Estimating Deformation Modulus and Bearing Capacity of Deep Soils from Dynamic Penetration Test

Shan, Shihan; Pei, Xiangjun; Zhan, Weiwei

doi:10.1155/2021/1082050

Cited by 3 publications

(2 citation statements)

References 26 publications

(27 reference statements)

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“…It is a good bearing layer that can withstand even large structures, such as high-rise condominiums. Therefore, in this study, if the N value is greater than 50 or more within the range of more than 3 m in a row, it is called the bearing layer [20]. Notably, the study introduces "bearing layer depth A" as a standardized measure, adjusting for elevation differences by subtracting elevation from all bearing layer depths.…”

Section: Overview Of Predictive Analytics For Bearing Layer Depthmentioning

confidence: 99%

Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations

Cong,

Inazumi

2024

Smart Cities

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This paper addresses challenges and solutions in urban development and infrastructure resilience, particularly in the context of Japan’s rapidly urbanizing landscape. It explores the integration of smart city concepts to combat land subsidence and liquefaction, phenomena highlighted by the 2011 Great East Japan Earthquake. Additionally, it examines the current situation and lack of geoinformation and communication technology in the concept of smart cities in Japan. Consequently, this study employs advanced technologies, including smart sensing and predictive analytics through kriging and ensemble learning, with the objective of enhancing the precision of geotechnical investigations and urban planning. By analyzing data in Setagaya, Tokyo, it develops predictive models to accurately determine the depth of bearing layers that are critical to urban infrastructure. The results demonstrate the superiority of ensemble learning in predicting the depth of bearing layers. Two methods have been developed to predict undetected geographic data and prepare ground reality and digital smart maps for the construction industry to build smart cities. This study is useful for real-time analysis of existing data, for the government to make new urban plans, for construction companies to conduct risk assessments before doing their jobs, and for individuals to obtain real-time geographic data and hazard warnings through mobile phones and other means in the future. To the best of our knowledge, this is the first instance of predictive analysis of geographic information being conducted through geographic information, big data technology, machine learning, integrated learning, and artificial intelligence.

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Section: Overview Of Predictive Analytics For Bearing Layer Depthmentioning

confidence: 99%

Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations

Cong,

Inazumi

2024

Smart Cities

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“…An N-value of 50 or more suggests a very solid layer, suitable as a load-bearing layer capable of supporting large structures such as high-rise condominiums. Therefore, in this study, continuous strata with an Nvalue exceeding 50 over a length of more than 3 m are designated as load-bearing layers [36].…”

Section: Procedures On Soil Liquefaction Risk Predictionmentioning

confidence: 99%

AI-Driven Prediction and Mapping of Soil Liquefaction Risks for Enhancing Earthquake Resilience in Smart Cities

Katsuumi,

Cong,

Inazumi

2024

Preprint

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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 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.

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Elastic‐Viscoplastic Constitutive Model of Soil under Cyclic Loading

You³

et al. 2021

Advances in Civil Engineering

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The creep problems are often involved in soil under cyclic loading, and its behaviors of soil under cyclic loading sparks many arguments in current research field. To propose one new model to demonstrate these creep behaviors of soil under cyclic loading, the cyclic loading was simplified equivalently, and the elastic-viscoplastic model (EVPM) for soil under cyclic loading was established based on the Bingham model. The yield criterion for soil under cyclic loading with constant amplitude was proposed following the simplified load. A constitutive equation based on the EVPM was constructed by using the flow criterion related to the yield criterion. Meanwhile, the parameters of EVPM were identified and discussed. In addition, the case analysis of the EVPM was also performed. The results indicate that the stable and destructive creep behaviors of soil under cyclic loading could be well described by the recommended EVPM, and the obtained parameters in the model exhibited a clear regularity with the increase of dynamic stress amplitude. Besides, the established model could be selected to predict the stable and destructive creep behavior of soil under cyclic loading.

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Estimating Deformation Modulus and Bearing Capacity of Deep Soils from Dynamic Penetration Test

Cited by 3 publications

References 26 publications

Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations

Integration of Smart City Technologies with Advanced Predictive Analytics for Geotechnical Investigations

AI-Driven Prediction and Mapping of Soil Liquefaction Risks for Enhancing Earthquake Resilience in Smart Cities

Elastic‐Viscoplastic Constitutive Model of Soil under Cyclic Loading

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