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Structural Classification of Joints01:20

Structural Classification of Joints

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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.
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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Multi-Class Concrete Defect Classification Using Guided Semantic-Spatial Fusion and Squeeze-Excitation Enhanced

Ali Mahmoud Mayya1, Nizar Faisal Alkayem2,3

  • 1Computer and Automatic Control Engineering Department, Faculty of Mechanical and Electrical Engineering, Latakia University, Latakia 2230, Syria.

Materials (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning framework for detecting multiple concrete defects. The enhanced model significantly improves classification accuracy for structural integrity assessments.

Keywords:
DenseNet201deep learningdefect detectionmulti-class classification

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Materials Science

Background:

  • Concrete structures are prone to defects impacting safety and maintenance.
  • Accurate defect detection and quantification are essential for structural health monitoring.
  • Existing deep learning methods often lack multi-class defect identification capabilities.

Purpose of the Study:

  • To develop an advanced deep learning framework for multi-class concrete defect detection and localization.
  • To enhance the accuracy and reliability of automated concrete defect classification.
  • To create a practical tool for non-destructive measurement of concrete defects.

Main Methods:

  • A novel dataset of 2029 concrete defect images across five categories was compiled.
  • The DenseNet201 model was enhanced with a guided semantic-spatial fusion module and squeeze-and-excitation architecture.
  • Attention mechanisms were integrated to improve feature representation and defect region tracking.

Main Results:

  • The proposed framework achieved a 5.6% accuracy improvement over the original DenseNet201 model.
  • Experimental validation demonstrated the model's superiority in multi-class defect identification.
  • The developed model effectively detects and localizes various concrete defects.

Conclusions:

  • The enhanced deep learning framework offers superior performance for multi-class concrete defect detection.
  • The study provides a reliable method for non-destructive measurement and classification of concrete defects.
  • Integration into a graphical user interface facilitates practical application in structural maintenance.