A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale.

Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion.

Building loss assessment using deep learning algorithm from typhoon Rusa.

Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the increasing damage, but the results have yet to meet market demand. Therefore, this study proposes a method that uses a deep learning algorithm to predict the damage caused by typhoons.

A quantitative analysis of multi-decadal shoreline changes along the East Coast of South Korea.

South Korea's east coast is facing several issues related to coastal erosion because of sea-level rise, typhoon-induced storm surges, and various coastal development projects. In recent decades, high storm waves have frequently appeared on the east coast, causing casualty, beach erosion, and coastal infrastructure damage, drawing significant public attention. Thus, we analyzed the multi-decadal shoreline changes to understand the coastal dynamics and the forces responsible for the spatio-temporal changes along the 173 km coastline.