Zheng Guan – författare

Site characterization is indispensable to good geotechnical or rock engineering practice as every site is unique, but technical, budget, time, or access constraints typically result in only a tiny fraction of the underground soil and rock in a site being visually inspected, sampled, or tested. This leads to a long- lasting challenge of sparse measurements in geo- sciences and engineering. This book introduces Bayesian compressive sensing or sampling (BCS) as a highly efficient spatial data analytic and simulation method for the efficient modelling of spatial geo- data from sparse measurements, with quantified reliability and uncertainty to further optimize site characterization. It provides the necessary theory and computational tools for setting up and solving a sparse spatial data modeling problem using BCS. This book suits graduate students, academics, researchers, and engineers interested in site characterization from sparse measurements in geotechnical and rock engineering, and also those modeling other spatially varying phenomena such as air quality data, soil or water pollution data, and meteorological data. This is supplemented with a software called Analytics of Sparse Spatial Data using Bayesian compressive sampling/ sensing and illustrative examples, and enables hands- on experience of spatial data analytics and simulation using sparse measurements.

Geotechnical Aspects of Underground Construction in Soft Ground comprises a collection of 68 contributions, including 55 technical papers, 6 General Reports, 5 Keynotes, 1 Fujita Lecture, and 1 Bright Spark Lecture presented at the 11th International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground (IS-Macau 2024), held in Macao SAR, China, on June 14-17, 2024.The symposium is the latest in a series that began in New Delhi in 1994 and was followed by symposia in London (1996), Tokyo (1999), Toulouse (2002), Amsterdam (2005), Shanghai (2008), Rome (2011), Seoul (2014), Sao Paulo (2017), and Cambridge (2022). This symposium was organized by the University of Macau, Civil Engineering Laboratory of Macau, and the Macau Association for Geotechnical Engineering under the auspices of TC204 of ISSMGE. The book includes contributions from more than 15 countries on the research, design, and construction of underground works in soft ground. The theme of IS-Macau 2024 is “Tunnelling and Underground Construction for Smart Cities”. The contributions cover the following topics:Basic properties and soil improvement in soft groundConstitutive and Numerical ModellingInnovative analysis and design in tunneling and underground constructionSmart monitoring and visualization technologies for tunneling and underground constructionSustainability and resilience of underground infrastructureField case studiesSimilar to previous editions, GEOTECHNICAL ASPECTS OF UNDERGROUND CONSTRUCTION IN SOFT GROUND serves as an invaluable resource offering insights into the contemporary methods of analyzing, designing, and executing tunnels and deep excavations within soft ground environments, crucial for the advancement of smart cities. The book is particularly aimed at academics and professionals interested in geotechnical and underground engineering.

Site characterization is indispensable to good geotechnical or rock engineering practice as every site is unique, but technical, budget, time, or access constraints typically result in only a tiny fraction of the underground soil and rock in a site being visually inspected, sampled, or tested. This leads to a long- lasting challenge of sparse measurements in geo- sciences and engineering. This book introduces Bayesian compressive sensing or sampling (BCS) as a highly efficient spatial data analytic and simulation method for the efficient modelling of spatial geo- data from sparse measurements, with quantified reliability and uncertainty to further optimize site characterization. It provides the necessary theory and computational tools for setting up and solving a sparse spatial data modeling problem using BCS. This book suits graduate students, academics, researchers, and engineers interested in site characterization from sparse measurements in geotechnical and rock engineering, and also those modeling other spatially varying phenomena such as air quality data, soil or water pollution data, and meteorological data. This is supplemented with a software called Analytics of Sparse Spatial Data using Bayesian compressive sampling/ sensing and illustrative examples, and enables hands- on experience of spatial data analytics and simulation using sparse measurements.