The use of acoustic emission for damage assessment of composite materials and life prediction under spectrum fatigue loading

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Effect of Alkaline Treatment on Mechanical Performance of Natural Fibers-reinforced Plaster: Optimization Using RSM

In the last decades, eco-friendly materials are playing an interesting role. Particularly, many studies deal with the use of natural fibers as a replacement to synthetic fibers in the plaster matrix. This study has examined the flexural properties of treated natural sisal, flax and jute fibers with different concentrations of NaOH (1.5%, 2% and 4%) and fiber length (5, 10 and 20 mm) reinforced plaster mortars. In this study, the effect of experimental parameters was determined by using analysis of variance ANOVA test. In addition, the response surface methodology (RSM) and desirability function (DF) are also used to optimize the output responses with maximizing the flexural properties. Finally, the experimental results are in good agreement with those obtained statistically

Optimization of Palm Rachis Biochar Waste Content and Temperature Effects on Predicting Bio-Mortar : ANN and RSM Modelling

In the current study, new prediction models were suggested to predict the compressive strength, porosity, and thermal conductivity of bio-mortar samples by replacing cement by weight with pyrolysis of Washingtonia filifera waste biochar (WFWB). Bio-mortars containing different biochar contents were prepared with the addition of 1%, 2%, 3%, 4%, and 5% pyrolyzed biochar at different temperatures 300°C, 400°C, and 500°C. The mortar samples produced were evaluated for compressive strength at 7 and 28 days. Relation between compressive strength porosity and thermal conductivity values (dependent values), and biochar replacement ratios and pyrolysis temperatures (independent values) was predicted by artificial neural network (ANN) machine learning techniques based on the Levenberg Marquardt algorithm. The results revealed compressive strength increase at 28 days of nearly 12%, 3%, and 2% at 1% optimal biochar replacement content for WFWB500, WFWB400, and WFWB300 specimens, respectively. Moreover, these results provide confidence in the manufacturability of bio-mortars with a compressive strength of 63.81 MPa at 28 days using only 1% substitution of WFWB500 and a thermal conductivity coefficient of 0.52 W/m.K. The importance measure of the variables shows that the most influential variables are the percentage of biochar. The statistical and experimental results also revealed satisfactory agreement

Mechanical Properties of Natural Cellulosic Yucca treculeana L. Fiber for Biocomposites Applications: Statistical Analysis

Yucca treculeana L. (YT) is increasingly being considered by researchers worldwide as a replacement option for synthetic fibers such as glass fibers. The objective of this study is to determine the tensile quasi-static room temperature mechanical parameters of YT fibers with gauge lengths (GL) of 10, 20, 30, and 40 mm. A comprehensive tensile test program was conducted on 120 fibers grouped in four series to determine the influence of their variability on the tensile strength, ultimate strain at break and elastic Young’s modulus of the YT fibers. The values of the tensile mechanical properties of YT fibers exhibit a large dispersion of results, which is a characteristic of natural fibers, therefore requiring statistical study. For the purpose of studying this dispersion, some statistical tools such as two- and three-parameter Weibull distribution at 95% CI confidence level and one-way analysis of variance (ANOVA) were used

Improving the Mechanical Performance of Biocomposite Plaster/ Washingtonia filifera: Optimization Comparison Between ANN and RSM Approaches

The present research is an extension of a previous paper published by the authors. In the first part of the research, the flexural properties of Washingtonia filifera (WF) fiber-reinforced plaster composite treated with sodium bicarbonate were explored using response surface method statistics. In the current study, the data was analyzed using artificial neural network tool. The main objective of the current research is to model the flexural properties of an environmentally friendly gypsum biocomposite reinforced with treated and untreated WF fibers using response surface method and artificial neural networks. For this purpose, the study reports a comparative approach between models predicted by response surface methodology (RSM) and artificial neural networks (ANNs). The statistical results as root mean square error and coefficient of determination reveal that ANN and RSM are effective techniques for bending properties prediction of plaster/WF biocomposites. In addition, ANN and RSM models correlate highly with the experimental data. However, artificial neural network model displayed more accuracy

Effect of Water Absorption on the Behavior of Jute and Sisal Fiber Biocomposites at Different Lengths: ANN and RSM Modeling

Artificial neural networks (ANN) and response surface method (RSM) modeling were used to study the effect of water absorption on the behavior of jute (JF/epoxy) and sisal (SF/epoxy) biocomposite. The specimens were laminated with short fibers of different lengths (5 mm, 10 mm, and 15 mm) of jute and sisal until saturation lasted about 25 days. The kinetic behavior of water uptake was recorded and then compared to the curves predicted by the two models. The results showed that the water absorption of the jute and sisal biocomposites was fairly lower for the 5 mm length compared to the 10 mm and 15 mm lengths. The optimized solution is attained with 1.00 desirability for 14.73 mm fiber length and 482.85 h immersion time. The RSM model error percentages were higher than the ANN model indicating better accuracy in predicting the water uptake of biocomposites by the ANN model compared to RSM. The results of this investigation offer benefits for the application of jute and sisal fiber biocomposites at the elaboration and application stage; it is easy for engineers to identify the swelling factor of these biocomposites without the need for experiments, thus avoiding costs and time

Response Surface Methodology Optimization of Palm Rachis Biochar Content and Temperature Effects on Predicting Bio-Mortar Compressive Strength, Porosity and Thermal Conductivity

Since cement manufacturing contributes significantly to CO2 pollution worldwide, biochar provides a novel opportunity to integrate it into the construction materials and eventually substitute a portion of cement usage. Consequently, this investigation aims to explore the response surface methodology (RSM) to predict compressive strength and identify optimal values for each parameter that affects the preparation of bio-mortars cement with biochar produced from Washingtonia filifera waste pyrolysis (WFWB). The status of the 7-days and 28-days samples compressive strength, porosity, as well as thermal conductivity of the bio-mortars produced by partially substituting cement at different rates (0%, 1%, 2%, 3%, 4%, and 5%) with pyrolyzed WFWB biochar at varying temperatures (300°C, 400°C, and 500°C) were examined experimentally and statistically. Moreover, the parameter effect on the experimental data performance was identified through analysis of variance (ANOVA). Results indicated an increase in the compressive strength at 28 days of almost 12%, 3%, and 2% at an optimum biochar content of 1% for the WFWB500, WFWB400 and WFWB300 samples respectively. In addition, the findings confer the feasibility of producing bio-mortars with a compressive strength of 63.81 MPa at 28 days with a 1% replacement rate of WFWB500 and coefficient of thermal conductivity of 0.52 W/m.K