Multivariate geotechnical zonation of seismic site effects with clustering-blended model for a city area, South Korea

Kim, Hansaem; Sun, Chang‐Guk; Lee, Moon-Gyo; Cho, Hyung-Ik

doi:10.1016/j.enggeo.2021.106365

Cited by 10 publications

(1 citation statement)

References 62 publications

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“…It demands not just technical adaptations but also a cultural shift. Training personnel, adapting to new decision-making paradigms, and ensuring that the integration does not disrupt ongoing operations are all significant challenges [48].…”

Section: Integration With Existing Systemsmentioning

confidence: 99%

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Akbar Firoozi,

Asghar Firoozi

2023

Artificial Intelligence

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Within the domain of geotechnical engineering, risk assessment is pivotal, acting as the linchpin for the safety, durability, and resilience of infrastructure projects. While traditional methodologies are robust, they frequently require extensive manual efforts and can prove laborious. With the onset of the digital era, machine learning (ML) introduces a paradigm shift in geotechnical risk assessment. This chapter delves into the confluence of ML and geotechnical engineering, spotlighting its enhanced predictive capabilities regarding soil behaviors, landslides, and structural resilience. Harnessing modern datasets and rich case studies, we offer an exhaustive examination that highlights the transformative role of ML in reshaping geotechnical risk assessment practices. Throughout our exploration of evolution, challenges, and future horizons, this chapter emphasizes the significance of ML in advancing and transforming geotechnical practices.

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Section: Integration With Existing Systemsmentioning

confidence: 99%