• Computers and Concrete
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• v.33 no.3
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• pp.275-284
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• 2024

This work presents a comprehensive investigation of buckling behavior of bidirectional functionally graded imperfect beams exposed to several thermal loading with general boundary conditions. The nonlinear governing equations are derived based on 2D shear deformation theory together with Von Karman strain-displacement relation. The beams are composed of two different materials. Its properties are porosity-dependent and are continuously distributed over the length and thickness of the beams following a defined law. The resulting equations are solved analytically in order to determine the thermal buckling characteristics of BDFG porous beams. The precision of the current solution and its accuracy have been proven by comparison with works previously published. Numerical examples are presented to explore the effects of the thermal loading, the elastic foundation parameters, the porosity distribution, the grading indexes and others factors on the nonlinear thermal buckling of bidirectional FG beam rested on elastic foundation.

• Advances in nano research
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• v.16 no.1
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• pp.1-9
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• 2024

SiO₂-coated magnetic nanoparticles (Fe₃O₄@SiO₂ NPs) were modified by 3-nitrobenzelidenmalononitrile and used as green linkages for removal of Hg²⁺ form the wastewater. In this research, it has been attempted to refer to the harmful effects of mercury ions for living things and how to remove such ions using very easy and practical technique. This study shows that by optimizing the test conditions, the efficiency of the removal of harmful ions such as mercury from the water contaminated with these ions can be increased. Conditions such as temperature, speed of agitation, pH of solution were tested for removal of mercury ions. The advantages of this method over other methods listed in the article are the rapid and easy nanocry synthesis. The generated and modified Fe₃O₄@SiO₂ nanoparticles were characterized by X-ray diffraction, fourier transform infrared and scanning electron microscopy spectroscopy. The results show that the synthesized magnetic nanoparticles have the excellent performance for the removal of mercury(II) ion from the waste water.

• Steel and Composite Structures
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• v.50 no.4
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• pp.475-487
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• 2024

The usage of fiber-reinforced polymer materials increases in the construction sector due to their advantages in terms of high mechanical strength, lightness, corrosion resistance, low density and high strength/density ratio, low maintenance and painting needs, and high workability. In this study, it is aimed to improve mechanical properties of GFRP box profiles, produced by pultrusion method, by filling the polymer concrete into them. Within the scope of study, hybrid use of polymer concrete produced with GFRP box profiles was investigated. Hybrid pressure and bending specimens were produced by filling polymer concrete (polyester resin manufactured with natural sand and stone chips) into GFRP box profiles having different cross-sections and dimensions. Behavior of the produced hybrid members was investigated under bending and compression tests. Hollow GFRP_xx profiles, polymer-filled hybrid members, and nominative polymeric concrete specimens were tested as well. The behavior of the specimens under pressure and bending tests, and their load bearing capacities, deformations and changes in toughness were observed. According to the test results; It was deduced that hybrid design has many advantages over its component materials as well as superior physical and mechanical properties.

• Advances in nano research
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• v.16 no.2
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• pp.165-173
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• 2024

The production of multipurpose cotton fabrics aimed at elevating the quality of aerobic and dance sportswear is explored in this study. Powder metallurgy, known for its high efficiency in manufacturing technological components with minimal waste, is employed as a method for fabricating brush ferrules for painting. The utilization of iron-copper material, prepared through powder metallurgy, enhances the strength and quality of the brush ferrules. A microscopic analysis reveals a robust interconnection between the particles of each layer achieved through isostatic pressure, resulting in a favorable microstructure. The relative density and strength of parts produced from copper-iron powder exhibit an increase with higher pressure levels. The application of this material in brush ferrules ensures their durability and longevity, thereby supporting the creation of artwork. The evolution of art over time reflects changing ideas and possibilities, and technological advancements have significantly improved artistic tools. The role of tools in artistic expression is paramount, and the integration of powder metallurgy materials in brush ferrules fortifies their artistic importance. In summary, this study underscores the advantages of powder metallurgy in augmenting the quality of art tools and facilitating artistic creation.

• Advances in nano research
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• v.16 no.5
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• pp.445-458
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• 2024

The objective of this paper is to present free vibration and static stability analyses of rotating composite beams reinforced with carbon nanotubes (CNTs) under uniform thermal loads. Beam structural equations and CNT-reinforced composite (CNTRC) beam formulations are derived based on Timoshenko beam theory (TBT). The temperature-dependent properties of the beam material, such as the elastic modulus, shear modulus, and material density, are assumed to vary over the thickness according to the rule of mixture. The beam material is modeled as a mixture of single-walled carbon nanotubes (SWCNTs) in an isotropic matrix. The SWCNTs are aligned and distributed in the isotropic matrix with different patterns of reinforcement, namely the UD (uniform), FG-O, FG-V, FG- Λ and FG-X distributions, where FG-V and FG- Λ are asymmetric patterns. Numerical examples are presented to illustrate the effects of several essential parameters, including the rotational speed, hub radius, effective material properties, slenderness ratio, boundary conditions, thermal force, and moments due to temperature variation. To the best of the authors' knowledge, this study represents the first attempt at the finite element modeling of rotating CNTRC Timoshenko beams under a thermal environment. The results are presented in tables and figures for both symmetric and asymmetric distribution patterns, and can be used as benchmarks for further validation.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.539-554
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• 2024

This study explores development of prediction model for seismic site classification through the integration of machine learning techniques with horizontal-to-vertical spectral ratio (HVSR) methodologies. To improve model accuracy, the research employs outlier detection methods and, synthetic minority over-sampling technique (SMOTE) for data balance, and evaluates using seven machine learning models using seismic data from KiK-net. Notably, light gradient boosting method (LGBM), gradient boosting, and decision tree models exhibit improved performance when coupled with SMOTE, while Multiple linear regression (MLR) and Support vector machine (SVM) models show reduced efficacy. Outlier detection techniques significantly enhance accuracy, particularly for LGBM, gradient boosting, and voting boosting. The ensemble of LGBM with the isolation forest and SMOTE achieves the highest accuracy of 0.91, with LGBM and local outlier factor yielding the highest F1-score of 0.79. Consistently outperforming other models, LGBM proves most efficient for seismic site classification when supported by appropriate preprocessing procedures. These findings show the significance of outlier detection and data balancing for precise seismic soil classification prediction, offering insights and highlighting the potential of machine learning in optimizing site classification accuracy.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• Structural Engineering and Mechanics
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• v.90 no.5
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• pp.517-530
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• 2024

This paper presents a new four-unknown equivalent single layer (ESL) refined plate theory for the buckling analysis of functionally graded (FG) rectangular plates with all simply supported edges and subjected to in-plane mechanical loading conditions. The present model accounts for a parabolic variation of transverse shear stress over the thickness, and accommodates correctly the zero shear stress conditions on the top and bottom surfaces of the plate. The material properties are supposed to vary smoothly in the thickness direction through the rules of mixture named power-law gradation. The governing equilibrium equations are formulated based on the total potential energy principle and solved for simply supported boundary conditions by implementing the Navier's method. A numerical result on elastic buckling using the current theory was computed and compared with those published in the literature to examine the accuracy of the proposed analytical solution. The effects of changing power-law exponent, aspect ratio, thickness ratio and modulus ratio on the critical buckling load of FG plates under different in-plane loading conditions are investigated in detail. Moreover, it was found that the geometric parameters and power-law exponent play significant influences on the buckling behavior of the FG plates.

• Wind and Structures
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• v.38 no.6
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• pp.445-459
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• 2024

To model the aeroelasticity in vortex-induced vibrations (VIV) of slender tubular towers, this paper presents an approach where the aerodynamic damping distribution along the height of the structure is calculated not only as a function of the normalized lateral oscillation but also considering the local incoming wind velocity ratio to the critical velocity (velocity ratio). The three-dimensionality of aerodynamic damping depending on the tower's displacement and the velocity ratio has been observed in recent studies. A contour map model of aerodynamic damping is generated based on the forced vibration tests. A sectional calculation procedure based on the spectral method is developed by defining the aerodynamic damping locally at each increment of height. The proposed contour map model of aerodynamic damping and the sectional calculation procedure are validated with full-scale measurement data sets of a rotorless wind turbine tower, where good agreement between the prediction and measured values is obtained. The prediction of cross-wind response of the wind turbine tower is performed over a range of wind speeds which allows the estimation of resulting fatigue damage. The proposed model gives more realistic prediction in comparison to the approach included in current standards.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.