• Steel and Composite Structures
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• 제35권5호
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• pp.627-634
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• 2020

The past years were marked by an increase in the use of wood waste in civil and mechanical constructions. Date palm waste remains also one of the most solicited renewable and recyclable natural resources in the composition of composite materials. In Algeria, a great amount of this type of plant wastes accumulates every year. In order to make use of this waste, a new wood-epoxy composite material based on date palm petiole particleboard is developed. It makes use of date palm petiole particleboard as reinforcement and epoxy resin as matrix. The size of the particles reinforcement are between 1~3 mm and proportion of reinforcement used is 37%. In this work, experimental and numerical studies are conducted in order to characterize the wood fibre-epoxy plates. Firstly, experimental modal analysis test was carried out to determine Young's modulus of the elaborated material. Then, in order to validate the results, compression test was conducted. Furthermore, additional information about the shear modulus of this material is obtained by performing an experimental modal analysis to extract the first torsional mode. Moreover, a finite element model is developed using ANSYS software to simulate the vibration behaviour of the plates. The results show a good agreement with the experimental modal analysis, which confirms the values of Young's modulus and shear modulus.

• Structural Engineering and Mechanics
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• 제47권6호
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• pp.819-838
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• 2013

This paper summarises the results of an experimental study to investigate the flexural behaviour of reinforced concrete beams strengthened using carbon-fibre reinforced polymer (CFRP) laminate in four-point bending. The experimental parameters included are the reinforcing bar ratio ${\rho}_s$ and preload level. Four bar ratios were selected (${\rho}_s=0.13$ to 0.86%), representing the section of two longitudinal tensile reinforcements, with diameters of 8, 14, 16, and 20 mm in order to reveal the effect of bar ratio on failure load and failure mode. Eight beams that could be considered "full-scale" in size, measuring 200 mm in width, 400 mm in total height and 2300 mm in length, were tested. Three beams were selected with different bar ratios (${\rho}_1$, ${\rho}_2$, ${\rho}_3$), and considered as control specimens (without ), while three other beams identical to the control beams with the same CFRP laminates ratio and a seventh beam with ${\rho}_{min}$ (the lowest bar ratio) were also used. In the second part of the study, two beams with the bar ratio ${\rho}_2$ were preloaded at two levels, 50 and 100% of their ultimate loads, and then repaired. This experimental investigation was consolidated using an analytical model. The experimental and analytical results indicate that the flexional capacity and stiffness of strengthened and repaired beams using CFRP laminate were increased compared to those of control beams, and the behaviour of repaired beams was nearly similar to the undamaged and strengthened beams; unlike the ductility of strengthened beams, which was greatly reduced compared to the control.

• Geomechanics and Engineering
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• 제28권6호
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Steel and Composite Structures
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• 제51권3호
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• pp.243-260
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• 2024

Thirteen self-compacting recycled concrete filled aluminium tubular (SCRCFAT) columns were tested under concentric compression loads. The effects of the replacement ratio of the recycled concrete aggregate (RCA) and steel fibre (SF) reinforcement on the structural performance of the SCRCFAT columns were studied. A control specimen (C000) was cast with normal concrete without SF to be reference for comparison. Twelve columns were cast using RCA, six columns were cast using concrete incorporating 2% SF while the rest of columns were cast without SF. Failure mode, ductility, ultimate load capacity, axial deformation, ultimate strains, stress-strain response, and stiffness of the SCRCFAT columns were studied. The results showed that, the peak load of tested SCRCFAT columns incorporating 5-100 % RCA without SF reduced by 2.33-11.28 % compared to that of C000. Conversely, the peak load of tested SCRCFAT columns incorporating 5-100% RCA in addition to 2% SF increased by 21.1-40.25%, compared to C000. Consequently, the ultimate axial deformation (Δ) of column C100 (RCA=100% and SF 0%) increased by about 118.9 % compared to C000. The addition of 2% SF to the concrete mix decreased the axial deformation of SCRCFAT columns compared to those cast with 0% SF. Moreover, the stiffness of the columns cast without SF decreased as the RCA % increased. In contrast, the columns stiffness cast with 2% SF increased by 26.28-89.7 % over that of C000. Finally, a theoretical model was proposed to predict the ultimate loads tested SCRCFAT columns and the obtained theoretical results agreed well with the experimental results.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.