so that they could run landslide models of them and determine the safest places for people to build. “We could create probabilistic models for any area that we had data for, showing a range of possible events, from very likely and not so bad, to unlikely and very bad. Showing this information on maps would be very useful for planning purposes.” With the right amount of data, Iverson believes, he could give any planner a fairly realistic prediction about whether future cities might be in danger of getting buried in mud slides the next time a storm hits.
By destroying buildings and causing mud slides, Takhirov and Iverson are able to study disaster as scientifically as possible. What they’ve learned has already affected how cities are built, and how people evacuate flood zones. As we move into the future, however, we want cities that can do more than collapse without killing us. We want cities (and city emergency services) that can change instantly in response to imminent danger. Such cities, though they sound like science fiction, are already in the process of being designed.
Smart Cities and Disaster Prediction
City planners looking to the future often talk wistfully about data acquisition. With enough data about how natural disasters have unfolded in the past, prediction becomes much easier—especially when computers are involved, juggling thousands of data points every nanosecond to create a likely model of the future. That’s why IBM recently launched its Smarter Cities program, which is essentially a suite of software and services that the company sells to cities whose governments want to predict everything from traffic and crime patterns to the best exit strategy in a flood. The goal is to create cities whose traffic, food systems, energy grids, water management, and even health care are managed in a “smart” way, based on real-time data that reveals what’s needed where. This “big data” can come from almost any networked gadgets, including sensors, mobile phones, and GPS devices.
George Thomas, a former structural engineer, heads up the company’s Smarter Cities sales efforts, and has helped implement the program in several urban areas around the world. One of their first projects was to reduce traffic in Stockholm. First, IBM installed cameras over heavily trafficked roads near downtown to gather data. Once they had enough information, they were able to predict peak traffic hours every day. To reduce traffic, the city installed a ring of sensors around the city center that identify the license plates of every car passing through. If cars pass through during a period of high traffic congestion, drivers will automatically be charged a “congestion tax” at the end of the month. Almost immediately, the city found that more people took public transportation, carbon emissions went down, and city revenues went up. Most important, the traffic snarls around the Swedish city were gone.
One of the group’s current projects uses data that engineers like Iverson have been gathering about how mud slides and floods behave. Their goal is to give residents of Rio de Janeiro two days’ warning before the notoriously flood-prone region is inundated with water and mud gushing down from the mountains that ring the city. In the past, emergency responders have only had a six-hour window in which to evacuate, but that’s not long enough. With the city set to host the Olympics and soccer’s World Cup, Rio’s mayor decided to work with IBM to create a system that could predict floods as far in advance as possible. They needed what Thomas called a city operating system—a piece of software that could integrate streaming data from sensors on local flood plains and weather monitors. Working with all this data, the city’s operating system could convert many types of information into a predictive model that would change in real time. With the new system in place, people in Rio will have a full 48 hours to leave their homes and get out of the city before disaster strikes.
Of course, even the best-prepared regions are still going to suffer setbacks. Japan was unprepared for the calamity of the March 2011 earthquake and tsunami. Though the damaged Fukushima Daiichi Nuclear Power Station did have flood-protection walls, they weren’t high enough. And switches that would have brought backup power to the plant’s cooling units hadn’t been adequately flood-proofed either. Could a predictive system with enough data have helped disaster workers prepare for the event? Possibly, though in the wake of the disaster, officials discovered that workers had known about problems at Fukushima for years without addressing them. Predictions are only helpful if city builders are willing to act on them.
The question, as always, is how to build based on what we know. Takhirov thinks the solution lies in the building code, which changes as new discoveries are made. But as Iverson explained, it’s not always easy to change cities that have already been built. All we can do in those situations is to make our cities “smarter.” That’s why engineers around the world are gathering all the data they can about disasters that might hit, in order to offer accurate predictions about when they’ll happen, and how to escape. Cities are more than buildings; they are the people who inhabit them. The philosophy of disaster science is that it doesn’t matter if our structures are damaged as long as people survive. Those people will come back to rebuild the city.
As we’ll see in the next chapter, engineers weren’t the first ones to come up with the idea of modeling urban disasters to save lives. It’s a strategy that works with pandemics, too.
16. USING MATH TO STOP A PANDEMIC
ENGINEERS WHO WANT to prevent natural disasters from destroying cities are still in the process of gathering enough data to predict dangers before they happen. But epidemiologists have been using predictive models for a century and a half. In the 1850s, a doctor named John Snow carefully mapped every incidence of cholera he could find in London, eventually determining that a single well was ground zero for the disease outbreak. It was the first great triumph of epidemic modeling, or using maps and data to figure out how infectious disease spreads through a city. Today, we have decades of data like Snow’s to help epidemiologists predict future paths of infection—hopefully stopping pandemics before they spread.
The next pandemic could start with viruses. Or bacteria, the way the Black Death did. It could even start with a bird or pig; with just the right combination of genetic material, a pathogen can jump from an animal into a human host. If the pathogen is infectious enough, the pandemic could kill 50 million people, the way the Spanish flu did in 1918. If it’s highly virulent, or develops resistance to treatment halfway through an outbreak, it could kill billions.
One of the most realistic pandemic scenarios in recent years came out of Hollywood, in a movie called Contagion. It offered a step-by-step look at how nations and health departments would respond to a viral outbreak, and how it would spread quickly via travelers all over the world. And—spoiler alert—it also gives us a very plausible scenario for the disease origin. A development company in China has been cutting down forests, displacing the local bat population. With no more natural habitat, the bats wind up nesting in barns full of pigs, and their virus-laced guano falls into the pig mush. Eventually a visiting American is exposed to one of the pigs, and she takes the virus with her back to the United States. Thus
a pandemic virus is born, partly the result of human meddling in the environment, and partly the result of our cosmopolitan living arrangements.
So what are we going to do about it?
When planning to survive a pandemic, there are two basic questions: What is killing people, and how should we organize an international response to it that minimizes death and economic damage? Generally we can answer the first question in a laboratory—and often we can come up with a treatment and vaccine there, too. As difficult as it is to identify a killer microbe and come up with a way to fight it, it’s the second question that keeps scientists and policy-makers up at night.
Even if we manage to whip up a cure for the pandemic, it won’t do any good if we can’t get it to people in time. That’s why modeling possible pandemic-outbreak scenarios has become its own scientific subfield, combining everything from medicine and genetics to statistical analysis and game theory. Pandemic modelers are usually experts in mathematics, creating gamelike computer simulations to aid in predictions, as well as maps and charts representing how a pandemic might spread. They also draft charts of survival. The pandemic modeler’s goal is to figure out what groups like the World Health Organization (WHO) and local medical groups can do to intervene and change the odds. They answer questions about how much vaccine we would need to prevent a pandemic from infecting a small town and how much for a large city. Using known patterns of infection, they can figure out roughly how much a quarantine will slow the spread of a pandemic, and the minimum number of antiviral drugs a country should stockpile to prevent mass death.
Scatter, Adapt, and Remember: How Humans Will Survive a Mass Extinction Page 18
