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

Abstract

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.

Optimizing Gene Expression Programming to Predict Shear Capacity in Corrugated Web Steel Beams

“…This resurgence of interest in corrugated web steel systems has coincided with a growing trend in the structural engineering community towards embracing machine learning techniques for modeling and analysis. Researchers have proposed the use of machine learning algorithms, such as K-Nearest Neighbors, XGBoost, CatBoost, Random Forest, and support vector machines, to develop more accurate formulae for structural design and to enhance SHM for bridges [6][7][8].…”