Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall

Friaa, Houda; Hellara, Myriam Laroussi; Stefanou, Ioannis; Sab, Karam; Dogui, Abdelwaheb

doi:10.1007/s11012-020-01134-0

Cited by 7 publications

(5 citation statements)

References 55 publications

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“…Further simplifying the hardware needed, use of ANNs removed the need for computationally heavy numerical modelling by considering a number of parameters from 1D and 2D models and representative "unit cells" of the structure. ANNs were used to establish coefficients that could represent and accurately describe the out-of-plane performance of a wall [53]. Although this study did not present an application involving a fire scenario, the method and philosophy of using AI would be applicable in other loading situations, including that of extreme heat and fire.…”

Section: Use Of Ai For the Assessment Of Thermal Performance Of Build...mentioning

confidence: 99%

Meta-Narrative Review of Artificial Intelligence Applications in Fire Engineering with Special Focus on Heat Transfer through Building Elements

Bakas

Kontoleon

2023

Fire

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Artificial intelligence (AI), as a research and analysis method, has recently been gaining ground in the ever-evolving scientific field of fire engineering in buildings. Despite the initial delay in utilising machine learning and neural networks due to the shortfall of available computational power, a review of cutting-edge scientific research demonstrates that scientists are now exploring and routinely incorporating such systems in their research processes. As such, a considerable volume of new research is being produced comprising applications of AI in fire engineering. These findings and research questions ought to be summarised, organised, and made accessible for further investigation and refinement. The present study aims to identify recent scientific publications relating to artificial intelligence applications in fire engineering, with particular focus on those tackling the issue of heat transfer through building elements. The method of the meta-narrative review, as implemented in the field of medical advancement research, is discussed, adapted, and finally utilised to weave a narrative that enables the reader to follow the most recent, influential, and impactful works. Efforts are made to uncover trends in the search for heat transfer models and properties under fire loading using AI. The review concludes with our thoughts on how future research can enrich the current findings on heat transfer in buildings exposed to fire actions and elevated temperatures.

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Section: Use Of Ai For the Assessment Of Thermal Performance Of Build...mentioning

confidence: 99%

Meta-Narrative Review of Artificial Intelligence Applications in Fire Engineering with Special Focus on Heat Transfer through Building Elements

Bakas

Kontoleon

2023

Fire

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“…Friaa et al [16] used ANNs to predict the elastic membrane and bending constants of the equivalent Love-Kirchhoff plate of hollow concrete blocks masonry wall. Drosopoulos and Stavroulakis [17] used the ML approach in multi-scale computational homogenization to obtain the masonry wall's non-linear response.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks

Thango,

Drosopoulos,

Motsa

et al. 2023

Infrastructures

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A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.

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“…Regarding continuum models, the first step consists in choosing the constitutive "homogenised" law. If the first constitutive laws or failure surfaces were often empirical (Dhanasekar et al 5 for instance), other methods such as AI-based methods (Asteris et Plevris, 6 Friaa et al 7 for instance) have expanded a lot over the last few years. Another, and maybe the most rigorous, way to take into account the material anisotropy due to the presence of joints is to resort to multiscale methods, which are often used for masonry.…”

Section: Introductionmentioning

confidence: 99%

“…If the first constitutive laws or failure surfaces were often empirical (Dhanasekar et al 5 . for instance), other methods such as AI‐based methods (Asteris et Plevris, 6 Friaa et al 7 . for instance) have expanded a lot over the last few years.…”

Section: Introductionmentioning

confidence: 99%

3D analytical and numerical upper‐bound homogenization approaches to the in‐plane strength domain of a running‐bond masonry wall

Donval

Pham

Hassen

et al. 2023

Num Anal Meth Geomechanics

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The present paper proposes a new homogenization method in order to determine the in-plane strength domain of a single-wythe, running-bond masonry wall. Two approaches, one analytical and one numerical, relying on the limit analysis/yield design framework, are introduced. The main novelty of these two approaches is that they allow to take into account a finite strength for the blocks and the mortar, as well as a non-zero thickness for the joints, without any specific assumption on the state of stress or strain of the structure. For that purpose, 3D virtual failure mechanisms are considered. A comprehensive parametric study is performed, showing that in many cases, the analytical approach is as precise as the numerical one for a much lower computational cost. Finally, the strength domains obtained by the proposed approaches are compared to the results of an extensive experimental study on reduced-scale masonry wallettes available in the literature, showing a good agreement in the compression-compression range.

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Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall

Cited by 7 publications

References 55 publications

Meta-Narrative Review of Artificial Intelligence Applications in Fire Engineering with Special Focus on Heat Transfer through Building Elements

Meta-Narrative Review of Artificial Intelligence Applications in Fire Engineering with Special Focus on Heat Transfer through Building Elements

Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks

3D analytical and numerical upper‐bound homogenization approaches to the in‐plane strength domain of a running‐bond masonry wall

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