Predicting the behaviour of
reinforced concrete structures Prof George Markou Dr Nikolaos P Bakas
A multidisciplinary project that involves machine learning algorithms in civil engineering was launched in 2018. The main objective of the project was to develop design formulae to predict the maximum capacity of reinforced concrete structural members through the use of software-generated data that would be made available free of charge to train machine learning algorithms.
This innovative approach has been extended to the structural problem that aims to predict the fundamental period of reinforced concrete structures during the seismic design and assessment of framing systems by accounting for the soil structure interaction phenomenon. Investigating the dynamic response of structures is of significant importance, particularly when it is essential to predict the fundamental period of structures in an accurate and realistic manner. This is crucial to reassure safe designs and a sustainable built environment when designing for seismic loading conditions. 42
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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Artificial intelligence (AI) and machine learning algorithms are involved in a variety of scientific and industrial tasks, and contribute to the solution of corresponding problems, from data modelling and analysis to automatic literature reviews, face recognition and autonomous vehicles. In recent years, researchers and professionals have been fetching AI algorithms in an unusually wide range of scientific, technological and business fields. Regardless of the database, their basic purpose is to develop a prediction algorithm, which will be accomplished by a mathematical model to describe the complex interactions among
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some input variables and a corresponding response. The relationship between independent and dependent variables is often highly non-linear, and mathematical models aim to form a generalised relationship that can objectively link them. The final aim is to develop a numerical model that can predict new outputs for new, out-of-sample inputs, which may be within the given domain (interpolation) or even outside the domain (extrapolation). Predicting out of the limits of a given domain is a hard and highly unstable problem without interesting results. However, a numerical solution was recently published, with extended prediction horizons. This significant breakthrough provided new insights and mathematical tools for this research project.
