Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 Prediction of Compressive Strength of High-Performance Concrete Using Gradient Boosting Regression and Ada Boost Regression models Prediction of Compressive Strength of High-Performance Concrete Using Gradient Boosting Regression and Ada Boost Regression models 598 606 28597 10.48311/mme.2026.118769.82933 FA Amin Khazaee Department of Civil Engineering, Esfarayen University of Technology, Esfarayen, Iran 0009-0007-8688-6306 Ali Khazaee Department of Electrical Engineering, University of Bojnord, Bojnurd, Iran Journal Article 2026 02 05 Compressive strength is one of the most critical performance indicators of high-performance concrete (HPC), playing a key role in the safety, durability, and load-bearing capacity of structures. Due to the time-consuming and costly nature of conventional laboratory testing methods for determining this parameter, the application of intelligent data-driven approaches has gained increasing attention as an efficient and accurate alternative. In this study, to predict the compressive strength of high-performance concrete, two models—Gradient Boosting Regression and Ada Boost Regression—were employed as advanced machine learning algorithms. The dataset used in this research consists of 1,030 experimental HPC samples collected from the University of California, Irvine database, including various mix design parameters such as cement content, water, blast furnace slag, fly ash, superplasticizer, aggregates, and specimen age. Initially, correlation analysis was conducted to examine the relationships between input variables and compressive strength, revealing that cement content, concrete age, and superplasticizer dosage have the strongest positive correlations, while water content exhibits the most significant negative correlation with compressive strength. Subsequently, the proposed model was trained and evaluated using a ten-fold cross-validation strategy. Subsequently, the two proposed models were trained and evaluated using 10-fold cross-validation. The results demonstrated that the Gradient Boosting Regression model possesses higher accuracy and reliability in predicting the compressive strength of high-performance concrete across all evaluation metrics. The findings of this study highlight the strong potential of boosting-based machine learning algorithms as reliable and cost-effective alternatives to traditional experimental methods for the design and performance assessment of advanced concrete materials. Compressive strength is one of the most critical performance indicators of high-performance concrete (HPC), playing a key role in the safety, durability, and load-bearing capacity of structures. Due to the time-consuming and costly nature of conventional laboratory testing methods for determining this parameter, the application of intelligent data-driven approaches has gained increasing attention as an efficient and accurate alternative. In this study, to predict the compressive strength of high-performance concrete, two models—Gradient Boosting Regression and Ada Boost Regression—were employed as advanced machine learning algorithms. The dataset used in this research consists of 1,030 experimental HPC samples collected from the University of California, Irvine database, including various mix design parameters such as cement content, water, blast furnace slag, fly ash, superplasticizer, aggregates, and specimen age. Initially, correlation analysis was conducted to examine the relationships between input variables and compressive strength, revealing that cement content, concrete age, and superplasticizer dosage have the strongest positive correlations, while water content exhibits the most significant negative correlation with compressive strength. Subsequently, the proposed model was trained and evaluated using a ten-fold cross-validation strategy. Subsequently, the two proposed models were trained and evaluated using 10-fold cross-validation. The results demonstrated that the Gradient Boosting Regression model possesses higher accuracy and reliability in predicting the compressive strength of high-performance concrete across all evaluation metrics. The findings of this study highlight the strong potential of boosting-based machine learning algorithms as reliable and cost-effective alternatives to traditional experimental methods for the design and performance assessment of advanced concrete materials. https://mme.modares.ac.ir/article_28597_06ac68cc0db1c6c3992eb55812168e55.pdf

Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 Manufacturing Process and Bending Behaviour of Sandwich Panels made by Unidirectional Thermoplastic Composite Skin with Wood Core Manufacturing Process and Bending Behaviour of Sandwich Panels made by Unidirectional Thermoplastic Composite Skin with Wood Core 609 620 28446 10.48311/mme.2026.119262.82977 FA Mostafa Babaei Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran Mohammad Golzar Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran 0000-0003-3873-7262 Hadi Ghorbani Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran Journal Article 2026 02 16 In recent years, significant advances in materials science and engineering have enabled the development and application of novel materials in engineered structures. Among these materials, composites have attracted increasing attention due to their high strength-to-weight ratio, favorable mechanical properties, and design flexibility. One important application of these materials is the fabrication of sandwich panels with lightweight cores and stiff skins, which can serve as suitable alternatives to conventional materials in sectors such as construction and transportation. In the present study, the fabrication and mechanical characterization of bio-based wood-core composite sandwich panels were investigated. Thermoplastic samples were fabricated using the hot-press method, while thermoset samples were produced by the vacuum-assisted resin infusion process. The face sheets of the thermoplastic panels consisted of glass fiber–reinforced polyamide 6, whereas glass fiber–reinforced epoxy resin was used as the face sheet in thermoset samples. Mechanical performance was assessed by three-point bending tests, and parameters such as face bending stress, core bending stress, and overall flexural strength were obtained experimentally. Furthermore, indices of strength-to-weight ratio and energy absorption were calculated and compared across the different systems. Finally, the results were benchmarked against findings from previous studies to highlight performance improvements and potential advantages of the proposed configurations. In recent years, significant advances in materials science and engineering have enabled the development and application of novel materials in engineered structures. Among these materials, composites have attracted increasing attention due to their high strength-to-weight ratio, favorable mechanical properties, and design flexibility. One important application of these materials is the fabrication of sandwich panels with lightweight cores and stiff skins, which can serve as suitable alternatives to conventional materials in sectors such as construction and transportation. In the present study, the fabrication and mechanical characterization of bio-based wood-core composite sandwich panels were investigated. Thermoplastic samples were fabricated using the hot-press method, while thermoset samples were produced by the vacuum-assisted resin infusion process. The face sheets of the thermoplastic panels consisted of glass fiber–reinforced polyamide 6, whereas glass fiber–reinforced epoxy resin was used as the face sheet in thermoset samples. Mechanical performance was assessed by three-point bending tests, and parameters such as face bending stress, core bending stress, and overall flexural strength were obtained experimentally. Furthermore, indices of strength-to-weight ratio and energy absorption were calculated and compared across the different systems. Finally, the results were benchmarked against findings from previous studies to highlight performance improvements and potential advantages of the proposed configurations. https://mme.modares.ac.ir/article_28446_55f333b00c3cd872e9afc52918b0fd46.pdf

Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 Experimental Investigation of Projectile Nose Shape Effects on the Low-Velocity Impact Response of Sandwich Panels with 3D-Printed Lattice and Corrugated Cores Experimental Investigation of Projectile Nose Shape Effects on the Low-Velocity Impact Response of Sandwich Panels with 3D-Printed Lattice and Corrugated Cores 621 633 28604 10.48311/mme.2026.118880.82955 FA Ali Akbar Mobasseri Department of Aerospace Engineering, Malek-Ashtar University of Technology, Tehran, Iran Mahmood Zabihpoor Department of Aerospace Engineering, Malek-Ashtar University of Technology, Tehran, Iran 0000-0002-2648-8070 Majid Jamal-Omidi Department of Aerospace Engineering, Malek-Ashtar University of Technology, Tehran, Iran Journal Article 2026 01 20 This study experimentally investigates the effect of projectile nose shape on the low-velocity impact response of sandwich panels with 3D-printed lattice and corrugated cores. The face sheets were manufactured from glass-fiber-reinforced polymer composites, and the cores were fabricated from polylactic acid (PLA) using the fused deposition modeling (FDM) process. Drop-weight impact tests were conducted at different energy levels using blunt, hemispherical, and conical projectiles. Force–time and displacement–time histories were recorded, the absorbed energy was obtained from the corresponding force–displacement curves, and post-impact inspections were performed to identify damage mechanisms. The results show that projectile nose geometry significantly influences contact conditions, stress concentration, and failure modes: blunt projectiles generally produce higher peak forces, whereas conical projectiles promote localized penetration and more severe localized damage. Regarding core architecture, corrugated cores exhibited higher initial stiffness under a considerable portion of the tested conditions, while lattice cores provided superior specific energy absorption at certain impact energy levels. Overall, the findings indicate that the relative advantages of each core type depend on impact energy and nose geometry, emphasizing that core selection should be guided by the intended performance criteria (initial stiffness versus energy absorption) and loading conditions. This study experimentally investigates the effect of projectile nose shape on the low-velocity impact response of sandwich panels with 3D-printed lattice and corrugated cores. The face sheets were manufactured from glass-fiber-reinforced polymer composites, and the cores were fabricated from polylactic acid (PLA) using the fused deposition modeling (FDM) process. Drop-weight impact tests were conducted at different energy levels using blunt, hemispherical, and conical projectiles. Force–time and displacement–time histories were recorded, the absorbed energy was obtained from the corresponding force–displacement curves, and post-impact inspections were performed to identify damage mechanisms. The results show that projectile nose geometry significantly influences contact conditions, stress concentration, and failure modes: blunt projectiles generally produce higher peak forces, whereas conical projectiles promote localized penetration and more severe localized damage. Regarding core architecture, corrugated cores exhibited higher initial stiffness under a considerable portion of the tested conditions, while lattice cores provided superior specific energy absorption at certain impact energy levels. Overall, the findings indicate that the relative advantages of each core type depend on impact energy and nose geometry, emphasizing that core selection should be guided by the intended performance criteria (initial stiffness versus energy absorption) and loading conditions. https://mme.modares.ac.ir/article_28604_9e4c4267803c7e60e916f65a2d0e49cc.pdf

Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 A Case-Based Evaluation of Thermophysical Property Changes on Helicopter Powertrain Lubricant Performance A Case-Based Evaluation of Thermophysical Property Changes on Helicopter Powertrain Lubricant Performance 635 647 28064 10.48311/mme.2026.115539.1000 FA Ali Mehrabi Flight and Engineering Department, IAMU, Tehran, Iran 0000-0002-5266-674X Journal Article 2025 09 20 In this study, the evolution of thermophysical and rheological properties of Aeroshell Turbine Oil 500 during its service life was experimentally investigated in two helicopter platforms operating under different conditions: a Military Attack Helicopter (MAH) subjected to severe thermal and mechanical loads, and a Military Utility Helicopter (MUH) operating under comparatively milder conditions. Engine and gearbox oil samples were collected over 200 hours of operation at 25-hour intervals. The experimental analysis included measurements of viscosity, thermal conductivity, density, and specific heat capacity, along with the evaluation of thermal diffusivity and rheological behavior. The results indicated that oil degradation in the engine was significantly more severe than in the gearbox. Kinematic viscosity increased by 34% in the MAH engine and 25% in the MUH, leading to a proportional rise in frictional losses. Thermal conductivity decreased by 23.7% in the MAH and 18.9% in the MUH, reflecting a reduction in oil cooling capability. In both platforms, specific heat decreased while density increased, indicating structural changes associated with thermal aging. Rheological analysis showed Newtonian behavior in the gearboxes of both helicopters. However, engine oil exhibited nearly Newtonian behavior after prolonged operation and transitioned to mild shear-thinning behavior in the MAH engine during the final service stage. These findings highlight the importance of continuous monitoring of oil thermophysical properties in high-load aircraft systems and provide a basis for optimizing maintenance strategies, lubricant selection, and thermal management system design. In this study, the evolution of thermophysical and rheological properties of Aeroshell Turbine Oil 500 during its service life was experimentally investigated in two helicopter platforms operating under different conditions: a Military Attack Helicopter (MAH) subjected to severe thermal and mechanical loads, and a Military Utility Helicopter (MUH) operating under comparatively milder conditions. Engine and gearbox oil samples were collected over 200 hours of operation at 25-hour intervals. The experimental analysis included measurements of viscosity, thermal conductivity, density, and specific heat capacity, along with the evaluation of thermal diffusivity and rheological behavior. The results indicated that oil degradation in the engine was significantly more severe than in the gearbox. Kinematic viscosity increased by 34% in the MAH engine and 25% in the MUH, leading to a proportional rise in frictional losses. Thermal conductivity decreased by 23.7% in the MAH and 18.9% in the MUH, reflecting a reduction in oil cooling capability. In both platforms, specific heat decreased while density increased, indicating structural changes associated with thermal aging. Rheological analysis showed Newtonian behavior in the gearboxes of both helicopters. However, engine oil exhibited nearly Newtonian behavior after prolonged operation and transitioned to mild shear-thinning behavior in the MAH engine during the final service stage. These findings highlight the importance of continuous monitoring of oil thermophysical properties in high-load aircraft systems and provide a basis for optimizing maintenance strategies, lubricant selection, and thermal management system design. https://mme.modares.ac.ir/article_28064_b9c317d06906da03e3d4798fe5824e31.pdf

Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 An Extension to "Robust cooperative multiple flexible-joint arms control using the q-Bernstein-Schurer operators as the uncertainty approximator: A singular perturbation approach" An Extension to "Robust cooperative multiple flexible-joint arms control using the q-Bernstein-Schurer operators as the uncertainty approximator: A singular perturbation approach" 649 663 28135 10.48311/mme.2026.117226.82872 FA Karim Bayat Department of Electrical Engineering Control , ST.C., Islamic Azad University, Tehran, Iran 0009-0007-0680-7672 Alireza Izadbakhsh Department of Electrical Engineering , Ga. C., Islamic Azad University, Garmsar, Iran 0000-0001-5644-1735 Ali Jamali Department of biomedical engineering , Sha. C., Islamic Azad University, Shahrood, Iran 0000-0002-9427-121X Amir Hooshang Mazinan Department of Electrical Engineering Control , ST.C., Islamic Azad University, Tehran, Iran 0000-0002-8810-9574 Journal Article 2025 10 31 Abstract: This paper introduces a robust adaptive controller tailored for collaborative multiple robots and equipped with elastic joints. It utilizes a simple model of manipulator dynamics, treating all other dynamics as lumped uncertainty. The proposed approach integrates Function Approximation Techniques (FAT), specifically Bernstein-type rational functions, to estimate lumped uncertainty. Recent advancements have utilized FAT-based robust adaptive controllers for uncertainty estimation. However, our innovation distinguishes itself from prior research by minimizing the required regressor matrices. This advantage becomes particularly pronounced as the number of manipulators and their degrees of freedom increase. In addition, the coefficients of the Bernstein-type rational functions are adjusted by the adaptation laws derived from stability analysis, which are not presented in the previous literature. To the best of our knowledge, this paper marks the first engineering application of Bernstein-type rational functions for function approximation in adaptive form. Stability analysis guarantees that all error signals remain uniformly ultimately bounded (UUB). The theoretical advancements are validated by employing two elastic joint manipulators to transport a rigid object. The outcomes are also compared with two advanced approximation techniques to show the precision and effectiveness of the proposed controller design. The results exhibit the usefulness of the proposed control scheme, facing uncertainties and disturbances Abstract: This paper introduces a robust adaptive controller tailored for collaborative multiple robots and equipped with elastic joints. It utilizes a simple model of manipulator dynamics, treating all other dynamics as lumped uncertainty. The proposed approach integrates Function Approximation Techniques (FAT), specifically Bernstein-type rational functions, to estimate lumped uncertainty. Recent advancements have utilized FAT-based robust adaptive controllers for uncertainty estimation. However, our innovation distinguishes itself from prior research by minimizing the required regressor matrices. This advantage becomes particularly pronounced as the number of manipulators and their degrees of freedom increase. In addition, the coefficients of the Bernstein-type rational functions are adjusted by the adaptation laws derived from stability analysis, which are not presented in the previous literature. To the best of our knowledge, this paper marks the first engineering application of Bernstein-type rational functions for function approximation in adaptive form. Stability analysis guarantees that all error signals remain uniformly ultimately bounded (UUB). The theoretical advancements are validated by employing two elastic joint manipulators to transport a rigid object. The outcomes are also compared with two advanced approximation techniques to show the precision and effectiveness of the proposed controller design. The results exhibit the usefulness of the proposed control scheme, facing uncertainties and disturbances https://mme.modares.ac.ir/article_28135_3f12cc3464d7a80a7706f970dc254bb7.pdf

Tarbiat Modares University Modares Mechanical Engineering 2476-6909 26 8 2026 07 23 Experimental investigation of the synergistic effects of porous structure and stabilized boiling surface on pool boiling under reduced nanofluid concentration Experimental investigation of the synergistic effects of porous structure and stabilized boiling surface on pool boiling under reduced nanofluid concentration 665 687 28743 10.48311/mme.2026.119885.83000 FA Mohammad Ghorbani Department of Mechanical Engineering, SR.C., Islamic Azad University, Tehran, Iran Masoud Zareh Department of Mechanical Engineering, SR.C., Islamic Azad University, Tehran, Iran 0000-0001-5613-7047 Cyrus Aghanajafi Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran Mohammad Behshad Shafii Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran Journal Article 2026 04 29 In this study, the synergistic effects of a honeycomb porous structure (HPS) and stabilized boiling surfaces on the pool boiling heat transfer characteristics of SiO₂ nanofluid under reduced concentration conditions is experimentally investigated. Dissipation of high heat fluxes is a major challenge in thermal systems, as approaching the critical heat flux (CHF) can lead to thermal instability and equipment damage. Surface stability is also essential for long-term operation, since instability during successive boiling cycles increases surface superheat and reduces system reliability. In addition, low-concentration nanofluids are preferred in practical applications to minimize nanoparticle deposition. First, the boiling surface was stabilized using a 2 wt% nanofluid under successive and prolonged boiling cycles. Then, the concentration was reduced to 0.5 wt%, leading to the formation of new stabilized layers on the initial surface. This approach preserves thermal performance while reducing deposition. After that, a HPS was attached to the stabilized surface. This structure increases the effective heat transfer area, enhances capillary liquid supply, and facilitates vapor removal. The results show that surface stability is maintained despite the reduced concentration. The combination with the porous structure improves thermal performance. Specifically, CHF increases by 35.6% and 39.8%, and the boiling heat transfer coefficient (BHTC) increases by 27.9% and 25.3%, compared to the stabilized surface without porous structure, for nanofluids with nanoparticle sizes of 20-30 and 60-70 nm, respectively. In addition, the porous structure increases the surface superheat at the onset of nucleate boiling (ONB). In this study, the synergistic effects of a honeycomb porous structure (HPS) and stabilized boiling surfaces on the pool boiling heat transfer characteristics of SiO₂ nanofluid under reduced concentration conditions is experimentally investigated. Dissipation of high heat fluxes is a major challenge in thermal systems, as approaching the critical heat flux (CHF) can lead to thermal instability and equipment damage. Surface stability is also essential for long-term operation, since instability during successive boiling cycles increases surface superheat and reduces system reliability. In addition, low-concentration nanofluids are preferred in practical applications to minimize nanoparticle deposition. First, the boiling surface was stabilized using a 2 wt% nanofluid under successive and prolonged boiling cycles. Then, the concentration was reduced to 0.5 wt%, leading to the formation of new stabilized layers on the initial surface. This approach preserves thermal performance while reducing deposition. After that, a HPS was attached to the stabilized surface. This structure increases the effective heat transfer area, enhances capillary liquid supply, and facilitates vapor removal. The results show that surface stability is maintained despite the reduced concentration. The combination with the porous structure improves thermal performance. Specifically, CHF increases by 35.6% and 39.8%, and the boiling heat transfer coefficient (BHTC) increases by 27.9% and 25.3%, compared to the stabilized surface without porous structure, for nanofluids with nanoparticle sizes of 20-30 and 60-70 nm, respectively. In addition, the porous structure increases the surface superheat at the onset of nucleate boiling (ONB). https://mme.modares.ac.ir/article_28743_67a83eab0da5d5e1357e82cee4ea9907.pdf