Structural Problems Research Articles

SwapInpaint2: Towards high structural consistency in identity-guided inpainting via background-preserving GAN inversion

In this work, we propose SwapInpaint2 to enhance the naturalness of identity-guided face inpainting. The previous version, SwapInpaint, relied on a generic inpainting model for content infer, bringing the problems of unnatural structure to the results. Our method addresses this issue by utilizing an inversion-based Background-preserving Attribute Extractor and an improved Embedding Integration Generator to provide high-quality attribute embeddings and produce high-naturalistic results. Comparison experiments with state-of-the-art models demonstrate that our new method achieves superior structural consistency and stronger style alignment both in terms of quality and quantity.

Historical evolution of structural optimization techniques for steel skeletal structures including industrial design applications

This study covers the review of algorithms developed for the optimum design of steel skeletal structures from the first article published in 1960 until to date. The paper initially describes the mathematical formulation of a simple truss structural design problem. The early optimum design algorithms that were based on mathematical programming techniques where the design variables are assumed to be continuous are reviewed. The optimum design of steel framed structures necessitates the selection of steel profiles from the standard list of discrete steel sections and requires the satisfaction of design code provisions. Both mathematical programming and optimality criteria techniques need more capability to produce solution to this type of optimum design problems. Soft computing techniques emerged recently provide solution directly without needing any approximation. These techniques are classified and reviewed and their use in obtaining the optimum solution of actual industrial steel design applications is given.

Propose Theoretical Foundations for Analyzing the Dynamic Control and Optimizing the Structure of Multifunctional Carbon Fiber-Reinforced Material Plate Integrated Piezoelectric

In the fields of aviation, energy, construction, biomedicine, chemical technology, and some other fields, the use of functionally graded materials (FGMs) plates, multifunctional carbon nanofibers reinforced material plates, piezoelectric plates, and several others are increasingly popular. The behavior of material plates can be analyzed through partial differential equations (PDEs). However, the PDEs are for complex problems such as solid-liquid interactions, thermoelectric mechanical environments, functional material plates in multi-physics environments, and others that are very difficult or impossible to find a solution. In many numerical methods have been researched and developed, the finite element method (FEM) is a widely used and effective method to find approximate solutions of the PDEs. But the FEM has certain limitations in element techniques, discretizing the weak form for the plate structure problems with many degrees of freedom significantly, and affects the accuracy and efficiency of calculation. Proposing improvements to traditional FEM combines dynamic control analysis in the presence of piezoelectric crystals and optimization method algorithms, meeting the increasing requirements in analyzing the behavior of carbon nanofiber material plates used in many fields. On this basis, the article presents some theoretical foundations to solve the problem of dynamic control and optimizing the structure of multifunctional carbon nanofiber-reinforced material plates with integrated piezoelectric crystal.

Perspective: Better Privacy Theorists for Better Data Stewards

The U.S. Census Bureau's use of differential privacy (DP) fundamentally changed how academic DP researchers perform outreach with official statistics stakeholders. In this perspectives piece, I propose ways for us in this community to improve those processes by being more receptive to the practical concerns raised by building DP systems. First, I discuss how academic DP work fundamentally differs from the policy decisions needed to implement DP systems and why this distinction has political consequences. Through examples and discussions from workshops, I show how the DP community largely asked applied stakeholders to communicate on DP's theoretical terms, when such an ask foreclosed important considerations relevant for the Census Bureau's policy problems. Second, I discuss how existing polarization between theoretical and empirical privacy researchers unintentionally seeped into the ways we communicated about DP, pointing to why both perspectives are necessary in different ways for policy conversations. Finally, I conclude by discussing how these issues are not unique to data privacy work, but instead reflect structural problems in translating theoretical science into practice. These ideas are presented in service of a single goal: to ensure DP theory supports substantive, privacy-aware data processing and dissemination in practice for essential data curators.

Problems and Countermeasures of China's Foreign Exchange Derivatives Market

China's foreign exchange derivatives market continues to develop, but some problems remain. This essay predominantly analyzes the shortcomings in China's foreign exchange derivatives market and proposes corresponding countermeasures. This essay mainly focuses on the following four issues in China's foreign exchange derivatives market. They are trading scale and liquidity problems, insufficient product innovation, market participant structure problems, and insufficient market trust and risk awareness. Four corresponding countermeasures are proposed, which are proposed from four angles: appropriately lowering the threshold for foreign exchange derivatives business of small and medium-sized financial institutions, promoting innovation of market derivatives, strengthening market diversification, and foreign exchange derivatives supervision. This article aims to analyze China's derivatives market from multiple angles and put forward appropriate suggestions for optimizing China's foreign exchange derivatives market structure.

Machine learning-based acoustic emission technique for corrosion-induced damage monitoring in reinforced concrete structures

Early-stage detection of corrosion in reinforcement embedded inside concrete, generally considered as the major problem in structures under extreme marine environments, can help to schedule timely maintenance/repair and to avoid any catastrophic failure. However, quantitative evaluation of the degree of deterioration, especially at a very early stage, i.e., before its appearance on the surface, is extremely difficult due to the complex process in heterogeneous material. This study aims to characterize the critical damage stages in reinforced concrete specimens under accelerated conditions using the Acoustic emission (AE) damage quantification methods supported by machine learning (ML) techniques. The stages of corrosion-induced damage are predicted by a two-step predictive model that involve both regression and classification tasks. Three supervised regression models — support vector regression (SVR), relevance vector machine (RVM) and kernel ridge regression (KRR) —are first adopted to evaluate the amount of current. Then the output from regression model is given as an input to the classification problem to predict the discrete class label output. Characterized damage stages are validated based on mass loss and crack width measurements. The results show that the corrosion initiation (at day 3) and crack formation (at day 7) can be determined from the sudden change in acoustic parameters. Further, a double layer (supervised-unsupervised) model is proposed which is found be more effective in correctly detecting the progression of damage. The findings indicate that the proposed approach that combines corrosion detection by analysing AE signals with ML technique can accurately and robustly predict corrosion severity.

Experimental investigation and numerical simulation of chloride diffusion in rubber concrete under dry-wet cycles

Chloride erosion caused by the dry-wet cycles is one of the most significant factors leading to durability problems in concrete structures. In this paper, the diffusion behavior of chloride in rubber concrete with varying rubber content is studied by experimental investigation and numerical simulation based on setting up artificial dry-wet cycles exposure conditions. The determination of chloride concentration in rubber concrete is achieved through chloride concentration titration, and as a result, the surface chloride concentration and the apparent chloride diffusion coefficient are expressed as a function of time, thus providing a theoretical guide for Comsol Multiphysics to perform numerical simulations. The findings indicate that the chloride diffusion behavior in rubber concrete complies with Fick's second law and that rubber can effectively reduce the diffusion rate of chloride in rubber concrete. Furthermore, the surface chloride concentration and apparent chloride diffusion coefficient are fitted with high accuracy according to the experimental data, which ensures the precision of the numerical simulation. In addition, numerical simulation results indicate that the diffusion of chloride in rubber concrete is impeded by the aggregate, that is, chloride diffusion is faster in areas where the local aggregate volume fraction is small. Also, it is evident that the presence of interfacial transition zone (ITZ) negatively affects the resistance of rubber concrete to chloride diffusion.

Optimization of structural reinforcement assessment for architectural heritage digital twins based on LiDAR and multi-source remote sensing

This study investigates the geometric modelling of architectural heritage digital twins constructed based on multi-source point cloud data and its effectiveness in structural reinforcement assessment. Particular emphasis has been placed on the use of static stiffness rules to identify areas of structural weakness in the geometric models of digital twins and the need for their reinforcement, in order to prevent potential structural problems and to ensure the long-term preservation of the built heritage. Taking Yingxian wooden pagoda as a study case, based on the collection of multi-source point cloud data, the digital twin geometric model is constructed through fine modelling, decoupling of digital models, and geometric transformation. This enhances the true reflection of the column-architrave structure morphology, providing a more accurate model for structural stress analysis. Based on verifying the accuracy of the digital twin geometric model, the instability conditions are identified through static stiffness rules and the deformation values at multiple points are analyzed, enabling precise identification of weak areas in the column-architrave structure. Two types of reinforcement measures are designed and simulated for the structural weak areas identified through the geometric modelling, and the optimal reinforcement scheme is obtained after detailed analysis, according to which specific adjustments and optimization strategies are proposed to enhance the overall stability and durability of the structure. The results showed that the maximum deformation value of 4.65 mm existed in column M2W23, which required reinforcement. Aluminum reinforcement reduced the deformation to 3.5 mm (24.7% reduction), while CFRP fabric reinforcement was more effective, reducing the deformation to 2.8 mm (39.7% reduction), showing high stability. The research results demonstrate the potential application of digital twin technology in architectural heritage preservation and restoration, providing methodological and empirical guidance for heritage preservation research.

Displacing the displaced: the response to the protracted precarious situation of Syrian refugees in Türkiye during the Covid-19 pandemic

ABSTRACT The Covid-19 crisis exposed deeply-rooted systemic problems for Syrians in Türkiye, especially continuous instability in accessing public health and social support services. By reviewing legal amendments and policies, this article examines the legal situation and the humanitarian challenges they encounter during the pandemic in Türkiye. Although the Turkish government provides free access and includes Covid-19 treatment within the emergency scope, the barriers impoverished the Syrian refugees accessing those services. This article does not address all aspects of the impact of Covid-19, thus emphasising how Turkish legal policies on refugee maintenance have constrained the life of Syrians in Türkiye. The study implies the multiple barriers, such as registration requirements and structural problems of the asylum policies restricting the Syrian refugee’s access to healthcare and State services in all aspects. The article concludes by demonstrating that the precarious legal status brings even more vulnerability at times of crisis for the Syrian refugees in Türkiye.

Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data

Masonry arches and vaults are common historic structural elements that frequently experience asymmetric loading due to seismic action or abutment settlements. Over the past few decades, numerous studies have sought to enhance our understanding of the structural behavior of these elements for the purpose of preventive conservation. The assessment of the structural performance of existing constructions typically relies on effective numerical models guided by a set of unknown input parameters, including geometry, mechanical characteristics, physical properties, and boundary conditions. These parameters can be estimated through deterministic optimization functions aimed at minimizing the discrepancy between the output of a numerical model and the measured dynamic and/or static structural response. However, deterministic approaches overlook uncertainties associated with both input parameters and measurements. In this context, the Bayesian approach proves valuable for estimating unknown numerical model parameters and their associated uncertainties (posterior distributions). This involves updating prior knowledge of model parameters (prior distributions) based on current measurements and explicitly considering all sources of uncertainties affecting observed quantities through likelihood functions. However, two significant challenges arise: the likelihood function may be unknown or too complex to evaluate, and the computational costs for approximating the posterior distribution can be prohibitive. This study addresses these challenges by employing Approximate Bayesian Computation (ABC) to handle the intractable likelihood function. Additionally, the computational burden is mitigated through the use of accurate surrogate models such as Polynomial Chaos Expansions (PCE) and Artificial Neural Networks (ANN). The research focuses on setting up numerical models for simple structural systems (tie-rods) and inferring unknown input parameters, such as mechanical properties and boundary conditions, through Bayesian updating based on observed structural responses (modal data, strains, displacements). The main novelties of this research regard, on the one hand, the proposal of a methodology for obtaining a reliable estimate of the axial force in ancient tie-rods accounting for different sources of uncertainty and, on the other hand, the application of ABC to obtain the structural identification inverse problem solution.

Reconstruct incomplete relation for incomplete modality brain tumor segmentation

Different brain tumor magnetic resonance imaging (MRI) modalities provide diverse tumor-specific information. Previous works have enhanced brain tumor segmentation performance by integrating multiple MRI modalities. However, multi-modal MRI data are often unavailable in clinical practice. An incomplete modality leads to missing tumor-specific information, which degrades the performance of existing models. Various strategies have been proposed to transfer knowledge from a full modality network (teacher) to an incomplete modality one (student) to address this issue. However, they neglect the fact that brain tumor segmentation is a structural prediction problem that requires voxel semantic relations. In this paper, we propose a Reconstruct Incomplete Relation Network (RIRN) that transfers voxel semantic relational knowledge from the teacher to the student. Specifically, we propose two types of voxel relations to incorporate structural knowledge: Class-relative relations (CRR) and Class-agnostic relations (CAR). The CRR groups voxels into different tumor regions and constructs a relation between them. The CAR builds a global relation between all voxel features, complementing the local inter-region relation. Moreover, we use adversarial learning to align the holistic structural prediction between the teacher and the student. Extensive experimentation on both the BraTS 2018 and BraTS 2020 datasets establishes that our method outperforms all state-of-the-art approaches.

An interactive reference-point-based method for incorporating user preferences in multi-objective structural optimization problems

Although multi-objective evolutionary algorithms (MOEAs) are often used to solve multi-objective structural optimization problems (MOSOPs), the incorporation of decision-maker (DM) preferences into MOEAs to search for regions of interest from the Pareto front has received limited attention in these problems. Recently, three MOEAs have shown promising results in solving MOSOPs incorporating DM’s preferences based on a priori reference points. However, providing this information can be challenging as the DM may not know an initial reference point for the MOSOP. Therefore, this study analyzes three interactive multi-objective optimization (IMO) algorithms, called I-NSGA-II, I-GDE3, and I-GDE3+APM, in which the reference points are progressively incorporated during the search process. These IMOs are applied to benchmark MOSOPs and evaluated using well-known performance indicators. The results demonstrate that I-NSGA-II outperforms I-GDE3 and I-GDE3+APM, which have similar overall performances. Additionally, the IMOs considered here outperformed their respective a priori approaches in MOSOPs where the reference points are located at the central sector of the Pareto front. Also, we identified the performance indicators in which the approaches considered in this work performed better than their corresponding a priori ones. Furthermore, the research findings suggest that these IMOs can generate good results without defining a priori reference points, making them a promising approach for solving MOSOPs and enabling DMs to make better and more reliable decisions.

Decision-making in structural engineering using BHARAT-II method

This paper presents a simple and effective multi-attribute decision-making method, named as BHARAT-II (Best Holistic Adaptable Ranking of Attributes Technique - II), to choose the best alternatives for different structural engineering related problems. Two case studies are presented to demonstrate the proposed multi-attribute decision-making method. The first case study addresses the problem of (a). selecting the best construction method for a bridge out of 4 available methods considering 7 selection attributes, (b). selecting best structural system of bridge out of 7 structural systems considering 11 selection attributes, and (c). selecting best construction material out of 4 materials considering 4 selection attributes; the second case study addresses the problem of selecting the best structural system of a housing project by considering 4 alternative structural systems and 5 selection attributes (i.e., criteria) involving 19 sub-attributes (sub-criteria). The results of the proposed BHARAT-II decision-making method are compared with those of other well-known multi-attribute decision-making methods. The proposed method is shown to be simple to implement, providing a logical way for allocating weights to the selection attributes and adaptable to solve the best alternative selection problems of structural engineering.

Optimal truss design with MOHO: A multi-objective optimization perspective.

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo's behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO's solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

The future of renewable energy in Iran's energy policy

Renewable energy has become a very important issue in the world. Access problems, high prices, and environmental issues of fossil fuels have caused the world to turn to renewable energy. Using the quantitative-qualitative combined methodology and the theoretical framework of energy security, this research has tried to answer the question of what the future of renewable energy in Iran is. The hypothesis of this research is that despite the adoption of programs and policies for the productivity and development of renewable energy in Iran, sanctions and investment problems make the development of renewable energy in Iran a structural problem. The findings of the research indicate that in the long term, solving structural challenges such as sanctions and investment and encouraging investment in renewable energy can provide a transition path by diversifying energy sources. So, the article tries to analyze the situation of renewable energy in terms of opportunities and challenges and show a vision of the future of this energy in Iran.

One-Step Spraying Strategy for Fabricating Bioinspired, Graphene-Based, and Multifunctional-Integrated Coatings on Structural Steel with Good Water Repellency, Fireproofing, Anticorrosion, and Durability.

Traditionally, many coatings were merely concentrated on settling the inherent fire protection problem of steel structures, while surface contamination and corrosion susceptibility should also be considered. Concurrently addressing these problems in fireproof efficiency and surface multifunctionality has become an issue of great significance in further expanding the application value in industrial and daily scenarios. Based on this condition, ecofriendly, graphene-based, and superhydrophobic coatings with multifunctional integration were constructed on steel via a one-step spraying method. The as-prepared coatings mainly consist of epoxy resin (EP), silicone resin (SR), a cyclodextrin-based flame retardant (MCDPM), expandable graphite (EG), and multilayered graphene (MG). The results demonstrate that the water contact angle (WCA) and water sliding angle (WSA) of as-prepared coatings can reach 156.8 ± 1.6 and 5.8 ± 0.7°, respectively, revealing good water repellency and self-cleaning properties. The coatings can also exhibit adequate adaptability for various substrates including wood, polyurethane foam, and cotton fabrics. Besides, good durability and robustness of coatings have been also verified via acid/alkali immersion, outdoor exposure, O2/plasma etching, and linear abrasion tests. Simultaneously, the coatings can exhibit excellent anticorrosion capacity for steel materials via a double barrier effect. Most importantly, the coatings have exhibited the lowest backside temperature (234.5 °C) during fire impact tests, suggesting excellent fireproof and heat insulation performance. This fact can be ascribed to the conjunct action between the physical/chemical charring process of flame retardants and the remarkable thermal stability of graphene. Consequently, this article can be expected to further promote the development and application of multifunctional-integrated coatings for steel structures in more fields.

An Evaluation Model for Node Influence Based on Heuristic Spatiotemporal Features.

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks.

A novel type-2 decision mechanism for dynamic parameter adaptation: theory and application in mathematical and structural problems

Metaheuristic algorithms are stochastic-based search techniques widely used for solving different types of optimization problems. These methods mostly adjust their search behavior using pre-defined search pattern(s) regardless of the current problem specifications. Therefore, integrating them with logical auxiliary modules can significantly enhance their search efficiency by enabling them to dynamically adapt their search behavior. The present study introduces a novel decision-making approach that employs interval type-2 fuzzy logic to balance the search behavior during optimization process. The proposed approach, designed as a stand-alone module with the flexibility to be integrated into various algorithms, is incorporated into the Interactive Search Algorithm. The developed reinforced technique is named Type-2 Fuzzy Interactive Search Algorithm. Performance of the proposed method is tested on different unconstrained mathematical functions and constrained structural and mechanical optimization problems. The attained results are compared with standard ISA method and seven other metaheuristic techniques through a suite of numerical and statistical evaluations. Drawing from the obtained results, the integration of the type-2 fuzzy decision module significantly enhances the algorithm's search capability. This improvement is evident in terms of stability, accuracy, and computational cost. It is worth noting that the employed comparative performance index for the proposed method indicates improvements of 3.38, 13.09, 16.61, and 27.23 percent compared to the best solution found by the selected methods for engineering problems.

Lack of skilled medical personnel as a structural problem

The healthcare system in Germany is characterized by a comprehensive local patient care. Nevertheless, due to the increasing lack of medical personnel bottlenecks are impending, which are not only of a temporary nature. The increasing demography-related needs for care, insufficient healthcare competence of many people and the inadequate prevention will strengthen the demand for medical and nursing personnel. At the same time, physicians and nursing personnel from the high birth rate baby boomer years are leaving the healthcare system. This age cohort must now be replaced by a younger workforce; however, in Germany too few physicians are being trained when measured against the requirements. A marked increase in the number of university study places in medicine will not be able to alleviate the deficit in the short term but prospectively there is no way past an expansion of capacities. The decline in panel physicians, especially in general practitioner care, is accompanied by a clear increase of employees in the outpatient sector. The desire for reduced working hours is clearly recognizable throughout all age cohorts. The part-time quota is increasing. The discrepancy between desired and actual working times is largely underestimated. The increased part-time quota already shows that something must fundamentally change to be able to provide sufficient medical manpower and working hours for the treatment of patients. The association between increased part-time quota and dissatisfaction with the working situation in hospitals is obvious and has repercussions for the medical care. In a multifactorial process the causes and sequelae of bottlenecks in skilled personnel are mutually strengthened in a negative spiral. In the short term, the deficit in medical personnel can only be counteracted by a better cooperation between the outpatient and inpatient fields of care and by a massive reduction in bureaucracy. The digitalization can without doubt contribute to the relief of the healthcare system. Telemedical applications can improve the treatment in rural and structurally weak regions.

A profile function to predict surface subsidence for down-cut slicing panels into inclined coal seam of the Barapukuria coal mine, Bangladesh

Mining-induced subsidence, a typical consequence of underground mining, can cause environmental and structural problems in the mine area. Predicting surface subsidence for inclined and deep seams has been less emphasized than horizontal seams, mainly due to the complexities of extracting such coal seams. The prediction of surface subsidence and the dynamics of subsidence in down-cut slicing panels is more complex and crucial in practice. This study presents a novel profile function method for forecasting surface subsidence resulting from downward slicing panels in the inclined coal seam mining of the Barapukuria coal mine. The calculated maximum subsidence depth is 10.23 meters whereas the measured maximum water depth of the subsided lake of the Barapukuria coal mine is 10.5 meters. The results indicate a strong correlation between the predicted and the measured values.