Mingfeng Huang – författare
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3 produkter
3 produkter
High-Rise Buildings under Multi-Hazard Environment
Assessment and Design for Optimal Performance
Inbunden, Engelska, 2016
1 589 kr
Skickas inom 10-15 vardagar
This book discusses performance-based seismic and wind-resistant design for high-rise building structures, with a particular focus on establishing an integrated approach for performance-based wind engineering, which is currently less advanced than seismic engineering. This book also provides a state-of-the-art review of numerous methodologies, including computational fluid dynamics (CFD), extreme value analysis, structural optimization, vibration control, pushover analysis, response spectrum analysis, modal parameter identification for the assessment of the wind-resistant and seismic performance of tall buildings in the design stage and actual tall buildings in use. Several new structural optimization methods, including the augmented optimality criteria method, have been developed and employed in the context of performance-based design. This book is a valuable resource for students, researchers and engineers in the field of civil and structural engineering.
High-Rise Buildings under Multi-Hazard Environment
Assessment and Design for Optimal Performance
Häftad, Engelska, 2018
1 637 kr
Skickas inom 10-15 vardagar
This book discusses performance-based seismic and wind-resistant design for high-rise building structures, with a particular focus on establishing an integrated approach for performance-based wind engineering, which is currently less advanced than seismic engineering.
- Nyhet
Hybrid Data-Model and AI-Driven Approach for Structural Monitoring in Hazardous Construction
Inbunden, Engelska, 2026
323 kr
Skickas inom 10-15 vardagar
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.