A termék adatai:

ISBN13:9783031434549
ISBN10:3031434544
Kötéstípus:Keménykötés
Terjedelem:554 oldal
Méret:235x155 mm
Nyelv:angol
Illusztrációk: 39 Illustrations, black & white; 277 Illustrations, color
700
Témakör:

Knowledge Transfer in the Sustainable Rehabilitation and Risk Management of the Built Environment

KNOW-RE-BUILT. Proceedings of the Online International Multiplier Event/Conference, December 15-16, 2021
 
Kiadás sorszáma: 1st ed. 2024
Kiadó: Springer
Megjelenés dátuma:
Kötetek száma: 1 pieces, Book
 
Normál ár:

Kiadói listaár:
EUR 267.49
Becsült forint ár:
110 379 Ft (105 123 Ft + 5% áfa)
Miért becsült?
 
Az Ön ára:

88 303 (84 098 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 22 076 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.
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  példányt

 
Rövid leírás:

This book showcases the valuable contributions made during the online event entitled The International Conference on Knowledge Transfer in the Sustainable Rehabilitation and Risk Management of the Built Environment. The conference was held on December 15?16, 2021, and was organized as a multiplier event of the European project Rehabilitation of the Built Environment in the Context of Smart City and Sustainable Development Concepts for Knowledge Transfer and Lifelong Learning (RE-BUILT). This book specifically retains the same main themes explored in the book titled Critical Thinking in the Sustainable Rehabilitation and Risk Management of the Built Environment ? CRIT-RE-BUILT. The papers included in this book are mostly authored by partners in the project?s consortium and cover various aspects of civil engineering knowledge transfer in crucial areas, to address different perspectives and significant challenges related to the sustainable built environment. The book seeks to provoke ideas and discussions, particularly in the areas where risk management and sustainable rehabilitation of the built environment intersect, ranging from reducing hazard risks to enhancing sustainable rehabilitation efforts in the field

Hosszú leírás:

This book showcases the valuable contributions made during the online event entitled The International Conference on Knowledge Transfer in the Sustainable Rehabilitation and Risk Management of the Built Environment. The conference was held on December 15?16, 2021, and was organized as a multiplier event of the European project Rehabilitation of the Built Environment in the Context of Smart City and Sustainable Development Concepts for Knowledge Transfer and Lifelong Learning (RE-BUILT). This book specifically retains the same main themes explored in the book titled Critical Thinking in the Sustainable Rehabilitation and Risk Management of the Built Environment ? CRIT-RE-BUILT. The papers included in this book are mostly authored by partners in the project?s consortium and cover various aspects of civil engineering knowledge transfer in crucial areas, to address different perspectives and significant challenges related to the sustainable built environment. The book seeks to provoke ideas and discussions, particularly in the areas where risk management and sustainable rehabilitation of the built environment intersect, ranging from reducing hazard risks to enhancing sustainable rehabilitation efforts in the field

Tartalomjegyzék:
Modelling of Critical Slip Surface Geometry for Sustainable Slope Stability Analysis.- Landslide Displacement Prediction with Machine Learning Techniques.- Influence of the Reconstruction of the Sofia Ring Road on the Travel Time of Fire and Rescue Service Vehicles.- Protection of Buildings in the Former Quarry Area Against Effects of Rockfall.- Remedial Measures for a Rainfall-Induced Creeping Landslide: A Case Study.- Hydrogeologic Framework of the Gardunha Mountain.- The Underestimated Study of the Geology and Historical Topography of the Construction Site as a Factor in the Cracking of a New Building Near a Small River.- Assessing the Near-Future Behavior of a Landslide: Development and Preliminary Results of a Machine Learning Algorithm.