Structural Dynamic Systems Research Articles

Multivariate risk assessment for offshore structures by Gaidai risk evaluation method under an accumulation of fatigue damage, utilizing novel deconvolution scheme

Current study presents a state-of-the-art structural spatiotemporal multimodal risk assessment methodology, that is particularly well-suited for multimodal structural dynamics, either recorded physically or numerically simulated over a representative timelapse. Offshore Jacket-type offshore platform, operating in Bohai Bay waters had been selected for the method’s verification. This investigation had shown that, in the presence of in-situ environmental stressors, it is possible to appropriately estimate dynamic structural system’s failure and damage risks. High dimensionality of engineering dynamic structural systems, alomg with nonlinear nonstationary inter-correlations between critical structural components, often present challenges for contemporary reliability methods, mostly limited to univariate and bivariate systems. Operating Jacket platform, subjected to in situ wave loads, had been chosen for this case study to benchmark advocated risk evaluation methodology. Number of hotspot stresses had been selected to represent multivariate structural system. Advocated multimodal method had been proven to be suitable for robust assessment of operational failure/damage risks, as well as for accurate structural life projection. Novel non-parametric deconvolution extrapolation scheme had been employed, providing enhanced numerical stability. Given increased safety concerns within offshore, naval, marine engineering, advocated multimodal reliability methodology may be utilized for safer and economically more viable structural design.

Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks

Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks

Exploration of Collaborative Design Practice and Potential Issues of Blockchain Technology in the Whole Process Project Management of Construction Engineering

With the development of social economy, urban planning has been paid more and more attention. At present, China is implementing low-carbon environmental planning in stages, stages and stages with the development goal of building two types of society, namely, national master plan and strategic development. This paper analyzes low-carbon environmental planning based on blockchain, then expounds the specific content of whole process project management, and completes the design of green building index system based on low-carbon environmental planning. At the same time, the design method of new dynamic structure and index system of green building is expounded. The comparative analysis shows that the material loss cost of the new environmental protection building engineering and index system is low, and the material utilization rate is high, accounting for more than 95%.

Ocean windspeeds forecast by Gaidai multivariate risk assessment method, utilizing deconvolution scheme

The current work presents spatiotemporal analysis of areal windspeeds, using state-of-the-art Gaidai multivariate hazards evaluation approach. Environmental dynamic systems being challenging to forecast using existing reliability methods due to their high dimensionality and intricate nonlinear cross-correlations between different environmental system essential dimensions or components. Developing novel multivariate reliability methods for dynamic environmental systems may assist contemporary research on the global climate change effects. Multivariate Gaidai risks evaluation approach is especially well-suited for multi-dimensional structural and environmental dynamic systems, that have been monitored physically or numerically simulated across a representative time-lapse. The current study made use of raw in-situ windspeed data, collected by NOAA (i.e., National Oceanic and Atmospheric Administration) buoys near the Hawaiian Islands in the North Pacific. Resilient island communities/infrastructure can be designed, using advocated multivariate risk/hazard assessment methodology, particularly those that are close to the ocean and hence subject to extreme weather.As was demonstrated in this study, even given limited underlying dataset, it is feasible to conservatively estimate environmental system’s hazard risks. Novel non-parametric deconvolution extrapolation scheme has been utilized to accurately assess hazard risks.

A partial decomposition cutting method with CF-discrepancy for points selection in stochastic seismic analysis of structures

The probability density evolution method is renowned for its effectiveness in conducting stochastic seismic response analyses of structures with uncertain parameters. Within this method, the points selection strategy, particularly in high-dimensional problems, is of paramount importance to achieving a balance between accuracy and efficiency. This paper proposes a novel point selection method designed to capture the probabilistic response of structural dynamic systems. The method starts by generating an initial uniform point set within a unit cube, using an improved number-theoretical method with a large number size. It then employs a partial decomposition cutting method to select a small number of samples from this initial uniform point set, which are subsequently scaled to the unit cube to serve as the representative points. These representative points are then transformed into the original random-variate space, and the corresponding assigned probabilities are computed accordingly. To enhance accuracy, a characteristic function-based discrepancy is proposed and applied to rearrange the representative points in the original random-variate space. The effectiveness of this method is demonstrated through two numerical examples, along with comparisons to results obtained using Monte Carlo Simulation and other comparable point sets.

Assessment of different optimal sensor placement methods for dynamic monitoring of civil structures and infrastructures

The optimal sensor placement is a critical aspect in the structural health monitoring of structures, as the number and layout of sensors directly impact the quality and usefulness of collected data. When developing a monitoring system, it is of paramount importance to use the minimum number of sensors to gain the maximum amount and quality of information. However, in the case of a limited number of sensors, their location on the structure becomes crucial. This study aims to assess the potential of several optimal sensor placement methods for the development of cost-efficient and well-dimensioned dynamic structural health monitoring systems to be implemented in a wide range of civil engineering structures and infrastructures. Indeed, the construction stock consists of a great variety of civil engineering structures and infrastructures that significantly differ from each other in terms of typology, historical period, construction techniques, materials, etc. Some of the most widely used optimal sensor placement methods are selected and applied in real case studies, and the obtained sensor configurations are discussed and compared. The results illustrated in this paper will contribute to defining good practice in the optimal sensor placement procedure for typical civil engineering structures and infrastructures.

Harmonic-modal hybrid frequency approach for parameterized non-linear dynamics

Structural dynamics systems are represented by discretized partial differential equations, whose solutions depend on various parameters. Developing high-fidelity numerical models for multi-dimensional systems or those with multiple parameters can be computationally expensive, particularly if the systems are non-linear. Consequently, the concept of a precalculated library of the system's response to a wide range of parameters is appealing. Thus, a global non-linear space-frequency solver is proposed that produces a low-rank representation of the solution using Modal Basis analysis known as the Harmonic Modal Hybrid Method. The DEIM is also used to accelerate its convergence by creating a reduced basis of the non-linear function(s) based on either calculated or experimental values. The optimized solver is then employed for rapid offline computations to construct surrogate models that can give real-time predictions of the parametrized dynamic response using the sPGD technique. These models serve as building blocks for virtual twins that necessitate near-instantaneous calculations when the system parameters and/or conditions are changed. A proof of concept is illustrated by using this technique to analyze a well-known non-linear system, the cantilevered beam with a non-linear cubic spring attached to its end. This method can be easily extended to solve other dynamical systems quickly and effectively.

Mode selection for component mode synthesis with guaranteed assembly accuracy

In this work, a modular approach is introduced to select the most important eigenmodes for each component of a composed structural dynamics system to obtain the required accuracy of the reduced-order assembly model. To enable the use of models of complex (structural) dynamical systems in engineering practice, e.g., in a design, optimization and/or control context, the complexity of the models needs to be reduced. When the model consist of an assembly of multiple interconnected structural components, component mode synthesis is often the preferred model reduction method. The standard approach to component mode synthesis for such system is to select the eigenmodes of a component that are most important to accurately model the dynamic behaviour of this component in a certain frequency range of interest. However, often, a more relevant goal is to obtain, in this frequency range, an accurate model of the assembly. In the proposed approach, accuracy requirements on the level of the assembly are translated to accuracy requirements on component level, by employing techniques from the field of systems and control. With these component-level requirements, the eigenmodes that are most important to accurately model the dynamic behaviour of the assembly can be selected in a modular fashion. We demonstrate with two structural dynamics benchmark systems that this method based on assembly accuracy allows for a computationally efficient selection of eigenmodes that (1) guarantees satisfaction of the assembly accuracy requirements and (2) results in most cases in reduced-order models of significantly lower order with respect to the industrial standard approach in which component eigenmodes are selected using a frequency criterion.

Anomaly detection in structural dynamic systems via nonlinearity occurrence analysis using video data

Dynamic loads in disaster events, such as earthquakes, often cause structural damages that severely affect serviceability and even reduce the safety of civil structures or render them unsafe. Most of these damage scenarios can cause the transition from a linear to nonlinear behavior in structural dynamics. Therefore, extracting such nonlinear behaviors is an effective strategy for structural anomaly detection. In recent years, the rapid development of media devices has led to the widespread use of video data, primarily because of the ease of acquisition and lack of contact measurement. This study developed a novel method for detecting anomalies due to structural nonlinearity from video data that could capture target dynamic behaviors via non-contact measurement. The method is based on optical flow, extended repulsive force networks, and morphological operations for extracting singularities due to nonlinear events in the estimated motion vector field in video data. Subsequently, shaking table tests on a three-story shear building model containing a novel controllable hinge bearing verified the effectiveness of the developed method. The results of the feature enhancement process demonstrate the accurate localization of anomalous regions and effective mitigation of unwanted interference using the proposed method. The proposed method facilitates the use of video-based technology to evaluate rapid damage conditions post-earthquake.

Gaidai multivariate risk assessment method for cargo ship dynamics

ABSTRACT Contemporary cargo vessel transport constitutes a vital part of the global trade and economy. Thus, it is of primary engineering and design importance to further develop more efficient novel risk analysis and risk assessment methods for large cargo ships. Classical risk assessment and risk analysis methodsmay not always have advantages of efficiently dealing with dynamic system’s high dimensionality along with cross-correlations between various critical dimensions. Current study benchmarks state-of-art Gaidai spatiotemporal structural risk assessment method, suitable for multivariate structural dynamic systems versus a well-established bivariate risk assessment method. As an example, for this risk assessment study, an operating container vessel has been selected. The vessel hull had been subjected to large deck panel stresses and accelerations during cross-Atlantic sailing. Risks of losing cargo containers are caused by extreme dynamics being one of the primary concerns for vessel cargo transport. Gaidai multivariate risk assessment methodology, advocated here, opens up new possibilities of predicting efficiently, yet simply potential structural damage/failure risks for nonstationary, nonlinear, multivariate cargo ship dynamic systems, as a whole. Note that advocated novel multivariate risk assessment method may be applied to a wide range of complex marine and offshore engineering systems.

A novel time-domain approach for identifying nonlinear structural dynamical system with explicit model based on observer/Kalman filter identification method

In this paper, a novel time-domain method for identifying nonlinear structural dynamical system with explicit model is proposed. By exploiting the observer/Kalman filter identification (OKID) and nonlinear feedback interpretation, the nonlinear parameters of the system are estimated accurately. The effectiveness of the proposed nonlinear OKID (NOKID) method is demonstrated through three numerical examples, namely a five degree-of-freedom system with one nonlinear spring, a three degree-of-freedom system with three nonlinear springs and a nonlinear rectangular membrane structure with all edges clamped. Comparing with the time-domain nonlinear subspace identification (TNSI) method, the proposed NOKID method can identify the nonlinear parameters without system order determination, and the identification results obtained by the proposed method are real numbers and have no dependency on frequency.

A non-iterative partitioned computational method with the energy conservation property for time-variant dynamic systems

A non-iterative partitioned computational method with the energy conservation property is proposed in this study for calculating a large class of time-variant dynamic systems comprising multiple subsystems. The velocity continuity conditions are first assumed in all interfaces of the partitioned subsystems to resolve the interface link forces. The Newmark integration scheme is subsequently employed to independently calculate the responses of each system based on the obtained link forces. The proposed method is thus divided into two computational modules: multi-partitioned structural analyzers and an interface solver, providing a modular solution for time-variant systems. The proposed method resolves the long-standing problem of iterative computation required in partitioned time-variant systems. More specifically, the proposed method eliminates the need for time-variant matrix formation and the utilization of complex iterative procedures in partitioned computations, which significantly improves computational efficiency. The derivation process and theoretical demonstration of the proposed method are thoroughly presented through a representative example, i.e., a vehicle-rail-sleeper-ballast time-variant system. The proposed method’s accuracy, energy conservation property, and efficiency are systematically demonstrated in comparison with the results of the global model, highlighting its superior performance. A more general example provided in Appendix C demonstrates that the proposed method is not confined to the analysis of vehicle-rail-sleeper-ballast systems but applies to other structural dynamic systems.

Perspectives on solution-based small angle X-ray scattering for protein and biological macromolecule structural biology.

Small-angle X-ray scattering (SAXS) is used to extract structural information from a wide variety of non-crystalline samples in different fields (e.g., materials science, physics, chemistry, and biology). This review provides an overview of SAXS as applied to structural biology, specifically for proteins and other biomacromolecules in solution with an emphasis on extracting key structural parameters and the interpretation of SAXS data using a diverse array of techniques. These techniques cover aspects of building and assessing models to describe data measured from monodispersed and ideal dilute samples through to more complicated structurally polydisperse systems. Ab initio modelling, rigid body modelling as well as normal-mode analysis, molecular dynamics, mixed component and structural ensemble modelling are discussed. Dealing with polydispersity both physically in terms of component separation as well as approaching the analysis and modelling of data of mixtures and evolving systems are described, including methods for data decomposition such as single value decomposition/principle component analysis and evolving factor analysis. This review aims to highlight that solution SAXS, with the cohort of developments in data analysis and modelling, is well positioned to build upon the traditional 'single particle view' foundation of structural biology to take the field into new areas for interpreting the structures of proteins and biomacromolecules as population-states and dynamic structural systems.

Self-powered wireless structural sensors for long-term monitoring of bridges

The present work introduces the design of a self-powered wireless static and dynamic structural monitoring system developed by Wisepower Srl and deployed on some bridges along the S.S. 675 "Umbro-Laziale" (former "Civitavecchia - Orte" junction) since 2018. The most significant aspect of the developed monitoring system lies in the fact that it consists of a wireless network of MEMS accelerometers (noise spectral density in all axes: 22.5 μg/√Hz, and sensitivity: 3.9 μg/LSB) powered by energy harvesting systems, which can convert environmental energy (i.e. vibrations and light) into electrical energy collected in batteries, so extending the life and minimizing the maintenance cycles of the devices. Vibration-based energy harvesting is achieved through a non-linear resonator, which utilizes a wider spectrum of frequencies compared to traditional linear vibration energy harvesters owing to its non-linear dynamics. The efficiency of the system is further enhanced by the combination of solar panels, overcoming the limitations associated with traditional wiring or batteries, widening the lifespan of the electronics, and reducing the maintenance costs. The paper reports the analysis method and the data elaboration of some ambient acceleration records, validating the system for natural frequencies identification.

Pacific Ocean Windspeeds Prediction by Gaidai Multivariate Risks Evaluation Method, Utilizing Self-Deconvolution

Abstract The current study advances research on the consequences of global climate change by utilizing the novel Gaidai multivariate risks evaluation methodology to conduct spatiotemporal analysis of areal windspeeds. Multidimensional structural and environmental dynamic systems that have been either physically observed or numerically simulated over a representative time-lapse are particularly suitable for the Gaidai risks evaluation methodology. Current research also presents a novel non-parametric deconvolution extrapolation method. As this study has shown, given in situ environmental input, it is possible to accurately predict environmental system hazard risks, based even on a limited underlying dataset. Furthermore, because of their complex nonlinear cross-correlations between various environmental system-critical dimensions or components and large dimensionality, environmental dynamic systems are difficult to handle using traditional methods for evaluating risks. In the North Pacific, close to the Hawaiian Islands, NOAA buoys gathered raw in situ wind speed data, which has been utilized in the current study. Areal ocean wind speeds constitute quite a complex environmental dynamic system that is challenging to analyze because of its nonlinear, multidimensional, cross-correlated nature. Global warming had impacts on ocean windspeeds in the recent decade. Developing novel state-of-the-art environmental system risk evaluation methods is a principal component of modern offshore structural analysis in light of adverse weather. The advocated novel risk/hazard assessment approach may be used for resilient island cities design, especially those that are near ocean shore and hence exposed to extreme weather.

A new time integration method based on state formulations for dynamic analysis of nonviscously damped systems

The mode superposition method (MSM), which is used for dynamic response calculations of nonviscously damped systems, requires state–space eigensolutions, which incur a high computational cost. This study proposed an alternative symmetric formulation-based time integration method for analyzing structural dynamic systems. An anelastic displacement field (ADF) model was introduced to capture damping mechanisms in nonviscous systems. The differential equation describing the ADF model was transformed into an augmented coupled system via the introduction of dissipative variables. Two symmetric state formulations based on coupled augmented systems were derived for nonviscous systems. Consequently, alternative asymmetric state formulations were derived for nonviscous systems involving the ADF model. Thereafter, a new method was developed based on these state–space formulations by assuming linear approximations. A numerical implementation of the new method for dynamic systems was demonstrated. Three benchmark examples of discrete and continuous systems were considered to evaluate the asymmetric formulation and the computational efficiency, accuracy, stability, and time-step-size sensitivity of the new method. Further, the response of the new method was validated using MSM and precise time integration (PTI) methods. An asymmetric state formulation was also validated against MSM and direct frequency response methods. The results revealed that the new method accurately matched the MSM and PTI methods at various time steps. The results also showed that the new method was more efficient than the MSM and PTI methods, rendering it suitable for solving large-scale structural dynamics problems.

Characterizing egress components for wheelchair users in dormitory building fires

People with disabilities are among the most vulnerable groups in building fires. According to the U.S. Fire Administration, an estimated 700 home fires involve people with physical disabilities each year. In parallel, the National Fire Protection Association estimates that 11% of civilian fire deaths were people with disabilities. Despite these statistics, the current body of literature shows few studies focused on the evacuation of disabled people. To bridge this knowledge gap, this paper presents findings on the evacuation processes of wheelchair users in a low-rise apartment (dormitory) building. More specifically, we simulate 1–3 wheelchair users in a dormitory building at our home institution via 327 simulations to examine evacuation time as well as identify structural aids and barriers. As a byproduct of this research, a new dynamic structural ranking system of egress components is proposed for wheelchair users, and a series of suggestions for structural modifications to improve the egressibility of the simulated building are provided.

Numerical-Computational Model for Dynamic Nonlinear Analysis of Frames With Semi-Rigid Connection Considering the Damping Effect

Objective: the scope of this work is to present a numerical-computational model for transient dynamic nonlinear analysis of frames with geometric nonlinearity and semi-rigid connection. Methodology: the equation of motion, which describes the structural dynamical system, is solved by the a-Generalized direct integration method associated with the standard Newton-Raphson method. The structures are discretized through the co-rotational formulation of the Finite Element Method considering the Euler-Bernoulli beam theory. The semi-rigid connection of structural members (beam-column and beam-support) is simulated by a connection element with zero length, which is described in terms of axial, translational and rotational stiffness. Results and conclusion: From the numerical results of three structural systems (bi-fixed beam, L-shaped frame, and single-story frame) obtained with the free Scilab program, it is concluded that the definition of the type of connection is an important factor to be considered in the analysis of frames subjected to dynamic loads. Furthermore, structural damping, which is a measure of energy dissipation, drives the structure from a vibrating state to a resting state. Research implications: Structural Engineering has been designing systems that cannot be analyzed and dimensioned without dynamic effects being considered. lack of knowledge of the levels and characteristics of the dynamic response can lead to system failure during the application of repetitive loading due to accumulation of structural damage. In this sense, the numerical model can represent a valuable engineering tool when it comes to the dynamic analysis of plane metallic structures with geometric nonlinearity.

Sparse Bayesian machine learning for the interpretable identification of nonlinear structural dynamics: Towards the experimental data-driven discovery of a quasi zero stiffness device

Data-driven discovery of governing laws for complex nonlinear structural dynamic systems remains a challenging issue of paramount importance. This work addresses the above issue by leveraging the available noisy data and integrating sparse Bayesian machine learning (ML) techniques to discover the governing equations. The problem of discovery is re-cast as the automatic relevance determination of models (model selection) from the library of potential candidate basis terms and their coefficients are determined (parameter identification) using sparse Bayesian linear regression. Two sparsity promoting ML algorithms based on relevance vector machines have been employed. Both these approaches use Bayesian statistics and quantify the uncertainty associated with the model predictions. Results from four representative numerical examples of nonlinear structural dynamics illustrate excellent performance of both proposed approaches. The results have been validated with the true governing equations and time response data. Comparison has also been made with a recent and popular sparse discovery approach. Finally, the proposed framework is applied to real datasets that were generated from an in-house designed experimental setup of a quasi zero stiffness device and good performance has been observed.

A time variant uncertainty propagation method for high-dimensional dynamic structural system via K-L expansion and Bayesian deep neural network.

In this paper, a time variant uncertainty propagation (TUP) method for dynamic structural system with high-dimensional input variables is proposed. Firstly, an arbitrary stochastic process simulation (ASPS) method based on Karhunen-Loève (K-L) expansion and numerical integration is developed, expressing the stochastic process as the combination of its marginal distributions and eigen functions at several discrete time points. Secondly, the iterative sorting method is implemented to the statistic samples of marginal distributions for matching the constraints of covariance function. Since marginal distributions are directly used to express the stochastic process, the proposed ASPS is suitable for stationary or non-stationary stochastic processes with arbitrary marginal distributions. Thirdly, the high-dimensional TUP problem is converted into several high-dimensional static uncertainty propagation (UP) problems after implementing ASPS. Then, the Bayesian deep neural network based UP method is used to compute the marginal distributions as well as the eigen functions of dynamic system response, the high-dimensional TUP problem can thus be solved. Finally, several numerical examples are used to validate the effectiveness of the proposed method. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.