In earthquake-prone areas like Japan, there is a need for better prediction of soil stability to mitigate liquefaction risks. Towards this end, researchers have used machine learning models, including artificial neural networks and bagging techniques, to create accurate 3D maps of bearing layers using data from 433 locations in Setagaya, Tokyo.
Building safer cities with AI: Machine learning model enhances urban resilience against liquefaction." ScienceDaily. ScienceDaily, 28 October 2024. <www.sciencedaily.comShibaura Institute of Technology. . Building safer cities with AI: Machine learning model enhances urban resilience against liquefaction.
Soil liquefaction that results in infrastructure damage has long been a point of contention for urban planners and engineers. Accurately predicting the soil liquefaction risk of a region could help ... During an earthquake, soil can weaken through subsidence and liquefaction. These processes can cause buildings to collapse as the soil becomes unable to support their weight. Researchers have now ...
Many factors influence where a landslide will occur, including the shape of the terrain, its slope and drainage areas, the material properties of soil and bedrock, and environmental conditions like ... Soil liquefaction was a major feature of the 2011 Christchurch, New Zealand earthquake that killed 185 people. Researchers developed a machine learning model to predict the amount of lateral movement ...'Well-Man' Thrown from Castle Identified from 800-Year-Old Norse Saga
Construction Engineering And Construction Earthquakes Natural Disasters Tsunamis Computer Modeling Mathematical Modeling Information Technology
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