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This study examined the water uptake (WU) behavior of recycled high-density polyethylene (RHDPE). The biocomposites made of palm waste Washingtonia (PWW) fibers were treated with 3% sodium bicarbonate (NaHCO3) for 24 h. Several RHDPE materials reinforced with different concentrations of PWW fibers (5 to 30 wt.%) until saturation, or roughly 25 days, were developed to investigate the WU kinetics and diffusion in biocomposites. An artificial neural network (ANN) and the response surface methodology (RSM) methods were applied to model the behavior of uptake measured in experiments and optimize the immersion period and PWW fiber content in RHDPE/PWW biocomposite WU. The WU moved swiftly during the first test phase and was completed after 400 h of soaking. The outcomes demonstrated a perfect fit between the observed and anticipated data. Findings showed that the ANN models’ training, test, and validation correlation coefficients were 0.9984, 0.9955, and 0.9723, respectively, for predicting WU. Concerning accuracy and reliability, the ANN model outperformed the RSM model, making it suitable for various industrial uses. The results offer helpful information for professionals to consider when developing and implementing PWW fiber biocomposites.

The building sector is one of the most dynamic in terms of energy consumption, consuming about 40% of the world’s energy. This same sector is also responsible for about 1/3 of the world’s greenhouse gas emissions. In recent years, the adoption of composite materials, particularly those strengthened through the use of natural fibers is growing in all areas. This increase is the direct result of the important performances offered by these materials and that includes lightness, thermal, and acoustic insulation along with respect for the environment. This led to the integration of materials, such as bio composites or bio sandwiches, into various building structures constructions relating to civil engineering. However, numerous researches related to bio composites showed the need to explore them further particularly concerning the issue of moisture absorption as the presence of water affects the behavior of plant fibers both in terms of swelling and degradation. It is within this context that the present study focuses on modeling the water absorption behavior of bio sandwich materials having an agglomerated cork core associated with fibers extracted from the plant Quercussuber L. Fick’s law and Artificial Neural Network (ANN) are applied to model the experimental results pertaining to this absorption behavior. The experimental investigation starts by placing the original samples in tank filled with distilled water at an ambient temperature of 25°C. Mass samples are later and periodically taken on specimen with cork core having different thicknesses (5, 10, and 20 mm) as well as on laminated skin sandwiches made of short flax fibers until saturation that lasted around 25 days. The two Fick’s diffusion characteristic parameters represented by the mass gain at saturation (Mm) and the diffusion coefficient (D) were determined analytically and water absorption kinetics behavior was recorded and later compared to the curves predicted by Fick’s laws. Statistical processing of the results was carried out through the application of the analysis of variance ANOVA.

Natural fiber composites are being increasingly used in several fields, owing to their considerable cost, weight, and environmental benefits. The objective of this research is to study the effect of water absorption on the behavior of laminated biocomposites using flax (F) and sisal (S) fiber biocomposites with different stacking sequences (3S and 3F) and fiber orientation at 90° (F090s and S090s), as well as hybridizations (F/4S90/F and S/4F90/S) to reinforce an epoxy matrix. The kinetics of water diffusion within the proposed biocomposites was monitored and analyzed through a model-based optimization approach using artificial neural network (ANN) method. The results of this study showed that the amount of water absorbed by different types of biocomposites increased with time according to Fick’s law. Non-hybrid flax and sisal fiber composites (UD and (0/90°)s oriented) had lower water absorption rates than the hybrid orientation. It was also observed that sisal fiber was most sensitive to water absorption than flax. A high correlation of experimental data with the model was revealed which denoted that the ANN learning process was perfectly performed. Therefore, it was inferred that the ANN approach can accurately predict biocomposites water absorption.

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.