Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine

TY - JOUR

T1 - Prediction of damage intensity to masonry residential buildings with convolutional neural network and support vector machine

AU - Jędrzejczyk, Adrian

AU - Firek, Karol

AU - Rusek, Janusz

AU - Alibrandi, Umberto

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/12

Y1 - 2024/12

N2 - 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 F1 score. The comparisons revealed only minor differences between the models. Despite the small range of damage intensity, the models created were able to achieve prediction results of around 80%. The SVM model had better results for training set accuracy, while the CNN model achieved higher values for F1 score and average precision for the test set. The results obtained justify the adoption of the CNN methodology as effective in the context of predicting the damage intensity of masonry residential buildings located in mining terrains.

AB - 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 F1 score. The comparisons revealed only minor differences between the models. Despite the small range of damage intensity, the models created were able to achieve prediction results of around 80%. The SVM model had better results for training set accuracy, while the CNN model achieved higher values for F1 score and average precision for the test set. The results obtained justify the adoption of the CNN methodology as effective in the context of predicting the damage intensity of masonry residential buildings located in mining terrains.

KW - Convolutional neural network

KW - Damage intensity

KW - Damage intensity prediction

KW - Masonry buildings

KW - Residential buildings

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85198635041&partnerID=8YFLogxK

U2 - 10.1038/s41598-024-66466-3

DO - 10.1038/s41598-024-66466-3

M3 - Journal article

C2 - 39009680

AN - SCOPUS:85198635041

SN - 2045-2322

VL - 14

JO - Scientific Reports

JF - Scientific Reports

IS - 1

M1 - 16256

ER -