Chapters authored
Application of Machine Learning in Geotechnical Engineering for Risk Assessment
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.
Part of the book: Machine Learning and Data Mining Annual Volume 2023
Non-seismic and Complex Source Tsunami: Unseen Hazard
Tsunamis, commonly induced by undersea earthquakes, are formidable natural hazards capable of causing widespread devastation. This comprehensive chapter examines the complex dynamics of tsunamis, their generation mechanisms, and their broad-reaching impacts. The multifaceted nature of tsunami triggers, both seismic and non-seismic, is dissected, highlighting the role of undersea earthquakes, landslides, volcanic eruptions, and meteorological events in driving these devastating natural phenomena. The intricate interplay of seismic parameters such as magnitude, depth, and activity type is elaborated, underscored by an insightful case study on the 2011 Tohoku Earthquake and Tsunami. A pivotal part of the discussion lies in the exploration of non-seismic triggers of tsunamis, an area often overshadowed in tsunami studies. The impact of landslide-induced and volcanically triggered tsunamis is considered alongside the contentious topic of meteorologically influenced tsunami events. Delving further into the genesis of tsunamis, the chapter explores the influences of bathymetry and tectonic structures, particularly in the context of non-seismic tsunami generation. The chapter serves as a beacon for continuous research and predictive modeling in the field of tsunami studies, emphasizing the necessity for societal preparedness and strategic risk mitigation against these potent natural disasters.
Part of the book: Earthquake Ground Motion