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

Introduction

The use of artificial intelligence (AI) and machine learning (ML) in structural analysis and design problems has been increasing due to the methods’ abilities to handle complex nonlinear structural systems under extreme actions (Markou & Bakas, 2021a, 2021b; Thai, 2022). The purpose of the chapter is to discuss the use of AI and ML in structural engineering design based on current work performed by the authors and analyse the future perspectives related to this technology. The applications that are presented herein include the development of predictive formulae for the calculation of the shear capacity of slender reinforced concrete (RC) beams without stirrups, predicting the shear capacity of deep beams without stirrups reinforced with fibre reinforced polymer (FRP) rebars, determining the fundamental period of RC as well as steel structures, and determining the deflection of curved steel I-beams. In some cases, the formulae currently available for the determination of the above structural problems are oversimplified or are not readily available to practising engineers (Ababu et al., 2022), therefore, this is discussed in the relevant sections of this chapter that follow.

It is the objective of this chapter to show the reader the development of closed mathematical form design formulae that can predict the mechanical behaviour of structures using an out-of-the-box approach (Bakas & Markou, 2019), while artificial neural networks (ANN) and other ML methods were adopted. According to the proposed approach, by using finite element software and nonlinear analysis through the use of ReConAn FEA (2020) software, datasets are developed that are used for the development of predictive models. This approach aims to marry state-of-the-art 3D detailed modelling with AI and ML algorithms for the development of predictive models that exhibit a more accurate calculation of the mechanical response of structural members and structures. Furthermore, given that AI and ML algorithms are an effective and efficient tools used to predict analysis outputs of computationally demanding engineering problems, their use by the civil engineering community has emerged over the last decade and increases exponentially (Bakas et al., 2021).

A dataset is obtained from the results of the analysis of these models and is then used to train AI and ML algorithms that can develop predictive models. One of the main obstacles in training and testing models is the lack of datasets that refer to a specific structural problem. This is attributed to the limitations in relation to performing a large number of experiments for any structural problem, where a sufficient number of output data will be made available for the training of our AI and ML models (van der Westhuizen et al., 2022). For this reason, the proposed approach foresees the substitution of the actual experiment through the use of 3D detailed finite element analysis, hence, to be able to proceed with this approach an extensive validation of the adopted finite element algorithm has to be performed. This is also going to be presented by referencing the research work performed towards validating the research finite element analysis (FEA) software ReConAn FEA (2020) that was used for the development of the datasets presented in this work. Additionally, it is important to note that the proposed approach foresees, where feasible, the validation of the predictive models through the use of additional datasets that derived from physical experiments and were found in the international literature.

For the case where physical experiments are not available, the accuracy of the proposed predictive models is validated using out-of-sample data, while for all the structural problems presented herein the mean absolute percentage error (MAPE) and other error parameters are determined and compared to that of the design formulae currently available in the international literature. As is going to be shown in this chapter, the predictive models derived by using AI or ML algorithms proved to outperform design code formulae currently used to determine the mechanical properties and the strength of structures or their members. All studies presented in the chapter show strong evidence that a combination of FEA and ML methods provides reliable predictions that can be used in improving design code formulae currently used to calculate the shear capacity of slender and deep beams, determining the fundamental period of RC and steel structures as well as determining the deflection of curved steel I-beams.