Researchers at Shibaura Institute of Technology, led by Professor Shinya Inazumi, have developed a machine learning model to enhance urban resilience against liquefaction in earthquake-prone areas. Utilizing artificial neural networks and bagging techniques, the model creates accurate 3D maps of soil layers to identify stable sites for construction and improve disaster preparedness. Published in “Smart Cities” on October 8, 2024, the study highlights the critical role of data-driven strategies in urban development.
In the quest to enhance urban resilience against liquefaction, especially in earthquake-prone regions like Japan, a novel machine learning model has been developed to accurately predict soil stability. This model generates contour maps highlighting the depth of soil bearing layers, which is crucial for city planners striving to mitigate liquefaction risks. Researchers, including Professor Shinya Inazumi and his student Yuxin Cong from Shibaura Institute of Technology, have employed advanced techniques, such as artificial neural networks (ANNs) and bagging methods, to create precise 3D maps of soil layers across 433 locations in Setagaya, Tokyo. This predictive capability not only aids in identifying stable construction sites but also enhances disaster preparedness, thereby fostering safer urban development. Liquefaction, a phenomenon induced by intense seismic activity where saturated soil loses its strength and behaves like a liquid, poses a significant threat to infrastructure. Historical incidents, such as the Tōhoku earthquake of 2011 and the 6.2 magnitude quake in Christchurch, underscore the critical nature of this challenge. In their recent study published in “Smart Cities” on October 8, 2024, the researchers revealed that their machine learning models provide a high-precision approach for predicting soil behavior during seismic events. The researchers’ data collection incorporated standard penetration tests and mini-ram sounding tests to document bearing layer depths, which were instrumental in training the ANN. Enhancements from using bagging techniques led to a 20% increase in prediction accuracy, culminating in valuable contour maps. These maps serve as essential tools for civil engineers in site selection and assist disaster management professionals in risk assessment. The researchers advocate for the incorporation of their methods in smart city initiatives, highlighting the pivotal role of data-driven strategies in urban planning and resilience.
The phenomenon of soil liquefaction poses a significant threat to infrastructure, particularly in regions that experience frequent seismic activity. During earthquakes, the intense shaking can cause saturated, loose soils to behave like a liquid, resulting in severe structural damage, such as the sinking of buildings and the destruction of roadways and utility systems. This risk is exacerbated in urban areas, where increasing population densities amplify the impact of natural disasters. As city planners and disaster management authorities grapple with these challenges, the need for advanced predictive tools has become paramount. Machine learning, particularly through models like ANNs, offers a promising solution by enabling the precise mapping of soil conditions and enhancing the safety of construction practices in these vulnerable areas.
The development of a machine learning model that predicts soil stability in earthquake-prone areas represents a significant Advancement in urban disaster resilience. By providing detailed contour maps and identifying areas vulnerable to liquefaction, this research lays the groundwork for safer infrastructure planning and more effective disaster management strategies. The approach not only enhances the capacity of city planners to make informed decisions regarding construction sites but also underscores the importance of integrating technology into urban resilience frameworks. Moving forward, the continued refinement of these predictive models will be vital in addressing the complexities of soil behavior, ultimately contributing to the cultivation of safer and smarter urban environments.
Original Source: www.preventionweb.net
