{"product_id":"9789819586875","title":"Hybrid Data-Model and AI-Driven Approach for Structural Monitoring in Hazardous Construction","description":"\u003cp\u003eThis 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 \u0026amp; 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.\u003c\/p\u003e","brand":"Qiang Li","offers":[{"title":"Default Title","offer_id":53111392534846,"sku":"9789819586875","price":0.0,"currency_code":"ZAR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4357\/7662\/files\/9789819586875.jpg?v=1773066141","url":"https:\/\/exclusivebooks.co.za\/products\/9789819586875","provider":"Exclusive Books Online","version":"1.0","type":"link"}