Artificial intelligence - finite element method - hybrids for efficient nonlinear analysis of concrete structures

Kraus, Michael; Bischof, Rafael; Kaufmann, Walter E.; Thoma, Karel

doi:10.14311/app.2022.36.0099

Cited by 4 publications

References 13 publications

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Artificial Intelligence for Predicting Reuse Patterns

Armeni,

Raghu,

De Wolf

2024

Circular Economy and Sustainability

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Artificial intelligence, and specifically the subfields of computer vision and machine learning, has become a topic with great potential for predicting reuse patterns in the built environment. With sensors that collect visual data becoming more readily available, new opportunities are created to digitalise the built environment by applying technologies from these fields. Applications include exploring the design space, monitoring construction progress, and improving building performance during operation. Using these applications to increase circularity in the built environment requires information about in-use building products and their attributes (e.g. type, material, size, geometry, condition, etc.). This information is a starting point for many downstream circular processes and a core component of circular databases, which can enable designers, constructors, and facility managers to follow a circular paradigm. Many advancements have been made in academia and industry towards extracting such information from visual and other building data, e.g. for the downstream processes of predicting material reusability or automating the maintenance of building facades. This chapter presents efforts on this front and highlights the gaps in adopting and utilising these technologies for the circular built environment, including challenges in developing comprehensive systems for their deployment and in robustly evaluating them. It also discusses business and organisational considerations with respect to adoption, utilisation, and development of the technologies in the circular context.

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Artificial Intelligence for Predicting Reuse Patterns

Armeni,

Raghu,

De Wolf

2024

Circular Economy and Sustainability

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Strength Lab AI: a mixture-of-experts deep learning approach for limit state analysis and design of monolithic and laminate structures made of glass

Kraus,

Bischof,

Riedel

et al. 2024

Glass Struct Eng

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The demand for transparent building envelopes, particularly glass facades, is rising in modern architecture. These facades are expected to meet multiple objectives, including aesthetic appeal, durability, quick installation, transparency, and both economic and ecological efficiency. At the heart of facade design, particularly for structural glass elements, lies the assurance of structural integrity for ultimate and serviceability limit states with a requisite level of reliability. However, current structural engineering assessments for glass and glass laminate designs, especially in the geometrically non-linear setting, are time-consuming and require significant expertise. This study develops a customized Mixture-of-Experts (MoE) neural network architecture to overcome current limitations. It calibrates it on synthetically generated stress and deformation data obtained via parametrized Finite-Element-Analysis (FEA) of glass and glass laminate structures under both geometrically linear and nonlinear conditions for several joint support and loading conditions. Our findings reveal that the MoE model outperforms baseline models in predicting laminate deflections and stresses, offering a substantial increase in computational efficiency, compared to traditional linear and non-linear FEA, at high accuracy. The MoE is integrated within a novel web-based glass design and verification tool called Strength Lab AI and provided to the engineering public for future use. These results have profound implications for advancing engineering practice, offering a robust tool for the intricate structural design and analysis of glass and glass laminate structures.

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