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Related Concept Videos

Temperature Dependent Deformation01:12

Temperature Dependent Deformation

254
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
254
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

284
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
284
Structural Classification of Joints01:20

Structural Classification of Joints

5.8K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

498
Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
The insights from the bending moment diagram extend to...
498
Plastic Deformations01:19

Plastic Deformations

244
Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
244
Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

567
One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
567

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Related Experiment Videos

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks.

Xianglong Luo1, Wenjuan Gan1, Lixin Wang2

  • 1School of Information and Engineering, Chang'an University, Xi'an 710064, China.

Computational Intelligence and Neuroscience
|May 14, 2021
PubMed
Summary
This summary is machine-generated.

Structural deformation prediction is improved using a novel Temporal Convolutional Network (TCN) model. This TCN approach enhances accuracy and efficiency in forecasting structural movements, outperforming traditional methods.

Related Experiment Videos

Area of Science:

  • Structural Engineering
  • Machine Learning
  • Time Series Analysis

Background:

  • Structural deformation is an inevitable challenge in structural engineering, often resulting in nonstationary and nonlinear monitoring data.
  • Accurate prediction of structural deformation is crucial for structural health monitoring but remains a difficult problem.
  • Traditional prediction methods face limitations in handling the complexities of deformation data.

Purpose of the Study:

  • To propose a novel structural deformation prediction model based on Temporal Convolutional Networks (TCNs).
  • To address the limitations of traditional methods for predicting nonstationary and nonlinear structural deformation data.
  • To optimize TCN model hyperparameters using orthogonal experiments for improved performance.

Main Methods:

  • Utilized a one-dimensional dilated causal convolution within the TCN architecture to minimize parameters, expand receptive fields, and prevent data leakage.
  • Employed orthogonal experiments to systematically optimize TCN network hyperparameters, identifying the best parameter combination.
  • Leveraged the long-term memory capabilities of TCNs to effectively mine internal time characteristics of structural deformation data.

Main Results:

  • The optimized TCN model demonstrated high consistency between predicted and actual structural deformation values.
  • Optimized TCN parameters led to significant reductions: 44.15% in RMSE, 82.03% in MAPE, and 66.48% in MAE, with a 45.41% decrease in running time.
  • Compared to Wavelet Neural Networks (WNN), Deep Belief Network-Support Vector Regression (DBN-SVR), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) models, the TCN model achieved average reductions of 26.88% in RMSE, 62.16% in MAE, and 40.83% in MAPE.

Conclusions:

  • The proposed TCN-based model offers a robust and efficient solution for structural deformation prediction.
  • Hyperparameter optimization via orthogonal experiments significantly enhances the model's predictive accuracy and computational efficiency.
  • The TCN model outperforms several established machine learning models in predicting complex structural deformation data.