Enhancing Urban Resilience Against Liquefaction Through Machine Learning

Researchers from the Shibaura Institute of Technology have developed a machine learning model to enhance urban resilience against liquefaction in earthquake-prone areas. By creating detailed 3D maps of bearing soil layers, the model aids city planners in identifying stable construction sites and improving disaster preparedness, thus contributing to safer urban development.

In modern urban planning, particularly in earthquake-prone regions such as Japan, the necessity for effective risk management related to soil stability has become increasingly pressing. To address this, researchers from the Shibaura Institute of Technology, led by Professor Shinya Inazumi and student Yuxin Cong, have developed an innovative machine learning model that significantly enhances the understanding of soil behavior during seismic events, specifically targeting the phenomenon of liquefaction. Utilizing advanced techniques such as artificial neural networks (ANNs) and bagging methodologies, the team successfully constructed detailed three-dimensional maps of bearing soil layers from geological data collected at 433 sites in Setagaya, Tokyo. These contour maps serve as essential tools for urban planners, aiding in the identification of stable construction sites and enhancing overall disaster preparedness. Given that liquefaction poses severe hazards to infrastructure—exemplified by historical earthquakes that have resulted in widespread damage and significant disruption—this predictive capability stands to substantially bolster urban resilience against such natural calamities. Furthermore, the study demonstrates a 20% enhancement in prediction accuracy through the integration of diverse datasets, marking a notable advancement in geotechnical engineering. As urban areas continue to expand, the adoption of such data-driven strategies will facilitate safer, more informed decisions in urban development, ultimately contributing to the establishment of smarter cities that can better withstand the challenges posed by earthquakes and their subsequent effects.

The ongoing risk of liquefaction during earthquakes results from loose, water-saturated soils losing their structural integrity when subjected to intense shaking. Historically, significant earthquakes like the 2011 Tōhoku earthquake in Japan and others have shown how devastating liquefaction can be, leading to extensive infrastructure damage and public safety concerns. To mitigate these risks, accurate assessments of soil stability are crucial for urban planners and civil engineers. The utilization of machine learning in these assessments provides a cutting-edge approach to geotechnical analysis, allowing for comprehensive mapping and identification of both stable and vulnerable areas. The ability to predict soil behavior not only aids in construction but also plays a vital role in disaster management and emergency preparedness.

The integration of artificial intelligence through machine learning models presents a transformative opportunity for urban resilience in earthquake-prone regions. By enabling detailed predictive analytics of soil behavior, particularly concerning liquefaction risks, urban planners are better equipped to make informed decisions that prioritize public safety and infrastructure integrity. The successful development of 3D soil layer maps illustrates the profound impact such technologies can have on enhancing urban resilience, ensuring future cities are not only smarter but also significantly safer for their inhabitants.

Original Source: www.preventionweb.net