Composites Part C Open Access最新文献

{"title":"Axial compressive performance of sustainable BFRP-confined rectangular columns using recycled brick aggregates","authors":"Chisanuphong Suthumma ,&nbsp;Ali Ejaz ,&nbsp;Muhammad Jawed Iqbal ,&nbsp;Ekkachai Yooprasertchai ,&nbsp;Qudeer Hussain ,&nbsp;Gritsada Sua-iam ,&nbsp;Burachat Chatveera ,&nbsp;Preeda Chaimahawan ,&nbsp;Panumas Saingam","doi":"10.1016/j.jcomc.2025.100653","DOIUrl":"10.1016/j.jcomc.2025.100653","url":null,"abstract":"<div><div>This study examines the mechanical behavior of basalt FRP confined rectangular concrete columns using crushed brick aggregates, addressing a research gap. While previous work focused on circular and square columns, this is the first to explore rectangular ones. The use of waste brick aggregates promotes sustainability. The study aims to assess the mechanical properties, expecting improvements in strength and ductility, and could lead to broader applications of basalt FRP. A total of 32 rectangular specimens were tested to evaluate the influence of aggregate type, concrete grade, and number of BFRP layers (0, 2, 4, and 6) on axial compressive performance. Results showed that BFRP confinement significantly enhanced strength and ductility, with maximum gains of 81% in strength and 230% in strain observed in low-strength natural aggregate concrete. Although recycled brick aggregate concrete (RBAC) exhibited lower stiffness, BFRP still provided up to 23% strength improvement. The effectiveness of confinement reduced with increasing unconfined strength. Post-peak analysis revealed that additional BFRP layers delayed stiffness degradation, promoting more ductile failure. Experimental elastic modulus closely matched ACI predictions in natural aggregate (NA) specimens but was overestimated in RBAC due to its higher porosity. The findings demonstrate the viability of BFRP confinement for enhancing the structural performance of sustainable concrete, while emphasizing the need for aggregate-specific design considerations. Design-oriented modelling was adopted to predict the complete stress-strain response of BFRP-confined concrete incorporating both natural and recycled brick coarse aggregates. A two-branch idealization of the compressive response was performed. Several key points were identified and predicted by using nonlinear regression analysis. The proposed approach closely predicted the response of BFRP-confined concrete.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100653"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critical review on laser-assisted paint removal from carbon fibre-reinforced polymer: Insights into process parameters, material integrity, and numerical modelling","authors":"Shiyao Zhu ,&nbsp;Jojibabu Panta ,&nbsp;Richard (Chunhui) Yang ,&nbsp;Lin Ye ,&nbsp;Y.X. Zhang","doi":"10.1016/j.jcomc.2025.100654","DOIUrl":"10.1016/j.jcomc.2025.100654","url":null,"abstract":"<div><div>Laser-based paint stripping has emerged as a precise, efficient, and environmentally sustainable technique for removing paints/coatings from carbon fibre-reinforced polymer (CFRP) composites. This review presents a comprehensive analysis of laser-material interaction mechanisms that govern paint removal, including thermal ablation, thermally induced interfacial failure, plasma shock wave generation, and photochemical bond disruption. The influences of thermal and optical properties of CFRP and paint on interaction dynamics and removal behaviours are critically examined. The key laser processing parameters are systematically analysed in relation to stripping efficiency, substrate preservation, and thermal loading. Experimental methods used for monitoring process response and evaluating removal quality are also reviewed. Numerical modelling approaches based on the finite element method are discussed, with a focus on simulating transient heat transfer, interfacial stresses, and coupled effects. Limitations of current models in capturing the complexity of pulsed laser interaction with multilayered paint-composite structure are addressed. The review highlights that while laser stripping offers selective, damage-free paint removal, challenges remain in managing thermal effects, ensuring layer-specific selectivity, and achieving process scalability. Addressing these challenges is essential for translating laser-based stripping into reliable maintenance solutions for aerospace, defence, renewable energy, and automotive industries.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100654"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid approach for predicting fatigue life of fiber-reinforced polypropylene composite (PPGF40): Integrating micromechanical modelling","authors":"Mohammadali Shirinbayan ,&nbsp;Samia Nouira ,&nbsp;Jihed Zghal ,&nbsp;Joseph Fitoussi","doi":"10.1016/j.jcomc.2025.100660","DOIUrl":"10.1016/j.jcomc.2025.100660","url":null,"abstract":"<div><div>This paper presents a hybrid approach for predicting the fatigue life of PPGF40. The approach combines micromechanical modeling with empirical techniques, based on an intrinsic relationship. Micromechanical modeling is used to analyze the material's monotonic behavior. The study presents a micromechanical model, based on Mori and Tanaka's approach, for simulating damage at the fiber-matrix interface. The model incorporates a local criterion and linearizes the plastic behavior of the matrix using the secant modulus method. The model parameters are identified by comparing them with experimental stiffness reduction results, and S-N curves for different modeled orientations (0°, 45°, and 90°) are presented. The study concludes by establishing the Tsai-Wu fatigue failure criterion based on hybrid modeling results, demonstrating its usefulness in designing structures such as tailgates. The versatility of the micromechanical model extends to other microstructures upon validation. This methodology provides a framework for linking process, microstructure, and properties, and can be coupled in the future with microstructure prediction tools, such as Moldflow, to support fatigue optimization in PPGF40 and similar materials.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100660"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a novel glass laminate with GFRP interlayers","authors":"Feyza Nur Yildirim ,&nbsp;Mithila Achintha","doi":"10.1016/j.jcomc.2025.100655","DOIUrl":"10.1016/j.jcomc.2025.100655","url":null,"abstract":"<div><div>This paper presents establishment of materials and a fabrication method for fabrication of a new laminated glass type with a Glass Fibre Reinforced Polymer (GFRP) interlayer. The developed novel Glass–GFRP Laminate (GGL) is strong and ductile compared to contemporary PolyVinyl Butyral (PVB)-based laminated glasses. The paper shows that annealed glass and an acrylic-based clear casting resin at viscosity 300–400 mPa^.s can be used to fabricate the Glass–GFRP Laminate (GGL) using a Modified Vacuum-Infusion-Process -based (MVIP) method. By carrying out a detailed experimental investigation focused on different glass and resin types, aspect ratio of the laminate specimens, GFRP thickness and number of GFRP layers, and humidity at fabrication, the key design parameters that ensure greater load resistance of the GGL (more than twice the load capacity and more than four times the flexural stiffness) compared to an equivalent thickness PVB-based laminated glass reference specimens are established and discussed in the paper. Finally, the results of percentage of passing of the visible light through the GGL specimens are investigated using a spectrometer. The percentage of passing of the visible light was determined to be 60–75 % and 50–60 % along the lateral and through-thickness directions of the laminate, respectively.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100655"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Failure modes and energy absorption in Glass Reinforced aluminum (GLARE) hybrid laminates subjected to three-point bending","authors":"Shreyas Anand,&nbsp;Nachiket Dighe,&nbsp;Pranshul Gupta,&nbsp;René Alderliesten,&nbsp;Saullo G.P. Castro","doi":"10.1016/j.jcomc.2025.100651","DOIUrl":"10.1016/j.jcomc.2025.100651","url":null,"abstract":"<div><div>This paper investigates 3-point bending failure of five different types of GLARE laminates (2A, 2B, 3, 4A and 4B). 73 configurations (419 specimens), with different stacking sequences and aluminum layer thicknesses are tested. Failure mechanisms, effect of stacking sequence, effect of aluminum rolling direction, effect of displacement rate and energy absorption are analyzed. Configurations with predominantly 0°glass fiber layers fail with delamination as the major failure mode, while configurations with predominantly 90°glass fiber layers fail with central cracking as the major failure mode. GLARE 3, with 1:1 ratio of 0°and 90°fibers, fail with an equal mix of delamination and central cracking. A semi-analytical framework that can be used to predict the force versus displacement curve for central cracking failure is proposed and validated.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100651"},"PeriodicalIF":7.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Viscoelastic high-damping vibration attenuation of sandwich FG-GPLRC face sheets by incorporating full nonlinear effects","authors":"Hamidreza Rostami ,&nbsp;Sattar Jedari Salami","doi":"10.1016/j.jcomc.2025.100650","DOIUrl":"10.1016/j.jcomc.2025.100650","url":null,"abstract":"<div><div>This article deals with the study of geometrically and materially nonlinear free-damped vibration analysis of Sandwich beams incorporating flexible cores governed by various frequency-dependent viscoelastic models, surrounded with top and bottom face sheets reinforced through a functionally graded distribution of graphene platelets (GPLs) in large deformation. In fact, two types of nonlinearities are considered in the formulation: one arising from the nonlinear strain-displacement relationship, and the other due to the viscoelastic material behavior in the sandwich beam. To analyze the impact of including nonlinear terms in both geometric and material behavior—which has not been reported in the literature—the results are computed by adopting the geometrically nonlinear von Kármán assumptions for the core and the face sheets on one hand, and by employing a viscoelastic core material with complex frequency-dependent Young's/shear modulus that induces material nonlinearity on the other. Based on the Extended Higher-Order Sandwich Panel Theory (EHSAPT), a set of coupled nonlinear governing equations is derived using the Lagrangian technique. As a progressive step, this is the first time that a displacement control technique has been enhanced to simultaneously account for both geometric and material nonlinearities in order to obtain the vibrational characteristics of a system, making it valid for large vibration amplitudes and high damping. To validate the approach, the results obtained from EHSAPT are compared with available data in the literature. Additionally, the problem is also examined by applying Euler–Bernoulli and Timoshenko beam theories to the face sheets and core, respectively. The complex nonlinear eigenvalue problem is solved, and the natural frequencies and loss factors of the viscoelastically damped sandwich beam are calculated. Parametric studies are discussed in detail to investigate the effects of weight fraction, graphene platelet distribution pattern, core-to-face sheet thickness ratio, boundary conditions, viscoelastic core temperature, and vibration amplitude. The results provide valuable and practical insights, showing that considering appropriate ranges of geometry and material in large-amplitude nonlinear vibrations of frequency-dependent viscoelastic core sandwich beams leads to improved design and industrial optimization.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100650"},"PeriodicalIF":7.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structure–property relationships in 3D-printed onyx-based composites reinforced with continuous fibers: role of temperature and fiber orientation","authors":"Vishista Kaushik,&nbsp;Suresh Kurra,&nbsp;Ramesh Adusumalli","doi":"10.1016/j.jcomc.2025.100649","DOIUrl":"10.1016/j.jcomc.2025.100649","url":null,"abstract":"<div><div>This study investigates the flexural performance of 3D-printed continuous fiber-reinforced composites, focusing on the influence of fiber types, orientation, and temperature. Using a carbon, glass, kevlar fiber- and Onyx matrix- filaments, specimens were fabricated as 24 or 30-layer composites. Three-point bending tests were conducted under different temperatures. The results reveal a significant influence of fiber type and orientation. Carbon fiber composite showed the highest strength of 281 MPa at 0° orientation and 127 MPa at 90° orientation at RT. At -20 °C, Carbon, Glass and Kevlar composites revealed flexural strength of 422, 308 and 188 MPa respectively (0°). Similarly, with an increase in temperature, a decrement in flexural properties can be observed in all the fiber types. The modulus for kevlar decreased from 8.29 to 5.71 to 4.15 GPa with an increase in temperature from -20 to 27 to 85 °C. Additionally, microscopic analysis highlights the failure mechanisms, including fiber pull-out, delamination, and matrix softening. Grey relation analysis used two mutually conflicted parameters (strength, cost) and reported the best and worst composite amongst 18 combinations considered. The findings provide valuable insights for optimizing the design of 3D-printed composites at different fiber orientations and temperatures enhancing their applicability in structural applications.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100649"},"PeriodicalIF":7.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating the creep rupture time of GFRP bars using machine learning","authors":"M.Talha Junaid ,&nbsp;Ahed Habib ,&nbsp;Mazen Shrif ,&nbsp;Samer Barakat","doi":"10.1016/j.jcomc.2025.100648","DOIUrl":"10.1016/j.jcomc.2025.100648","url":null,"abstract":"<div><div>Fiber-reinforced polymer (FRP) bars are increasingly utilized in civil structures due to their advantages in terms of corrosion resistance and a high strength-to-weight ratio. Current research on long-term durability, particularly under sustained loading (creep-rupture), has not yet fully explored the use of methods like machine learning to accurately predict the creep rupture time of FRP bars. This study seeks to address this gap by applying machine learning techniques to estimate the creep rupture time of glass fiber-reinforced polymer (GFRP) bars. The motivation for this research comes from the shortcomings of traditional models, which are often inadequate for capturing the complex nonlinear behavior of materials subjected to long-term stress. This research aims to evaluate the effectiveness of different machine learning models, including neural networks, support vector machines, and ensemble methods, in predicting the creep behavior of GFRP bars. Within the study context, a large dataset consisting of 435 experimental tests is collected from the literature. In the testing phase, the optimized neural network achieved an RMSE of 926.29 h and an R² of 0.99 on a heterogeneous dataset that also included bars tested under environmental conditioning reported in the source studies. Gaussian process regression and support vector machines also performed well, albeit with higher errors. Sensitivity analysis revealed that the level of sustained stress and bar diameter were the most critical factors for environmentally conditioned bars. Importantly, the predictors reflect standard design and material descriptors (diameter, fiber content, modulus, UTS, sustained stress) and, when reported, environmental conditioning, which together capture the primary sources of variability relevant to civil engineering practice. Overall, the findings suggest that machine learning, particularly through optimized neural networks, offers a powerful tool for predicting complex material behavior and improving the reliability of GFRP-reinforced structures. This study contributes to the field by highlighting the potential of machine learning to enhance the precision of long-term performance predictions for engineering materials, facilitating improved design and material selection in critical infrastructure.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100648"},"PeriodicalIF":7.0,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Material card’s identification by inverse method and multi-scale optimization approach through eigenmode analysis of unidirectional Elium®/flax laminated biocomposites","authors":"Ameny Ketata ,&nbsp;Zouhaier Jendli ,&nbsp;Mondher Haggui ,&nbsp;Abderrahim El Mahi ,&nbsp;Anas Bouguehca ,&nbsp;Mohamed Haddar","doi":"10.1016/j.jcomc.2025.100647","DOIUrl":"10.1016/j.jcomc.2025.100647","url":null,"abstract":"<div><div>This article presents a mixed finite element method (FEM) and experimental inverse identification approach for determining the ply-level elastic properties of unidirectional (UD) Elium®/flax composites. Using the global dynamic response of UD laminates, the intrinsic mechanical properties are identified. Material uncertainties are accounted for, and engineering constants are determined over a broad frequency range through a response surface methodology (RSM)-based sensitivity analysis and meta-modeling approach. A multi-objective optimization process based on a non-dominated sorting genetic algorithm (NSGA) is employed to minimize differences between experimental and numerical frequency responses. The sensitivity analysis reveals that the first seven vibration modes are primarily influenced by the longitudinal modulus (<em>E</em>₁) and the shear modulus (<em>G</em>₁₂), with <em>E</em>₁ having a dominant effect in UD configurations. The optimization process, conducted using HyperStudy™, demonstrates good agreement between the numerical and experimental frequencies. However, the use of a global error function reveals certain limitations, as it may fail to smooth out local deviations, making it challenging to precisely identify mismatches in individual vibration modes. In summary, these findings provide valuable insights into the dynamic behavior of Elium®/flax composites and offer a robust method for determining material properties card for future complex composite structures.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100647"},"PeriodicalIF":7.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven prediction of failure loads in damaged FRP composites under four-point flexure","authors":"James A. Quinn,&nbsp;Ourania Patsia,&nbsp;Gabrielis Cerniauskas,&nbsp;Dongmin Yang,&nbsp;Dilum Fernando,&nbsp;Edward D. McCarthy","doi":"10.1016/j.jcomc.2025.100646","DOIUrl":"10.1016/j.jcomc.2025.100646","url":null,"abstract":"<div><div>This study investigates the use of machine learning (ML) as a tool to make predictions of the criticality of delamination damage in fiber-reinforced polymer (FRP) composites subjected to four-point flexural loading. An extensive experimental campaign was conducted on polyester-glass FRP specimens. Most specimens were manufactured with a polytetrafluoroethylene film inserted at one interlaminar location to simulate delamination damage. Damage size, damage location through the laminate thickness, and the number of plies in the laminate, were each varied in the test matrix. The strength of damaged specimens was normalized against the strengths of corresponding pristine reference specimens to obtain a measure of damage criticality. Data augmentation techniques were subsequently utilized on the experimental data to synthetically generate a larger dataset for training, validating and testing the ML model. Output predictions of specific strength from the ML model proved very accurate for both the training dataset and the test dataset, meaning the ML model can accurately and near instantaneously predict the specific four-point flexure strength of new delamination damage cases. The method presented could be expanded to include new specimen characteristics and loading scenarios, or be combined with non-destructive testing techniques to enable data-backed, rapid decision making when delamination damage is detected in asset maintenance programs. The results highlight the effectiveness of data-driven methods for predicting the failure loads and apparent static strengths of damaged FRP composites and provide information on the most influential delamination features affecting the strength of FRP under flexure loads.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"18 ","pages":"Article 100646"},"PeriodicalIF":7.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}