Francis / Miresco / Melhado Advances in Information Technology in Civil and Building Engineering

Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation
1. Auflage 2025
ISBN: 978-3-031-87364-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 629, 671 Seiten, eBook

Reihe: Lecture Notes in Civil Engineering

This book gathers the latest advances, innovations, and applications in the field of information technology in civil and building engineering, presented at the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE), held in Montreal, Canada on August 25-28, 2024. It covers highly diverse topics such as BIM, construction information modeling, knowledge management, GIS, GPS, laser scanning, sensors, monitoring, VR/AR, computer-aided construction, product and process modeling, big data and IoT, cooperative design, mobile computing, simulation, structural health monitoring, computer-aided structural control and analysis, ICT in geotechnical engineering, computational mechanics, asset management, maintenance, urban planning, facility management, and smart cities. Written by leading researchers and engineers, and selected by means of a rigorous international peer-review process, the contributions highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaborations.

Zielgruppe

Research

Autoren/Hrsg.

Francis, Adel

Miresco, Edmond

Melhado, Silvio

Inhaltsverzeichnis

Transparency and Trust Evaluating XAI for Critical Infrastructure Systems A Comprehensive Analysis.-Quantum computing in civil engineering Potentials and Limitations.- A contribution to process oriented graph data management for structural data exchange.- Machine Learning and Internet of Things for Construction Air Pollution Monitoring and Prediction.- Exploring the potential of BIM models for deriving synthetic training data for Machine Learning applications.- Natural Language Communication with Sensor Data through a LLM integrated Protocol a Case Study.- Integrating Machine Learning for Cyber Risk Analysis in Construction 4 0.- Automated Hierarchical Object Clustering for Multi Image Documentation A Comprehensive Image Analysis Pipeline.