ArticleScientific reports2024
Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Life cycle assessment and multicriteria decision making analysis of additive manufacturing processes towards optimal performance and sustainability.Scientific reports · 2025Article
- Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining.Scientific reports · 2025Article
- Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine.Scientific reports · 2024Article
- Energy Criterion for Fracture of Rocks and Rock-like Materials on the Descending Branch of the Load-Displacement Curve.Materials (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During their life cycle, buildings are subjected to damage that reduces their performance and can pose a significant threat to structural safety. This paper presents the results of research into the creation of a model for predicting damage intensity of buildings located in mining terrains. The basis for the research was a database of technical and mining impact data for 185 masonry residential buildings. The intensity of damage to buildings was negligible and ranged from 0 to 6%. The Convolutional Neural Network (CNN) methodology was used to create the model. The Support Vector Machine (SVM) methodology, which is commonly used for analysis of this type of issue, was used for comparisons. The resulting models were evaluated by comparing parameters such as accuracy, precision, recall, and F
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Registered trials
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