Jędrzejczyk Adrian, Firek Karol, Rusek Janusz, Alibrandi Umberto
Faculty of Geo-Data Science, Geodesy and Environmental Engineering, AGH University, Kraków, Poland.
Department of Civil and Architectural Engineering, Aarhus University, Aarhus, Denmark.
Sci Rep. 2024 Jul 15;14(1):16256. doi: 10.1038/s41598-024-66466-3.
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 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 F 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.
在其生命周期中，建筑物会遭受损害，这会降低其性能，并可能对结构安全构成重大威胁。本文介绍了一项研究成果，该研究旨在创建一个预测位于采矿地区的建筑物损坏强度的模型。该研究的基础是一个包含185栋砖石结构住宅建筑的技术和采矿影响数据的数据库。建筑物的损坏强度可忽略不计，范围为0%至6%。采用卷积神经网络（CNN）方法创建该模型。支持向量机（SVM）方法常用于此类问题的分析，被用于进行比较。通过比较诸如准确率、精确率、召回率和F分数等参数对所得模型进行评估。比较结果显示各模型之间只有细微差异。尽管损坏强度范围较小，但所创建的模型能够达到约80%的预测结果。SVM模型在训练集准确率方面有更好的结果，而CNN模型在测试集的F分数和平均精确率方面取得了更高的值。所获得的结果证明，在预测位于采矿地区的砖石结构住宅建筑的损坏强度方面，采用CNN方法是有效的。
