Affiliation:
1. Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan
2. College of Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan
Abstract
In response to increasing urbanization and the need for infrastructure resilient to natural hazards, this study introduces an AI-driven predictive model designed to assess the risk of soil liquefaction. Utilizing advanced ensemble machine learning techniques, the model integrates geotechnical and geographical data to accurately predict the potential for soil liquefaction in urban areas, with a specific focus on Yokohama, Japan. This methodology leverages comprehensive datasets from geological surveys and seismic activity to enhance urban planning and infrastructure development in smart cities. The primary outputs include detailed soil liquefaction risk maps that are essential for effective urban risk management. These maps support urban planners and engineers in making informed decisions, prioritizing safety, and promoting sustainability. The model employs a robust combination of artificial neural networks and gradient boosting decision trees to analyze and predict data points, assessing soil susceptibility to liquefaction during seismic events. Notably, the model achieves high accuracy in predicting soil classifications and N-values, which are critical for evaluating soil liquefaction risk. Validation against an extensive dataset from geotechnical surveys confirms the model’s practical effectiveness. Moreover, the results highlight the transformative potential of AI in enhancing geotechnical risk assessments and improving the resilience of urban areas against natural hazards.
Reference46 articles.
1. Sustainable development of smart cities: A systematic review of the literature;Trindade;J. Open Innov. Technol. Mark. Complex.,2017
2. Smart cities and sustainable development goals (SDGs): A systematic literature review of co-benefits and trade-offs;Sharifi;Cities,2024
3. Smart cities and sustainable development;Su;Reg. Stud.,2023
4. Mishra, R.K., Kumari, C.L., Krishna, P.S.J., and Dubey, A. (2022). Smart cities for sustainable development: An overview. Smart Cities for Sustainable Development, Springer.
5. Smart cities: Advances in research—An information systems perspective;Ismagilova;Int. J. Inf. Manag.,2019