Analysis Of Complex Structural Systems Research Articles

PSDM: A parametrized structural dynamic modeling method based on digital twin for performance prediction

Digital twin (DT) technology is a powerful tool that accurately represents physical entities and provides real-time monitoring and reliability analysis capabilities for decision-makers and managers. However, modeling complex systems in structural health monitoring using DTs can be repetitive and tedious with existing approaches. To address these issues, we propose a new approach called the Parametrized Structural Dynamic Modeling(PSDM) method. This approach effectively parametrizes dynamic systems in structural health monitoring by leveraging Proper Orthogonal Decomposition (POD) to represent and analyze complex structural behaviors. It also incorporates model reduction and kernel functions. By integrating physics-based insights and advanced modeling methodologies, the PSDM method aims to bridge the gap between the physical and digital domains, enabling accurate and efficient analysis of complex structural systems. To verify its accuracy and efficiency, we apply the PSDM method to two engineering cases: the wing cantilever beam and the telehandler boom. The results demonstrate that the online computational cost of the PSDM method is lower than Finite Element Methods (FEM), thereby improving the computational efficiency of DT modeling technology for complex-large structures. Thus, this research presents a feasible method for implementing structural reliability analysis for engineering equipment.

Value of information-based risk and fatigue management for offshore structures

ABSTRACT This paper proposes a framework for the planning of the structural integrity and risk management (SIRM) of offshore structures by utilizing the value of information and action (VoIA) analysis. Multiple structural health information sources, i.e. inspections, structural health monitoring (SHM) and damage detection systems (DDS), are modeled in dependency of the information acquirement state. In the probabilistic modeling, it is distinguished whether the information are predicted or already obtained. Two deteriorating structural system modeling approaches are illustrated and the pre-posterior and posterior updating of the structural system performance is formulated. To enhance the efficiency of the analysis of complex structural systems, a response surface method for load modeling is derived and the model uncertainty of the response surface is explicitly modeled. The formulated approaches are applied to a generic structural system and a typical deepwater jacket platform analyzing four SIRM scenarios. The optimal SIRM strategy is identified by maximizing the VoIA. The results show that the implementation of SIRM can reduce the failure risks and the expected total costs over the service life compared to a scenario without SIRM. It is also observed that information from SHM and DDS can reduce future inspection efforts and enhance the value of SIRM.

BOUT DEVELOPMENT OFMETHODS OF ANALYSISAND ASSESSMENT OF VULNERABILITYOF SPATIAL PLATE-SHELL REINFORCED CONCRETESTRUCTURES WITH ALLOWANCE FOR PHYSICAL NON-LINEARITIES, CRACK FORMATION ANDINDUCED ANISOTROPY

Themodern stage of modelling of behavior of reinforced concrete structures is associated with the widespread use of numerical methods. Thedistinctive paper is devoted todevelopment and numerical implementation of methods of structural analysis including progressive collapse analysis of spatial plate-shell reinforced concrete structures with allowance for physical nonlinearity, crack formation and inducedanisotropy. The relevance of the research topic is substantiated, the current status of research on this topic in Russia and abroad (including various aspects dealing with types of diagrams for modelling of reinforced concrete structures,construction of general deformation models of reinforced concrete, strength criteria for reinforced concrete structures and methods of structural analysis) is analyzed, the goals, objectives and boundaries of the study are determined, the provisions constituting scientific novelty, theoretical significance and practical significance are formulated, publications on the topic are under consideration. It should be noted that generallyfurther improvement and modifications of reinforced concrete models and their integration incontemporarysoftware systems for structural analysis remain very important. It is assumed that developing methods of analysis of reinforced concrete structures will replace multi-iterative approaches to the solution of physically nonlinear problems and move from the practically possible high-precision analysis of individual structures to the analysis of complex structural systems with allowance for various factors of physical nonlinearity and anisotropy. As a result,reliability of design solutions will increase significantly. The strength criteria used in this way, in turn, will also eliminate a number of errors in existing methods for strength analysis.

A PROBABILISTIC ANALISYS OF THE FORCES IN THE PILES OF A CONTAINER TERMINAL WHARF UNDER SHIP BERTHING ACTIONS

ABSTRACTIn this work a probabilistic structural analysis of a container terminal wharf is presented. Through the application of the Monte Carlo Simulations in a finite element structural model, the statistics of the axial forces at the pile heads were evaluated. Considering only ship berthing conditions, but varying equipment type, number and positions, ten external load combinations were analyzed and compared. Both finite element model and probabilistic analysis were performed in ANSYS software, v.16, on its APDL Mechanical and Probabilistic Design System packages respectively. The random input parameters considered were the concrete structure, pavement and rear landfill self-weight, live loads and ship sizes, for which statistical parameters were attributed based on bibliography or data collection. PIANC’s berthing energy method for fender systems design was applied for the evaluation of ship berthing forces acting on the structure. Considering the axial forces as the main parameters in the design of partially embedded piles, the random output parameters obtained were the minimum and maximum axial forces on each simulation. That approach, applied due to the possibility of ANSYS to be programmed through its Parametric Design Language (APDL), proved to be a powerful tool for probabilistic analysis of complex structural systems. The results present a comparison between the statistical parameters and probability density curves for the ten cases analyzed, by which the worst combinations from a probabilistic point of view could be defined. Keywords: Container terminal berth; Probabilistic Analisys; Monte Carlo Method; Finite Element Method; ANSYS.

A probabilistic framework based on statistical learning theory for structural reliability analysis of transmission line systems

This paper describes a novel application of statistical learning theory to structural reliability analysis of transmission lines considering the uncertainties of climatic variables such as, wind speed, ice thickness and wind angle, and of the resistance of structural elements. The problem of reliability analysis of complex structural systems with implicit limit state functions is addressed by statistical model selection, where the goal is to select a surrogate model of the finite element solver that provides the value of the performance function for each conductor, insulator or tower element. After determining the performance function for each structural element, Monte Carlo simulation is used to calculate their failure probabilities. The failure probabilities of towers and the entire line are then estimated from the failure probabilities of their elements/components considering the correlation between failure events. In order to quantify the relative importance of line components and provide the engineers with a practical decision tool, the paper presents the calculation of two types of component importance measures. The presented methodology can be used to achieve optimised design, and to assess upgrading strategies to increase the line capacity.

Reliability analysis of structures using stochastic response surface method and saddlepoint approximation

Stochastic response surface method (SRSM) is a technique used for reliability analysis of complex structural systems having implicit or time consuming limit state functions. The main aspects of the SRSM are the collection of sample points, the approximation of response surface and the estimation of the probability of failure. In this paper, sample points are selected close to the most probable point of failure and the actual limit state surface (LSS). The response surface is fitted using the weighted regression technique, which allows the fitting points to be weighted based on their distance from the LSS. The cumulant generating function (CGF) of the response surface is derived analytically. The saddlepoint approximation (SPA) method is utilized to compute the probability of failure of the structural system. Finally, four numerical examples compare the proposed algorithm with the traditional quadratic polynomial SRSM, Kriging based SRSM and direct MCS.

Finite element modelling of hysteresis, degradation and failure of dowel type timber joints

A method for macro modelling timber-to-timber dowel type joints under shear loads using finite elements is presented. The proposed model, which includes a combination of nonlinear one-dimensional elements, is able to properly represent the hysteretic behaviour, stiffness degradation and failure of this type of joints. Experimental data from mechanical tests was used to define the macro model parameters. The proposed methodology does not require a high computational processing cost and, consequently, can be used for the nonlinear analysis of complex structural systems with a large number of connections.

Evaluation of a substructured soft-real time hybrid test for performing seismic analysis of complex structural systems

The hybrid test method is a relatively recently developed dynamic testing technique that uses numerical modelling combined with simultaneous physical testing. The concept of substructuring allows the critical or highly nonlinear part of the structure that is difficult to numerically model with accuracy to be physically tested whilst the remainder of the structure, that has a more predictable response, is numerically modelled. In this paper, a substructured soft-real time hybrid test is evaluated as an accurate means of performing seismic tests of complex structures. The structure analysed is a three-storey, two-by-one bay concentrically braced frame (CBF) steel structure subjected to seismic excitation. A ground storey braced frame substructure whose response is critical to the overall response of the structure is tested, whilst the remainder of the structure is numerically modelled. OpenSees is used for numerical modelling and OpenFresco is used for the communication between the test equipment and numerical model. A novel approach using OpenFresco to define the complex numerical substructure of an X-braced frame within a hybrid test is also presented. The results of the hybrid tests are compared to purely numerical models using OpenSees and a simulated test using a combination of OpenSees and OpenFresco. The comparative results indicate that the test method provides an accurate and cost effective procedure for performing full scale seismic tests of complex structural systems.

Modified subset simulation method for reliability analysis of structural systems

A “Regenerative Adaptive Subset Simulation” (RASS) method is proposed for performing the reliability analysis of complex structural systems. Proposed modifications to the classic subset simulation method include the implementation of advanced Markov Chain processes to combine the benefits of a Markov Chain regeneration process, a Delayed Rejection and Adaptive sample selection algorithms and a Componentwise sampling model. The proposed modifications help to overcome the limitations of the original Metropolis–Hasting algorithm used in the subset simulation which include the “burn-in problem” and the difficulty of the selection of the proposal probability function. Several illustrative examples are presented to demonstrate the efficiency of the proposed simulation and compare its results to those of other methods. The results show that RASS is robust and efficient in estimating the probability of failure of structural systems with complex failure regions, large numbers of random variables, and small probabilities of failure.

Dynamic modeling of complex structures in a broad frequency range.

A general method, the so‐called finite substructurE method (FSEM), is presented for the dynamic analysis of complex structural systems. In this method, a complex structure is considered as a collection of a finite number of basic structural components such as beams, plates, and shells. Instead of seeking a numerical solution at a number of discrete or grid points, the current displacement solution is sought, over each component, as a continuous field in the form of Fourier series expansions. Thus, the number of degrees of freedom, which now represent the Fourier expansion coefficients, can be substantially reduced in comparison with a grid‐based solution for the same spatial resolution. Mathematically, the resulting system tends to be better conditioned than those in the finite element methods as the number of DOF’s increases with frequency. The robustness of this model for high frequency applications can be further improved by incorporating statistical processes into the modeling method to properly reflect the means or account for the uncertainties of certain input variables. The proposed substructure method is considerably different from the existing substructure techniques in that no modal data are required for any component. Numerical examples are presented to demonstrate the reliability of this method.

Uncertainty analysis of complex structural systems

AbstractIn this paper methods for uncertainty propagation for complex structural systems are discussed. Only a limited class of methods is available for this purpose. It is shown that only numerical procedures allow the uncertainty assessment and reliability estimation of such systems. In particular, the perturbation procedure, the Karhunen–Loève reduction scheme, the polynomial chaos expansion, direct and advanced Monte Carlo simulation (MCS), as well as random matrix theory meet these requirements. It is shown that advanced MCS procedures proved to be the most versatile approach. Copyright © 2009 John Wiley & Sons, Ltd.

A two-step, two-field hybrid method for the static and dynamic analysis of substructure problems with conforming and non-conforming interfaces

The need for assembling independent finite element substructure solutions arises in several engineering and scientific problems including the design and analysis of complex structural systems, component mode synthesis, global/local analysis, adaptive refinement and parallel processing. In this paper, we discuss the solution of such problems by a two-field hybrid method where the substructures are jointed with low-order polynomial or piece-wise polynomial Lagrange multipliers and present a Rayleigh-Ritz based smoothing procedure for improving the accuracy of the computed coupled solution in the presence of various substructure heterogeneities. We consider both conforming and nonconforming substructure meshes, and demonstrate the benefits of the proposed two-step solution method with several examples from structural mechanics.

Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation

This paper examines the application of Neural Networks (NN) to the reliability analysis of complex structural systems in connection with Monte Carlo Simulation (MCS). The failure of the system is associated with the plastic collapse. The use of NN was motivated by the approximate concepts inherent in reliability analysis and the time consuming repeated analyses required for MCS. A Back Propagation algorithm is implemented for training the NN utilising available information generated from selected elasto-plastic analyses. The trained NN is then used to compute the critical load factor due to different sets of basic random variables leading to close prediction of the probability of failure. The use of MCS with Importance Sampling further improves the prediction of the probability of failure with Neural Networks.

Influence of Mass Distribution, Shear Deformation, and Rotatory Inertia on Flutter Loads

Classical problems of columns under tangential (follower) loads are analyzed for stability. A generalized Ritz technique is used in conjunction with a computer-oriented algorithm suitable for the flutter analysis of complex structural systems. A brief description of this technique is presented. The numerical approach allows for easy consideration of additional effects in classical problems such as the Beck and Leipholz columns. The influence of shear deformation, rotatory inertia, and concentrated mass at the tip of the column is shown in graphs and discussed.

Coupled systems subjected to determinate and random input

A method is presented for the analysis of complex structural systems which can be subdivided into any number of component systems arbitrarily interconnected at discrete points. Using experimentally or analytically determined receptances (frequency response functions) characterizing the mechanical properties of the component systems, receptance are derived which characterize the mechanical propertues of the entire coupled system. The requirement of system continuity at the coupling points gives rise to condutions of equilibrium and compatibility at the connections. These conditions aer modified allowing for the presence of elastic and/or dissipative coupling units with negligible masses between the couplin points, thus, adding condiderable practical flexibility to the method. Keeping the contributions of the individual component systems identified, it is then shown how the receptances enter response calculations for the entire system which is subjected to determine or random excitations.