Search Results (5)

In recent years, there has been a growing use of cold-formed steel (CFS) structures in the field of civil engineering. The objective of this study is to utilize gene expression programming (GEP) in order to forecast the ultimate bearing capacity of cold-formed steel columns. The buckling resistance of built-up back-to-back cold-formed (BCF) thin-walled tube columns under axial compression, and of cold-formed thick-walled steel columns under combined axial compression and bending, is examined in this paper. The data were collected from various studies to develop and verify the proposed model, with training and testing sets of 160 and 14, and 2000 and 500, respectively. The performance of the genetically developed GEP models was evaluated and compared with that of the mechanical models specified in American and Chinese specifications. The GEP models demonstrated significantly better performance compared with that of the code-specified models. The results generated by the GEP models demonstrate stronger alignment with both experimental data and analytical predictions. This study also demonstrates the capability of the GEP models to calculate the ultimate bearing capacity, with the proposed mechanical models being used as a reference for calculations. Full article

This paper investigates the effect of fiber-reinforced composites (FRPs) on the mechanical properties of concrete under ambient conditions. It begins with an examination of the various types of FRP and their advantages, followed by a review of isostructural models for passively restrained concrete under ambient conditions. These models are categorized into two main groups: those assuming constant confining stresses and those that incorporate stress constraints related to the loading history. Recent studies have highlighted the significant role of stress paths in determining the stress–strain behavior of concrete. Traditional methods for predicting the FRP-constrained concrete reinforcement bond at room temperature are increasingly being replaced by machine learning techniques, such as Artificial Neural Networks (ANNs) and Genetic Expression Programming (GEP), which offer superior accuracy in predicting the FRP-constrained concrete bond strength and the compressive properties of FRP-confined concrete columns. In particular, experimental results show that the compressive strength of FRP-confined concrete columns can increase by up to 30–250%. This review offers valuable insights into the effects of FRP on concrete and contributes to the advancement of engineering design practices. Full article

The effect of moisture on the fracture resistance of asphalt concrete is a significant concern in pavement engineering. To investigate the effect of the water vapor concentration on the fracture properties of asphalt concrete, this study first designed a humidity conditioning program at the relative humidity (RH) levels of 2%, 50%, 80%, and 100% for the three types of asphalt concrete mixtures (AC-13C, AC-20C, and AC-25C).The finite element model was developed to simulate the water vapor diffusion and determine the duration of the conditioning period. The semi-circular bending (SCB) test was then performed at varying temperatures of 5 °C, 15 °C, and 25 °C to evaluate the fracture energy and tensile strength of the humidity-conditioned specimens. The test results showed that the increasing temperature and the RH levels resulted in a lower peak load but greater displacement of the mixtures. Both the fracture energy and tensile strength tended to diminish with the rising temperature. It was also found that moisture had a significant effect on the tensile strength and fracture energy of asphalt concrete. Specifically, as the RH level increased from 2% to 100% (i.e., the water vapor concentration rose from 0.35 g/m³ to 17.27 g/m³), the tensile strength of the three types of mixtures was reduced by 34.84% on average, which revealed that the water vapor led to the loss of adhesion and cohesion within the mixture. The genetic expression programming (GEP) model was developed to quantify the effect of water vapor concentrations and temperature on the fracture indices. Full article

The central aim of this study is to evaluate the effect of polyethylene terephthalate (PET) alongside two supplementary cementitious materials (SCMs)—i.e., fly ash (FA) and silica fume (SF)—on the 28-day compressive strength (CS_28d) of cementitious grouts by using. For the gene expression programming (GEP) approach, a total of 156 samples were prepared in the laboratory using variable percentages of PET and SCM (0–10%, each). To achieve the best hyper parameter setting of the optimized GEP model, 10 trials were undertaken by varying the genetic parameters while observing the models’ performance in terms of statistical indices, i.e., correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), comparison of regression slopes, and predicted to experimental ratios (ρ). Sensitivity analysis and parametric study were performed on the best GEP model (obtained at; chromosomes = 50, head size = 9, and genes = 3) to evaluate the effect of contributing input parameters. The sensitivity analysis showed that: CS_7d (30.47%) > CS_1d (28.89%) > SCM (18.88%) > Flow (18.53%) > PET (3.23%). The finally selected GEP model exhibited optimal statistical indices (R = 0.977 and 0.975, RMSE = 2.423 and 2.531, MAE = 1.918 and 2.055) for training and validation datasets, respectively. The role of PET/SCM has no negative influence on the CS_28d of cementitious grouts, which renders the PET a suitable alternative toward achieving sustainable and green concrete. Hence, the simple mathematical expression of GEP is efficacious, which leads to saving time and reducing labor costs of testing in civil engineering projects. Full article

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. Full article