Assessment of the Structural Response of Steel Reinforced and Steel-Fibre Reinforced Concrete Structures with 3D Detailed Modeling: Limitations and Remedies

Papadrakakis, Manolis; Markou, George

doi:10.1007/978-3-030-87312-7_38

Cited by 2 publications

References 20 publications

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A general framework of high-performance machine learning algorithms: application in structural mechanics

Markou,

Bakas,

Chatzichristofis

et al. 2024

Comput Mech

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Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.

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A general framework of high-performance machine learning algorithms: application in structural mechanics

Markou,

Bakas,

Chatzichristofis

et al. 2024

Comput Mech

View full text Add to dashboard Cite

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Use of AI and ML Algorithms in Developing Closed-Form Formulae for Structural Engineering Design

Markou,

Bakas,

Megan van der Westhuizen

2023

Advances in Civil and Industrial Engineering

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The design and analysis of structures is performed with the use of national and international design codes that usually suggest the use of semi-empirical formulae. Often the formulae are oversimplified, in some cases are not available to engineers, or are time-consuming and challenging to implement. The objective of this chapter is to demonstrate the use of artificial intelligence and machine learning to develop more accurate formulae for different types of applications related to structural design. The applications that are discussed in this work include predicting the shear capacity of reinforced concrete slender and deep beams without stirrups, calculating the fundamental period of reinforced concrete and steel structures, and predicting the deflection of horizontally curved steel I-beams.

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Assessment of the Structural Response of Steel Reinforced and Steel-Fibre Reinforced Concrete Structures with 3D Detailed Modeling: Limitations and Remedies

Cited by 2 publications

References 20 publications

A general framework of high-performance machine learning algorithms: application in structural mechanics

A general framework of high-performance machine learning algorithms: application in structural mechanics

Use of AI and ML Algorithms in Developing Closed-Form Formulae for Structural Engineering Design

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