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The purpose of this paper is to check the feasibility of using biomaterial such as of Phragmites-Australis (PA) in cement paste to achieve sustainable building materials.

In this study, cement pastes were prepared by adding locally produced PA fibers in four different volumes: 0%, 0.5%, 1% and 2% for a duration of 180 days. Bottles and prisms were subjected to chemical shrinkage (CS), drying shrinkage (DS), autogenous shrinkage (AS) and expansion tests. Besides, prism specimens were tested for flexural strength and compressive strength. Furthermore, a mathematical model was proposed to determine the variation length change as function of time.

The experimental findings showed that the mechanical properties of cement paste were significantly improved by the addition of 1% PA fiber compared to other PA mixes. The effect of increasing the % of PA fibers reduces the CS, AS, DS and expansion of cement paste. For example, the addition of 2% PA fibers reduces the CS, expansion, AS and DS at 180 days by 36%, 20%, 13% and 10%, respectively compared to the control mix. The proposed nonlinear model fit to the experimental data is appropriate with R2 values above 0.92. There seems to be a strong positive linear correlation between CS and AS/DS with R2 above 0.95. However, there exists a negative linear correlation between CS and expansion.

The PA used in this study was obtained from one specific location. This can exhibit a limitation as soil type may affect PA properties. Also, one method was used to treat the PA fibers.

The utilization of PA fibers in paste may well reduce the formation of cracks and limit its propagation, thus using a biomaterial such as PA in cementitious systems can be an environmentally friendly option as it will make good use of the waste generated and enhance local employment, thereby contributing toward sustainable development.

To the authors best knowledge, there is hardly any research on the effect of PA on the volume stability of cement paste. Therefore, the research outputs are considered to be original.

To contribute to the identification of the parameters influencing the behavior of textile-reinforced concrete (TRC), the purpose of this paper is to investigate the flexural behavior of TRC-based plates under four-point bending notably designed in the context of sustainable development and the substitution of mortar components with natural and abundant materials.

An extensive experimental campaign was focused about two main parameters. The first one emphases the textile reinforcements, such as the number of layers, the nature and the textile mesh size. In the second step, the composition of the mortar matrix was explored through the use of dune sand as a substitute of the river one.

Test results in terms of load-displacement response and failure patterns were highlighted, discussed and confronted to literature ones. As key findings, an increase of the load-bearing capacity and ductility, comparable to the use of an industrially produced second textile layer was recorded with the use of dune sand in the mortar mix design. The designed ecofriendly samples with economic concerns denote the significance of obtained outcomes in this research study.

The novelty of the present work was to valorize the use of natural dune sand to design new TRC samples to respond to the environmental and economical requirements. The obtained values provide an improved textiles–matrix interface performance compared to classical TRC samples issued from the literature.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

The purpose of this study is to investigate how the amount of material used and printing parameters affect the mechanical and water sorption properties of acrylonitrile butadiene styrene printed parts.

The specimens were printed using different printing parameters such as shell number, infill pattern and printing orientation, while accounting for the amount of material used. The mechanical properties of the printed parts were then evaluated using tensile, compression and flexural tests, along with sorption tests.

The results revealed that the maximum tensile stress of 31.41 MPa was obtained when using 100% infill and a horizontal printing orientation. Similarly, the maximum flexural strength and compression of 40.5 MPa and 100.7 MPa, respectively, were obtained with 100% infill. The printing orientation was found to have a greater impact on mechanical behavior compared to the number of shells or infill patterns. Specifically, the horizontal printing orientation resulted in specimens with at least 25% greater strength compared to the vertical printing orientation. Furthermore, the relationship between the amount of material used and strength was evident in the tensile and flexural tests, which showed a close correlation between the two.

This study’s originality lies in its focus on optimizing the amount of material used to achieve the best strength-to-mass ratio and negligible water infiltration. The findings showed that specimens with two shells and a 60% infill density exhibited the best strength-to-mass ratio.

To investigate the mechanical properties of geopolymer concrete at elevated temperatures.

The investigation involved studying the influence of partially replacing fly ash with ground granulated blast furnace slag (GGBS) at different proportions (5%, 10%, 15%, 20% and 25%) on the composition of the geopolymer. This approach aimed to examine how the addition of GGBS impacts the properties of the geopolymer material. The chemical NaOH was purchased from the local supplier of Jamshedpur. The alkali solution was prepared with a concentration of 12 M NaOH to produce the concrete. After several trials, the alkaline-to-binder ratio was determined to be 0.43.

The compressive strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 35.42 MPa, 41.26 MPa, 44.79 MPa, 50.51 MPa and 46.33 MPa, respectively. The flexural strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 5.31 MPa, 5.64 MPa, 6.12 MPa, 7.15 MPa and 6.48 MPa, respectively. The split tensile strength values at 28 days for specimens FG1, FG2, FG3, FG4 and FG5 are 2.82 MPa, 2.95 MPa, 3.14 MPa, 3.52 MPa and 3.31 MPa, respectively.

This approach allows for the examination of how the addition of GGBS affects the properties of the geopolymer material. Four different temperature levels were chosen for analysis: 100 °C, 300 °C, 500 °C and 700 °C. By subjecting the geopolymer samples to these elevated temperatures, the study aimed to observe any changes in their mechanical.

The industrial application of continuous glass fabric-reinforced polymer composites (GFRPCs) is growing; however, the manufacturing boundedness of complex structures and the high cost of molds restrict their use. This research proposes a three-dimensional (3 D) printing process for GFRPCs that allows low-cost and rapid fabrication of complex composite parts.

The composite is manufactured using a digital light processing (DLP) based Vat-photopolymerization (VPP) process. For the composites, suitable resin material and glass fabrics are chosen based on their strength, stiffness, and printability. Jacob's working curve characterizes the curing parameters for adequate adhesion between the matrix and fabrics. The tensile and flexural properties were examined using UTM. The fabric distribution and compactness of the cured resin were analyzed in scanning electron microscopy.

The result showed that the object could print at a glass fabric content of 40 volume%. In DLP-based VPP printing technology, the adequate exposure time was found to be 30 seconds for making a GFRPC. The tensile strength and Young's modulus values were increased by 5.54 and 8.81 times, respectively than non-reinforced cured specimens. The flexural strength and modulus were also effectively increased to 2.8 and 3 times more than the neat specimens. In addition, the process is found to help fabricate the functional component.

The experimental procedure to fabricate GFRPC specimens through DLP-based AM is a spectacular experimental approach.

This study focuses on the structural performance assessment of hybrid polymer composites for pick-and-place robot grippers used in critical infrastructure. This research involved the creation of composite materials with different nanoparticle concentrations, followed by extensive testing to assess the mechanical properties of the materials, such as strength, stiffness and durability.

The composites comprised bidirectional interply inclined carbon fibers (C), S-glass fibers (SG), E-glass (EG), an epoxy matrix and silica nanoparticles (SNiPs). During construction, the composite materials must be carefully layered using quasi-static sequence techniques (45°C1/45°SG2/45°EG2/45°C1/45°EG2/45°SG2/45°C1) to obtain the epoxy matrix reinforcement and bonding using 0, 2, 4 and 6 wt. % of silica nanoparticles.

According to various test findings, the 4 wt. % of SNiPs added to polymer plates exhibits the maximum strength outcomes. The average results of the tensile and flexural tests for the polymer composite plates with 4 wt. % addition SNiPs were 127.103 MPa and 223.145 MPa, respectively. The average results of the tensile and flexural tests for the plates with 0 wt.% SNiPs were 115.457 MPa and 207.316 MPa, respectively.

The authors hereby attest that the research paper they have submitted is the result of their own independent and unique labor. All of the sources from which the thoughts and passages were derived have been properly credited. The work has not been submitted for publication anywhere and is devoid of any instances of plagiarism.

1. The study enhances the engineering materials for innovative applications.

2. The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

3. Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

The study enhances the engineering materials for innovative applications.

The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

This study aims to evaluate the development of composites made of epoxy (E) resin with different weight percentages of polypropylene (PP) and graphene oxide (Go) to form nanocomposite plates.

A hand lay-up process was used to develop 21 different composites, with varying concentrations of PP (5%–35%) and Go (5%–35%). A ternary composite of E matrix was produced by combining binary fillers PP and Go (5%–35%) in a 1:1 ratio to a (95%–5%) solution. With the help of adopting the melt condensation deal to extract Go, the modified Hummers method was used to make Go platelets.

Through field emission scanning electron microscopy (FESEM) and X-ray diffraction investigations, the particulate’s size and structural characteristics were identified. Based on the FESEM analysis of the collapsed zones of the composites, a warp-and-weft-like structure is evident, which endorses the growth yield strength, flexural modulus and impact strength of the composites.

The developed nanocomposites have exceptional mechanical capabilities compared to plain E resin, with E resin exhibiting better tensile strength, modulus and flexural strength when combined with 10% PP and 10% Go. When compared to neat E resin, materials formed from composites have exceptional mechanical properties. When mixed with 10% PP and 10% Go, E resin in particular displays improved tensile strength (23 MPa), tensile modulus (4.15 GPa), flexural strength (75.6 MPa) and other attributes. Engineering implications include automobile side door panels, spacecraft applications, brake pads and flexible battery guards.