Simulations Done by Supercomputers Are Actively Analyzing Safer Cities

 Recent data and statistics have suggested that for the first time in the history of human civilization, approximately fifty percent of the entire population has now shifted towards urban areas, which is a remarkable thing as far as advancements are concerned and might be a building block for the next step in human evolution. And, some scientists are even suggesting that migration towards urban areas has only started to intensify, and it will further increase steadily, if not exponentially, in the coming years. In 2018, the United Nations had published research that comes the year 2050, nearly 68 percent of the earth’s population would have migrated towards the urban counties. 

However, the greater the population, the more difficult it is to maintain and sustain those areas. And, maintaining urban cities in a safe, sustainable (renewable sources of energy), and secure way has become a priority, if not a concern, for urban planners. And, they are now seeking the aid of sophisticated technologies such as machine learning, supercomputers, and artificial intelligence to help them roll out a plan for an ambitious project that can practically be manifested in a data-driven and sustainable way. And relevant to this is a piece of news that researchers from NHERI SimCenter of NSF have designed and progressed an analysis toolkit that aids users in pinpointing the exact resilience of a particular city along with its structures. 

Well, first, you should know that the Sim Center is renowned for analyzing and researching natural hazards. The institute has developed a toolkit called “BRAILS” that utilizes artificial technology to analyze large-scale objects. BRAILS employs several concepts to analyze and research the resilience of city structures, including machine learning, computer vision, and deep learning. Apart from that, it also assesses risks and analyzes the street level and satellite imagery and the characteristics of the city buildings. 

Charles Wang, who is the lead developer of the BRAILS team, has recently given an interview to Aaron Dubrow of TACC has mentioned the fact that their works are relevant to a scenario when we want to predict the ramifications of a potential hazard on all edifices and buildings situated in an area but in the absence of attributes of all structures. In these cases, simulations done by artificial intelligence could grant users useful information to make the accurate simulation of a potential calamitous event on an entire area. He also added that the team also has neural network models to give some idea about the information regarding each edifice and buildings from the images that are available and different other sources of data. 

Conclusion 

The researchers have tried out the capabilities of BRAILS by putting it under different scenarios based on real-life hazardous events such as the earthquake in San Francisco, etc. The team predicted that their model could deliver 90% accurate statistics of all the buildings around the proximity of the impacted zone. 

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