Shanghai, the most populated city in China, is sinking at an average rate of 2-4 cm per year. Although that may not sound like much, the downward shift can cause the collapse of buildings and underground tunnels, endangering lives and costing money. Recently, the National Natural Science Foundation of China has granted funding to a Nottingham University researcher to develop a computer program to identify which buildings and other structures are moving the most and are at greatest risk of collapse.
Andrew Sowter, a mathematician and scientist at the University of Nottingham Ningbo, China, (UNNC) is developing software that analyzes satellite images of Shanghai over the past several years, which shows how much the land has moved across the coastal city. The program can accurately measure the land’s movement down to the millimeter. Along with researchers at Tongji University in Shanghai, Sowter is also analyzing data from the ground to confirm the satellite data.
While Shanghai is being used as a case study, several other cities in China are also sinking and could benefit from the research. Many of the sinking cities are coastal cities, such as Ningbo, which is currently constructing an underground rail system. Like Shanghai, Ningbo has a rapidly growing population and is built on water-logged land. Rapid urban development has also required groundwater to be pumped into the cities, contributing to the sinking.
In 2003, subsidence was blamed for the collapse of an eight-storey building in Shanghai’s inner-city Bund region, which is known for its iconic commercial real estate.
The pumping of groundwater to cater for a massive, growing population has been a significant contributor to subsidence. The problem has been exacerbated by the country’s decades-long building boom amid rapid urbanisation, said Sowter.
Sowter is working in collaboration with Shanghai’s Tongji University, which is gathering ground information to confirm the results of data gathered from space.
’We are advancing and refining existing computer programs so that we can identify risks with greater confidence of the accuracy of the results. Rather than just measuring the problem, we are also improving the models to map and identify priority areas,’ he said.
Sowter said that the technology he is developing can be applied to other risks associated with land, such as earthquake zones, high-risk flood areas, land deformation from mining, and glacier movements. It can, for example, help authorities prevent landslides by detecting where land is starting to move at the stage when changes are slight.