Plant-based macromolecules such as lignocellulosic fibers are one of the promising bio-resources to be utilized as reinforcement for developing sustainable composites. However, due to their hydrophilic nature and weak interfacial bonding with polymer matrices, these fibers are mostly incompatible with biopolymers. The current research endeavor explores the novel eco-friendly oxalic acid (C2H2O4. 2H2O) treatment of sisal fibers (SF) with different concentrations (2, 5, and 8 % (w:v)) and exposure duration (4, 8, and 12 h). Optimum treatment conditions were achieved through the single fiber strength testing of SFs. The tensile strength of the treated fiber with 8 % concentration and 12 h exposure duration (TSF/8/12) increased by approximately 60 % compared to untreated SF. Fourier transform infrared spectroscopy (FTIR), morphological observation, X-ray diffraction (XRD), and thermogravimetric analysis (TGA) of untreated and treated fibers confirmed that TSF/8/12 has better mechanical and crystallinity behavior than its counterparts. The thermal stability and maximum degradation temperature of the TSF/8/12 are 232 degrees C and 357 degrees C. Sustainable composites were fabricated by introducing the treated SFs (30 wt%) as reinforcement in a bio-based poly (butylene succinate) (bio PBS) matrix. The experimental evaluation of mechanical properties, thermal degradation behavior, and water absorption established that treated fiber-reinforced biocomposites (bio PBS/TSF/8/12) have strong interfacial bonding between constituents that resulted in better thermal stability and decreased water uptake than untreated sisal fiber (USF)based composites (bio PBS/USF). The results of the soil degradation confirmed that SFs expedite the rate of degradation of composites due to the increased availability of hydroxyl groups.

The energy absorption capacity (EAC) of earthen materials significantly influences the safety of civil projects. Furthermore, the development of machine learning techniques, including Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models, entails financial and non-financial benefits by reducing the need for performing expensive, exhausting and time-consuming laboratory tests. This study investigates the EAC of sandy soil reinforced by three different forms of processed lignocellulosic fiber pulps. The studied influence parameters included fiber type, curing time, effective confining pressure, and fiber content. Artificial neural network (ANN) models were developed to assess the EAC of the reinforced specimens and evaluate the impact of studied parameters. The analysis of each fiber type was carried out using Multiple Linear Regression (MLR) methods. The specimens, subjected to a 7-day curing period and reinforced with 2% of lignocellulosic fibers of 1.5 mm in length, exhibited the greatest EAC values. Sensitivity analysis identified effective confining pressure as the most influential factor on the EAC of the reinforced specimens. This study demonstrates the advantageous impact of processed lignocellulosic fibers, which are environmentally harmless substances, in enhancing the EAC of sandy soil and its ductility response. As a result, this decreases the likelihood of unexpected and catastrophic failures. This research also demonstrates the high capability of ANN-based models in predicting EAC at various influence parameters.