Search results

From the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (l_eff/d) ratio on shear strength of RC slender beams.

The researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and l_eff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and l_eff/d ratios for the selected 12 shear models for RC beams.

Increasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing l_eff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and l_eff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing l_eff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.

The study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.

The findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.

The study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.

This study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

The purpose of this study is to compare the competition and productivity of the US freight rail transportation industry for the past 41 years (1980 ∼ 2020), which consists of the two periods, before and after the abolishment of the Interstate Commerce Commission (ICC) in 1995.

This study investigates any relationships between the market concentration index values and labor productivity values in the separate two periods, and how the existence of a regulatory body in the freight transportation market impacted the productivity of the freight rail transportation industry by using a Cobb–Douglas production function on annual financial statement data from the US stock exchange market.

This study found that, after the abolishment of the ICC: (1) the rail industry became less competitive, (2) even if the rail industry had an increasing labor productivity trend, there was a strong negative correlation between the market concentration index and labor productivity and (3) the rail industry’s total factor productivity was decreased.

This study is to find empirical evidence of the effect of the ICC abolishment on the competition and productivity levels in the US freight rail transportation industry using a continuous data set of 41-year financial statements, which is unique compared to previous studies.

The novelty addressed here is undertaken by using tailor-made and fully characterized starch nanoparticles (SNPs) having a particle size ranging from 80 to 100 nm with a larger surface area, biodegradability and high reactivity as a starting substrate for cadmium ions and basic dye removal from wastewater effluent. This was done via carboxylation of SNPs with citric acid via esterification reaction using the dry preparation technique, in which a simple, energy-safe and sustainable process concerning a small amount of water, energy and toxic chemicals was used. The obtained adsorbent is designated as cross-linked esterified starch nanoparticles (CESNPs).

The batch technique was used to determine the CESNPs adsorption capacity, whereas atomic adsorption spectrometry was used to determine the residual cadmium ions concentration in the filtrate before and after adsorption. Different factors affecting adsorption were examined concerning pH, contact time, adsorbent dose and degree of carboxylation. Besides, to validate the esterification reaction and existence of carboxylic groups in the adsorbent, CESNPs were characterized metrologically via analytical tools for carboxyl content estimation and instrumental tools using Fourier-transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) morphological analysis.

The overall adsorption potential of CESNPs was found to be 136 mg/g when a 0.1 g adsorbent dose having 190.8 meq/100 g sample carboxyl content at pH 5 for 60 min contact time was used. Besides, increasing the degree of carboxylation of the CESNPs expressed as carboxyl content would lead to the higher adsorption capacity of cadmium ions. FTIR spectroscopy analysis elucidates the esterification reaction with the appearance of a new intense peak C=O ester at 1,700 cm⁻¹, whereas SEM observations reveal some atomic/molecules disorder after esterification.

The innovation addressed here is undertaken by studying the consequence of altering the extent of carboxylation reaction expressed as carboxyl contents on the prepared CESNPs via a simple dry technique with a small amount of water, energy and toxic chemicals that were used as a sustainable bio nano polymer for cadmium ions and basic dye removal from wastewater effluent in comparison with other counterparts published in the literature.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a significant challenge. To address that, monitoring of transient temperature distribution concerning time is a critical input. Finite element analysis (FEA) is considered a decisive engineering tool in quantifying temperature and RS in all manufacturing processes. However, computational time and prediction accuracy has always been a matter of concern for FEA-based prediction of responses in the GMA-DED process. Therefore, this study aims to investigate the effect of finite element mesh variations on the developed RS in the GMA-DED process.

The variation in the element shape functions, i.e. linear- and quadratic-interpolation elements, has been used to model a single-track 10-layered thin-walled component in Ansys parametric design language. Two cases have been proposed in this study: Case 1 has been meshed with the linear-interpolation elements and Case 2 has been meshed with the combination of linear- and quadratic-interpolation elements. Furthermore, the modelled responses are authenticated with the experimental results measured through the data acquisition system for temperature and RS.

A good agreement of temperature and RS profile has been observed between predicted and experimental values. Considering similar parameters, Case 1 produced an average error of 4.13%, whereas Case 2 produced an average error of 23.45% in temperature prediction. Besides, comparing the longitudinal stress in the transverse direction for Cases 1 and 2 produced an error of 8.282% and 12.796%, respectively.

To avoid the costly and time-taking experimental approach, the experts have suggested the utilization of numerical methods in the design optimization of engineering problems. The FEA approach, however, is a subtle tool, still, it faces high computational cost and low accuracy based on the choice of selected element technology. This research can serve as a basis for the choice of element technology which can predict better responses in the thermo-mechanical modelling of the GMA-DED process.

This paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.

The scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.

The score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.

Unlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

The construction industries at present are focusing on designing sustainable concrete with less carbon footprint. Considering this aspect, a Fibre-Reinforced Geopolymer Concrete (FGC) was developed with 8 and 10 molarities (M). At elevated temperatures, concrete experiences deterioration of its mechanical properties which is in some cases associated with spalling, leading to the building collapse.

In this study, six geopolymer-based mix proportions are prepared with crimped steel fibre (SF), polypropylene fibre (PF), basalt fibre (BF), a hybrid mixture consisting of (SF + PF), a hybrid mixture with (SF + BF), and a reference specimen (without fibres). After temperature exposure, ultrasonic pulse velocity, physical characteristics of damaged concrete, loss of compressive strength (CS), split tensile strength (TS), and flexural strength (FS) of concrete are assessed. A polynomial relationship is developed between residual strength properties of concrete, and it showed a good agreement.

The test results concluded that concrete with BF showed a lower loss in CS after 925 °C (i.e. 60 min of heating) temperature exposure. In the case of TS, and FS, the concrete with SF had lesser loss in strength. After 986 °C and 1029 °C exposure, concrete with the hybrid combination (SF + BF) showed lower strength deterioration in CS, TS, and FS as compared to concrete with PF and SF + PF. The rate of reduction in strength is similar to that of GC-BF in CS, GC-SF in TS and FS.

Performance evaluation under fire exposure is necessary for FGC. In this study, we provided the mechanical behaviour and physical properties of SF, PF, and BF-based geopolymer concrete exposed to high temperatures, which were evaluated according to ISO standards. In addition, micro-structural behaviour and linear polynomials are observed.

This study aims to demonstrate a modified wire arc additive manufacturing (AM) named non-transferring arc and wire AM (NTA-WAM). Here, the build plate has no electrical arc attachment, and the system’s arc is ignited between tungsten electrode and filler wire.

The effect of various deposition conditions (welding voltage, travel speed and wire feed speed [WFS]) on bead characteristics is studied through response surface methodology (RSM). Under optimum deposition condition, a single-bead and thin-layered part is fabricated and subjected to microstructural, tensile testing and X-ray diffraction study. Moreover, bulk texture analysis has been carried out to illustrate the effect of thermal cycles and tensile-induced deformations on fibre texture evolutions.

RSM illustrates WFS as a crucial deposition parameter that suitably monitors bead width, height, penetration depth, dilution, contact angle and microhardness. The ferritic (acicular and polygonal) and lath bainitic microstructure is transformed into ferrite and pearlitic micrographs with increasing deposition layers. It is attributed to a reduced cooling rate with increased depositions. Mechanical testing exhibits high tensile strength and ductility, which is primarily due to compressive residual stress and lattice strain development. In deposits, ϒ-fibre evolution is more resilient due to the continuous recrystallisation process after each successive deposition. Tensile-induced deformation mostly favours ζ and ε-fibre development due to high strain accumulations.

This modified electrode arrangement in NTA-WAM suitably reduces spatter and bead height deviation. Low penetration depth and dilution denote a reduction in heat input that enhances the cooling rate.

This paper aims to evaluate the mechanical behaviour of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) filaments for fusion filament fabrication (FFF). PC/ABS have emerged as a promising material for FFF due to their excellent mechanical properties. However, the optimal processing conditions and the effect of the blending ratio on the mechanical properties of the resulting workpieces are still unclear.

A statistical factorial matrix was designed, including infill pattern, printing speed, nozzle size, layer height and printing temperature as factors (with three levels). A total of 810 workpieces were printed using PC/ABS blends filament with the FFF. The workpieces’ finishing and mass were evaluated. Tensile tests were performed. Analysis of variance was performed to determine the main effects of the processing conditions on the mechanical properties.

The results showed that the PC/ABS (70/30) exhibited higher tensile. Tensile rupture corresponded to 30% of the tensile strength. The infill pattern showed the highest contribution to the responses. The concentric pattern showed higher tensile strength. Tensile strength and mass ratio demonstrated the influence of mass on tensile strength. The influence of printing parameters on deformation depended on the blend proportions. Higher printing speed and lower layer height provided better quality workpieces.

This study has implications for the design and manufacturing of three-dimensional printed parts using PC/ABS filaments. An extensive experimental matrix was applied, aiming at a complete understanding of mechanical behavior, considering the main printing parameters and combinations not explored by literature.