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Found 8 papers, 0 papers with code
Vision-aided nonlinear control framework for shake table tests
The signal generated by the control law of the adaptive control method will be implemented by a loop-shaping controller.
A reinforcement learning based construction material supply strategy using robotic crane and computer vision for building reconstruction after an earthquake
In this paper, the robotic crane with advanced AI algorithms is proposed to provide resources for infrastructure reconstruction after an earthquake.
Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision
Experimental results indicate that the proposed MAV-based inspection approach can effectively collect images from multiple viewing angles, and accurately assess critical RC column damages.
Dynamic Analysis of Nonlinear Civil Engineering Structures using Artificial Neural Network with Adaptive Training
In this study, artificial neural networks are developed with adaptive training algorithms, which enables automatic nodes generation and layers addition.
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking
Then, the target-free object tracking algorithm based on optical flow is implemented, to continuously monitor and quantify the rotation of structural bolts.
Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks
The proposed YOLO-v2 is used in combination with the classification neural network, which improves the identification accuracy for critical damage state of reinforced concrete structures by 7. 5%.
A Data-Driven Approach for Linear and Nonlinear Damage Detection Using Variational Mode Decomposition and GARCH Model
Afterwards, the GARCH model is utilised to represent the statistics of IMFs.
A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network
In this paper, a novel deterioration and damage identification procedure (DIP) is presented and applied to building models.
