Zhu et al., 2023 - Google Patents
Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technologyZhu et al., 2023
- Document ID
- 10101056712471740131
- Author
- Zhu Y
- Jia Q
- Zhang K
- Li Y
- Li Z
- Wang H
- Publication year
- Publication venue
- Frontiers of Structural and Civil Engineering
External Links
Snippet
Concrete is widely used in various large construction projects owing to its high durability, compressive strength, and plasticity. However, the tensile strength of concrete is low, and concrete cracks easily. Changes in the concrete structure will result in changes in …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5086—Mechanical design, e.g. parametric or variational design
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Truong et al. | An effective deep feedforward neural networks (DFNN) method for damage identification of truss structures using noisy incomplete modal data | |
Wang et al. | Seismic response prediction and variable importance analysis of extended pile-shaft-supported bridges against lateral spreading: Exploring optimized machine learning models | |
Song et al. | Multi-failure probabilistic design for turbine bladed disks using neural network regression with distributed collaborative strategy | |
Li et al. | A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams | |
Ihesiulor et al. | Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts | |
Liu et al. | Flaw detection in sandwich plates based on time-harmonic response using genetic algorithm | |
CN114117840B (en) | Structural performance prediction method based on simulation and test data hybrid drive | |
Achouri et al. | Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF | |
Thirumalaiselvi et al. | Response prediction of laced steel-concrete composite beams using machine learning algorithms | |
Zhang et al. | Research on damage identification of hull girder based on Probabilistic Neural Network (PNN) | |
Lim et al. | Delamination detection in composite plates using random forests | |
Ghannadi et al. | The Differential Evolution Algorithm: An Analysis of More than Two Decades of Application in Structural Damage Detection (2001–2022) | |
Kuo et al. | GNN-LSTM-based fusion model for structural dynamic responses prediction | |
Dang et al. | Semi-supervised vibration-based structural health monitoring via deep graph learning and contrastive learning | |
Wu et al. | Pipeline damage identification based on an optimized back-propagation neural network improved by whale optimization algorithm | |
Ding et al. | Jaya-based long short-term memory neural network for structural damage identification with consideration of measurement uncertainties | |
Ning et al. | Prediction model for the failure behavior of concrete under impact loading base on back propagation neural network | |
Chen et al. | A vibration-based 1DCNN-BiLSTM model for structural state recognition of RC beams | |
Nguyen et al. | Evaluating structural safety of trusses using Machine Learning | |
Zhao et al. | Reliability-based support optimization of rockbolt reinforcement around tunnels in rock masses | |
Bandara | Damage identification and condition assessment of building structures using frequency response functions and neural networks | |
Fang et al. | A combined finite element and deep learning network for structural dynamic response estimation on concrete gravity dam subjected to blast loads | |
Afshari et al. | Deep learning-based methods in structural reliability analysis: a review | |
Zhao et al. | A conditional generative model for end-to-end stress field prediction of composite bolted joints | |
Zhu et al. | Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technology |