New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes: An Evolutionary Approach

Abstract

The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been utilized for establishing a prediction model for the axial behavior of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.

Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

“…Koza proposed a GP method, to provide an alternate method for fixed-length binary strings (used in GAs) [ 65 ]. This method is illustrated in Figure 1 which is adapted from [ 37 ]. Five main parameters to be defined throughout the GP methodology are the collection of the terminals (the constants and the input variables), the set of primitive functions (domain-explicit functions), the fitness evaluation, the control variables (cross-over and population size, etc.)…”

“…The phases covered in the process of GEP are illustrated in Figure 2 which is adapted from [ 37 ]. The method starts with the arbitrary formation of fixed-size chromosomes for all individuals; which are subsequently converted into expression trees (ET) and for each individual, the fitness strength is estimated.…”

“…This creates ambiguity in the prediction properties of GPC. Moreover, a rapid spike in the use of soft computing techniques to build an empirical model has been observed recently [ 36 , 37 ]. Gene expression programming (GEP) is one of the popular soft computing methods utilized by various researchers in several engineering perspectives.…”

“…The researchers use experimental and literature-based data for the prediction of compressive strength of sugar cane bagasse ash (SCBA) concrete via GEP [ 36 ]. Furthermore, the authors proposed a formula using GEP for estimating the axial capacity of concrete filled steel tube (CFST) with just 277 instances [ 37 ]. Furthermore, Nour et al [ 40 ] worked with GEP algorithms for the estimation of compressive strength of CFST containing recycled aggregates.…”

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

“…Moreover, using the previously mentioned technique in the recent and modern period of life is quite uneconomical. Thus, the modern methodologies of machine learning techniques can be adapted to predict the desired result in advance [ 16 ]. The prediction of variables can be done from regressions and machine learning models.…”