Homogenisation of masonry structures subjected to seismic loads through matrix/inclusion micromechanics

Drougkas, Anastasios; Sarhosis, Vasilis; D’Alessandro, Antonella; Ubertini, Filippo

doi:10.1016/j.istruc.2022.02.016

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…Masonry residential buildings, like other structures, are exposed to damage during their life cycle. Analysed in terms of structural mechanics, this damage is a process initiated when the allowable level of elastic deformation potential energy stored in a given structural element is exceeded 3 , 4 . This is a major criterion in the design of building structures and determines the maintenance of safety and acceptable levels of performance values 5 .…”

Section: Introductionmentioning

confidence: 99%

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

Jędrzejczyk,

Firek,

Rusek

et al. 2024

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jędrzejczyk,

Firek,

Rusek

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…When considering the failure phenomenon from the point of view of structural mechanics, this process is initiated when the permissible threshold of the potential elastic deformation energy stored in a given structural element is exceeded [7,8]. It is the main criterion in the design of building structures and determines the maintenance of safety and acceptable levels of serviceability [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

2022

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This paper presents the results of comparative studies on the implementation of machine learning methods in the damage intensity assessment of masonry buildings. The research was performed on existing residential buildings, subjected to negative impacts of the industrial environment induced by coal mining plants during their whole technical life cycle. The research was justified on the grounds of safety of use, as well as potential energy losses and CO2 emissions generated by the inefficient management of building materials resources resulting from poor planning of retrofitting. In this field, the research is in line with the global trends of large-scale retrofitting of existing buildings in European countries due to their thermal insulation parameters and seismic hazard. By combining this with the effects of material degradation throughout the technical lifecycle of buildings, the proposed methods allow for a more efficient approach to maintaining quality management of large groups of buildings, which is part of the sustainable development framework. Due to the multidimensionality of the undertaken problem and the necessity of mathematical representation of uncertainty, it was decided to implement a machine learning approach. The effectiveness of the following methods was analysed: probabilistic neural network, support vector machine, naive Bayes classification and Bayesian belief networks. The complexity of individual methods dictated the order of the adopted research horizon. Within such a research plan, both model parameters were learned, and model structure was extracted from the data, which was applied only to the approach based on Bayesian networks. The results of the conducted analyses were verified by assuming classification accuracy measures. Thus, a method was extracted that allows for the best realisation of the set research objective, which was to create a classification system to assess the intensity of damage to masonry buildings. The paper also presents in detail the characteristics of the described buildings, which were used as input variables, and assesses the effectiveness of the obtained results in terms of utilisation in practice.

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3D Semi‐Analytical and Numerical Upper‐Bound Homogenisation Approaches to the Out‐of‐Plane Strength Domain of a Running‐Bond Masonry Wall

Donval,

Hassen,

Pham

et al. 2024

Num Anal Meth Geomechanics

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The present contribution proposes a new semi‐analytical homogenisation approach to determine a running‐bond masonry wall's in‐ and out‐of‐plane strength domain based on the yield design framework. The main novelty of such an approach is that it does not rely on simplifying assumptions such as infinitely thin joints or plane stress state within the blocks, by making use of 3D virtual failure mechanisms in the kinematic approach. The new semi‐analytical approach is then compared to a state‐of‐the‐art numerical implementation of the kinematic approach of yield design, relying on semi‐definite programming. Several comparisons show a good agreement between the semi‐analytical and the numerical approaches and outline the computational efficiency of the semi‐analytical approach as well as the fact that it is very well suited for engineering design purposes. Both proposed approaches are then compared to existing approaches based on the limit analysis or yield design framework.

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Homogenisation of masonry structures subjected to seismic loads through matrix/inclusion micromechanics

Cited by 11 publications

References 38 publications

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

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

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

3D Semi‐Analytical and Numerical Upper‐Bound Homogenisation Approaches to the Out‐of‐Plane Strength Domain of a Running‐Bond Masonry Wall

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