Lightweight Characteristics Research Articles

Piezoelectric Energy Harvesting: From Fundamentals to Advanced Applications

Piezoelectric energy harvesting (PEH) has surfaced as an innovative technology for supplying power to low‐power electronic devices by converting mechanical energy into electrical energy. This technology utilizes the piezoelectric effect, in which specific materials produce an electric charge when they experience mechanical stress. Piezoelectric materials can be categorized into three main types: single crystal, composite, and polymeric. Single‐crystal materials exhibit elevated piezoelectric coefficients and stability; however, they tend to be costly and fragile. Composite materials integrate piezoelectric ceramics with polymer matrices, enhancing flexibility and lowering costs. Polymeric materials exhibit lightweight, flexible, and biocompatibility characteristics, rendering them ideal for wearable and implantable applications. Although PEH presents considerable promise, it is essential to tackle challenges, including low power output, material constraints, and environmental influences. Future investigations will focus on creating innovative materials that exhibit improved piezoelectric characteristics, refining device architecture for optimal energy conversion, and incorporating piezoelectric harvesting technology into intelligent systems. By addressing these challenges and investigating creative solutions, PEH can significantly advance sustainable and self‐powered electronic devices.

TransSMPL: Efficient Human Pose Estimation with Pruned and Quantized Transformer Networks

Existing Transformers for 3D human pose and shape estimation models often struggle with computational complexity, particularly when handling high-resolution feature maps. These challenges limit their ability to efficiently utilize fine-grained features, leading to suboptimal performance in accurate body reconstruction. In this work, we propose TransSMPL, a novel Transformer framework built upon the SMPL model, specifically designed to address the challenges of computational complexity and inefficient utilization of high-resolution feature maps in 3D human pose and shape estimation. By replacing HRNet with MobileNetV3 for lightweight feature extraction, applying pruning and quantization techniques, and incorporating an early exit mechanism, TransSMPL significantly reduces both computational cost and memory usage. TransSMPL introduces two key innovations: (1) a multi-scale attention mechanism, reduced from four scales to two, allowing for more efficient global and local feature integration, and (2) a confidence-based early exit strategy, which enables the model to halt further computations when high-confidence predictions are achieved, further enhancing efficiency. Extensive pruning and dynamic quantization are also applied to reduce the model size while maintaining competitive performance. Quantitative and qualitative experiments on the Human3.6M dataset demonstrate the efficacy of TransSMPL. Our model achieves an MPJPE (Mean Per Joint Position Error) of 48.5 mm, reducing the model size by over 16% compared to existing methods while maintaining a similar level of accuracy.

Kinematic Analysis of a Cam-Follower-Type Transplanting Mechanism for a 1.54 kW Biodegradable Potted Cabbage Transplanter

Widespread use of plastic seedling pots has been attributed to their light weight and durable characteristics. However, these pots have limitations in facilitating efficient root establishment. Recent studies indicate that biodegradable seedling pots not only enhance seedling resilience but are also environmentally sustainable through natural decomposition. This study presents a kinematic analysis of a cabbage transplanting mechanism specifically under development for biodegradable seedling pots, focusing on position, velocity, acceleration, and power. The optimization of link combinations within the transplanting mechanism was analyzed to enhance the transplantation process, focusing on achieving precise depth and spacing for potted seedlings. A kinematic model of the mechanism was developed and simulated using commercial mechanical design and simulation software, followed by validation through performance tests. The proposed transplanter comprised a four-bar-linkage mechanism consisting of a driving link, a driven link, a connecting link, and a guide bar. Simulation trials were conducted by varying the main arm link length while keeping machine forward speed and mechanism driving speed fixed. Results indicated that the optimal mechanism parameters included a driving link of 50 mm, a connecting arm of 120 mm, a guide bar of 120 mm, and an end-effector link of 220 mm. A dibbling hopper length of 153 mm was identified as the most effective for operation. With these recommended link lengths, validated velocities of the end hopper in the ‘X’ and ‘Y’ directions were 284 mm/s and 1379 mm/s, respectively, while corresponding accelerations were measured at 1241 mm/s2 and 8664 mm/s2. The driving power requirement was calculated to be 17.4 W. These findings suggest that the developed mechanism provides effective planting performance, evidenced by a high degree of seedling uprightness and minimal soil disturbance. This study supports the use of biodegradable pots in mechanized transplanting as a viable alternative to conventional plastic pots, with potential benefits for both agricultural efficiency and environmental sustainability.

Biomimetic Nanostructured Polyimine Aerogels with Graded Porosity, Flame Resistance, Intrinsic Superhydrophobicity, and Closed-Loop Recovery.

Polymer aerogels, with their porous and lightweight features, excel in applications such as energy storage, absorption, and thermal insulation, making them a sought-after new material. However, the covalent cross-linking networks of current polymer aerogels result in unsustainable manufacturing and processing practices, persistently depleting our finite natural resources and causing significant global environmental impacts. Herein, we have constructed a high-performance dynamic covalent cross-linking aerogel network using biobased materials, with its structure and green sustainability akin to those of plants in nature. Abundant reversible cross-linking points endow the aerogel with ultrafast degradation capabilities, enabling allow for closed-loop chemical monomer recovery and reprocessing. Furthermore, utilizing the highly active reversible network, net-zero emission material reuse and reprocessing can be achieved. Additionally, the controlled dynamic aerogel network features a multilevel roughness nanostructured surface similar to lotus leaf and a biomimetic pore structure, contributing to significant anisotropy. The distinctive structure and composition endow the dynamic aerogel with high compressive strength (2.2 MPa) vertically, low thermal conductivity (0.0257 W/(m·K)) horizontally, and outstanding fire resistance (LOI is as high as 36%). Notably, the aerogel demonstrates the highest hydrophobicity among polyimine materials, with a contact angle of 154°. Furthermore, those dynamic aerogels have excellent performance in a variety of potential applications such as oil-water separation, directional transport, and phase change energy storage, and it is anticipated that these applications will greatly benefit from systematic upgrades in recyclability and reprocessing.

Separation and Dynamic Binding Mechanism of 3D Model Animation by Integrating Improved Genetic Algorithm and WebAR

With the development of the economy, the demand for 3D model animation in fields such as games, film and television, and education is constantly increasing. At present, many 3D model animations are built based on genetic algorithm. However, due to the slow convergence speed and poor local search ability of genetic algorithm, this model still has many shortcomings. Therefore, a cellular automaton is used to optimize the genetic algorithm, and a cellular genetic algorithm is proposed. It is integrated with WebAR technology, which has the characteristics of lightweight and low cost, aiming to improve the separation and dynamic binding mechanism of 3D model animation. Experimental analysis showed that when the number of chromosome genes was 90, the vertex gene value of the research algorithm was the highest, ranging from 281 to 501, and the convergence was the highest. The file size of the model dataset based on the research method was reduced by 50% compared with before. The FPDT based on the animation separation and dynamic binding mechanism was decreased by 15.2 s on average. To sum up, it can be seen that the designed 3D model animation is more detailed and the animation dynamic is also the best. This research method can significantly improve the separation and dynamic binding mechanism of 3D model animation. This research will improve the integration of genetic algorithm and WebAR technology, which can not only effectively deal with complex 3D models and animations, but also has broad application prospects.

The Transformation of Batik Motif: Transforming Interior Spaces to be More Aesthetic with Batik Patterned Metal Ceilings

Batik continues to evolve beyond its traditional function. This study explores the innovative transformation of batik motifs into metal ceilings as a representation of the integration of traditional aesthetics with modern technology in contemporary interior design. The main objective of the study is to analyse the potential for developing batik-patterned metal ceilings that combine cultural values with technological innovation. The research methodology uses a mixed qualitative and quantitative approach, including literature studies, design observations, and manufacturing technology experiments. The study focuses on the transformation of ceplok batik motifs, especially kawung, into metal panels using stamping and forming techniques. Aluminium and copper were chosen as the main materials due to their lightweight, corrosion-resistant, and design flexibility characteristics. The research process involved the development of Appropriate Technology for stamping and forming metal that allows the creation of ceilings with precise batik motif details. Experimentation includes designing special dies, setting metal forming parameters, and finishing. The results of the study indicate the success of transforming traditional batik motifs into metal ceilings that have high aesthetic value and practical functions. This innovation not only produces interior design products, but also makes a significant contribution to the preservation and development of cultural heritage through a contemporary approach. The study concludes that the transformation of batik motifs into metal ceilings successfully brings together tradition and modernity. This approach opens up new opportunities in interior design that respect cultural heritage while taking advantage of advances in manufacturing technology, while expanding the appreciation of batik in a more dynamic and innovative context.

MSDFNet: multi-scale detail feature fusion encoder–decoder network for self-supervised monocular thermal image depth estimation

Abstract Currently available thermal image depth estimation methods are difficult to efficiently extract fine multi-scale feature information from thermal images and suffer from the problem of blurring details at the edges of the estimated depth map. To address these challenges, this paper proposes MSDFNet, a multi-scale detail feature fusion encoder–decoder network, for self-supervised monocular thermal image depth estimation. The model is based on a channel expansion hourglass residual lightweight feature encoder, which can capture rich and fine-grained multi-scale feature information with low computational effort. MSDFNet utilizes a detail feature weight evaluation decoder to fuse cross-scale features and reevaluate the importance of each feature, thereby emphasizing critical edge information at multiple scales. Additionally, MSDFNet incorporates a depth consistency loss function, which provides self-supervised signals for the detailed features of thermal images and improves the optimization of network performance. The method is applied to the ViViD++ and MS2 datasets and achieves state-of-the-art depth estimation performance compared to existing state-of-the-art algorithms. In the Indoor Dark scenario of the ViViD++ dataset, the Abs Rel, Sq Rel, RMSE, and RMSE log error metric values of MSDFNet are reduced by 6.71%, 11.92%, 9.09%, and 5.73%, respectively, while the accuracy metric values δ < 1.25 i , i = 1 , 2 , 3 were improved by 4.18%, 1.13%, and 0.2%, respectively. In addition, MSDFNet proves its excellent generalization ability on the MS2 dataset. The Abs Rel and RMSE error values in the night scene are reduced by 45.6% and 30.09%, respectively, and the accuracy δ < 1.25 i , i = 1 , 3 is improved by 20.95% and 1.33%, respectively. The Abs Rel and RMSE values in the rainy day scenario are reduced by 1.33% and 1.21%, respectively, and the accuracy δ < 1.25 i , i = 1 , 3 is improved by 0.24% and 0.83%, respectively.

Preparation of carboxylated-silk nanofibers by the one-pot method of maleic acid hydrolysis.

In this study, a maleic acid (MA) hydrolysis one-pot method is proposed to prepare carboxylated silk nanofibers (MA-SNFs). The MA concentration, hydrolysis temperature, and processing time were optimized. Combined with high-pressure homogenization, MA-SNFs with a carboxyl content of 0.617±0.019mmol/g and length of 333±116nm were obtained with a yield of 54.90±1.98% at the optimal conditions: 50wt% of MA concentration, 110°C of hydrolysis temperature and 120min of processing time. The morphology, chemical structure, and crystal structure of raw silk fibroin (SF), acid-hydrolyzed silk fibroin and nanofibers were studied. Furthermore, MA was reused several times with a recovery rate of >94% and maintained almost the same treatment effect. Finally, due to the presence of carboxyl groups, MA-SNF hydrogels were successfully prepared by an acetic coagulation vapor bath which exhibited excellent self-supporting ability and mechanical properties as well as a more sensitive pH response compared with regenerated silk fibroin solution and other non-carboxylated silk nanofibers. The MA-SNF aerogels had the characteristics of light weight, high strength and porous with cross-linked nanostructures.

Robust, lightweight, thermally-insulating felt assembled by hollow and continuous yttria-stabilized ZrO2 fibers

Advanced hollow ceramic fiber materials with excellent thermal insulation and lightweight characteristics are urgently needed in rapidly developing areas such as aerospace. However, creating such hollow ceramic fiber materials has proven to be extremely challenging for current synthetic techniques. Here, we introduce a facile approach for the production of hollow yttria-stabilized zirconia (YSZ) fiber felts through sol–gel solution centrifugal spinning. It is found that the hollow structure can be effectively controlled by adjusting the mass ratios of inorganic salts and PVP, as well as the calcination temperatures. The resulting hollow YSZ felts exhibit low density (38 mg cm−3), robust tensile strength (439.56 ± 24.23 kPa), significant stretchability (>10 %), exceptional oxidation and high-temperature resistance, refractory properties, and outstanding thermal insulation characteristics (0.019 W/m K−1 at room temperature and 0.103 W/m K−1 at 1000℃). The successful synthesis of such fascinating materials may provide new insights into the design and development of hollow ceramic fibers.

3D printed high–strength polyimide aerogel metamaterials for sound absorption and thermal insulation

The structural manufacturing of high–performance polyimide (PI) aerogels is challenging because of the insufficient mechanical strength and rheological properties of sol–gel ink, and PI aerogels have limited application. In this study, a PI aerogel–based Helmholtz resonator (PIHM) and its acoustic metamaterial were innovatively constructed using freeze casting–assisted extrusion printing process and building block–assembly strategy, featuring a micro–perforated panel (MPP) sound–absorbing structure. The PIHM with a density of 0.294g·cm−3 achieved a compressive strength of 10.2MPa because of its periodically distributed honeycomb topology. Moreover, the PIHM not only retained the lightweight and thermal insulation characteristics of aerogels but also exhibited excellent sound–absorption performance because of the dual dissipation of sound waves by the MPP structure and PI aerogel framework. By serially connecting PIHMs with different aerogel pore sizes and leveraging the distinct acoustic properties of the layered structure along with the significant increase in relative mass resistance and acoustic impedance, the acoustic metamaterial achieved absorption coefficient peaks of 0.82–0.91 at 622–726Hz, considerably widening the bandwidth with an absorption coefficient greater than 0.8. Finally, the composite sound–absorbing panel fabricated from a PIHM combined with common building materials demonstrated strong practicability and versatility. This research has pioneered a viable method for manufacturing PI aerogels with functional structures through 3D printing, expanding their application in the field of sound absorption and thermal insulation, and paving the way for the study of aerogel metamaterials in construction.

Investigating the characteristics of alkali-activated fly ash lightweight blocks at lower curing temperatures

Investigating the characteristics of alkali-activated fly ash lightweight blocks at lower curing temperatures

Low light recognition of traffic police gestures based on lightweight extraction of skeleton features

With the increasing demand for information interaction between intelligent vehicles and traffic managers, understanding traffic police commands has become an essential element in achieving human–computer interaction. The aim of this study is to investigate the low light traffic police gesture recognition based on lightweight extraction of skeleton features. A two-stage recognition framework for eight gesture commands is proposed. The first stage utilizes convolutional neural networks to acquire the human skeleton under low light. The second stage utilizes a recurrent neural network to construct a lightweight feature extraction model for gesture recognition. HRnet acquires spatial features of the action, high-resolution and multi-scale fusion circumvents the effect of low-light intensity. Self-attention (SA) mechanism and MobileNet lightweight feature extraction have addressed the time-consuming issue in the feature extraction phase. The improved GRU network is used to extract time domain features, the fusion of spatio-temporal features improves the recognition accuracy. For training and validation, a gesture dataset (TPGD) containing 3391 gesture instances is constructed. The proposed method obtains 94.75% accuracy in the TPGD test set, demonstrating its effectiveness. This study holds significant importance for enhancing the level of intelligence in traffic management, promoting the development of intelligent transportation systems, and ensuring road traffic safety.

Mesoscopic simulation study on density gradient metallic foam sandwich panels under hypervelocity impact

The high stiffness of sandwich panel shield ensures the survival of satellites and spacecrafts, making them extensively utilized in practical aerospace engineering. Metallic foams are exceptionally appropriate for spacecraft debris shields owing to their light weight and superior energy absorption characteristics. The internal mesostructure of a metallic foam plays a crucial role in determining its protective performance. At the mesoscale, the density gradient metallic foam exhibits a greater potential for protection compared to uniform metallic foam under hypervelocity impact. Therefore, this study investigates the behavior of density-gradient foams under hypervelocity impact. By leveraging three-dimensional Voronoi tessellation in conjunction with the background mesh-mapping algorithm, this study constructed mesoscopic finite element models of the layered and continuous-density gradient metallic foam, considering the internal structure of randomness. Subsequently, the Finite Element-Smoothed Particle Hydrodynamics (FE-SPH) adaptive method in LS-DYNA was employed to conduct numerical simulations of the hypervelocity impact. First, the simulation was validated through a comparison with the experiment. Based on the results of the numerical simulations, the characteristics of the debris cloud and the damage within the foam were analyzed. It was determined that the protection mechanism of the density gradient foam sandwich panel under hypervelocity impact involved a coupling effect between the domino and microchannel effects. The different damage characteristics of layered density gradient foam sandwich panels were analyzed. According to this mechanism, foam sandwich panels with different density-gradient configurations were designed and their protective performances were compared to determine the optimal density-gradient configuration to provide valuable insights into the optimal design of protective structures.

Flexural Behavior of Innovative Glass Fiber-Reinforced Composite Beams Reinforced with Gypsum-Based Composites.

Glass Fiber-Reinforced Composite (GFRP) has found widespread use in engineering structures due to its lightweight construction, high strength, and design flexibility. However, pure GFRP beams exhibit weaknesses in terms of stiffness, stability, and local compressive strength, which compromise their bending properties. In addressing these limitations, this study introduces innovative square GFRP beams infused with gypsum-based composites (GBIGCs). Comprehensive experiments and theoretical analyses have been conducted to explore their manufacturing process and bending characteristics. Initially, four types of GBIGC-namely, hollow GFRP beams, pure gypsum, steel-reinforced gypsum, and fiber-mixed gypsum-infused beams-were designed and fabricated for comparative analysis. Material tests were conducted to assess the coagulation characteristics of gypsum and its mechanical performance influenced by polyvinyl acetate fibers (PVAs). Subsequently, eight GFRP square beams (length: 1.5 m, section size: 150 mm × 150 mm) infused with different gypsum-based composites underwent four-point bending tests to determine their ultimate bending capacity and deflection patterns. The findings revealed that a 0.12% dosage of protein retarder effectively extends the coagulation time of gypsum, making it suitable for specimen preparation, with initial and final setting times of 113 min and 135 min, respectively. The ultimate bending load of PVA-mixed gypsum-infused GFRP beams is 203.84% higher than that of hollow beams, followed by pure gypsum and steel-reinforced gypsum, with increased values of 136.97% and 186.91%, respectively. The ultimate load values from the theoretical and experimental results showed good agreement, with an error within 7.68%. These three types of GBIGCs with significantly enhanced flexural performance can be filled with different materials to meet specific load-bearing requirements for various scenarios. Their improved flexural strength and lightweight characteristics make GBIGCs well suited for applications such as repairing roof beams, light prefabricated frames, coastal and offshore buildings.

Mine underground object detection algorithm based on TTFNet and anchor-free

Abstract To solve the problem of poor object detection effect caused by uneven light and high noise in underground mines, this study proposes a TTFNet (training-time-friendly network)-based object detection algorithm for underground mines. First, CenterNet and TTFNet algorithms are introduced, then pooling is introduced into CSPNet basic structure to design a lightweight feature extraction network, at the same time optimizing the feature fusion way in the original algorithm, optimizing residual shrinkage network structure, and introducing it into object detection task. Experiments were conducted on the established underground data set. The results show that compared with the original algorithm, our proposed algorithm can still maintain similar accuracy while significantly reducing model parameters; compared with other anchor-based detection algorithms, it has achieved similar overall performance.

Effect of Heat Treatment on Mechanical Characteristics and Microstructure of Aluminium Alloy AA6061

Aluminium alloy 6061 (AA6061) is widely used across various industries. It has untapped potential in diverse sectors due to its exceptional strength, lightweight characteristics and corrosion resistance. It finds value in aerospace, automotive, marine, sports gear, architecture, renewable energy, electronics, healthcare devices, packaging and defence. This study investigates the impact of heat treatment on AA6061 on its microstructural and mechanical properties (tensile strength and microhardness). The main goals of this study involve assessing the mechanical properties of AA6061 after subjecting it to solution heat treatment at various temperatures and time intervals. Furthermore, the study aims to understand the relationship between microstructural alterations and mechanical properties in AA6061 following heat treatment. A combination of tensile tests, hardness tests, and scanning electron microscopy (SEM) to examine the material's microstructure. The findings reveal that the sample subjected to the heat treatment of 450°C for 60 minutes exhibited the highest tensile strength, boasting an impressive 130.9 MPa, coupled with a remarkable hardness of 19.8 HRB. Conversely, the sample treated at 550°C for 60 minutes displayed the most significant elongation percentage, reaching a remarkable 46%. These results can be explained by analysing the microstructure. Finer grain boundaries led to increased tensile strength and hardness, while a larger dendritic microstructure was linked with lower tensile strength and hardness. Remarkably, the second one led to higher elongation percentages. Therefore, precise control of the heating temperature and duration is crucial to enhance the performance of aluminium alloy, AA6061. With continuous research, alloy improvement and creative design, AA6061 have the potential to enhance performance and efficiency in these areas, driving technological advancements and better products.

Cluster analysis of acoustic emission signals for bending damage mechanism of flax fiber reinforced composites under hydrothermal aging

Despite the eco-friendly and lightweight features, natural fiber reinforced composites (NFRCs) are more susceptible under hydrothermal conditions than artificial synthesis fibers reinforced composites. Cluster analysis on damage evolution of hydrothermal aged NFRCs by using acoustic emission techniques and machine learning also remains scare. To figure out these challenges, unidirectional flax fiber reinforced composites (FFRCs) were prepared and submerge into distilled water at three different temperatures to systematically study the influence and damage mechanism under hydrothermal aging by acoustic emission (AE). After hydrothermal aging for 60 days, AE signals revealed that the FFRCs were more likely to cause defects in the early loading stage at the higher temperature. Cluster analysis showed an increase in the proportion of delamination during the process of failure. The proportion of fiber breakage AE signals with higher PF also increased. Microscope view proved the bending damage behavior of FFRCs after hydrothermal aging.

Shuffle-PG: Lightweight feature extraction model for retrieving images of plant diseases and pests with deep metric learning

Disease and pest diagnosis plays a critical role in managing and controlling the damage caused by plant diseases and pests. This study employs a content-based image retrieval approach to diagnose diseases and pests, suggesting similar candidate images to assist in decision-making. Previous research in disease and pest diagnosis has relied on large models for feature extraction, posing challenges for deployment in resource-constrained environments like mobile devices. To address these challenges, this study proposes a lightweight feature extraction model, Shuffle-PG, which integrates the computationally efficient ShuffleNet v2 model with pointwise group convolution. Additionally, a method for fine-tuning the feature extraction model using deep metric learning based on contrastive loss was developed to enhance discriminative feature extraction. To validate the effectiveness of the proposed method, experiments were conducted using plant disease and pest datasets specifically collected for this study. The results show that the proposed Shuffle-PG model uses approximately 20 times fewer parameters and reduces computational costs by an order of magnitude compared to existing benchmark models, while achieving higher mean average precision scores of 97.7 % and 98.8 % for the disease and pest datasets, respectively.

Wind-Induced Vibration Control of High-Rise Buildings with Double-Skin Façades Using Distributed Multiple Tuned Façade-Dampers-Inerters

To address the shortcomings of tuned mass dampers (TMD), such as excessive internal space occupation and overlarge physical mass, this paper proposes a tuned façade damper inerter (TFDI) that utilizes parts of the outer façades of double-skin façades (DSF) as damping mass, capitalizing on the lightweight and efficient characteristics of inerters. The TFDI effectively resolves the challenge of multi-layer connections of inerters in high-rise buildings by utilizing corridor space. By vertically distributing TFDIs, a distributed multiple TFDI (d-MTFDI) system is formed. The configuration and motion of equations of this system are presented, and the control effectiveness is validated using wind tunnel test data. Two tuning modes are further proposed: unified tuning mode and distributed tuning mode. For the unified tuning mode, analytical expressions for optimal tuning frequency and damping ratio are derived; for the distributed tuning mode, numerical optimization methods are employed to determine the optimal tuning frequency range and damping ratio. Comparative results indicate that the distributed tuning mode achieves higher control efficiency than the unified tuning mode, with a significant reduction in the required optimal damping ratio. Furthermore, comparisons with d-MTMD demonstrate that d-MTFDI significantly enhances wind-induced vibration control performance.

Reliability and Fire Performance of Built-up (Battened) Cold-formed Steel Columns: Numerical study

Cold-formed steel (CFS) columns play a crucial role in modern construction due to their lightweight, prefabricated, and recyclable characteristics, contributing significantly to structural safety and reliability. However, unprotected CFS columns lose their load-bearing capacity within 10-15 minutes under fire conditions. This study reviews recent advancements aimed at improving the fire resistance of CFS columns, focusing on factors such as steel thickness, grade, cross-sectional shape, and fire protection materials. Using ABAQUS software, validated against experimental data, parametric studies reveal that thicker sections and higher-grade steels enhance fire resistance, delaying structural failure. Fire protection strategies, such as plasterboard encasement, further bolster safety. The Complex cross-sectional shape demonstrated the highest load capacity (178.51 kN), while G450 steel outperformed other grades in both load capacity and fire resistance. Columns with 1.95 mm thickness provided the longest failure time (73.49 minutes).