Building safer cities with AI: Machine learning model enhances urban resilience against monetization

In earthquake-prone regions such as Japan, better prediction of soil stability is needed to mitigate liquefaction risks. To this end, the researchers used machine learning models, including artificial neural networks and bagging techniques, to create accurate 3D maps of load-bearing layers using data from 433 sites in Setagaya, Tokyo. This approach can identify stable construction sites, enhance disaster planning, and contribute to safer urban development, making cities more resilient to liquefaction risks.

As urban areas expand, the risk of natural disasters becomes a pressing concern for city planners and disaster management authorities. In earthquake-prone countries like Japan, one critical risk to infrastructure is liquefaction, a phenomenon where severe tremors cause water-saturated soil to lose its strength and behave like a liquid. Liquefaction can cause buildings to sink into the soil, crack foundations, and collapse roads and utilities such as water lines.

Soil liquefaction accompanies every major earthquake, and the damage is extensive. The 2011 Tohoku earthquake in Japan caused liquefaction that damaged 1,000 homes. In Christchurch, the 6.2 magnitude earthquake caused liquefaction, destroying 80% of the water and sewerage networks. In 2024, the Noto earthquake caused widespread flooding, affecting 6,700 homes.

To make cities more resilient to the effects of liquefaction, Professor Shinya Inazumi and his student Yuxin Kong from the Shibura Institute of Technology in Japan have developed machine learning models that predict how soil will react during earthquakes. These models use geological data to create detailed 3D maps of soil layers, identifying areas that are stable and those most susceptible to liquefaction. Unlike manual soil testing methods, which cannot cover every site, this approach provides a broader and more detailed view of soil behavior

In their latest study published in Smart Cities on October 8, 2024, they used artificial neural networks (ANNs) and ensemble learning techniques to accurately estimate the depth of load-bearing layers, a critical indicator of how stable soil is and how likely it is to do so. Experience liquefaction during an earthquake.

“This study establishes a high-accuracy prediction method for unknown points and areas, demonstrating the great potential of machine learning in geotechnical engineering. These improved prediction models facilitate safer and more efficient infrastructure planning, which is critical for earthquake-prone areas, ultimately contributing to “In order to develop safer and smarter cities,” says Professor Inazumi.

Predicting areas with deep, stable aquifers helps identify locations where soil can provide better support for buildings, especially during events such as liquefaction. The researchers collected bore depth data from 433 points in Setagaya-ku, Tokyo, using standard penetration tests and small ram sounding tests. In addition to the depth of the aquifer, they also recorded basic information about each location, such as longitude, latitude, and elevation.

The data was used to train the ANN to predict aquifer depth at 10 sites, using actual site measurements to evaluate the accuracy of the predictions. To improve the accuracy of these predictions, the researchers applied a technique called bagging (bootstrap pooling), which involves training the model multiple times on different subsets of the training data. This approach resulted in a 20% improvement in prediction accuracy.

Using the predicted values, the researchers created a contour map showing the depth of the aquifers within a one-kilometre radius around four selected sites in the Setagaya district. This map is a valuable visual aid for civil engineers, helping them identify suitable construction sites with stable soil conditions. It also helps disaster management experts identify areas most vulnerable to soil liquefaction, allowing for better risk assessment and mitigation strategies.

The researchers envision their method as a key enabler for the growth of smart cities, emphasizing the importance of data-driven strategies in guiding urban development and infrastructure planning. Discussing the matter, Professor Inazumi says: “This study provides a foundation for safer, more efficient and cost-effective urban development. By integrating advanced AI models into geotechnical analysis, smart cities can better mitigate liquefaction risks and enhance overall urban resilience.” The future implications of this study are optimistic.

Going forward, the researchers plan to enhance the accuracy of their model by incorporating additional ground conditions and developing specialized models for coastal and non-coastal areas, taking into account the influence of groundwater, an important factor in liquefaction.

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