Search results

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN).

The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis).

Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects.

By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry.

Digital transformation and innovation-driven development have become an international consensus. The purpose of this paper is to examine the effects of relationships, mechanisms and economic consequences between digital transformation and enterprise innovation quality in order to provide a benchmark for developing countries to implement digital transformation strategies and innovation-driven strategies and provide a major support for economic recovery in the post-coronavirus disease 2019 (COVID-19) era.

Using microdata from A-share listed enterprises in Shanghai and Shenzhen from 2010 to 2021, this study examines the relationship between digital transformation and enterprise innovation quality and further reveals the internal logic and economic consequences of digital transformation to improve enterprise innovation quality through the mediating effect and moderating effect models.

The results demonstrate that digital transformation is beneficial for improving enterprise innovation quality. The heterogeneity test demonstrates that digital transformation has a larger effect on improving enterprise innovation quality in non-state-owned enterprises and eastern enterprises in China. The mechanism test demonstrates that digital transformation can improve enterprise innovation quality by improving internal control quality and analyst attention. Furthermore, with the increase in enterprise innovation inputs, digital transformation plays a significantly stronger role in improving enterprise innovation quality. The extended analysis demonstrates that digital transformation can significantly improve enterprise financial performance by improving innovation quality.

First, the construction of the core explanatory variable digital transformation index in this study is based on the Python data analysis software, which calculates the frequency of digital transformation in the text of the business situation analysis portion of the annual report of the listed companies and then obtains the degree of digital transformation of the company in this year. There may be some deviation from the degree of digital transformation in the actual production and operation of enterprises. Second, in addition to internal control quality and analyst attention, are there other mediating mechanisms for the impact of digital transformation on the quality of enterprise innovation? Third, whether the moderating effect of innovation input on digital transformation and innovation quality is related to human capital factors of the research and development (R&D) team, such as the technical background of R&D personnel, etc.

This study enriches the relevant theories of digital transformation and broadens the research boundaries of digital transformation and enterprise innovation. This study's result provides an empirical basis for enterprises to improve enterprise innovation quality and financial performance from the perspective of digital transformation at the micro level and points out specific practical directions, combining theory with practice.

This study aims to improve the stability and obstacle surmounting ability of the traditional wall-climbing robot on the surface of the ship, a wheel-track composite magnetic adsorption wall-climbing robot is proposed in this paper.

The robot adopts a front and rear obstacle-crossing mechanism to achieve a smooth crossover. The robot is composed of two passive obstacle-crossing mechanisms and a frame, which is composed of two obstacle-crossing magnetic wheels and a set of tracks. The obstacle-crossing is realized by the telescopic expansion of the obstacle-crossing mechanism. Three static failure models are established to determine the minimum adsorption force for the robot to achieve stable motion. The Halbach array is used to construct the track magnetic circuit, and the influence of gap, contact area and magnet thickness on the adsorption force is analyzed by parameter simulation.

The prototype was designed and manufactured by the authors for static failure and obstacle crossing tests. The prototype test results show that the robot can cross the obstacle of 10 mm height under the condition of 20 kg load.

A new structure of wall-climbing robot is proposed and verified. According to the test results, the wall-climbing robot can stably climb over the obstacle of 10 mm height under the condition of 20 kg load, which provides a new idea for future robot design.

Damage to the pipe structure and increased bolt stress in operating tunnels will significantly affect the long-term stability of shield tunnels. However, due to the large number of segments and complex bolt installation methods, it is difficult to directly obtain the conditions of segments and bolts. Convergence deformation, as an important indicator for the health assessment of operating tunnels, is easy to collect and can directly reflect the structural state of tunnel segments and bolts.

Taking the typical sections of Hangzhou Metro Line 1 and Line 3 as an example, the current structural damage is simulated by monitoring tunnel convergence deformation. First, based on the disease data, the relationship between convergence deformation and segment leakage, segment misalignment as well as the longitudinal joint opening between two segments is analyzed statistically. The stress of bolts, steel bars and segments under different convergence deformations is simulated by Midas. Then a reinforcement measure of circumferential internal tension steel rings is proposed.

It is found that when the segment convergence exceeds 85 mm, although the overall force on the segment has not reached the material limit, there are certain safety risks in the structure. After comparison, the reinforcement measure has a relatively significant effect on controlling the lateral deformation of segments.

The results have important guiding significance for the evaluation of structural diseases, early warning and control of structural diseases at different convergence deformations.

This study introduces the heritage city risk dimension of the urban rail transit (URT) projects. It aims to identify the risk factors affecting URT projects within the unique context of heritage-rich cities, exploring their interrelation and evaluating critical factors.

The research adopts a multi-case exploratory study to identify the unique challenges faced by URT projects in heritage-rich environments, followed by a comprehensive risk assessment framework integrating Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and Risk Interaction Network (RIN) analysis to assess identified risks in the context of Kathmandu Valley. Additionally, a risk response action is simulated using RIN analysis.

About 16 risk factors were identified from the case studies and evaluated using the proposed risk assessment methodology. The study reveals a highly interconnected risk environment, with heritage impact-related factors exerting the strongest causative influence on cost and social engagement factors. Community opposition (R8) shows the highest betweenness centrality, indicating its central position in risk propagation across the network. Cost-related risk, social demand contingency (R2) ranked as the most critical. Simulations of a targeted risk avoidance strategy showed that addressing only three key high-betweenness centrality factors (R5, R8 and R15) reduced overall risk interactions by 46%, simplifying the risk network, reducing project complexity and improving manageability.

The findings emphasize that project managers, urban planners and policymakers should integrate heritage preservation concerns when planning and executing URT projects in heritage-rich cities. Moreover, the research highlights that effective community engagement serves as a key strategy for reducing risk propagation and plays a crucial role in overall project risk management.

The study contributes to the underexplored context of URT projects in heritage-rich cities, providing a comprehensive risk management framework for identifying and assessing project risks intersecting with urban development imperatives and heritage conservation objectives.

Mega construction projects (megaprojects) require technological innovation cooperation (TIC) to address complex construction demands and the interests of multiple stakeholders. Although TIC has been extensively discussed at the firm level, a significant gap remains in understanding megaprojects at the project level. This paper aims to identify TIC’s influencing factors and transmission paths and discuss stakeholders’ TIC mechanisms at the project level.

Based on case analysis, expert interviews, literature analysis and the Delphi method, this paper identifies the influencing factors of TIC in megaprojects at the project level. A structural system of these influencing factors is constructed by interpretive structural modeling (ISM), developing various mechanisms for TIC from bottom to top. The Matriced’ Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) method validates the driving forces and dependencies of the influencing factors, clarifying their roles and positions within the system. Additionally, the TIC mechanism is constructed.

The research findings identify 26 influencing factors categorized into four hierarchical levels: cooperative relationships, cooperative behavior, cooperative performance and technological innovation risks. Regarding direct factors, resource sharing affects goal congruence and communication effectiveness in megaprojects, affecting TIC’s satisfaction and trust. Most factors exist in the middle layer, and bridging the upper and lower levels depends on stakeholder collaboration. The root factors in the independent group significantly impact TIC, including policy circumstances, high technical requirements and limited site conditions. Addressing these issues influences improvements in other factors. The development of a digital resource-sharing platform, the enhancement of innovation incentives, the optimization of benefit distribution mechanisms and the improvement of risk-sharing mechanisms are essential for the effective operation of the TIC mechanism.

This study contributes to identifying and classifying challenges and opportunities in TIC. It explores transmission paths for enhancing TIC and presents strategies for successfully implementing and delivering megaprojects.

Introduction: Based on the given experiences, many government institutions failed in their strategic management and planning for managing COVID-19. Meanwhile, when a crisis disrupts a system, institutions lose their direction and fail to make necessary responses.

Purpose: The current study highlighted the impact of social justice and modern governance in providing equitable healthcare services and dealing with crises during the COVID-19 pandemic in developing countries.

Methodology: Cross-country analyses were used based on captured secondary data. We evaluated several indices, including, for example, Crisis Index Indicators, Worldometers, and the Global Health Security (GHS) Index 2019.

Findings: According to the GHS (2019) data, public health service delivery equity was ineffective, socially unjust, and unfair treatment was experienced in the context of the conflict-affected countries. Most conflict-affected countries (Iraq, Nigeria, Afghanistan, and Venezuela) did not have guidelines or public reports committing to providing prioritized healthcare services to the public and healthcare workers. The experience of conflict-affected countries has shown that healthcare disparities still exist. While many governments in conflict-affected countries failed to give equitable access to healthcare services during the COVID-19 pandemic to the public.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

The main objectives of this paper are to develop a novel perturbation method (PM) to solve the complex-orthogonal eigenvalue problem and further propose an exact complex mode superposition method (CMSM) for the non-proportionally rate-independent damped systems.

A novel PM is developed to solve the eigenvalue problem. The PM reduced the N-order generalized complex eigenvalue problem into a set of n algebraic equations by the perturbation theory. The convergence and accuracy of the PM are demonstrated by several numerical examples. Further, an exact CMSM is presented. The influences of the imaginary part response of the modal coordinate and the off-diagonal elements of the damping matrix as well as the modal truncation on the solution by CMSM are discussed to illustrate the effectiveness of the developed CMSM.

The eigenvalues obtained by PM would converge to the exact ones with the increase of the modal numbers. For seismic response, the influence of the imaginary part solutions of the modal coordinate would increase with the increase of the coupling factor. The contribution of higher modes to acceleration response is greater than that to the displacement. The cumulative mode contribution coefficient of acceleration is developed to estimate the numbers of the complex modes for the acceleration seismic response by the CMSM.

1. An eigenvalue perturbation method for a rate-independent damped system is proposed. 2. PM is carried out by the real mode and accomplishes the reduction of the matrix. 3. CMSM is established for rate-independent damped systems. 4. CMSM considers the effect of imaginary part solutions of the modal coordinate. 5. Modal truncation index is developed to estimate the complex mode number for CMSM.