Structural Analysis Approach Research Articles

Paving the path to urban flood resilience by overcoming barriers: A novel grey structure analysis approach

Paving the path to urban flood resilience by overcoming barriers: A novel grey structure analysis approach

Tailoring Optical and Magnetic Properties in Mn2+-Substituted 2D Magnetic Semiconductor SnS2: A Powder XRD Approach for Electronic Structure Analysis

Tailoring Optical and Magnetic Properties in Mn2+-Substituted 2D Magnetic Semiconductor SnS2: A Powder XRD Approach for Electronic Structure Analysis

A New CDM-Based Approach for the Nonlinear Numerical Structural Analysis of Flax Fiber Reinforced Plastic

Fibre-reinforced polymers based on natural fibers, such as flax fibers, exhibit pronounced nonlinear orthotropic material behavior. This presents a significant challenge in finite element analysis (FEA) simulations, as the nonlinear constitutive models available in commercial FEA tools are difficult to apply and fail to capture all the material’s specific characteristics. Relying on initial or reduced secant moduli in linear quasi-static analyses of deformations or stress states can result in inaccurate outcomes and overly optimistic strength predictions, particularly in compression-dominated cases. However, with appropriate modifications, classical laminate theory (CLT) can be adapted for nonlinear analysis. This involves iteratively updating the components of the stiffness matrix for the unidirectional (UD) ply during the calculation process based on the current strain state and stress interactions. This study presents and discusses a computational algorithm for the FEA software ABAQUS/CAE 2019, which incorporates material-related orthotropic nonlinearities and stress-dependent interactions within the CLT framework. The algorithm represents a single-scale material model at the meso level (UD ply) and is based on the concept of orthotropic elasto-damage within the framework of continuum damage mechanics (CDM) theory. Numerical implementation is achieved through a user-defined field (USDFLD) subroutine, accompanied by a pre-processing Python script for managing experimental data, computing data fields, and calculating parameters. As shown below, this type of implementation appears justified compared to a user material subroutine (UMAT) subroutine in terms of computational efficiency and practicality.

Analytical Finite-Integral-Transform and Gradient-Enhanced Machine Learning Approach for Thermoelastic Analysis of FGM Spherical Structures with Arbitrary Properties

Analytical Finite-Integral-Transform and Gradient-Enhanced Machine Learning Approach for Thermoelastic Analysis of FGM Spherical Structures with Arbitrary Properties

Computational approaches for structural analysis of wood specimens

Abstract The structure tensor (ST), also named a second-moment matrix, is a popular tool in image processing. Usually, its purpose is to evaluate orientation and to conduct local structural analysis. We present an efficient algorithm for computing eigenvalues and linking eigenvectors of the ST derived from a material structure. The performance and efficiency of our approach are demonstrated through several numerical simulations. The proposed approach is evaluated qualitatively and quantitatively using different two-dimensional/three-dimensional wood image types. This article reviews the properties of the first- and second-order STs, their properties, and their application to illustrate their usefulness in analyzing the wood data. Our results demonstrate that the suggested approach achieves a high-quality orientation trajectory from high-resolution micro-computed tomography ( μ {\rm{\mu }} CT)-imaging. These orientations lead to establishing a description of fiber orientation states in thermo-mechanical models for fiber-reinforced composite materials. We conclude with an overview of open research and problem directions.

Factors Affecting the Implementation of the Digital Twins in the Construction Industry: An Interpretive Structural Modelling Analysis

With the extensive use of digital technologies, the modern construction industry has made progress in improving productivity and safety. Despite this, construction productivity remains among the lowest in the industry. With the development of Industrial Revolution 4.0, the construction industry has also benefited from it, forming the idea behind Construction 4.0, which is founded on: the digitization of the construction sector and the industrialization of the construction procedure. The current use of digital twins in the construction industry is considered among the most successful and influential ways to achieve Construction 4.0, optimize construction links, and improve coordination among stakeholders. Many factors are impacting the construction industry's adoption of digital twins. Previous studies have failed to propose a practical model to facilitate the implementation of digital twins in the construction industry. This study fills this gap. First, this study conducted a systematic literature review (SLR) based on Web of Science and Scopus databases to identify negative and positive factors. SLR identifies four aspects of factors, including technical factors, stakeholder factors, external factors, and economic factors. A conceptual model is then proposed using an interpretive structural modeling analysis approach. The model is used to describe the interaction of these factors in digital twins implementation, and finally, recommendations are made to mitigate the negative factors based on the model. This conceptual model helps guide digital twin implementation in the construction industry and helps build a knowledge system of digital technology. The findings will support practitioners in the construction industry using or planning to use digital twins.

Shear Stress Solutions for Curved Beams: A Structural Analysis Approach.

The shear stress on isotropic curved beams with compact sections and variable thickness is investigated. Two new solutions, based on Cook's proposal and the mechanics of materials approach, were developed and validated using computational finite element models (FEM) for four typical cross-sections (rectangular, circular, elliptical, and triangular) used in civil and mechanical structures, constituting a novel approach to predicting shear stresses in curved beams. They predict better results than other reported equations, are simpler and easier for engineers to use quickly, and join the group of equations found using the theory of elasticity, thereby expanding the field of knowledge. The results reveal that both equations are suitable to predict the shear stress on a curved beam with outer/inner radii ratios in the interval 1<b/a ≤ 5 aspect ratios. There is a maximum relative difference between the present solutions and finite element models of 8% within 1<b/a ≤ 2, and a maximum of 16% in 2<b/a ≤ 5. Additionally, the neutral axis of the curved beam can be located with the proposed solution and its position matches with that predicted by FEM. The displacement at the top face of the end of the curved beam induces a difference in the shear stress results of 8.0%, 7.0%, 6.5%, and 2.9%, for the circular, rectangular, elliptical, and triangular cross-sections, respectively, when a 3D FEM solution is considered. For small b/a ratios (near 1), the present solutions can be reduced to Collignon's formula.

RNA tertiary structure and conformational dynamics revealed by BASH MaP.

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

Guitar music by Heitor Villa-Lobos: genre traditions of Brazilian choro

The relevance of the article is to consider Brazilian guitar music as an important component of the modern South American repertoire. On the example of choros of famous composer Heitor Villa-Lobos connections with the folklore traditions of the country, Afro-Brazilian, AfroCuban primary and derivative patterns of the genre have been revealed. The main objective of the study is to discover in H. Villa-Lobos's guitar music metrorhythmic and intonation-melodic archetypes of the Brazilian choro genre as an essential component of the southeastern regions of the country. The novelty of the article consists in the introduction to the Ukrainian musicological circulation of the genre-style analysis of the guitar works of H. VillaLobos, which, thanks to the names, are defined by the author as representatives of the specifics of this genre phenomenon. The methodology is based on an interdisciplinary approach and methods of historical, cultural, musicological, systemic, structural, and functional analysis, applied to the study of the genesis of the choro rhythmic archetype, primary ethnic sources, intonation-melodic manifestations and stylistic interactions in the composer's music. Results and conclusions. Choro is considered as one of the main heraldic genres of Brazilian musical culture, spread to a large extent in the southeastern regions of Rio de Janeiro and São Paulo. Among the local folk traditions of music-making in the middle of the 20th century choro (ancient wailing) and samba belong to the hybrid field of genres of Afro-Brazilian roots. The genesis of choro is connected with the local folklore of street musicians (choroens), as well as with tropical soundscapes and noise signals of the "polyphony of the city". Choro is characterized as a genre model, an instrumental composition of performers, singing, dancing and a figurative state of suffering. Linguistic and genre features of choro are revealed in the example of guitar works from the "Popular Brazilian Suite" by Heitor Villa-Lobos and his solo guitar composition from the largescale cycle of the same name "Chôros" for various ensembles of performers. Connections of choro with mazurka, ecossaise (scottish), waltz, gavotte are determined. The means of creating sound effects of ensemble playing thanks to the idiomatic resources of the solo guitar are considered. In the process of analytical research, a schematic generalization of the rhythmic choro archetype is proposed. The basic and derivative patterns of the archetype create a system of variant means of alternating rhythmic formulas that generalize the features of Afro-Cuban, Afro-Brazilian habanera, African lundu and batuk, European and Brazilian polka. The modernist means of choro representation are characterized thanks to the comparison of the guitar version with the language findings of the following parts of the grandiose cycle "Chôros": the use of Indian ritual-tribal folklore elements, techniques of sound imitation, stylistic allusions, a modern language arsenal, which together with the further development of the fundamental technique of playing the guitar with movements of the same positions along the guitar neck formed their individual and stylistic platform of musical thinking.

A novel deterministic sampling approach for the reliability analysis of high-dimensional structures

Overcoming the “curse of dimensionality” in high-dimensional reliability analysis is still an enduring challenge. This paper proposes an innovative deterministic sampling method designed to overcome this challenge. The approach starts with a two-dimensional uniform point set, generated using the good lattice point method. This set is then refined through the cutting method to produce a specific number of points. A novel generating vector is computed based on this method, enabling the generation of the targeted high-dimensional point set through a strategic dimension-by-dimension mapping. Notably, this method eliminates the need for complex congruence computation and primitive root optimization, enhancing its efficiency for high-dimensional sampling. The resulting point set is deterministic and uniform, greatly reducing variability in reliability analysis. Then, the proposed approach is integrated into the fractional exponential moment-based maximum entropy method with the Box–Cox transform. This integration efficiently recovers the probability distribution for the limit state function (LSF) with high-dimensional inputs, enabling precise assessment of the failure probability. The efficacy of the proposed method is demonstrated through three high-dimensional numerical examples, involving both explicit and implicit LSFs, highlighting its applicability for high-dimensional reliability analysis of structures.

Research on Corporate Social Responsibility in Public Relations: A Hybrid Review Through Topic Modeling Analysis and Way Forward

ABSTRACTDespite the growing significance of corporate social responsibility (CSR) in corporate public relations (PR) strategies, there has been a paucity of comprehensive reviews of the extant CSR literature within the PR discipline. The aim of this study is to investigate the relationship between the concept of CSR and the discipline of PR through a hybrid concept‐discipline‐focused review. We employed latent dirichlet allocation (LDA) via topic modeling as a novel approach for intellectual structure analysis. The dataset for this review comprised 246 peer‐reviewed articles on CSR published between 1975 and 2022. Based on the LDA results, five topics were identified and discussed: the political CSR stream, social media‐oriented CSR, consumer‐oriented CSR, CSR reporting, and business‐oriented CSR. Additionally, implications for both academic and practical applications were drawn, along with recommendations for future research. This study is pioneering in its contribution to the literature and practice of CSR‐related PR research, offering insights into the intellectual structures and trends within this field.

A reliability calculation method based on ISSA-BP neural network

PurposeTo address the shortcomings of the traditional back propagation (BP) neural network agent model, such as insufficient fitting accuracy and low computational efficiency, an improved method is proposed.Design/methodology/approachIn this study, an improved sparrow search algorithm (ISSA) is developed to optimize the reliability calculation of the BP neural network (ISSA-BP) using an enhanced BP neural network model. The traditional sparrow search algorithm is enhanced by incorporating a golden sine strategy to improve its position-updating mechanism, thereby overcoming its tendency to converge prematurely to local optima. Additionally, an opposition-based learning strategy is integrated to explore the reverse solution around the optimal solution of the sparrow search algorithm, mitigating the risk of local optima.FindingsThe results of the test function demonstrate that the proposed method significantly enhances fitting accuracy while maintaining computational efficiency. Finally, by applying this approach to the metro bogie frame as a case study, the structural reliability of the bogie frame is evaluated using the Monte Carlo method, providing valuable insights for subsequent analysis and structural optimization.Originality/valueThe use of the surrogate model approach for structural reliability analysis significantly improves solution efficiency. Furthermore, the integration of ISSA with the BP neural network enhances both fitting accuracy and computational efficiency, demonstrating the superiority and practicality of the proposed method.

Full-field Modal Analysis of a Tensegrity Column Using a Three-dimensional Scanning Laser Doppler Vibrometer with a Mirror

Abstract Tensegrity structures become important components of various engineering structures due to their high stiffness, light weight, and deployable capability. Existing studies on their dynamic analyses mainly focus on responses of their nodal points while overlook deformations of their cable and strut members. This study proposes a non-contact approach for experimental modal analysis of a tensegrity structure to identify its three-dimensional (3D) natural frequencies and full-field mode shapes, which include modes with deformations of its cable and strut members. A 3D scanning laser Doppler vibrometer is used with a mirror for extending its field of view to measure full-field vibration of a novel three-strut metal tensegrity column with free boundaries. Tensions and axial stiffnesses of its cable members are determined using natural frequencies of their transverse and longitudinal modes, respectively, to build its theoretical model for dynamic analysis and model validation purposes. Modal assurance criterion (MAC) values between experimental and theoretical mode shapes are used to identify their paired modes. Modal parameters of the first 15 elastic modes of the tensegrity column identified from the experiment, including those of the overall structure and its cable members, can be classified into five mode groups depending on their types. Modes paired between experimental and theoretical results have MAC values larger than 78%. Differences between natural frequencies of paired modes of the tensegrity column are less than 15%. The proposed non-contact 3D vibration measurement approach allows accurate estimation of 3D full-field modal parameters of the tensegrity column.

A novel computer vision and point cloud-based approach for accurate structural analysis of a tall irregular timber structure

Wood material has been widely used in critical heritage structures such as pagodas, totem poles, and large-scale sculptures. Conducting rigorous structural analysis is crucial to protect these structures in high-seismic regions. Typically, these types of structures are modeled using an equivalent finite element (FE) model which used simple cylinder with a constant cross section equal to the average of the top and bottom cross section. However, simplified equivalent FE models may not accurately consider the irregularities and complexities of these structures. In this paper, advanced computer vision and point cloud techniques were adopted to accurately and rapidly construct a FE model of a 30-meter irregular timber sculpture. This was achieved using video scans, computer vision-aided 3D reconstruction, point cloud processing, and mesh to solid element conversion. The refined FE model was used to conduct capacity check, mesh sensitivity study, pushover analysis, and response spectrum analysis. The results of the refined FE model were compared to an equivalent FE model. The results show: 1) the proposed numerical modeling methodology for structural analysis can efficiently and accurately measure the dimension of the irregular sculpture up to 98.2 % accuracy; 2) the lateral stiffnesses of the 30-meter irregular sculpture vary significantly (up to 42.6 %) from one direction to the other; 3) the equivalent FE model overestimated the shear and moment capacities by 20.6 % and 13.2 %, respectively; 4) on average, the equivalent FE model overestimated the shear and moment demands by 8.9 % and 5.5 %, respectively. Overall, the proposed application has demonstrated a fast, economical and accurate method to conduct seismic evaluation and design for irregular structures.

Network spillover effects and path analysis of shocks - An empirical study in China

The study of interconnections between various sectors of the national economy is crucial for understanding the pattern and pace of macroeconomic growth. This paper analyzes the macroeconomic impact of shocks occurring in specific sectors through both supply and demand perspectives and proposes a combination of bottom-up and top-down structural path analysis approaches to trace the transmission path of network spillover effects, where shocks in this paper refer to microeconomic productivity changes and network spillover is defined as the effect on GDP due to the propagation of shocks to other sectors. The research results found that the total spillover effect of primary and secondary industry sectors in China shows an inverted U-shape, and the total spillover effect of tertiary industry sectors shows an upward trend. A large total spillover effect of a sector does not mean that both upward and downward spillover effects are large; for example, the construction industry has high upward spillover effects and low downward spillover effects. The spillover effect of each production layer decreases as the path lengthens, and the distribution is L-shaped. In addition, by identifying the critical paths of spillover effects, we find that the spillover effects of labor-intensive industries, such as wholesale and retail, are decreasing year by year, and the spillover effects of the paths related to the information technology industry are gradually occupying an important position.

Enhancing Organizational Citizenship Behavior Through Leader Behavior and Job Satisfaction in Construction Company Associations

This study aims to elucidate the influence of job satisfaction and leader behavior variables in enhancing organizational citizenship behavior (OCB). The sample consists of large qualified contractor companies and medium qualified contractor companies. Respondents were identified using purposive sampling technique for each category within a target population. Correlational analysis is employed using the Generalized Structured Component Analysis (GSCA) approach, an inventive approach to the Structural Equation Model (SEM). The GSCA analysis is used to derive a powerful structural model for predictive purposes. The research results show that leader behaviour and job satisfaction have significantly and simultaneously effect on OCB. Keywords: behaviour, job satisfaction, Organizational Citizenship Behaviour (OCB)

Adaptive Leadership, Employee Recognition and Employee Engagement as Mediator to Employee’s Agility: The Point of View from Supervisors Level Up to Manager

There are three aims of this research: to determine the role of employee recognition on employee agility, the role of adaptive leadership on employee agility, and the role of employee engagement as a mediator between adaptive leadership and employee recognition on employee agility from supervisor’s level up to manager in a manufacturing company focuses in automotive in Jakarta. This research is based on a comprehensive review of existing literature on adaptive leadership, employee recognition, engagement, and agility. The research approach applied in this study is quantitative, using a Partial Least Square Structural Equation Modelling analysis approach. The data for this research was collected through purposive sampling, with the object of this research being one of the subsidiary companies that has implemented the employee agility approach. The findings of this research indicate that adaptive leadership does not have a significant positive influence on employee agility, nor does employee recognition. Additionally, employee engagement was found not to mediate the influence of employee recognition on employee agility.

A Review of Artificial Neural Networks (ANNs) as a Potential Predictive Tool for the Performance of Scissor-Type Deployable Bridges

After a severe disaster, some places may be unreachable for rescue operations due to bridge destruction. A scissor-type deployable bridge is a novel rescue technology that enables a lifeline to be quickly recovered during a catastrophic event. Several structural analysis approaches were used to predict the structural behavior of deployable bridges. Yet none of the prior studies used Artificial Neural Networks (ANNs) to predict the structural behavior of scissor-type deployable bridges. This research explores the potentiality of Artificial Neural Networks (ANNs) to predict the performance of a scissor-type deployable bridge. The study aims to leverage the capabilities of ANNs in modeling complex relationships to forecast key parameters related to the bridge's functionality. ANNs can assist engineers in optimizing the design parameters of scissor-type deployable bridges by predicting how different configurations affect total deformation and stress levels. The analysis involves training the neural network with relevant data to enable it to learn and generalize patterns, enabling more informed predictions for diverse scenarios. Lastly, the application of ANNs in simulating bridge behavior contributes to advancing research in structural engineering, particularly in the field of deployable structures, by providing insights into complex structural responses that are challenging to model analytically. Keywords: Deployable Bridge, Mobile Bridge, Scissor-Type Bridge, Aluminum Alloy, Artificial Neural Networks (ANNs), MATLAB

Management of innovative projects to ensure innovative development of enterprises

The subject of the study is theoretical provisions, methodology of adaptive management of innovation projects and innovative development of enterprises, methodological basis, practical proposals for its research in modern conditions. The purpose of the article is to summarize generalization of the theoretical basis of the features of innovative projects, the expediency of using organizational structures for managing the processes of their formation and implementation, analysis of adaptive methodologies for managing innovative projects to ensure the employment of workers, their innovative development, and development of practical recommendations on these issues for a working enterprise. The purpose is revealed by solving the following tasks: generalize the features of innovative projects that determine the success of their implementation; determine the feasibility of using organizational structures of enterprise management for the formation and implementation of innovative projects to ensure innovative development; identify and propose the use of adaptive management methodologies for innovative projects to achieve successful innovative development of enterprises; develop a hybrid organizational structure for innovation management; the main directions of innovative personnel management of enterprises to promote their employment and management of innovative development are proposed. To solve the tasks, the following methods were used: theoretical generalization, analysis, synthesis, induction and deduction, structural and logical analysis, systemic and situational approaches, the method of working day photography, Agile adaptive management methodology, graphic method, and the authors' own practical experience. The conclusions and results of the article are as follows: it is proved that the management of innovation projects should take into account their features, be based on the use of project, matrix and hybrid organizational management structures and the application of adaptive methodologies for managing innovation development and the formation and implementation of innovation projects, such as Agile, Scrum, Kanban, Lean. For the operating enterprise Nebozvid LLC, using adaptive management methodologies, a hybrid organizational structure of the project department has been developed to ensure the innovative development of the enterprise through the implementation of innovative projects with the organization of small project teams. The main trends in innovative personnel management of enterprises are proposed, the use of which contributes to the success of innovative project management to ensure the innovative development of enterprises.

Strength Lab AI: a mixture-of-experts deep learning approach for limit state analysis and design of monolithic and laminate structures made of glass

The demand for transparent building envelopes, particularly glass facades, is rising in modern architecture. These facades are expected to meet multiple objectives, including aesthetic appeal, durability, quick installation, transparency, and both economic and ecological efficiency. At the heart of facade design, particularly for structural glass elements, lies the assurance of structural integrity for ultimate and serviceability limit states with a requisite level of reliability. However, current structural engineering assessments for glass and glass laminate designs, especially in the geometrically non-linear setting, are time-consuming and require significant expertise. This study develops a customized Mixture-of-Experts (MoE) neural network architecture to overcome current limitations. It calibrates it on synthetically generated stress and deformation data obtained via parametrized Finite-Element-Analysis (FEA) of glass and glass laminate structures under both geometrically linear and nonlinear conditions for several joint support and loading conditions. Our findings reveal that the MoE model outperforms baseline models in predicting laminate deflections and stresses, offering a substantial increase in computational efficiency, compared to traditional linear and non-linear FEA, at high accuracy. The MoE is integrated within a novel web-based glass design and verification tool called Strength Lab AI and provided to the engineering public for future use. These results have profound implications for advancing engineering practice, offering a robust tool for the intricate structural design and analysis of glass and glass laminate structures.