A Hybrid Data-Model and Ai-Driven Approach for Structural Monitoring in Hazardous Construction (el. knyga) (skaityta knyga) | knygos.lt

Aprašymas

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.

>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.