Truss Design Research Articles

Topology optimization and diverse truss designs considering nodal stability and bar buckling

Ensuring the bar stability is crucial in truss design. However, unstable nodes lacking lateral support complicate the calculation of bar buckling lengths. bar buckling constraints make the feasible region of optimization problems concave, further complicating the solution process. Moreover, traditional truss optimization methods typically yield a single optimal result, limiting the design options available to engineers. In this study, nominal disturbing load conditions are applied to the structure to eliminate unstable nodes, thereby ensuring accurate buckling length calculations. Additionally, the advanced allowable stress iteration (AASI) approach is proposed to address truss optimization problems with bar buckling constraints. To generate geometrically diverse and structurally competitive trusses, we develop a bar-length penalty (BLP) method. To validate the effectiveness of these methods, three numerical studies are presented. The results demonstrate that the proposed AASI approach produces optimized structures free from unstable nodes and bar buckling. Compared to structures optimized using the allowable stress iteration (ASI) method, which can only optimize for a single load case, those designed with the new approach maintain bar stability under all load conditions. Compared to the traditional method of increasing the cross-sectional area of unstable bars to ensure stability, much lighter trusses can be generated while maintaining the same load-bearing capacity. By applying the proposed BLP method to penalize specific bars, it is possible to achieve optimized structures with distinct topologies, similar masses, and equivalent load-carrying capacities. The proposed methods provide valuable insights for truss optimization design.

An efficient weighted slime mould algorithm for engineering optimization

In engineering applications, optimal parameter design is crucial. While Slime Mould Algorithm (SMA) excels in parameter discovery under constrained conditions, it faces challenges in achieving global convergence and avoiding local opsecttimal traps in complex tasks. This paper introduces an enhanced variant of SMA, termed CCHSMA, which integrates a Chaotic Local Search (CLS) mechanism to improve initial population diversity and combines Covariance Matrix Adaptation (CMA) and Harris Hawks Optimization (HHO) strategies to enhance global search efficiency. CCHSMA aims to improve search quality and reduce the likelihood of getting trapped in local optima. We evaluated CCHSMA's effectiveness by benchmarking it against the standard SMA and its variants using 30 CEC2017 test functions, and compared its performance with seven notable meta-heuristic algorithms and ten advanced swarm intelligence variants. The experimental results demonstrate that CCHSMA outperforms the other algorithms tested on the benchmark functions. To further validate its practical utility, CCHSMA's performance was also benchmarked against leading algorithms in real-world engineering applications. This paper uses detailed statistical methods, including the Wilcoxon signed-rank test and the Friedman test, to validate the comparative results. Our findings show that CCHSMA outperforms other algorithms in solving complex engineering optimization problems such as tension/compression spring design, pressure vessel design, and three-bar truss design, proving to be a robust tool for complex engineering optimization. Its enhanced initial population diversity and improved global search efficiency are essential for effectively addressing diverse engineering challenges.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

A fast search method of buckling load for telescopic boom structure

The telescopic boom structures are extensively utilized in engineering applications involving large mobile cranes, aerial work platforms of significant height, and similar mechanical devices. These are predominantly fabricated using solid-webbed box types, latticed truss designs, or a combination thereof. Under load, they exhibit pronounced geometric nonlinear effects, with the relationship between load and displacement demonstrating marked nonlinear characteristics. This paper focuses on the geometric nonlinear modeling of slender multi flexible beam structures. The overall structural system is divided into several substructures, and the concept of multi flexible system modeling is borrowed to establish a follow-up connected body foundation on each substructure. By decomposing the node displacement and rotation in the substructure, the large displacement and large rotation of the node are decomposed into rigid body motion of the connected base and small displacement and small rotation relative to the connected base, effectively representing the large displacement and large rotation of the substructure as rigid body motion of the connected base. A modeling method for the geometric nonlinear analysis of slender and flexible multibody beam structures is proposed. By decomposing the nodal displacement and rotation within the substructures, the large displacement and rotation of the nodes are broken down into the rigid body motion of the body-fixed coordinate and small displacement and rotation relative to the body-fixed coordinate. This approach establishes the application conditions for calculating the structure’s virtual power of deformation using traditional beam elements with linear strain. A method for solving the instability load of slender and flexible multibody structures is proposed. This method integrates the characteristics of the system’s nonlinear equilibrium equations with respect to load, by deriving the system equations with respect to a load increment control parameter. This paper converts nonlinear equilibrium equations into first-order ordinary differential equation (ODE). The method proposed in this paper provides a more efficient solution for the buckling load of the telescopic boom. It can be used to systematically and quickly calculate the structure of the telescopic boom.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Endodontic Access Cavity Design and Fracture Resistance: A Systematic Review and Meta-Analysis of Conventional vs. Newer Access Cavity.

The era of minimally invasive dentistry has led to the development of new access cavity designs. The impact of various access cavity designs on the fracture resistance of teeth has been extensively studied. The primary aim of this systematic review and meta-analysis is to evaluate and compare the effects of recent modifications in endodontic access cavity design- specifically, conventional, conservative, and truss designs on tooth fracture resistance. Three independent reviewers searched studies across six different databases (PubMed, Scopus, EBSCOhost, BVS, Wiley, and Google Scholar) from January 2000 to July 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The articles were then screened using strict inclusion and exclusion criteria. A quality assessment was performed using a modified version of the quality assessment of in-vitro studies according to the QUIN (Quality Assessment Tool For In Vitro Studies) tool, categorizing the selected articles into low, moderate, and high risk of bias. Quantitative data synthesis was conducted to combine equivalent results using STATA. Forest plots were created with the level of significance set at 0.05 (p = 0.05). Out of 243 articles, 14 met the strict inclusion criteria. Among the selected articles, 11 showed a low risk of bias and three showed a moderate risk. The meta-analysis revealed that fracture resistance of conservative and truss access designs is significantly higher than that of conventional endodontic access, with a standardized mean difference (SMD) of 2.61 (95% 1.47 to 3.74; p-values <0.001) and SMD = -1.26 (95% confidence interval (CI): -1.81 to 0-0.71; p<0.001). The heterogeneity (I²) values for these comparisons were 92% and 65.6%, respectively. The extent of the access cavity has a substantial impact on tooth fracture resistance. Newer conservative and truss endodontic access designs offer better fracture resistance compared to conventional endodontic access.

Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications.

The dung beetle optimization (DBO) algorithm is acknowledged for its robust optimization capabilities and rapid convergence as an efficient swarm intelligence optimization technique. Nevertheless, DBO, similar to other swarm intelligence algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose the Move-to-Escape dung beetle optimization (MEDBO) algorithm in this paper. MEDBO utilizes a good point set strategy for initializing the swarm's initial population, ensuring a more uniform distribution and diminishing the risk of local optima entrapment. Moreover, it incorporates convergence factors and dynamically balances the number of offspring and foraging individuals, prioritizing global exploration initially and local exploration subsequently. This dynamic adjustment not only enhances the search speed but also prevents local optima stagnation. The algorithm's performance was assessed using the CEC2017 benchmark suite, which confirmed MEDBO's significant improvements. Additionally, we applied MEDBO to three engineering problems: pressure vessel design, three-bar truss design, and spring design. MEDBO exhibited an excellent performance in these applications, demonstrating its practicality and efficacy in real-world problem-solving contexts.

Constructability-driven design of frame structures with state-space search methods

In the design of frames and trusses, the relationship between the structural form, the construction sequence, and the structural behavior during construction is rarely systematically considered. This paper proposes a method to systematically consider constructability in design, specifically focusing on minimizing the maximum displacement or stress during construction. The paper presents the formulation of the optimal assembly sequencing problem as a state-space search with a unique design of the search heuristic and constraint bounding schemes. It proposes three variants of heuristic search algorithms for finding a feasible sequence, a diverse set of feasible sequences, and an optimal sequence, respectively. These algorithms are tested on a case study structure to showcase their ability to navigate the combinatorial space of assembly sequences, and provide means for designers to incorporate sequence-related performance into conceptual structural design.

Optimal truss design with MOHO: A multi-objective optimization perspective.

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO's solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

Novel constant-height deployable mechanisms with only revolute joints and constructed deployable ring trusses for satellite mesh reflector antennas

Deployable mechanisms play an increasingly crucial role in aerospace, yet their design still faces challenges: 1) The folded height increases while its deployed height decreases, which is not conducive to improving the folding performance; 2) Compared with revolute joints, prismatic joints have relatively weak reliability in complex space environments. To address the above challenges, novel constant-height deployable mechanisms with only revolute joints are proposed in this paper. The deployable mechanisms exhibit one degree of freedom, enabling them to attain modular expansion. Subsequently, the mobility properties of these deployable mechanisms are substantiated through screw theory. Assembly strategies for constant-height deployable mechanisms are proposed, and deployable ring trusses are further constructed to provide support structures for satellite mesh reflector antennas. The kinematics are analyzed to investigate the deployable characteristics of the proposed deployable mechanisms and ring trusses. The prototypes of a deployable mechanism and a ring truss are established to validate the designed deployable mechanisms. This work contributes novel types of deployable mechanisms with potential advantages, aiming to function as valuable references for the design and analysis of deployable trusses.

Theoretical and experimental research on the rocking truss system with post-tensioned rocking connection

The weak story failure often occurs in steel frame structures and the collapse risk of the frames will increase because of concentrated plastic deformation in the soft story. The work presented in this paper aims to avoid the soft story mechanisms and the improve self-centering capacity of steel frames by employing the innovative rocking truss. Compared with the previous rocking system, it is more flexible in application and suitable for retrofit of existing buildings. The theoretical approach to quantifying the stiffness demand of the rocking truss was proposed and presented two critical parameters (equivalent bending stiffness ratio η and the rotation stiffness ratio λ) for the design of the rocking truss. The 1/3 scaled steel frame with rocking truss (SFRT) was tested and theoretical analysis results were validated by test results. The finite element model of SFRT was built following the boundary conditions of the test specimen. The numerical results are consistent with the experimental observations. The nonlinear static and dynamic analysis results of the SFRT were compared with those of the same steel frame (SF) with a bracing system. It was found that the rocking truss can reduce the maximum and residual drifts of the main frame as expected, and the plastic development of the RBS at the beam end in SFRT is less than that in SF with a bracing system. The parametric analysis of the rocking connections with different values of prestressing force was carried out. The results confirm that the rotational stiffness and load-bearing capacity of the rocking connection can be accurately evaluated by the theoretical force-displacement curve. The effect of the equivalent bending stiffness ratio η and the rotation stiffness ratio λ on the lateral deformation pattern of structures was discussed by theoretical analysis, and the design procedure of the rocking truss is recommended to satisfy the demand of uniform inter-story drift and self-centering capacity.

A population-based DNN-augmented optimization method for designing truss structures

Truss design problems have been extensively investigated for various purposes. How to efficiently optimize truss structures and obtain diverse optimized designs is still a challenging task. In this paper, a population-based DNN (deep neural network) -augmented method is proposed to address this issue. In the proposed method, truss design problems are firstly transformed into a DNN-based design loss minimization problem. The DNN model is gradually generated in an adaptive updating manner, and is used to indicate the truss design problem. In addition, the transformed minimization problem can be efficiently solved by DNN computation and a population of trusses can be optimized accordingly. Through the investigation of four truss design problems (two standard optimization problems and two optimization problems considering the design diversity), the effectiveness of the proposed method is validated. In addition, three aspects affecting the performance of the proposed method are also investigated, and the robustness and applicability of the proposed method are demonstrated.

Learning cooking algorithm for solving global optimization problems

In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

A Probabilistic Approach to Design of Steel Trusses with Incomplete Statistical Data

A Probabilistic Approach to Design of Steel Trusses with Incomplete Statistical Data

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

A review of nature-inspired algorithms on single-objective optimization problems from 2019 to 2023

The field of nature inspired algorithm (NIA) is a vital area of research that consistently aids in solving optimization problems. One of the metaheuristic algorithm classifications that has drawn attention from researchers in recent decades is NIA. It makes a significant contribution by addressing numerous large-scale problems and achieving the best results. This research aims to identify the optimal NIA for solving single-objective optimization problems. The NIA discovered between 2019 and 2023 is presented in this study with a brief description. About 83 distinct NIAs have been studied in this study in order to address the optimization issues. In order to accomplish this goal, we have taken into consideration eight real-world single-objective optimization problems: the 3-bar truss design problem, the rolling element bearing, the pressure vessel, the cantilever beam, the I beam, the design of a welded beam, and the design of a spring. Based on a comparative study and bibliographic analysis, we have determined that two algorithms—the flow direction algorithm, and prairie dog optimization—give us the best results and optimal solutions for all eight of the engineering problems listed. Lastly, some perspectives on the limitations, difficulties, and future course are provided. In addition to providing future research guidelines, this will assist the novice and emerging researcher in providing a more comprehensive perspective on advanced NIA.

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

A Novel Gaussian Process Surrogate Model with Expected Prediction Error for Optimization under Constraints

Optimization, particularly constrained optimization problems (COPs), is fundamental in engineering, influencing various sectors with its critical role in enhancing design efficiency, reducing experimental costs, and shortening testing cycles. This study explores the challenges inherent in COPs, with a focus on developing efficient solution methodologies under stringent constraints. Surrogate models, especially Gaussian Process Regression (GPR), are pivotal in our approach, enabling the approximation of complex systems with reduced computational demand. We evaluate the efficacy of the Efficient Global Optimization (EGO) algorithm, which synergizes GPR with the Expected Improvement (EI) function, and further extend this framework to Constrained Expected Improvement (CEI) and our novel methodology Constrained Expected Prediction Error (CEPE). We demonstrate the effectiveness of these methodologies by numerical benchmark simulations and the real-world application of optimizing a Three-Bar Truss Design. In essence, the innovative CEPE approach promises a potent balance between solution accuracy and computational prowess, offering significant potential in the broader engineering field.

Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design

This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.

WrapToR shells: Concept introduction, experimental testing, and design space exploration

This work introduces a novel truss stiffener concept for lightweight structural panels as an alternative to sandwich panels or stringer stiffened panels. The Wrapped Tow Reinforced (WrapToR) truss concept is modified to create stiffened composite panel structures (referred to here as WrapToR Shells). A prototype WrapToR Shell unit cell is manufactured and tested under three-point bending, where it demonstrates high levels of specific stiffness and strength. This experimental result is benchmarked against idealised sandwich panels via a low fidelity comparative analysis, and the study shows that even a best-case sandwich panel of equivalent mass under a representative bending load has an 83% increase in displacement, highlighting the excellent stiffness truss reinforced panels can provide. The design space available to the truss reinforced panels is then explored using finite element analysis and a simple parameter sweep of key truss design variables. A Pareto frontier between the competing objectives of minimising mass and maximising rigidity is shown, and global trends in performance are highlighted. It is observed that increasing the size of the truss profile or longitudinal member has a near linear increase on EI/mass, whereas for increasing shear member angle the inverse is true. For increasing shear member diameter, a less obvious effect on EI/mass is seen, indicating a more complex relationship with the other design variables.