Related Experiment Video
Updated: Feb 14, 2026
05:30
Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
Published on: September 29, 2019
8.7K
Digital Image Correlation-Based Bolt Preload Monitoring
Linsheng Huo1, Liukun Zhao1, Aocheng Hu1
1State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|February 13, 2026
Summary
This study introduces a new non-contact method using Digital Image Correlation (DIC) for bolt preload monitoring. This approach precisely tracks surface strain to detect bolt loosening, enhancing structural safety.
Area of Science:
- Engineering
- Structural Health Monitoring
- Materials Science
Background:
- Bolt connections are critical in engineering structures but prone to loosening, posing safety risks.
- Existing bolt-loosening detection methods are often inefficient, inaccurate, or require contact sensors.
Purpose of the Study:
- To develop a novel non-contact method for monitoring bolt preload.
- To overcome the limitations of conventional bolt-loosening detection techniques.
Main Methods:
- Utilizing Digital Image Correlation (DIC) with an industrial camera to capture speckle images of bolt heads.
- Measuring surface strain on the bolt head before and after deformation.
- Calculating the strain field and tracking its changes to correlate with bolt preload.
Main Results:
- A linear relationship was established between the bolt head surface strain field and bolt preload.
- The DIC-based method demonstrated precise and efficient bolt preload monitoring.
- Experimental validation confirmed the accuracy and user-friendliness of the proposed technique.
Conclusions:
- The novel non-contact DIC method offers a precise, efficient, and user-friendly solution for bolt preload monitoring.
- This technique shows significant potential for applications in structural health monitoring and ensuring structural integrity.
Related Concept Videos
02:20Correlations
36.6K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.6K
01:27Correlation and Causation
43.0K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.0K
01:09Correlation
15.2K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.2K
00:53Correlation and Regression
3.5K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.5K
01:12Coefficient of Correlation
8.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.7K
01:23Correlation of Experimental Data
491
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
491