Ali Asghar Firoozi

By Ali Akbar Firoozi and Ali Asghar Firoozi

This chapter provides a comprehensive analysis of Soil-Structure Interaction (SSI), a key component in solving complex engineering challenges amidst rapid urbanization and changing environmental conditions. It elucidates the theoretical principles and practical implications of SSI, emphasizing its role in creating sustainable and resilient engineering solutions. The chapter explores the soil\'s response to different load scenarios, highlighting the impact on structural reliability and integrity. The narrative includes real-world case studies demonstrating the practical application of SSI principles, advocating their integration into contemporary construction methodologies for improved structural safety. It also outlines innovative strategies to tackle SSI-related challenges, such as employing advanced materials and computational models. Finally, the focus is placed on sustainability and resilience-driven solutions designed to withstand the tests of time and climate change. Serving as a valuable guide for various stakeholders in the field, this chapter underscores the significance of SSI in the development of environmentally conscious and structurally robust constructions.

Part of the book: Challenges in Foundation Engineering - Case Studies and Best Practices [Working title]

By Ali Akbar Firoozi and Ali Asghar Firoozi

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

By Ali Akbar Firoozi and Ali Asghar Firoozi

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