Zum Hauptinhalt springen
Nicht aus der Schweiz? Besuchen Sie lehmanns.de
Bayesian Compressive Sensing for Site Characterization - Yu Wang, Tengyuan Zhao, Yue Hu, Zheng Guan, Kok-Kwang Phoon

Bayesian Compressive Sensing for Site Characterization

Buch | Hardcover
258 Seiten
2025
CRC Press (Verlag)
978-1-032-45809-0 (ISBN)
CHF 287,95 inkl. MwSt
  • Lieferbar (Termin unbekannt)
  • Versandkostenfrei
  • Auch auf Rechnung
  • Artikel merken
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.
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.

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.

1. Introduction 2. Compressive Sensing (CS) 3. Analytics of sparse spatial data by BCS 4. Simulation of sparse spatial data 5. Sample size determination for site characterization 6. Adaptive sampling in site characterization 7. Software ASSD-BCS 8. Future Research and Challenges

Erscheinungsdatum
Reihe/Serie Challenges in Geotechnical and Rock Engineering
Zusatzinfo 18 Tables, black and white; 118 Line drawings, color; 6 Line drawings, black and white; 24 Halftones, color; 2 Halftones, black and white; 142 Illustrations, color; 8 Illustrations, black and white
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Gewicht 670 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Mathematik / Informatik Informatik Theorie / Studium
Technik Bauwesen
ISBN-10 1-032-45809-7 / 1032458097
ISBN-13 978-1-032-45809-0 / 9781032458090
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
eine Einführung mit Python, Scikit-Learn und TensorFlow

von Oliver Zeigermann; Chi Nhan Nguyen

Buch | Softcover (2024)
O'Reilly (Verlag)
CHF 27,85
Von den Grundlagen bis zum Produktiveinsatz

von Anatoly Zelenin; Alexander Kropp

Buch (2025)
Hanser (Verlag)
CHF 69,95