A Hybrid Data-Model and AI-Driven Approach for Structural Monitoring in Hazardous Construction
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Product details:
- Publisher Springer Nature Singapore
- Date of Publication 10 April 2026
- ISBN 9789819586875
- Binding Hardback
- No. of pages119 pages
- Size 235x155 mm
- Language English
- Illustrations X, 119 p. 93 illus., 84 illus. in color. 700
Categories
Long 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.
MoreTable of Contents:
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Introduction.- Hybrid Data-Model Driven Theory.- Hybrid Data-Model Driven Prediction of Dynamic Response for Tower Cranes under Typhoons.- Real-Time Displacement Monitoring for High-Formwork Support Structures Using Computer Vision.- Hybrid Data-Model Driven Update of Warning Thresholds and Short-Term Response Prediction for High-Formwork Support Structures.- Conclusions and Prospects.
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