Strategic guide for implementing and developing digital twin products, with real-world case studies from different industries
Infrastructure Digital Twins is an instructive and practical guide that offers tools on incorporating digital twins into existing processes, illustrated by real-world case studies across diverse sectors. Emphasizing the intersections of digital twin technology, sustainability, and cutting-edge software inventions, this book uncovers the potential to accelerate the global journey toward carbon neutrality and the United Nations Sustainable Development Goals.
Written by a an esteemed author, Infrastructure Digital Twins approaches the topic by discussing:
- Ground-breaking applications of digital twin technology demonstrated through real-world examples in transportation, construction, aviation, manufacturing, and healthcare sectors, bringing to light the transformative potential of emerging tech in these industries.
- Advantages offered by the confluence of digital twin technology and modern software engineering tools, including generative Artificial Intelligence (AI), enhanced interoperability and sophisticated relationship mapping between static and dynamic entities.
- Advanced semantics to leverage AI and data analytics, thus enabling accurate predictions of future scenarios, illuminating the path towards smarter, sustainable cities and infrastructure.
- Current challenges and future solutions for digital twin adoption in sustainable asset maintenance and management, focusing on innovative strategies to seamlessly develop digital twin products
Diving into a subject of growing significance across industries, Infrastructure Digital Twins serves as an essential guide for practicing professionals and executive engineering leaders. It is also applicable for technologists and entrepreneurs, as well as it can serve as a textbook for graduate-level courses focusing on digital twin applications, machine learning, and the emerging landscape of tech innovation in infrastructure sustainability.
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
ISBN-13
978-1-394-19516-9 (9781394195169)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Eva Agapaki, PhD (Gainesville, FL) is an Artificial Intelligence Professor in the M.E. Rinker, Sr. School of Construction Management, and the Director of the Digital Twins Lab. She is an Affiliated Faculty of the Electrical and Computer Engineering Department. She is also an innovation product leader with nearly 10 years of academic and industry experience in Digital Twinning, data analytics, and Artificial Intelligence applications in tech, sustainability, manufacturing and civil engineering sectors.
Autor*in
Rinker Sr School of Construction Management FL
