Tensile Membrane Structures Research Articles

Enhancing outdoor comfort through tensile membrane structures and pavement Surfaces: A case study report in Évora, Portugal

The Universal Thermal Climate Index (UTCI), developed in 2009 through collaboration among experts in human thermophysiology, aims to evaluate the relationship between the outdoor environment and human well-being. The case study focused on assessing the impact of shadowing and thermal masses on occupant outdoor thermal comfort as measured by the UTCI. The study specifically examined the effects of various designs of tensile membrane structures (TMS) on a U-shaped patio enclosed by nine maritime containers arranged in three groups of three levels in Évora, Portugal. The model tested fourteen TMS with varying slopes, overlap angles, lengths, repetitions, and four different pavements with distinct thermal mass layouts. The methodology employed dynamic simulation software, the energy engine EnergyPlus 2022, to assess the monthly variation of UTCI temperatures, comparing them to the scenario of an uncovered patio for each pavement configuration. The best-performing patio follows a TMS with a slope of 38.2°, an overlap of 74.4°, a length of 3.88 m, and five repetitions, accompanied by a full-concrete pavement. During winter (>9°C), the best-performing TMS underperforms against the uncovered patio by adding 1.1 °C but during summer (>26 °C), outperforms by reducing user discomfort by 9.4 °C. The global temperature deviation with the best-performing TMS sets at 3.3 °C, where the uncovered patio at 11.8 °C, is out of the comfort gap (9 °C to 26 °C) by 8.3 °C, benefiting user comfort across the year. The study highlights the potential of optimizing outdoor spaces based on UTCI measurements to create comfortable and user-friendly environments.

Gradient enhanced physics-informed neural network for iterative form-finding of tensile membrane structures by potential energy minimization

Tensile membrane structures (TMS) are overwhelmingly popular as practical design solutions for large roofing structures. The TMS designer, however, has to perform a “form-finding” as the stable configuration of TMS are not known beforehand. Although many form-finding approaches are currently available, most suffer from various implementation complications, particularly with regards to convergence issues stemming from improper hyper-parameter selection, mesh distortion and the choice of initial reference configuration. In this study, a new iterative form-finding methodology is proposed and implemented by the direct minimization of a potential energy functional. A mesh-free approach based on gradient enhanced physics-informed neural network (gPINN) is employed, which expands the applicability to most common TMS types while eliminating the aforementioned issues faced by traditional mesh-based algorithms. Furthermore, a new method for selecting the initial reference configuration using transfinite interpolation is proposed that is significantly better suited to form-finding than conventional approaches. Additionally, approximate distance functions are employed to impose exact boundary conditions. Extensive numerical case studies on frame- and cable-supported TMS, along with multi-dimensional parametric variations, show the efficacy and robustness of the proposed form-finding framework.

Dependance of Membrane Stress on the Supports Geometry of the Barrel-Vault Shaped Tensile Membrane Structures

Dependance of Membrane Stress on the Supports Geometry of the Barrel-Vault Shaped Tensile Membrane Structures

One-Way FSI Coupling with Steady-State and Transient CFD Analysis for the Umbrella Form of Tensile Membrane Structure

One-Way FSI Coupling with Steady-State and Transient CFD Analysis for the Umbrella Form of Tensile Membrane Structure

Form-finding of frame-supported non-minimal tensile membrane structures for anisotropic prestress using physics-informed neural networks

Form-finding of frame-supported non-minimal tensile membrane structures for anisotropic prestress using physics-informed neural networks

Intelligent damage classification for tensile membrane structure based on continuous wavelet transform and improved ResNet50

Traditional structural dynamic detection methods are difficult to accurately identify damage features in vibration signals from tensile membrane structures, thus a damage classification method for membrane materials based on continuous wavelet transform and an improved ResNet50 is proposed. This damage classification method takes ResNet50 as the backbone, and ResNet50 is improved by embedding the convolutional block attention modules and parameter-transfer learning. Firstly, the planar tensile membrane structure vibration test bench is built, and vibration acceleration signals of four damaged membrane materials under three vibration excitations are collected. Secondly, continuous wavelet transform is applied to perform time–frequency conversion on the raw signals. Finally, the proposed method is used for time–frequency image classifications, and compares with other mainstream networks. The feature mappings are discussed based on Grad-CAM. The results show that the proposed method can achieve accurate damage classification, and the precisions on three custom datasets are respectively 99.67%, 99.74%, and 97.20%.

Form-finding of tensile membrane structures with strut and anchorage supports using physics-informed machine learning

Due to their aesthetic appeal, light weight, and capacity to span vast expanses of space, tensile membrane structures (TMS) are very popular these days. TMS are usually supported by frames and boundary edge cables, but sometimes also by struts and anchorage supports. Traditional mesh-based form-finding algorithms, typically developed for the first two boundary types, suffer from convergence issues stemming from the choice of initial shape, choice of pseudo material properties, mesh distortion, hyper-parameter selection, etc. In order to address the issues with conventional mesh-based form-finding frameworks as stated above, a mesh-free form-finding framework for TMS is proposed utilizing a gradient-enhanced physics-informed neural network. The theory of functional connections is employed to precisely meet the TMS boundary conditions without depending on an additional penalty function used in conventional physics-informed neural network frameworks, thereby eliminating errors related to boundary constraints. The challenge of incorporating compressive strut supports in TMS form-finding is addressed by employing the principle of inverted forces, and modelling them as tension elements. 24 numerical studies, with a diverse set of TMS shapes and boundary supports, including benchmark problems, are presented demonstrating the effectiveness and accuracy of the proposed method. This method also allows the designer to explore various parametric (architectural) designs of TMS.

The tensile strength test and result comparison of new transparent architecture membrane material STFE

ABSTRACT STFE(structural, transparent, fluorinated, envelope) membrane material is a new type of transparent architecture membrane material. Compared with general architecture membrane materials, which have the disadvantage of cannot balance strength and transparency, STFE has both high transparency and certain tensile strength. In this study, tensile strength tests were conducted on this new type of membrane material, and data obtained from different test methods, such as clamping test, winding test, and dumbbell-shaped test, were compared, and the mechanism for the differences was analyzed. The results showed that the winding-type test with smooth surface contact yielded the highest and most accurate strength data, clamping tests due to the tendency of the specimen to slip out of the clamping end, dumbbell tests due to the stress concentration effect, and winding tests with rough surface contact due to material damage caused by pressure, all reduce the measured tensile strength values. The measured tensile strength of STFE membrane material was 4241N/5 cm in the warp direction and 4335N/5 cm in the weft direction. This indicates that STFE membrane material has sufficient strength to be used in tensile membrane structures.

Irradiation Analysis of Tensile Membrane Structures for Building-Integrated Photovoltaics

A dynamic development in building-integrated photovoltaics (BIPVs) has been observed in recent years. One of the manifestations of this trend is the integration of photovoltaic cells with tensile membrane structures, including canopies. Such solutions bring mutual benefits—the roofs provide a potentially large area for the application of photovoltaic cells while contributing to the improvement of the energy efficiency of the building. However, what is lacking is thorough research on the most favourable photovoltaic cell exposure within these roofs. This paper investigates the optimal position of photovoltaic cells in terms of energy gains related to exposure to solar radiation. Hypar geometries were simulated as the most characteristic of tensile membrane roofs and, simultaneously, the least obvious in the research context. Simulations were performed for 54 roof samples with the following geometric variables: roof height (1.0, 3.0 m) and membrane prestress (1:3, 1:1, 3:1). The research was conducted for three roof orientations defined by azimuth angles of 0, 22.5, and 45 degrees and three geographic locations, Oslo, Vienna, and Lisbon, representing Northern, Central, and Southern Europe, respectively. The Sofistik and Rhino + Ladybug software were used to create models and simulations. The study results show significant differences in the roof irradiation and, consequently, the optimal location of BIPVs depending on the above variables. Generally, it is the curvature that is the most important variable-less curved roofs are more irradiated and thus more suitable for BIPVs. Prestress and the azimuth angle are of lesser significance, but defining the optimal use of a BIPV depends on the adopted scenario regarding the percentage of membrane coverage with PVs—other recommendations concern the strategy of total or partial roof coverage with PV cells. The difference between optimally and incorrectly designed roofs may amount to a 50% electricity gain from PV cells.

Form-finding and determining geodesic seam lines using the updated weight method for tensile membrane structures with strut and anchorage supports

Form-finding and determining geodesic seam lines using the updated weight method for tensile membrane structures with strut and anchorage supports

Analysis and prestress optimisation of membrane structures with optimal fabric alignment using modified energy minimisation

The use of light-weight tensile membrane structures (TMS) has become popular, particularly for covering vast areas. The behaviour of these structures when subjected to external actions is dependent on the initial shape, prestress and material property. The load analysis and optimisation of TMS are still challenging due to different factors including membrane wrinkling, choice of material fibre orientation and practical design considerations. This paper formulates the load analysis process by defining a modified energy functional with respect to the material fibre basis direction. The energy functional is directly minimised using a quasi-Newton solver addressing convergence issues. Improved algorithms to find the wrinkling criteria and direction are proposed. The choice of the material fibre direction and optimal seam partitioning based on the principal curvature are also developed. A practical optimisation framework for TMS is formulated based on available guidelines. The integrated methodology is successfully tested and demonstrated on a wide variety of case studies and benchmark problems. The optimal material fibre basis provides a stiffer (hence, more stable) structure and reduces the induced shear. The integrated solution along with the optimisation framework provide the end user with an effective method and tool for the design of TMS.

Two-stage intelligent detection system of pixel-level classification and damage type recognition for membrane materials

Damaged membrane materials would destroy the structural stress balance and have an extremely harmful impact on the appearance and safety of the cable membrane structure. The main difficulty of the membrane damage detection using the deep learning algorithm is that there are many useless redundant data due to the monotonicity of membrane images and the diversity of damage types. Therefore, a deep learning dataset of normal and damaged membrane images is established through test platform of plane bidirectional tensile membrane structure, and a two-stage intelligent detection system of pixel-level classification and damage type recognition for membrane materials is proposed. The proposed system includes two parts: (1) The normal and damaged membrane images classification method is developed based on pixel difference, which is used to determine the normal membrane material threshold. (2) YOLOv5s is trained to achieve the rapid and accurate identification of the damaged membrane dataset. The results show that the pixel percentage of normal membrane images is [0,0.0073]. Moreover, the material object detection results show that YOLOv5s offers 98.50% excellent recognition accuracy of the damaged membrane images, and verify the applicability of deep learning to the research of damage to membrane materials.

Learning non-stationary and discontinuous functions using clustering, classification and Gaussian process modelling

Surrogate models have shown to be an extremely efficient aid in solving engineering problems that require repeated evaluations of an expensive computational model. They are built by sparsely evaluating the costly original model and have provided a way to solve otherwise intractable problems. A crucial aspect in surrogate modelling is the assumption of smoothness and regularity of the model to approximate. This assumption is however not always met in reality. For instance in civil or mechanical engineering, some models may present discontinuities or non-smoothness e.g., in case of instability patterns such as buckling or snap-through. Building a single surrogate model capable of accounting for these fundamentally different behaviours or discontinuities is not an easy task. In this paper, we propose a three-stage approach for the approximation of non-smooth functions which combines clustering, classification and regression. The idea is to split the space following the localized behaviors or regimes of the system and build local surrogates that are eventually assembled. A sequence of well-known machine learning techniques are used: Dirichlet process mixtures models (DPMM), support vector machines and Gaussian process modelling. The approach is tested and validated on two analytical functions and a finite element model of a tensile membrane structure.

Fast Aero-Structural Model of a Leading-Edge Inflatable Kite

Soft-wing kites for airborne wind-energy harvesting function as flying tensile membrane structures, each of whose shape depends on the aerodynamic load distribution and vice versa. The strong two-way coupling between shape and loading poses a complex fluid-structure interaction problem. Since computational models for such problems do not yet meet the requirements of being accurate and at the same time fast, kite designers usually work on the basis of intuition and experience, combined with extensive iterative flight testing. This paper presents a fast aero-structural model of leading-edge inflatable kites for the design phase of airborne wind-energy systems. The fluid-structure interaction solver couples two fast and modular models: a particle system model to capture the deformation of the wing and bridle-line system and a 3D nonlinear vortex step method coupled with viscous 2D airfoil polars to describe the aerodynamics. The flow solver was validated with several wing geometries and proved to be accurate and computationally inexpensive for pre-stall angles of attack. The coupled aero-structural model was validated using experimental data, showing good agreement in the deformations and aerodynamic forces. Therefore, the speed and accuracy of this model make it an excellent foundation for a kite design tool.

Hyperbolic Paraboloid Tensile Structure—Numerical CFD Simulation of Wind Flow in RWIND Software

Tensile membrane structures combine a prestressed roofing envelope material and supporting elements. To design these structures, there is a set of recommendations in the European Design Guide for Tensile Surface Structures and some other national standards. However, currently, there is no official standard related to the design process of tensile structures in the European Union. The structure studied in this project is considered as permanent roofing of an external testing device in the shape of a simple hyperbolic paraboloid without enclosing walls. Snow and wind loads were analyzed as the most critical types of loading in the location. Determining the value of the snow load is relatively simple according to the European standard. However, in the case of the wind load, this shape is not considered in the European standard and needs to be solved experimentally or by numerical simulation in a wind tunnel. The present contribution focuses on numerical analysis of the wind flow in RFEM software and simulation of the wind tunnel in RWIND software.

Form-finding analysis and optimization of cable membrane structure based on iterative correction of tension process

The current form-finding analysis of cable membrane structures does not consider the actual tensioning process, thus leading to a difference between the form-finding prestressed and tensioning working states. It will affect the morphology accuracy of membrane structures in use, especially in the field of space structures. This work proposes a form-finding optimization method based on the iterative correction of the tensioning process, which considers membrane stress and edge shape optimizations in order. The method focuses on two evaluation indices: the average membrane stress difference and the root mean square (RMS) of the position difference of the edge cables. This sequential optimization analysis is illustrated based on the examples of a sunshade structure for aerospace applications. The results show that these design requirements on the two evaluation indices can be satisfied after one membrane stress optimization and one edge shape optimization. Subsequently, the influence of the seams on the optimization analysis of tensile cable membranes is also investigated. Although seams increase the inhomogeneity of the membrane stress and edge shape difference, the proposed optimization framework based on the iterative correction of the tension process can still ensure that the design requirement can be satisfied. The work in this paper could form the basis for a high-precision study of tensile membrane structures in space.

Physics-informed neural networks for the form-finding of tensile membranes by solving the Euler–Lagrange equation of minimal surfaces

The use of light-weight tensile membrane structures (TMS) has been widely accepted and their usage is continually increasing, particularly for covering large areas. Such structures result in high material usage efficiency as bending and buckling issues are absent. Their design begins with finding an initial equilibrium state of the structure, under given stress levels and boundary conditions, called ‘form-finding’. Although several form-finding algorithms are present in the literature, many of these conventional approaches are highly sensitive to the choice of parameters of the chosen method. Other problems, especially that of a proper selection of initial shape and non-convergence in case of large number of degrees of freedom, are also major issues in implementation. This article explores the applicability of a recently developed machine learning algorithm, known as the ‘physics-informed neural network’ (PINN), for the purpose of form-finding. The PINN is used to solve the Euler–Lagrange Equation of minimal surfaces which corresponds to a stable minimal shape of the TMS constrained by the prescribed boundary conditions and constant prestress. The accuracy of the proposed mesh-free method is illustrated using several form-finding case studies for frame-supported TMS. It is observed that the aforementioned issues faced by conventional approaches are efficiently avoided in the proposed framework. A step-by-step methodology is also provided, along with a practical guideline for implementation.

Tensile Roof Structure

Membrane Structures are highly popular in architectural design now a days. There is trend of using membrane structures. It satisfies both attractive architect’s design as well as structural design. The preliminary type of structure most commonly used by man was Tents. As the name suggests, Tension fabric structures utilize fabric in complete tension, as a primary building material. Every part of structure is loaded only in tension with no requirement to resist bending or compression. Soap film model is the classic example of Tensile Structures. Assembly of tensile membrane structures creates a unique structural system, indeterminate in its behavior and nonlinear in its deflection patterns. Tensile structures are gaining popularity due to their light weight, structural efficiency, serviceability, aesthetic appearance, installation and dismantling feasibility, climate regulation effects and less maintenance expenditures. Due to its light weight and stretch property, they can be used on places such as stadiums, large parking etc. Computer aids like Form Finder, Dlubal RFEM and AutoCAD is used for modeling and analysis.

On computation of reliability index for tensile membrane structures based on limit state of deflection

PurposeThe present study is based on finding the structural response of a tensile membrane structure (TMS) through deformation. The intention of the present research is to develop a basic understanding of reliability analysis and deflection behavior of a pre-tensioned TMS. The mean value first-order second-moment method (MVFOSM) method is used here to evaluate stochastic moments of a performance function with random input variables. Results suggest the influence of modulus of elasticity, the thickness of the membrane, and edge span length are significant for reliability based TMS design.Design/methodology/approachA simple TMS is designed and simulated by applying external forces (along with prestress), as a manifestation of wind and snow load. A nonlinear analysis is executed to evaluate TMS deflection, followed by calculating the reliability index. Parametric study is done to consider the effect of membrane material, thickness and load location. First-order second moment (FOSM) is used to evaluative the reliability. A comparison of reliability index is done and deflection variations from μ − 3s to μ + 3s are accounted for in this approach.FindingsThe effectiveness of deflection is highlighted for the reliability assessment of TMS. Reliability and parametric study collectively examine the proposed geometry and material to facilitate infield design requirements. The estimated β value indicates that most suitable fabric material for a simple TMS should possess an elasticity modulus in the range of 1,000–1,500 MPa, the thickness may be considered to be around 1.00 mm, and additional adjustment of around 5–10 mm is suggested for edge length. The loading position in case of TMS structures can be a sensitive aspect where the rigidity of the surface is dependent on the pre-tensioning of the membrane.Research limitations/implicationsThe significance of the parametric study on material and loading for deflection of TMS is emphasized. Due to the lack of consolidated literature in the field combining reliability with deflection limits of a TMS, this work can be very useful for researchers.Practical implicationsThe present work outcome may facilitate practitioners in determining effective design methodology and material selection for TMS construction.Originality/valueThe significance of parametric study for serviceability criteria is emphasized. Parameters like pre-stress can be included in future parametric studies to witness in depth behavior of TMS. Due to lack of consolidated literature in the field combining reliability with deflection limits of a TMS, this work can be very useful for the researchers. The present work outcome may facilitate practitioners in determining effective design methodology and material selection for TMS construction.

Updated weight method: an optimisation-based form-finding method of tensile membrane structures

Updated weight method: an optimisation-based form-finding method of tensile membrane structures