Donna M. Huey
A perfect storm of risks threatens even the simplest of resiliency goals. There are key dangers to pay attention to while evaluating the relevance of context, contracting, and people as critical factors to achieve goals.
Starting with natural risks, resiliency is how vulnerable one is to hazards—understanding what the pattern or intensity of those hazards is, the response time, and how one can recover. There has been an 80 percent increase in the growth of climate-related disasters between 1980 and 2009. In 29 years, losses have doubled because of disaster. The increasing densities of urban centers, particularly in coastal cities, only push the limit of property and human losses. Even when attributing some of the loss increases to improved reporting, scientists argue two-thirds of the increase is ‘real.’ Certainly, if one were to dispute the increase in frequency, it would still be clear the rising costs are related to increased density in urban centers.
The key challenges of infrastructure risk include:
- adequately maintained structures;
- the pace of technology and new material adoption to improve asset life and performance; and
- prioritizing maintenance or recovery plans based on risk and life line analysis.
The American Society of Civil Engineers (ASCE) 2013, Report Card for America’s Infrastructure estimates the need for $3.6 trillion to raise our infrastructure to acceptable standards. Further, ASCE’s 2015 Report Card for New York’s Infrastructure showed only modest improvements with roads, bridges, and wastewater still reflecting ‘D’ grades.
Another risk to consider involves cyber security. Recent reports predict in the next five years a move from four billion to 30 billion in Internet-connected devices, with a trillion sensors emerging by 2022. Considering the prevalence of personal credit card hacks and identity theft, it is certainly within the realm of possibility there may be a data hack on a smart building or intelligent transport system. In 2014, many in the infrastructure community took careful note of the widely publicized study by Cesar Cerrudo on vulnerability of smart cities. Field tests have shown exposure to traffic sensors in several U.S. cities—couple this with a growing popularity of games and popular products.
A multi-variable problem
Problems arise while in isolation, but together the challenges become a multi-variable problem and infinitely more complex. Having personally worked in the IT side of the infrastructure industry for over 25 years, this author has become familiar with the use of systems’ engineering principles and progressive assurance to protect people from these multi-variable problems—testing individual pieces of hardware or code independently before connecting them together.
However, when the time comes to design or redevelop infrastructure, the luxury of a controlled setting is not always possible. Assumptions need to be made and the interdependencies of these variables must be modeled—it is the real-life, natural elements that will tell the true story of a successful design. Thus, the most important variable to be considered is people. Humans will use and interact with the infrastructure in the environment.
As a result, it is important to evaluate these multi-variable problems with a respect for context—taking into account economic risk, social risk, and the maturity of the community in place to maintain and sustain. To better understand, one can analyze the exponential increase of sensors. The devices come in many shapes and sizes and some can even generate their own power. They are helping designers and engineers understand how their designs are performing—a new live feedback loop has been created because the infrastructure can now ‘talk.’
For example, a new bridge structure loaded up with sensors can tell the operator everything from vibration to loads to wear and tear. Bridges in Atlanta or New York would be connected to servers with teams of people in well-staffed agencies or top-notch consultants evaluating, managing, and leveraging data to optimize maintenance, improve safety, and develop improved designs. Incoming data would feed decision support systems or asset management systems generating predictions, and automating work orders—a great example of making the best of all new technology has to offer.