Thanh / Nguyen-Ngoc / Bui-Tien Proceedings of the 5th International Conference on Sustainability in Civil Engineering - Volume 2
1. Auflage 2025
ISBN: 978-981-965206-8
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
ICSCE 2024, 23-25 October, Hanoi, Vietnam
E-Book, Englisch, 412 Seiten
Reihe: Lecture Notes in Civil Engineering
ISBN: 978-981-965206-8
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book contains the proceedings of the 5th International Conference on Sustainability in Civil Engineering, ICSCE 2024, held on October 23–25, 2024, in Hanoi, Vietnam. It presents the expertise of scientists and engineers in academia and industry in the field of bridge and highway engineering, construction materials, environmental engineering, engineering in Industry 4.0, geotechnical engineering, structural damage detection and health monitoring, structural engineering, geographic information system engineering, traffic, transportation and logistics engineering, and water resources, estuary, and coastal engineering. This book caters to academics, researchers, industrial practitioners, policymakers who are interested in sustainable development as an indispensable trend in the field of civil engineering.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Air Quality Prediction Using Deep Learning Approaches.- Utilizing Accelerometer Data and LSTM Model for Road Surface Detection.- Air Passenger Forecasting: Leveraging the Power of Autoregressive Models.- Performance Of Machine Learning For Predicting Load-Carrying Capacity Of Nonlinear Steel Trusses.- Evaluating Regression And Classification Surrogate Models For Sizing Optimization Of Nonlinear Steel Trusses.- Fuzzy Structural Analysis using Rao-kNNC-based Approach.- Multi-objective optimization of I-shaped steel plate girders using NSGA-II.- Using Classification and ANN Model to Predict Slump and Compressive Strength of Normal and High-Early Strength Concrete: A Study at a Concrete Batching Plant in Binh Thuan, Vietnam.- Application of Long Short-Term Memory Networks in Bridge Damage Detection.- An effective damage detection approach for a truss bridge using a hybrid deep learning model.- etc.