Browsing by Author "Ipek, Suleyman"

Now showing 1 - 3 of 3

Investigating the Rubberized Concrete-Filled Steel Tube Composite Columns and Developing Artificial Intelligence-Based Analytical Models for Ultimate Axial Strength Prediction
(Wiley, 2026) Katipoglu, Okan Mert; Simsek, Oguz; Ipek, Suleyman; Guneyisi, Erhan
The application of rubberized concrete (RuC) in structural systems has led to the development of a novel composite system: the rubberized concrete-filled steel tube (RuCFST). The characteristics of infill materials, particularly RuC, significantly influence the structural behavior and performance of these composite systems. This study aims to evaluate the ultimate axial strength of RuCFST columns, considering rubber aggregate content up to 75% and infill concrete compressive strength up to 63 MPa, and propose artificial intelligence (AI)-based design models using deep learning (CNN, DNN, Autoencoder), machine learning (CatBoost), hybrid AI/neuro-fuzzy (ANFIS), and unsupervised neural network (SOM) to predict the ultimate axial strength. A robust dataset was compiled from the literature, comprising the results of 131 RuCFST column specimens. The experimental results underwent a comprehensive statistical assessment using various analytical methods, including Pearson correlation coefficient analysis, histogram analysis, distribution fitting curve analysis, and box-and-whisker plot analysis. Additional statistical metrics such as Kolmogorov-Smirnov, skewness, and kurtosis values were also used. Furthermore, by considering various variables covering the geometrical and material properties of the RuCFST columns, the design models were developed using AI techniques. The statistical evaluation results indicated that the compiled dataset is statistically significant and encompasses various input and output parameters. The design models developed in this study displayed varying prediction performance. However, those created using the Autoencoder and ANFIS methods showed statistically satisfactory performance in determining the load-carrying capacity of the columns.
Citation - WoS: 6
Citation - Scopus: 5
Tensile strength and durability performances-based evaluation of 3D-printed and mold-cast fibrous geopolymer composites produced at various alkaline activator combinations
(ELSEVIER, 2024) Kasim Mermerdas; Lawand Waleed Khalid; Dillshad Khidhir Bzeni; Suleyman Ipek; Dara Jabar Jawad; Jawad, Dara Jabar; Bzeni, Dillshad Khidhir; Mermerdas, Kasim; Ipek, Suleyman; Khalid, Lawand Waleed
This study aimed to investigate the effects of the sodium silicate-to-sodium hydroxide ratio SH molarity and carbon fiber volume fraction on the tensile strength and durability properties of mold-cast and 3D-printed geopolymer composites including flexural and splitting tensile strengths ultrasonic pulse velocity water absorption sorptivity index and chloride penetration properties. For that purpose the ground-granulated blast furnace slag and fly ash were employed as alumino-silicate-rich raw material. In order to activate the alumino-silicate-rich raw material two sodium silicate/sodium hydroxide ratios of 1 and 2 and three sodium hydroxide molarities of 8M 10M and 12M were designated. Furthermore carbon fiber was incorporated into the geopolymer composites at three volume fractions: 0 % 0.3 % and 0.6 %. In total 18 nonfibrous and fibrous geopolymer composites were manufactured in two different ways: mold-cast and 3D- printed. The findings demonstrated that the strength and durability characteristics of 3D-printed specimens were inferior to those of mold-cast specimens attributed to the presence of weak interfacial bonds between layers. It has been observed that the incorporation of carbon fiber has the effect of improving tensile strength performance. Nevertheless the addition of carbon fiber led to a slight decrease in UPV values and an increase in water absorption and sorptivity index values. Also it adversely influenced the chloride penetration of geopolymer composites. The findings of the study also indicated that increasing the sodium hydroxide molarity had a positive impact on the strength and durability properties of the composites. Nevertheless it was observed that increasing the sodium silicate-to-sodium hydroxide ratio led to a decrease in both tensile strength and durability performances. Additionally the microstructure of the geopolymer composites was analyzed using scanning electron microscope (SEM) images.
Time-Based Fire Resistance Performance of Axially Loaded, Circular, Long CFST Columns: Developing Analytical Design Models Using ANN and GEP Techniques
(MDPI, 2025) Nassani, Dia Eddin; Ozelmaci Durmaz, C. Ozge; Ipek, Suleyman; Mete Guneyisi, Esra
Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (ts), concrete compressive strength (fc), steel yield strength (fsy), the slenderness ratio (L/D), and the load ratio (mu). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models' accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f approximate to 1; PI approximate to 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes.