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Bayesian Compressive Sensing for Site Characterization (Challenges in Geotechnical and Rock Engineering) - Tapa dura
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Yu Wang is Professor of Geotechnical Engineering at the Hong Kong University of Science and Technology, Hong Kong.
Tengyuan Zhao is Associate Professor of Geotechnical and Geological Engineering at Xi’an Jiaotong University, China.
Yue Hu is Humboldt Postdoctoral Fellow at Leibniz Universität Hannover, Germany.
Zheng Guan is Assistant Professor of Geotechnical Risk Assessment and Management at Delft University of Technology in the Netherlands.
Kok-Kwang Phoon is Cheng Tsang Man Chair Professor and President at Singapore University of Technology and Design (SUTD). He has twice won the American Society of Civil Engineers Norman Medal and is founding editor of the journal Georisk.
"Sobre este título" puede pertenecer a otra edición de este libro.
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BAYESIAN COMPRESSIVE SENSING FOR SITE CHARACTERIZATION (HB 2026)
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Bayesian Compressive Sensing for Site Characterization (Hardcover)
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Taylor & Francis Ltd, London, 2025
ISBN 10: 1032458097
ISBN 13: 9781032458090
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Hardcover. Condición: new. Hardcover. 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. Bayesian compressive sensing/sampling is a very efficient analytic and simulation method for modelling spatial geo-data from sparse measurements, to quantify reliability and uncertainty, and to optimize site characterization. This provides principles with software for graduate students, and geotechnical or environmental engineers. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032458090
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Bayesian Compressive Sensing for Site Characterization (Challenges in Geotechnical and Rock Engineering)
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Bayesian Compressive Sensing for Site Characterization (Challenges in Geotechnical and Rock Engineering)
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Bayesian Compressive Sensing for Site Characterization (Challenges in Geotechnical and Rock Engineering)
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Bayesian Compressive Sensing for Site Characterization
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Bayesian Compressive Sensing for Site Characterization (Hardcover)
Publicado por
Taylor & Francis Ltd, London, 2025
ISBN 10: 1032458097
ISBN 13: 9781032458090
Nuevo
Tapa dura
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: new. Hardcover. 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. Bayesian compressive sensing/sampling is a very efficient analytic and simulation method for modelling spatial geo-data from sparse measurements, to quantify reliability and uncertainty, and to optimize site characterization. This provides principles with software for graduate students, and geotechnical or environmental engineers. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781032458090
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Bayesian Compressive Sensing for Site Characterization
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Bayesian Compressive Sensing for Site Characterization (Challenges in Geotechnical and Rock Engineering)
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