|
|
books
| book details |
A Hybrid Data-Model and AI-Driven Approach for Structural Monitoring in Hazardous Construction
By (author) Qiang Li, By (author) Peixuan Wang, By (author) Bawar Iftikhar, By (author) Yan Jiang, By (author) Mingfeng Huang
|
|
| on special |
normal price: R 1 514.95
Price: R 1 439.95
|
| book description |
This open access book addresses a critical challenge in modern construction: ensuring the safety of hazardous and complex engineering structures, such as super-tall buildings and large-span structures characterized by their slenderness and scale. The widespread use of these critical structures necessitates advanced safety monitoring and early warning systems. Traditional data-driven methods often fall short in meeting the demands for real-time, accurate, and proactive alerts under complex construction environments and extreme conditions. Therefore, research into hybrid data-model driven monitoring and early-warning technologies holds significant engineering importance. (1) Hybrid Data-Model Driven Theory: A foundational framework is established, analyzing core models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory networks (BiLSTM), and AdaBoost. A novel CNN-BiLSTM-AdaBoost hybrid prediction model is proposed, along with an overall implementation framework. (2) Hybrid-Driven Prediction for Tower Crane Response under Typhoons: A hybrid method is developed to predict tower crane displacement under extreme typhoons. An IoT-based monitoring system collects real-world data, while a Finite Element Method (FEM) model supplements extreme-scenario data. Predictions using pure data-driven and hybrid methods are compared. (3) Real-Time Displacement Monitoring for High-Formwork Using Computer Vision: The M-DAVIM vision-based method is investigated. Controlled experiments quantify the impact of factors like light intensity, fog, camera angle, and vibration on measurement accuracy. Deployed at a real construction site in Ningbo, the system achieved sub-millimeter accuracy under optimal conditions (illuminance: 200-400 lux, target size >18 pixels) and demonstrated strong robustness, enabling real-time tracking of key nodal displacements. (4) Hybrid-Driven Warning Threshold Update & Short-Term Response Prediction for High-Formwork: A three-module framework is proposed: a vision system for monitoring, a hybrid module for determining and dynamically updating safety warning thresholds, and a prediction module using the CNN-BiLSTM-Adaboost algorithm for one-hour-ahead displacement forecasting and construction load inversion.
| product details |
Normally shipped |
Publisher | Springer Verlag, Singapore
Published date | 10 Apr 2026
Language |
Format | Hardback
Pages | 119
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-9-8195-8687-5
Readership Age |
BISAC | technology / construction / general
| other options |
|
|
To view the items in your trolley please sign in.
| sign in |
|
|
|
| specials |
|
|
Katie Daysh
Paperback / softback
384 pages
was: R 295.95
now: R 259.95
|
|
Victor Frankenstein driven by the mad dream of creating his own creature, experiments with alchemy and science to build a monster stitched together from dead remains.
|
This is indeed a story of mercy and the redemption it offers.
|
|
|
|
