Damage Inference of Truss Structure Based on Bayesian Updating Article Swipe
In the long-term use of civil engineering structures, affected by natural disasters and man-made disasters, its performance gradually deteriorated, so it is very important to find structural damage in time. This paper discusses the problem of damage inference for truss structures, and uses Bayesian updating theory as a solution. By combining Bayesian updating with structural damage inference method, Monte Carlo sampling method is used for repeated variable experiment and analysis. The experimental results show that in a specific truss structure, when the load point is applied at a specific position, the experimental effect is the best. Moreover, with the increase of load and measuring points, the accuracy of experimental results is gradually improved. This method obviously improves the existing problems of the traditional method, and proves its effectiveness and accuracy. The combination of structural damage inference and Bayesian updating will bring more intelligent, reliable and efficient structural health monitoring and maintenance methods to the field of engineering construction. Realizing real-time reliability update and damage assessment of structures has a positive impact on improving the safety, sustainability and longevity of structures.
Related Topics
Concepts
Truss
Bayesian inference
Computer science
Inference
Structural health monitoring
Bayesian probability
Reliability (semiconductor)
Monte Carlo method
Bayesian network
Reliability engineering
Field (mathematics)
Data mining
Machine learning
Algorithm
Artificial intelligence
Structural engineering
Engineering
Mathematics
Statistics
Pure mathematics
Physics
Power (physics)
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54097/80hx5930
- https://drpress.org/ojs/index.php/HSET/article/download/22736/22304
- OA Status
- diamond
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400998384
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400998384Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.54097/80hx5930Digital Object Identifier
- Title
-
Damage Inference of Truss Structure Based on Bayesian UpdatingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-16Full publication date if available
- Authors
-
Hongwei LiuList of authors in order
- Landing page
-
https://doi.org/10.54097/80hx5930Publisher landing page
- PDF URL
-
https://drpress.org/ojs/index.php/HSET/article/download/22736/22304Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://drpress.org/ojs/index.php/HSET/article/download/22736/22304Direct OA link when available
- Concepts
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Truss, Bayesian inference, Computer science, Inference, Structural health monitoring, Bayesian probability, Reliability (semiconductor), Monte Carlo method, Bayesian network, Reliability engineering, Field (mathematics), Data mining, Machine learning, Algorithm, Artificial intelligence, Structural engineering, Engineering, Mathematics, Statistics, Pure mathematics, Physics, Power (physics), Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 106 |
| abstract_inverted_index.affected | 8 |
| abstract_inverted_index.existing | 118 |
| abstract_inverted_index.improves | 116 |
| abstract_inverted_index.increase | 99 |
| abstract_inverted_index.man-made | 13 |
| abstract_inverted_index.positive | 169 |
| abstract_inverted_index.problems | 119 |
| abstract_inverted_index.reliable | 143 |
| abstract_inverted_index.repeated | 65 |
| abstract_inverted_index.sampling | 60 |
| abstract_inverted_index.specific | 77, 88 |
| abstract_inverted_index.updating | 44, 52, 138 |
| abstract_inverted_index.variable | 66 |
| abstract_inverted_index.Moreover, | 96 |
| abstract_inverted_index.Realizing | 158 |
| abstract_inverted_index.accuracy. | 129 |
| abstract_inverted_index.analysis. | 69 |
| abstract_inverted_index.combining | 50 |
| abstract_inverted_index.disasters | 11 |
| abstract_inverted_index.discusses | 32 |
| abstract_inverted_index.efficient | 145 |
| abstract_inverted_index.gradually | 17, 111 |
| abstract_inverted_index.important | 23 |
| abstract_inverted_index.improved. | 112 |
| abstract_inverted_index.improving | 172 |
| abstract_inverted_index.inference | 37, 56, 135 |
| abstract_inverted_index.long-term | 2 |
| abstract_inverted_index.longevity | 177 |
| abstract_inverted_index.measuring | 103 |
| abstract_inverted_index.obviously | 115 |
| abstract_inverted_index.position, | 89 |
| abstract_inverted_index.real-time | 159 |
| abstract_inverted_index.solution. | 48 |
| abstract_inverted_index.assessment | 164 |
| abstract_inverted_index.disasters, | 14 |
| abstract_inverted_index.experiment | 67 |
| abstract_inverted_index.monitoring | 148 |
| abstract_inverted_index.structural | 26, 54, 133, 146 |
| abstract_inverted_index.structure, | 79 |
| abstract_inverted_index.structures | 166 |
| abstract_inverted_index.combination | 131 |
| abstract_inverted_index.engineering | 6, 156 |
| abstract_inverted_index.maintenance | 150 |
| abstract_inverted_index.performance | 16 |
| abstract_inverted_index.reliability | 160 |
| abstract_inverted_index.structures, | 7, 40 |
| abstract_inverted_index.structures. | 179 |
| abstract_inverted_index.traditional | 122 |
| abstract_inverted_index.experimental | 71, 91, 108 |
| abstract_inverted_index.intelligent, | 142 |
| abstract_inverted_index.construction. | 157 |
| abstract_inverted_index.deteriorated, | 18 |
| abstract_inverted_index.effectiveness | 127 |
| abstract_inverted_index.sustainability | 175 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100411968 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I25355098 |
| citation_normalized_percentile.value | 0.13899638 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |