Structure Of Complex Systems Research Articles

Assessment of the Usage of Innovative Integrated Photogrammetry Method in Structural Behaviour Analysis: The Case of Mardin Melik Mahmut Mosque

The integration of photogrammetric modeling with the finite element method has emerged as a valuable approach in the practical analysis and determination of structural behavior. Photogrammetry, a technique that utilizes the analysis and processing of photographs to create three-dimensional models of real-world objects, offers a powerful tool for capturing accurate geometric data. On the other hand, the finite element method provides a numerical framework for the mathematical modeling and analysis of complex structural systems. This article presents a methodology that integrates these two modeling techniques by comparing their performance with integrated and classical models. Classical modelling is a technique performed in a finite element software and has been applied for many years. Accordingly, this study aims to reveal the limitations of photogrammetry, develop solutions to these limitations, and provide integrated model generation. The material definitions and load assignments were kept consistent across both modeling techniques for observing the differences in analysis. The study used SAP2000 and modal analysis was conducted to analyse the behaviour and to compare the geometrical properties of the models independent of region and time. The obtained results reveal a high level of similarity, with the natural vibration periods of the models demonstrating 98%, the stress values exhibiting 90%, and deformation values 90% similarity a result of the dynamic loads applied in the X-Y direction. Consequently, this study highlights the potential for further improvement in the integrated model generation process by identifying the advantages and disadvantages of each modeling technique.

Damages on complex structural systems: Effects on load paths

Damages on complex structural systems: Effects on load paths

A machine learning approach to predicting dynamical observables from network structure

Estimating the outcome of a given dynamical process from structural features is a key unsolved challenge in network science. This goal is hampered by difficulties associated with nonlinearities, correlations and feedbacks between the structure and dynamics of complex systems. In this work, we develop an approach based on machine learning algorithms that provides an important step towards understanding the relationship between the structure and dynamics of networks. In particular, it allows us to estimate from the network structure the outbreak size of a disease starting from a single node, as well as the degree of synchronicity of a system made up of Kuramoto oscillators. We show which topological features of the network are key for this estimation and provide a ranking of the importance of network metrics with much higher accuracy than previously done. For epidemic propagation, the k-core plays a fundamental role, while for synchronization, the betweenness centrality and accessibility are the measures most related to the state of an oscillator. For all the networks, we find that random forests can predict the outbreak size or synchronization state with high accuracy, indicating that the network structure plays a fundamental role in the spreading process. Our approach is general and can be applied to almost any dynamic process running on complex networks. Also, our work is an important step towards applying machine learning methods to unravel dynamical patterns that emerge in complex networked systems.

Analysis of the internal motions of thermoresponsive polymers and single chain nanoparticles.

Data-driven techniques, such as proper orthogonal decomposition (POD) and uniform manifold approximation & projection (UMAP), are powerful methods for understanding polymer behavior in complex systems that extend beyond ideal conditions. They are based on the principle that low-dimensional behaviors are often embedded within the structure and dynamics of complex systems. Here, the internal motions of a thermoresponsive, LCST polymer are investigated for two cases: (1) the coil-to-globule transition that occurs as the system is heated above its critical temperature and (2) intramolecularly crosslinked, single chain nanoparticles (SCNPs) both above and below the critical temperature (TC). Our results demonstrate that POD can successfully extract the key features of the dynamics for both polymer globules and SCNPs. In the globular state, our results show that the relaxation modes are distorted relative to the coil state and relaxation times decrease upon chain collapse. After randomly crosslinking a globule to produce a SCNP, we observe a further distortion of the relaxation modes that depends strongly upon the particular set of monomers that are crosslinked. Yet, different sets of crosslinked monomers produce similar relaxation times for the SCNP. We observe that for SCNPs below the critical temperature, the relaxation times decrease with increasing crosslink density while above the critical temperature, they increase as crosslink density increases. Finally, using UMAP we categorize the local structure of SCNPs and examine the influence of the local structure on SCNP relaxation dynamics.

A paradigm for building fire safety

The aim of study is to describe the fire safety paradigm using the concept of T. Kuhn, its components, and its role and significance for the further development of construction science, particularly the fire safety of buildings. The components of the fire safety paradigm form a complex structure (system) that is presented graphically to illustrate the interconnections and interactions between them. This structure is built by analogy with a three-dimensional coordinate system using linguistic quantities. Currently, it is not yet possible to assign a sequence of numbers representing the coordinates of a point in the space of this system. The three axes of this system determine the major groups of paradigm elements: - fire safety; - components; - activities and inputs. For each group, the components were distinguished, which were then briefly described and characterised, emphasising their mutual connections and importance for the fire safety of buildings. Some significant gaps in the systemic approach to fire safety in the EU were discussed and illustrated by the example of Grenfell Tower fire in London. The paradigm described is universal, and its universality is based on the possession of certain common attributes characteristic of the fire safety environment and their interpretation, as well as the manner in which fire safety entities implement them. A paradigm shift will result in the introduction of a fire toxicity criterion for the assessment of construction products, which, for unknown reasons, has so far only been implemented in relation to cables. The second necessary amendment is the addition of a requirement for the spread of fires on building facades.

Self-Powered Actively Controlled Lateral Suspensions of High-Speed Trains Using Energy-Regenerative Electromagnetic Damper and Model Predictive Control

Although traditional active suspension can offer superior riding comfort and maneuverability over its semiactive and passive counterparts, its reliance on an external power supply has hindered its widespread applications in vehicles. To overcome this deficiency, this paper proposes an innovative self-powered active suspension design for a high-speed train (HST), by leveraging the recently emerging H-bridge circuit-based electromagnetic damper (HB-EMD), allowing bidirectional power flow between the damper and controlled system. The capability of HB-EBD to achieve unique self-powered active skyhook control was previously proved in a simplified single degree-of-freedom (SDOF) structure under harmonic excitations; however, the feasibility of employing HB-EMD to realize active vibration control for more complex structural systems under stochastic excitations remains an unanswered question. One main challenge is designing a novel control algorithm that can simultaneously realize vibration control and self-powering objectives, which is unattainable by traditional active control algorithms. In this study, an ad hoc model predictive controller (MPC) is designed to guarantee the fulfillment of these dual objectives. To evaluate the performance of the proposed active suspension design, two separate HB-EMDs are implemented on the front and rear sides of the secondary lateral suspensions of an HST model subjected to stochastic track irregularities. At a speed of 200[Formula: see text]km/h, the proposed HB-EMDs with MPC could achieve a 55% reduction in the lateral acceleration of the car body in comparison with passive suspension, meanwhile maintaining energy harvesting performance with an average output power of 25.0[Formula: see text]W. In contrast, a traditional active linear quadratic Gaussian (LQG) controller consumes 72.7[Formula: see text]W power when performing comparable vibration reduction. This study, for the first time, validates the feasibility of designing a self-powered, actively controlled secondary lateral HST suspension system without relying on an external power source, which will potentially pave the way for a new active vibration control paradigm for other generic structures.

Matrix structural analysis of beams on elastic supports: implementation in Python

Technological resources have made it possible to model natural phenomena and engineering problems more accurately, thus allowing better results. This article presents the computational implementation of a procedure for matrix structural analysis of beams. Both rigid and elastic supports were considered to represent the contact between the structural model and the external environment. A computational code was created in Python language. The matrix method allows a detailed analysis of the structural response. The developed code calculates stiffness matrices and force vectors of beam elements, which are assembled into a global stiffness matrix and a global force vector, and the equilibrium equation system is then solved. Results such as nodal displacements and slopes, support reactions, and internal forces are obtained. Different support conditions, namely fixed, hinged, and elastic supports, as well as different loading conditions, were investigated for beams. Results highlight that the presence of elastic supports can significantly affect the overall structural behavior of the system. By incorporating these effects into the analysis, more accurate predictions of structural response can be obtained, leading to safer and more economical solutions. The article provides a basis for further investigation of the behavior of complex structural systems. The use of Python language can be justified as it provides a versatile and efficient platform for conducting structural analysis and is suitable for future advances in the field of structural engineering.

Advances in the application of network analysis methods in traditional Chinese medicine research

ObjectiveThis review aims at evaluating the role and potential applications of network analysis methods in the medicinal substances of traditional Chinese medicine (TCM), theories of TCM compatibility, properties of herbs, and TCM syndromes. MethodsLiterature was retrieved from databases, such as CNKI, PubMed, and Web of Science, using keywords, including "network analysis," "network biology," "network pharmacology," and "network medicine." The extracted literature included the biological network construction (including ingredient-target and target-disease relations), analysis of network topology characteristics (including node degree, clustering coefficient, and path length), network modularization analysis, functional annotation and so on. These studies were categorized and organized based on their research methods, application domains, and other relevant characteristics. ResultsNetwork analysis algorithms, such as network distance, random walk, matrix factorization, graph embedding, and graph neural networks, are widely applied in fields related to the properties, compatibility, and mechanisms of TCM. They effectively reflect the interactive relations within the complex systems of TCM and elucidate and clarify theories, such as the effective substances, the principles of TCM compatibility, the TCM syndromes, and the properties of TCM. ConclusionThe network analysis method is a powerful mathematical and computational tool that reveals the structure, dynamics, and functions of complex systems by analyzing the elements and their relations. This approach has effectively promoted the modernization of TCM, providing essential theoretical and practical tools for personalized treatment and scientific research on TCM. It also offers a significant methodological framework for the modernization and internationalization of TCM.

Self-assembly aggregation-induced emission from Eu (III) complexes with o-hydroxy-benzophenone ligands

In this work, europium (III) complexes were synthesized by using different o-hydroxy-benzophenone ligands, namely 2-hydroxy-4-octyloxybenzophenone (HL1), 4-allyloxy-2-hydroxybenzophenone (HL2) and 2-hydroxy-4-methoxybenzophenone (HL3). The structures and optical properties of the EuIII complexes E1, E2 and E3 were characterized by elemental analysis, infrared/fluorescence spectroscopy and mass spectrometry. The aggregation-induced emission properties from E1, E2 and E3 were systematically investigated in the acetone/water binary mixtures. At the same time, the corresponding agglomeration behavior and self-assembly structures in the EuIII complex system was confirmed through scanning electron microscopy. Excellent luminescent performance was illustrated in sample of E1 (the luminescence quantum yield of 21.36 % in solid state) which should be attributed to the aggregation-induced emission from better self-assembly behavior with longer carbon chain in ligand. The results of ultraviolet aging resistance from E1 reveal that the self-assembly behavior could not only improve the aggregation-induced emission performance, but also enhance the photostability of EuIII complex compared with the traditional β-diketone EuIII complex. This discovery may provide a facile strategy to design and prepare more promising rare earth complexes with good luminescence properties and strong photostability for application in solid state.

Quantification and Sensitivity Analysis of the Model Uncertainty in the Projectile-Barrel Coupled Artillery Dynamics System Based on Random Matrix Theory

Simplifications in the engineering of artillery firing dynamics modeling introduce model uncertainty, which seriously affects the model’s predictive performance. To this end, a typical variable-section hollow cylindrical cantilever beam structure in artillery firing dynamics is investigated in this paper. The statistical approximation methods quantify parameter and model uncertainty separately, and their respective confidence intervals were calculated and compared. The results show that the traditional parameter uncertainty approach overestimates the parameter uncertainty ranges, and introducing the model uncertainty approach dramatically improves the model prediction performance. Additionally, a projectile-barrel coupled artillery dynamics model considering model uncertainty is established, and its effectiveness is validated through the actual launch experiments. The sensitivity of the dynamic responses to the system matrix, namely model uncertainty, is further analyzed. This paper provides theoretical references for the study of uncertainty in artillery launch dynamics and proposes effective methods to enhance the predictability of computational models for complex structural systems.

Dynamic instability and nonlinear response analysis of nanocomposite sandwich arches with viscoelastic cores

This paper presents a comprehensive study of the nonlinear dynamic behavior and snap-through phenomena in sandwich arch structures with viscoelastic cores and carbon nanotube-reinforced nanocomposite face sheets. Subjected to uniform time-dependent pressure shocks, these arches exhibit complex snap-through behavior critical for practical engineering applications. Utilizing third-order shear deformation theory, the study accurately captures nonlinear behaviors. The viscoelastic core, modeled with the Kelvin-Voigt law, enhances damping and reduces vibration amplitudes. Numerical solutions are obtained using a Chebyshev-based Ritz method, Newmark integration, and Newton-Raphson method. The Budiansky-Ruth criterion evaluates dynamic buckling loads. Key findings include significant instability near buckling loads, increased buckling loads and vibration damping due to viscoelastic effects, reduced buckling loads with foam cores, improved performance with CNTs, and more pronounced CNT effects with greater deflections. Additional conclusions highlight the sensitivity of dynamic snap-through to geometric parameters and the superior accuracy of the proposed approach compared to traditional models. This research advances the understanding and design strategies for nonlinear sandwich arch structures, enhancing predictive capabilities in complex structural systems.

Influence of structure-soil-structure interaction on seismic response and seismic migration performance of building structures

Increasing numbers of complex structure systems with underground structures crossing a cluster of aboveground structures are being constructed in densely populated urban areas. The complex dynamic interaction between the underground structure, aboveground structure, and the surrounding soil, is called structure-soil-structure interaction (SSSI). The SSSI effects bring challenges to the seismic design of such complex structure systems. This study aims to analyze the influence of adjacent aboveground and underground structures on seismic response and seismic migration performance of building structures using dynamic finite element simulations. The direct method is used to establish structures and soil. A comprehensive numerical analysis is first presented to study the impact of adjacent aboveground and underground structures on target building structures by varying the distance, form, and size of adjacent structures, etc. Then, the seismic migration performance of building structures equipped with the tuned mass damper (TMD) is investigated. The numerical results indicate that the presence of adjacent aboveground structures has a limited impact on the seismic response of the target structure. The seismic response of the target structure is significantly affected by the adjacent underground structures. The TMD designed according to the traditional method may be detuned when the SSI effect is significant. The TMD performance is improved by considering the adjacent structures.

Physics-informed neural networks for localized assessment in Euler-Bernoulli beams

This study employs the Physics-Informed Neural Networks (PINNs) to address an inverse problem, specifically evaluating local changes in material properties within an Euler-Bernoulli beam through numerical simulations. In our numerical model, a viscoelastic material is strategically introduced to a localized zone of the beam, inducing non-constant and complex alterations in the storage modulus. The training process incorporates the partial differential equation into the neural network's loss function, coupled with essential stability conditions for the machine learning model, enhancing the efficiency of the training process. Notably, this methodology excels in characterizing structural behavior influenced by these intricate local changes in material properties, bypassing the need to identify the entire structure and consequently to deal with uncertainties related to boundary conditions. The effectiveness of the approach is validated through numerical simulations, underscoring its potential for applications in efficiently assessing and understanding local material alterations within complex structural systems.

Antioxidant Activity of Asphaltene and Resinous Components in the Magnetic Field

Using the voltammetric method of oxygen electroreduction, an analysis of the antioxidant activity of resin-asphaltene components isolated from 2 crude oils of different compositions before and after exposure to a magnetic field was carried out. For the studied asphaltenes, weakly polar and polar resins, an increase or decrease in antioxidant activity is observed with increasing sample concentration in the background electrolyte solution. Petroleum antioxidants are part of a complex colloidal structural system and are associated with it by associative interactions that arise as a result of changes in the size and activity of associates of the petroleum system as a whole.

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

Applying relational autonomy in US special education: Educators’ role in supporting parents’ decisions for deaf or hard-of-hearing children

ABSTRACT The concept of relational autonomy is widely applied in bioethics research to elucidate how patients’ abilities in agentively making medical decisions are constrained within a complex structural system and set of relations; thus, rather than being individualistic in nature, autonomy is relationally and interdependently situated. While this perspective is thought-provoking, it is seldom explored in settings outside of healthcare. An equally critical site of decision-making processes is found in special education, where parents of children with disabilities must contend with information from both teachers and physicians to make choices that best support their child’s learning and development. Families of deaf or hard-of-hearing (D/HH) children in particular are uniquely positioned to encounter decisions that intersect educational, medical, and sociocultural levels – that is, in the earliest years of a D/HH child’s life, parents must make decisions about the child’s medical care and whether they will receive hearing technology or treatments, which in turn shapes the inherently cultural decision of linguistic choice, which in turn shapes educational decisions about the type of schooling environment for the child. This study broadly asks: In the field of special education, how do parents make decisions for their D/HH child? Data is drawn from semi-structured, ethnographic interviews with 16 educators of the deaf or hard-of-hearing (EODs) across public, private, and state levels in California. IMPACT STATEMENT The findings can help contribute academic research into conversation with social justice-oriented work to improve language and educational access for D/HH children.

Research on the Parametric Generation Method of Model with Multi-Information for the Wooden Architectural Heritage

ABSTRACT There are many traditional wooden buildings with complex structural systems, which leads to tedious modelling processes, and challenges to achieve multidisciplinary collaboration between architecture and structure. This study establish a multi-information model using information and parameterization methods. First, this study identifies typical traditional Chuan-Dou style wood frames in Jingdezhen as an example and uses the parameterization method to quickly generate building information models based on the composition rules of traditional wooden buildings using the Revit-Dynamo platform. Second, by embedding mechanical parameters in the model components, the building and structure information are integrated into a multidimensional integrated model. Finally, a method for applying the generated multi-information model in the project is discussed. This building and structure multi-information model verifies the rationality of the component structure in the building model in real time, thereby optimizing the design scheme and achieving the full-scale recording of the detailed statistics of the building and structural information of the components. These research results have significantly improved the modelling speed of traditional wooden buildings, simplified the tedious process from architectural design to structural calculation, and provided an important research foundation for the intelligent and digital protection of architectural heritage.

Reliability evaluation of reinforced concrete arch bridges during construction based on LGSSA‐SVR hybrid algorithm

AbstractDuring the cantilever casting process of reinforced concrete arch bridges with cantilever cast‐in‐situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200 m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502–3.608. The indexes of buckling cable stress reliability are 3.806–6.784. The indexes of arch ring stress reliability are 4.379–7.562, and the indexes of buckling tower deflection reliability are 3.608–8.123.

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.

A scalable synergy-first backbone decomposition of higher-order structures in complex systems

In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.