Large-scale Structural Systems Research Articles

Long-span fiber composite truss made by coreless filament winding for large-scale satellite structural systems demonstrated on a planetary sunshade concept

Climate change necessitates exploring innovative geoengineering solutions to mitigate its effects—one such solution is deploying planetary sunshade satellites at Sun–Earth Lagrange point 1 to regulate solar radiation on Earth directly. However, such long-span space structures present unique technical challenges, particularly structural scalability, on-orbit manufacturing, and in-situ resource utilization. This paper proposes a structural concept for the sunshade’s foil support system and derives from that a component-level modular system for long-span fiber composite lightweight trusses using coreless filament winding. Within a laboratory-scale case study, the component scalability, as well as the manufacturing and material impacts, were experimentally investigated by bending deflection testing. Based on these experimental results, FE models of the proposed structural concept were calibrated to estimate the maximum displacement and mass of the foil support structure, while comparing the influences of foil edge length, orbital load case, and material selection.

Modified Iterative-Order-Reduction Substructuring Method with Interface Boundary Reduction

The iterative-order-reduction (IOR) substructuring method is an efficient model order reduction (MOR) technique for estimating eigenvalues and eigenvectors in large-scale structural systems. However, it can be computationally expensive when applied to models with highly refined meshes and a substantial number of substructures because it retains all the interface degrees of freedom (DOF), which increase the size of the reduced structural matrices. Moreover, the reduced structural matrices may suffer from ill-conditioning after a certain number of iterations, potentially yielding inaccurate results. This paper presents a modified IOR substructuring method that integrates interface boundary reduction to reduce the size of the original IOR substructuring model by decreasing the number of interface DOF. The performance of the proposed method is demonstrated through a comprehensive numerical comparison with the original IOR substructuring and other widely used MOR techniques, including the Craig–Bampton, enhanced Craig–Bampton, and iterated improved reduced system with a substructuring scheme. The results show that the proposed method achieves comparable accuracy while significantly reducing the computational time and memory usage.

2.M. Workshop: Fostering integrated care through a large-scale structural health system reform in Finland

Abstract Several countries have introduced health system reforms to foster integrated care within their health systems and in some case also between health and other welfare services such as social care. These integrated care initiatives have included reorganization of the work of professionals, developing patient pathways and removing barriers for organisations to work together. Comprehensive structural reforms to foster integrated care have not been that common. In health systems, like in public policy in general, small, micro-level reforms are common but only seldom large-scale structural reforms are enacted. Also integrated care is often fostered through small-scale reforms which are not always even scaled up from the local or regional level to national level policies. In Finland a large-scale structural reform was introduced in 2023 which can be marked as one of the most comprehensive health system reform in Europe over the past decades. As a result of the reform the responsibility for organizing health care, social services and rescue services was transferred from over 300 municipalities to 22 health and social care authorities which all get funding from the state. A key tool in pursuing the reform has been to implement administrative and financial integration both in terms of vertical integration within health system and horizontal integration between health care and social services. The workshop uses the Finnish health system reform as a case study to present how integrated care can shape the health reform agendas and implementation of the reform. We discuss how structural reformation can foster integrated care at patient level and how the rather radical restructuration of the Finnish health system looks from the wider European perspective. Key messages • One of the most comprehensive health-system reforms in Europe over the past decades was introduced in Finland in 2023. • The radical structural health-system reformation can foster integrated care at patient level and offers a learning opportunity to other European health systems.

An Adaptive Frequency Sampling Algorithm for Dynamic Condensation-Based Frequency Response Analysis

This paper proposed an efficient and adaptive frequency sampling algorithm for frequency response analysis using dynamic condensation-based reduced-order modeling. For the degree of freedom-based model reduction method, the reduced-order basis becomes a frequency-dependent matrix since the relationship between master and slave degrees of freedom stems from partial equations of a second-order dynamical system. Such frequency-dependency makes the analysis inefficient for investigating the frequency response of the system. Considering that the coverage of a local reduced-order basis at a single frequency varies depending on the frequency, a new frequency sampling algorithm was proposed with a strategy of constructing multiple local reduced-order models (ROMs) at sample frequencies. For adaptive sampling, the frequency range of a local ROM was evaluated, and a new sample was added if there was a gap between two adjacent ROMs. As a result, the accuracy of the local ROM can be estimated, and the efficiency in the online stage was greatly enhanced. The proposed method was verified by performing frequency response analysis with several numerical examples, including a large-scale structural and dynamic system.

A global-local hybrid strategy with adaptive space reduction search method for structural health monitoring

The difficulty in computational convergence poses challenges of application for traditional heuristic optimization algorithms to solve the optimization-based structural identification problem, especially for the large-scale and complex structural systems where considerable number of unknown parameters and degrees of freedom involved. Unlike the classic identification methods, in this paper, a novel hybrid strategy, coarsely exploring the relatively large search limits with the improved Jaya algorithm and adaptive search space reduction method in the global stage, and then fine-tuning the identified best solution with local optimization methods to the optimum in the local stage, is proposed and evaluated. The improved Jaya algorithm includes three improvements compared to its original version, fuzzy clustering competitive learning, experience learning and Cauchy mutation mechanisms. Gradient based Levenberg-Marquardt method, sequential quadratic programming method and non-gradient based Nelder-Mead simplex method are inserted as local mathematical optimizers to further enhance identification accuracy and efficiency. The superiority of proposed improved Jaya algorithm is validated in optimizing classical and CEC05 benchmark functions by comparing with several state-of-the-art algorithms. Furthermore, the effectiveness of proposed global-local hybrid method is verified by a numerical example of truss structure and an experimental test of the steel grid benchmark structure with incomplete set of noise-polluted measurements. The statistical results show that the improved Jaya algorithm and adaptive search space reduction method combined with sequential quadratic programming can achieve better performance in structural damage identification than other methods.

Effect of member grouping and pool size of discrete cross-sections on the optimal design of a large-scale 3D steel frame

This paper examines the effect of member grouping and selected pool size of discrete cross-sections on the optimal design of a large-scale steel frame structure. Member grouping is commonly applied to structural optimization problems to significantly reduce the solution space of candidate designs and improve the efficiency and robustness of the search to achieve near-optimum solutions. At the same time, discrete sets of cross-sectional steel shapes (profile lists) assigned to a particular member group are often limited to a subset of cross-sections to further limit the search space and ensure practicality in the final design. However, for large-scale structural systems (e.g., tall buildings), limiting the number of member groups coupled with the exclusion of lighter cross-sectional shapes may significantly impact the quality of the optimal solution (i.e., weight of the frame). This work presents a case study of a large-scale three-dimensional (3D) steel frame to evaluate the sensitivity of the optimal design to practical combinations of member groups and steel profile lists used in multi-story steel frames. A recently developed metaheuristic search algorithm was applied to the steel frame and independent optimization experiments were performed for 16 unique member grouping and profile list combinations (cases) to quantify their influence on the variability and quality of the optimal design. Results reveal a 33% difference in the optimal (final) weight between member group and profile list combinations producing the smallest and largest solution spaces. Yet, the lightest design (corresponding to the largest solution space) produced a greater number of unique steel shapes and required significantly higher computational cost.

Optimal sensor placement techniques for modal identification of historical masonry structures

Since destructive tests are not allowed for historical structures, numerical model updating using accelerometers has gained a lot of attraction in the last decade. Furthermore, another application of structural health monitoring is damage detection for near-real-time monitoring of cultural heritage assets of infrastructures such as masonry bridges. However, high cost is the main problem that discourages the use of large-scale structural health monitoring systems, and a modal pretest analysis is required to plan and optimize the modal tests procedure. For this purpose, various optimal sensor placement (OSP) techniques have been developed to derive the operational modal analysis results with a minimum number of sensors, leading to a lower cost. In this study, various OSP techniques have been applied to optimize sensor placement in two selected case studies. The first case study is a two-span masonry arch bridge in Rhodes, Greece and the second is a stone masonry tower located in Tønsberg, Norway. Baseline finite element models were developed before performing the ambient vibration tests and model updating process. The optimum sensor locations were detected using various techniques, and a comparative study was conducted on the results. Furthermore, the effect of considering soil-structure interaction on the OSP results was investigated.

Translational surface coupling along a line with non-conforming meshes

In the modelling of large-scale metal structures, comprising plated components intersecting along weld lines, the meshing efficiency and flexibility are limited by the requirements of nodal alignment and compliance of element size, shape and edge orientation throughout the domain. Such limitations necessitate the use of complex transitional meshes in intersection regions and result in highly complex global mesh configurations. This paper presents an original and systematic methodology for surface coupling along an arbitrary 1D interface, which is applicable to any type of 2D and 3D FEs, and which provides a systematic framework for: (i) geometric modelling of weld lines; (ii) coupling of regions with different levels of discretisation detail or element types within a system; and (iii) domain partitioning problems involving computationally heterogeneous partitions. The strategy is based upon a novel coupling element formulation, which uses the fundamental principles of the mortar method and an augmented Lagrangian Multiplier optimisation approach. Particular consideration is given to an element formulation that enforces rigid translational coupling, which has been implemented for employment with co-rotational Reissner-Mindlin shell elements. Various numerical examples are presented to demonstrate the accuracy, versatility and substantial computational benefits of the developed methodology for modelling large-scale metal structural systems.

Bayesian updating and identifiability assessment of nonlinear finite element models

A promising and attractive way of performing structural health monitoring (SHM) and damage prognosis (DP) of engineering systems is through utilizing a nonlinear finite element (FE) model. Often, FE models contain parameters that are unknown or known with significant level of uncertainty. Such parameters need to be estimated/updated/calibrated using data measured from the physical system. The Bayesian paradigm to model updating/calibration is attractive as it accounts, using a rigorous probabilistic framework, for numerous sources of uncertainties existing in the real-world. However, applying Bayesian methods to nonlinear FE models of large-scale civil structural systems is computationally very prohibitive. Additionally, non-identifiability of FE model parameters poses challenges in the model updating process. This paper presents Bayesian model updating and identifiability analysis of nonlinear FE models with a specific testbed civil structure, Pine Flat concrete gravity dam, as illustration example. Model updating is performed in the recursive mode using the unscented Kalman filter (UKF) and in the batch mode using the transitional Markov chain Monte Carlo (TMCMC) method. Limitations in terms of applicability and computational challenges of each method for model updating of large-scale nonlinear FE models are addressed and discussed. Identifiability and sensitivity analyses of the model are then performed using local and global methods. Local practical identifiability analysis using local sensitivity in conjunction with the Fisher information matrix is used to assess the parameter identifiability in a certain local region in the parameter space. Due to the nonexistence of a method to assess global practical identifiability, variance-based global sensitivity analysis (Sobol’s method) is used herein. Identifiability and sensitivity analysis results are used to choose the parameters to be included in the model updating phase.

System reliability of a gravity dam-foundation system using Bayesian networks

Traditionally, the reliability of a gravity dam-foundation system is evaluated using several typical dam sections. However, the performance of different dam sections may vary greatly even under the same environment and loading conditions due to the different dam structures and geological conditions. Therefore, an improved method was proposed in this study to assess the system reliability of a gravity dam-foundation system more reasonably. Based on the synchronous load increment in the whole dam and the response surface method (RSM), the critical failure modes were determined, and the RSM agent models for the strength failure and instability failure performance functions were constructed. Through the Monte Carlo simulation and the Bayesian progressive deduction, the method of system reliability was proposed. The application results of a gravity dam showed that the reliability in different dam sections was different for the same loading and environment conditions, and the system safety assessment was unreasonable using several typical dam sections. The proposed method overcame the defects of excessively searching the failure path, distorting the failure probability assessment, and incompletely describing the characteristics of the system using the traditional methods. It provides a new method for the system reliability evaluation of large-scale and complex structural systems.

Optimal analysis for optimal design of cyclic symmetric structures subject to frequency constraints

In general, the design optimization of large-scale structural systems is an extremely computationally expensive process. However, optimal structural analysis provides efficient and practical methods of structural analysis which can be employed as powerful tools to reduce significantly the computational time of structural analyses performed in the structural optimization process. In this paper, an efficient eigensolution method for free vibration analysis of rotationally repetitive structures is developed for the optimal design of cyclic symmetric structures subject to frequency constraints. Through the present eigensolution method, the initial free-vibration eigenproblem is decomposed into some sub-eigenproblems with much smaller dimensions using an efficient block-diagonalization technique. The main advantage of the present method is that it requires significantly less computational time and memory than the existing classical eigensolution method. Finally, two large-scale numerical examples are given to demonstrate the efficiency and accuracy of the present eigensolution method and compare it with the existing classical method in both terms of computational time and memory requirements. Numerical results indicate that the present eigensolution method not only guarantees the accuracy of the analysis results but also significantly reduces the computational time and memory required compared to the existing classical method. More importantly, the computational time of the optimization process is also significantly reduced. Optimization is carried out by a set-theoretical-based Jaya algorithm, named ST-JA. The proposed ST-JA aims to enhance the diversification and intensification capabilities of the original JA and strike a fine balance between them. Optimization results confirm that the proposed ST-JA outperforms the original JA and has superior or comparable performance to other state-of-the-art optimization algorithms.

Using the hierarchical Kriging model to optimize the structural dynamics of rocket engines

Traditional fine models are often inefficient in optimizing the dynamics of complex structural systems such as large liquid rocket engines. To enhance the design efficiency, this paper proposes a fast optimization method based on finite element modeling, central Latin hypercube experimental design with minimax criteria, hierarchical Kriging surrogate model, and combined multi-island genetic algorithm + nonlinear sequential quadratic programming approach. First, a low-frequency dynamic fine finite element model of the entire engine structure was established, and the sampling points were obtained by considering the experimental design and a hierarchical Kriging model that describes the relationship between the design variables of the engine structure parameters and the modal frequency characteristics. Next, the height of the frame, size of the gimbal mount, and stiffness and installation angle of the servo mechanism were considered as design variables. To satisfy the design requirements in terms of the first modal frequency of the engine structure and realize the optimal design of the modal characteristics, the global optimization of the high-precision Kriging model was performed in the design space by using the hybrid optimization algorithm. Finally, the optimization results were substituted into the finite element model to perform the modal analysis, and modal tests were conducted to verify the effectiveness of the optimization method in realizing large-scale structural system optimization. The results indicated that the first modal frequency of the engine structure was increased from 7.53 Hz to 11.50 Hz and the optimization results could satisfy the requirements for the dynamics design. The proposed optimization design method can considerably improve the optimization efficiency by ensuring high accuracy of the results. The presented modeling strategies and optimization concepts can be applied to several engineering applications and provide novel solutions for the dynamic optimization problems of large and complex structures.

NHERI Lehigh Experimental Facility With Large-Scale Multi-Directional Hybrid Simulation Testing Capabilities

The NHERI Lehigh Experimental Facility, as part of the NSF funded Natural Hazards Engineering Research Infrastructure (NHERI) site, was established in 2016 as an open-access facility. This facility enables researchers to conduct state-of-art research on natural hazard mitigation in civil infrastructure systems, including high performance numerical and physical testing to improve the resilience and sustainability of the civil infrastructure against natural hazards. The facility has the unique ability to conduct real-time multi-directional hybrid simulation (RTHS) on large-scale structural systems using 3D nonlinear numerical models combined with large-scale physical models of structural and non-structural components. The Lehigh Experimental Facility processes testbeds that include a testbed for testing lateral load resisting system characterization testbed, a non-structural component multi-directional dynamic loading simulator, full-scale and reduced-scale damper testbeds, a tsunami and storm surge debris impact force testbed, and a soil-foundation structure interaction testbed. This paper describes the infrastructure and capabilities of the NHERI Lehigh Experimental Facility. Developments by the facility in advancing large-scale RTHS are given. Examples of research projects performed by users of the facility are then provided, including large-scale RTHS of steel frame buildings with magneto-rheological (MR) dampers and nonlinear viscous dampers subject to strong earthquake ground motions; 3D multi-hazard large-scale RTHS of tall steel buildings subject to multi-directional wind and earthquake ground motions; characterization of a novel semi-active friction device based on band brake technology; and testing of cross-laminated timber self-centering coupled wall-floor diaphragm-gravity systems involving multi-directional loading.

On the stable mode selection for efficient component mode synthesis of geometrically nonlinear beams

A large-scale structural system containing a substantial amount of degrees of freedom requires many computations to achieve the optimum of the system. In particular, nonlinear structural analysis compels a recursive calculation in limited computational resources. Thus, a powerful tool, which improves the efficiency of analysis in the limitations, may be required. In structural dynamics, various methods related to model order reduction have been developed in the past decades [1–15]. In addition, several mode selection methods to enhance the rate of reduction have been reported in the Refs. [16–19]. Most of them are focused on dynamic analysis. In this paper, we focus on the nonlinear static response of reduced order model with a proper mode selection criterion. To construct the nonlinear reduced order model, modal derivatives [10–14], which are the quadratic enhancement of modal basis, have been employed. The degrees of freedom used to the model reduction have been selected via the singular value decomposition (SVD). Finally, the performance of the selection scheme for the static problem is verified via numerical examples.

An extended model-based observer for state estimation in nonlinear hysteretic structural systems

This paper proposes a model-based observer for state estimation in nonlinear hysteretic structural systems. The observer combines a nonlinear model and response measurements to estimate the complete dynamic response of the structure of interest. The feedback gain of the observer is computed by an iterative algorithm that accounts for the nonlinearity of the system and minimizes the trace of the state error covariance matrix. The main feature of the proposed observer (and its main advantage with respect to existing nonlinear state estimators) is that it is designed to be physically realizable as a nonlinear structural model, which allows the user to benefit from modeling capabilities available in finite element solvers. The performance of the proposed observer is studied in the context of a numerical example consisting of a nonlinear chain model with a Bouc-Wen model of hysteresis subjected to a base motion; the estimation results are compared to those obtained using an unscented Kalman filter. The observer is also validated experimentally using data from a six-story full-scale benchmark wood frame building tested by the NEESWood project at the E-Defense facility in Miki, Japan. The results show the effectiveness of the proposed approach to estimate the dynamic response of large-scale structural systems exhibiting a strong nonlinear behavior.

Vertex combination approach for uncertainty propagation analysis in spacecraft structural system with complex eigenvalue

Uncertainty propagation analysis is crucial to the safety and optimal design of spacecraft structural system in which high accuracy modelling is required. In consideration of convex uncertainties, a vertex combination approach based on the convex set theory is proposed to evaluate the influence of uncertain parameters on the complex eigenvalue of spacecraft structural system with non-proportional damping. The uncertain parameters are dealt with as a closed and bounded convex set. In the proposed method, complex eigenvalue problem with convex uncertainties can be converted into a family of equivalent eigenvalue problems without uncertainties, and the bound evaluation of eigenvalue is conducted by determining the extreme values of the deterministic complex eigenvalue problems. The advantage of this work is easy to integrate with existing codes for large-scale structural system, which allows us to carry out the extension of such deterministic finite element codes to deal with uncertain structural system with complex eigenvalue. Three numerical examples are performed to demonstrate the effectiveness and the accuracy of the proposed method.

Classical modal analysis and asymptotic modal analysis of structural and acoustic systems

The Classical Modal Analysis (CMA) method is commonly used to solve for the vibroacoustic response of large scale structural and acoustic systems. In this method, the modal equations of motion are developed and solved for the modal coefficients to form a modal series solution to predict the structural and acoustic responses. The CMA method generally works well in the low-to-medium frequency range. However, at high frequencies, the large number of modes required in the summation limits the usefulness of the CMA method and alternative methods, such as Statistical Energy Analysis (SEA) and Energy Finite Element Analysis (EFEA) have been developed. As an alternative to SEA and EFEA, the Asymptotic Modal Analysis (AMA) method has been developed in which the modal series summation in the CMA method is asymptotically approximated based on the overall frequency and band averaging of the modal series. The AMA method thereby predicts the band averaged frequency response in the high frequency range and also has the advantage of providing the spatial distribution of the response based on the summed modes in the frequency band. In this paper, an example of coupled cavity-plate system subject to external random white noise pressure load excitation is developed to illustrate the CMA and AMA methods and to address their accuracy.

A model-based fatigue damage estimation framework of large-scale structural systems

A model-based fatigue damage estimation framework is proposed for online estimation of fatigue damage, for structural systems by integrating operational vibration measurements in a high-fidelity, large-scale, finite element (FE) model and applying a fatigue damage accumulation methodology. To proceed with fatigue predictions, one has to infer the stress response time histories characteristics based on the monitoring information contained in vibration measurements collected from a limited number of sensors attached to a structure. Predictions, like the existence, the location, the time, and the extent of the damage, are possible if one combines the information in the measurements with information obtained from a high-fidelity FE model of the structure. Such a model may be optimized with respect to the data, using state-of-the-art FE model updating techniques. These methods provide much more comprehensive information about the condition of the monitored system than the analysis of raw data. The diagnosed degradation state, along with its identified uncertainties, can be incorporated into robust reliability tools for updating predictions of the residual useful lifetime of structural components and safety against various failure modes taking into account stochastic models of future loading characteristics. Fatigue is estimated using the Palmgren–Miner damage rule, S-N curves, and rainflow cycle counting of the variable amplitude time histories of the stress components. Incorporating a numerical model of the structure in the response estimation procedure, permits stress estimation at unmeasured spots. The proposed method is applied in a steel frame of a real city bus.

An iterative reduced-order substructuring approach to the calculation of eigensolutions and eigensensitivities

Substructuring methods are efficient to estimate some lowest eigensolutions and eigensensitivities of large-scale structural systems by representing the global eigenequation with small-sized substructural eigenmodes. Inclusion of more substructural eigenmodes improves the accuracy of eigensolutions and eigensensitivities, whereas decreases the computational efficiency adversely. This paper proposes a new iterative reduced-order substructuring method to calculate the eigensolutions and eigensensitivities of the global structure. A modal transformation matrix, relating the higher modes to the lower modes, is derived to transform the original frequency-dependent matrices of each substructure into frequency-independent ones. A simplified reduced-order eigenequation is then obtained through a few iterations performed on the modal transformation matrix and mass matrix. The eigensolutions and eigensensitivities of the global structure are calculated accurately with a small number of substructural eigenmodes retained, avoiding the inclusion of numerous substructural eigenmodes. Applications of the proposed method to a numerical frame and a practical large-scale structure demonstrate that the eigensolutions and eigensensitivities of the global structure can be calculated accurately with only a small number of substructural eigenmodes and a few iterations.

Numerical simulation of large-scale structural systems

Numerical simulation of large-scale structural systems