Hasil untuk "Structural engineering (General)"

Shuilin Wu, Xiangmei Liu, K. Yeung et al.

O. Artime, Marco Grassia, Manlio De Domenico et al.

Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected many-body systems characterized by macroscopic properties that cannot be trivially deduced from those of their microscopic constituents. Such systems are exposed to both internal, localized, failures and external disturbances or perturbations. Owing to their interconnected structure, complex systems might be severely degraded, to the point of disintegration or systemic dysfunction. Examples include cascading failures, triggered by an initially localized overload in power systems, and the critical slowing downs of ecosystems which can be driven towards extinction. In recent years, this general phenomenon has been investigated by framing localized and systemic failures in terms of perturbations that can alter the function of a system. We capitalize on this mathematical framework to review theoretical and computational approaches to characterize robustness and resilience of complex networks. We discuss recent approaches to mitigate the impact of perturbations in terms of designing robustness, identifying early-warning signals and adapting responses. In terms of applications, we compare the performance of the state-of-the-art dismantling techniques, highlighting their optimal range of applicability for practical problems, and provide a repository with ready-to-use scripts, a much-needed tool set. Complex biological, social and engineering systems operate through intricate connectivity patterns. Understanding their robustness and resilience against disturbances is crucial for applications. This Review addresses systemic breakdown, cascading failures and potential interventions, highlighting the importance of research at the crossroad of statistical physics and machine learning. A variety of biological, social and engineering complex systems can be defined in terms of units that exchange information through interaction networks, exhibiting diverse structural patterns such as heterogeneity, modularity and hierarchy. Owing to their interconnected nature, complex networks can amplify minor disruptions to a system-wide level, making it essential to understand their robustness against both external perturbations and internal failures. The study of complex networks’ robustness and resilience involves investigating phase transitions that usually depend on features such as degree connectivity, spatial embedding, interdependence and coupled dynamics. Network science offers a wide range of theoretical and computational methods for quantifying system robustness against perturbations, as well as grounded approaches to design robustness, identify early-warning signals and devise adaptive responses. These methods find application across a multitude of disciplines, including systems biology, systems neuroscience, engineering, and social and behavioural sciences. A variety of biological, social and engineering complex systems can be defined in terms of units that exchange information through interaction networks, exhibiting diverse structural patterns such as heterogeneity, modularity and hierarchy. Owing to their interconnected nature, complex networks can amplify minor disruptions to a system-wide level, making it essential to understand their robustness against both external perturbations and internal failures. The study of complex networks’ robustness and resilience involves investigating phase transitions that usually depend on features such as degree connectivity, spatial embedding, interdependence and coupled dynamics. Network science offers a wide range of theoretical and computational methods for quantifying system robustness against perturbations, as well as grounded approaches to design robustness, identify early-warning signals and devise adaptive responses. These methods find application across a multitude of disciplines, including systems biology, systems neuroscience, engineering, and social and behavioural sciences.

Zi-Jun Yong, Shaoqiang Guo, Ju-Ping Ma et al.

All-inorganic perovskite nanocrystals (NCs) have emerged as a new generation of low-cost semiconducting luminescent system for optoelectronic applications. The room-temperature photoluminescence quantum yields (PLQYs) of these NCs in the green and red spectral range approach unity. However, their PLQYs in the violet are much lower, and an insightful understanding of such poor performance remains missing. We report a general strategy for the synthesis of all-inorganic violet-emitting perovskite NCs with near-unity PLQYs through engineering local order of the lattice by nickel ion doping. A broad range of experimental characterizations, including steady-state and time-resolved luminescence spectroscopy, X-ray absorption spectra, and magic angle spinning nuclear magnetic resonance spectra, reveal that the low PLQY in undoped NCs is associated with short-range disorder of the lattice induced by intrinsic defects such as halide vacancies and that Ni doping can substantially eliminate these defects and result in increased short-range order of the lattice. Density functional theory calculations reveal that Ni doping of perovskites causes an increase of defect formation energy and does not introduce deep trap states in the band gap, which is suggested to be the main reason for the improved local structural order and near-unity PLQY. Our ability to obtain violet-emitting perovskite NCs with near-perfect properties opens the door for a range of applications in violet-emitting perovskite-based devices such as light-emitting diodes, single-photon sources, lasers, and beyond.

Sarah Alamdari, Nitya Thakkar, Rianne van den Berg et al.

Deep generative models are increasingly powerful tools for the in silico design of novel proteins. Recently, a family of generative models called diffusion models has demonstrated the ability to generate biologically plausible proteins that are dissimilar to any actual proteins seen in nature, enabling unprecedented capability and control in de novo protein design. However, current state-of-the-art diffusion models generate protein structures, which limits the scope of their training data and restricts generations to a small and biased subset of protein design space. Here, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional space. We show experimentally that EvoDiff generations express, fold, and exhibit expected secondary structure elements. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered regions, while maintaining the ability to design scaffolds for functional structural motifs. We validate the universality of our sequence-based formulation by experimentally characterizing intrinsically-disordered mitochondrial targeting signals, metal-binding proteins, and protein binders designed using EvoDiff. We envision that EvoDiff will expand capabilities in protein engineering beyond the structure-function paradigm toward programmable, sequence-first design.

Alexander LeClair, S. Haque, Lingfei Wu et al.

Automatic source code summarization is the task of generating natural language descriptions for source code. Automatic code summarization is a rapidly expanding research area, especially as the community has taken greater advantage of advances in neural network and AI technologies. In general, source code summarization techniques use the source code as input and outputs a natural language description. Yet a strong consensus is developing that using structural information as input leads to improved performance. The first approaches to use structural information flattened the AST into a sequence. Recently, more complex approaches based on random AST paths or graph neural networks have improved on the models using flattened ASTs. However, the literature still does not describe the using a graph neural network together with source code sequence as separate inputs to a model. Therefore, in this paper, we present an approach that uses a graph-based neural architecture that better matches the default structure of the AST to generate these summaries. We evaluate our technique using a data set of 2.1 million Java method-comment pairs and show improvement over four baseline techniques, two from the software engineering literature, and two from machine learning literature.

Zhengzhong Yuan, Chen Zhao, Z. Di et al.

Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. Although it has been possible to calculate the conditions for exerting complete control over a directed complex network, for undirected and weighted networks this calculation is inexact. Yuan et al. develop a general framework for determining the controllability of any complex network.

Amir Kazemi, F. Manteghi, Zari Tehrani

The rising demand for fossil fuels and the resulting pollution have raised environmental concerns about energy production. Undoubtedly, hydrogen is the best candidate for producing clean and sustainable energy now and in the future. Water splitting is a promising and efficient process for hydrogen production, where catalysts play a key role in the hydrogen evolution reaction (HER). HER electrocatalysis can be well performed by Pt with a low overpotential close to zero and a Tafel slope of about 30 mV dec–1. However, the main challenge in expanding the hydrogen production process is using efficient and inexpensive catalysts. Due to electrocatalytic activity and electrochemical stability, transition metal compounds are the best options for HER electrocatalysts. This study will focus on analyzing the current situation and recent advances in the design and development of nanostructured electrocatalysts for noble and non-noble metals in HER electrocatalysis. In general, strategies including doping, crystallization control, structural engineering, carbon nanomaterials, and increasing active sites by changing morphology are helpful to improve HER performance. Finally, the challenges and future perspectives in designing functional and stable electrocatalysts for HER in efficient hydrogen production from water-splitting electrolysis will be described.

E. Figueiredo, J. Brownjohn

Bridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical, and most resilient bridges are those that are well managed and maintained. In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. While there have been multiple review studies published on SHM and vibration-based structural damage detection for wider scopes, there have not been so many reviews on SHM of bridges in the context of the SPR paradigm. Besides, some of those reviews become obsolete quite fast, and they are usually biased towards applications falling outside of bridge engineering. Therefore, the main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges.

F. Rosso, A. Giordano, M. Barbarisi et al.

Franccois Maz'e, Faez Ahmed

Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace, mechanical, and civil engineering. Recently, generative adversarial networks (GANs) have emerged as a popular alternative to traditional iterative topology optimization methods. However, GANs can be challenging to train, have limited generalizability, and often neglect important performance objectives such as mechanical compliance and manufacturability. To address these issues, we propose a new architecture called TopoDiff that uses conditional diffusion models to perform performance-aware and manufacturability-aware topology optimization. Our method introduces a surrogate model-based guidance strategy that actively favors structures with low compliance and good manufacturability. Compared to a state-of-the-art conditional GAN, our approach reduces the average error on physical performance by a factor of eight and produces eleven times fewer infeasible samples. Our work demonstrates the potential of using diffusion models in topology optimization and suggests a general framework for solving engineering optimization problems using external performance with constraint-aware guidance. We provide access to our data, code, and trained models at the following link: https://decode.mit.edu/projects/topodiff/.

Rasha Jasim Al Karawi, Merool Vakil , Seyed Sina Mousavi

Despite the extended lifespan of concrete structures under ideal circumstances, the service life is significantly reduced due to the cracks created by various loads. Unwanted crack treatments are effective only on the exterior side of the accessible concrete constructions. Therefore, there is a great need to incorporate a self-healing (S-H) mechanism within the concrete matrix. This review discusses a general trend in developing S-H concrete in the construction industry. It consists of two parts: Part I introduces an overview of S-H concrete by describing the basic concepts and available S-H techniques (agents/materials) and analyzing their applications, critiques, and performance in various published studies. Part II critically discusses the conceptual life cycle cost, the maturity level of S-H concrete, the commercial situation, potential applications, field behavior, and challenges to be addressed in future years. The most important outcome of this review is a deeper understanding of the self-healing phenomenon and its potential applications. It is shown that self-healing technologies can give concrete power to heal and prepare itself, which reduces the need for regular maintenance, substantial cost savings, and environmental protection.

David Juračka, David Bujdoš, Petr Lehner

Research in the field of property analysis of 3D printed structural elements raises many new questions. A major challenge is to understand the behavior of the material, as the raw material and the resulting printed sample cannot be considered the same in this respect. In 3D printing, the properties of the sample change due to high temperatures, changes in the state of the raw material and different setups. Currently, there is no standard for determining certain properties, which leads to the need for appropriate use of experimental and numerical tools. This study highlights the results of tensile testing and numerical analysis of a special 3D printed shape. Different material settings were used to allow a complete inverse analysis of the specimen behavior and calibration between experiment and model. The model was created in Ansys software and was prepared in several variations to be as close as possible to the real specimen. Subsequently, the numerical model was subjected to a simplified fatigue analysis with respect to the S-N curves and the predicted fatigue life of the specimen was determined.

Elena-Raluca RADU

Abstract: This paper explores the design of logos in the aeronautical field, especially for the Faculty of Aerospace Engineering from Bucharest. The article aims to identify a new image for the Faculty of Aerospace Engineering in order to create a corporate identity manual to illustrate the use of the new logo in different situations and environments. The perfect candidate to represent the institution will be decided based on a survey which measures the public's receptivity to 5 proposed variants for the logo

I. Tomić, A. Penna, M. DeJong et al.

City centres of Europe are often composed of unreinforced masonry structural aggregates, whose seismic response is challenging to predict. To advance the state of the art on the seismic response of these aggregates, the Adjacent Interacting Masonry Structures (AIMS) subproject from Horizon 2020 project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA) provides shake-table test data of a two-unit, double-leaf stone masonry aggregate subjected to two horizontal components of dynamic excitation. A blind prediction was organized with participants from academia and industry to test modelling approaches and assumptions and to learn about the extent of uncertainty in modelling for such masonry aggregates. The participants were provided with the full set of material and geometrical data, construction details and original seismic input and asked to predict prior to the test the expected seismic response in terms of damage mechanisms, base-shear forces, and roof displacements. The modelling approaches used differ significantly in the level of detail and the modelling assumptions. This paper provides an overview of the adopted modelling approaches and their subsequent predictions. It further discusses the range of assumptions made when modelling masonry walls, floors and connections, and aims at discovering how the common solutions regarding modelling masonry in general, and masonry aggregates in particular, affect the results. The results are evaluated both in terms of damage mechanisms, base shear forces, displacements and interface openings in both directions, and then compared with the experimental results. The modelling approaches featuring Discrete Element Method (DEM) led to the best predictions in terms of displacements, while a submission using rigid block limit analysis led to the best prediction in terms of damage mechanisms. Large coefficients of variation of predicted displacements and general underestimation of displacements in comparison with experimental results, except for DEM models, highlight the need for further consensus building on suitable modelling assumptions for such masonry aggregates.

Changle Li, Song Lu, S. Divinski et al.

Grain boundary energy (GBE) and its temperature dependence in body-centered cubic (bcc) metals are investigated using ab initio calculations. We reveal a scaling relationship between the GBEs of the same grain boundary structure in different bcc metals and find that the scaling factor can be best estimated by the ratio of the low-index surface energy. Applying the scaling relationship, the general GBEs of bcc metals at 0 K are predicted. Furthermore, adopting the Foiles’s method which assumes that the general GBE has the same temperature dependence as the elastic modulus 𝑐 44 [Scr. Mater., 62 (2010) 231–234], the predicted general GBEs at elevated temperatures are found in good agreement with available experimental data. Reviewing two experimental methods for determining the general GBEs, we conclude that the two sets of experimental GBEs for bcc metals correspond to different GB structural spaces and differ by approximately a factor of 2. The present work puts forward an efficient methodology for predicting the general GBEs of metals, which has the potential to extend its application for homogeneous alloys without strong segregation of the alloying element and facilitates GB engineering for advanced alloy design.

陈世玺, 吴杨周, 于海丰 et al.

基于削弱耗能梁截面尺寸的原理，提出了一种新型带弯剪屈服耗能梁段的K形偏心支撑钢框架结构。为验证其抗震性能，完成了1个1∶2.6缩尺的单层、单榀、单跨模型的拟静力试验。结果表明，试验模型的塑性变形基本局限在耗能梁段上，且主要通过耗能梁端部上、下翼缘和中部的腹板耗散能量，试件表现出较好的耗能能力和延性。为进一步研究其抗震性能，在试验模型的基础上改变了中部剪切型耗能梁（简写为SL）的长度<italic>l</italic>_m，采用ABAQUS有限元软件建立了8个数值模型并对之进行了滞回性能分析，分析了滞回曲线、承载力、刚度及耗能能力等随<italic>l</italic>_m/<italic>e</italic>（<italic>e</italic>为耗能梁整体的长度）的变化规律，并给出了<italic>l</italic>_m/<italic>e</italic>的设计建议。总体上，提出的新型带弯剪屈服耗能梁段的K形偏心支撑钢框架结构具有较好的抗震性能，建议实际工程中采用。

Kang Peng, Longliang Wu, Yousef Zandi et al.

While adding superabsorbent polymer hydrogel particles to fresh concrete admixtures, they act as internal curing agents that absorb and then release large amounts of water and reduce self-desiccation and volumetric shrinkage of cement that finally result in hardened concrete with increased durability and strength. The entrainment of microscopic air bubbles in the concrete paste can substantially improve the resistance of concrete. When the volume and distribution of entrained air are adequately managed, the microstructure is protected from the pressure produced by freezing water. This study addresses the design and application of hydrogel nanoparticles as internal curing agents in concrete, as well as new findings on crucial hydrogel–ion interactions. When mixed into concrete, hydrogel particles produce their stored water to power the curing reaction, resulting in less volumetric shrinkage and cracking and thereby prolonging the service life of concrete. The mechanical and swelling performance qualities of the hydrogel are very sensitive to multivalent cations found naturally in concrete mixes, such as aluminum and calcium. The interactions between hydrogel nanoparticles and alkaline cementitious mixes are described in this study, while emphasizing how the chemical structure and shape of the hydrogel particles regulate swelling behavior and internal curing efficiency to eliminate voids in the admixture. Moreover, in this study, an artificial neural network (ANN) was utilized to precisely and quickly analyze the test results of the compressive strength and durability of concrete. The addition of multivalent cations reduced swelling capacity and changed swelling kinetics, resulting in fast deswelling behavior and the creation of a mechanically stiff shell in certain hydrogel compositions. Notably, when hydrogel particles were added to a mixture, they reduced shrinkage while encouraged the creation of particular inorganic phases within the void area formerly held by the swelled particle.

M. S. Egorov, V. N. Pustovoit, G. G. Tsordanidi et al.

Introduction. The development of modern technology imposes increasingly stringent requirements on materials operating under conditions of high pressures, speeds, deformations and aggressive media. The use of powder metallurgy methods in the creation of new materials makes it possible to provide a rational combination of production technology, structural and performance characteristics. Powder steels used in mechanical engineering are of great interest among the materials obtained by powder metallurgy. The article explores the possibility of manufacturing porous bearings made of iron powder for fan motors of domestic air conditioners instead of porous bearings made of bronze graphite.Problem Statement. To ensure long-term operation of fan motors from metal powders, it is necessary to create porous bearings without alloying additives with the required mechanical properties. This requires a series of experimental work to determine the dependences of mechanical and technological properties on the sintering temperature, compacting pressure and the porosity of samples.Theoretical Part. As a theoretical description, the use of a mold with an additional draining gap, which provides high bearing density at low compacting pressure, is analyzed. The effect of compacting pressure on the strength of sliding bearings under mechanical deformations depending on the sintering temperature is also considered.Conclusions. It was established in the work that during the sintering of sliding bearings at a temperature of 800-1100°C, a significant charge carburization occurs due to the decomposition of zinc stearate in closed pores. As a result, a ferritepearlite structure is formed, due to which the bearings are well calibrated and have high wear resistance when paired with a steel shaft. Optimum sintering modes and compacting pressures were selected, which showed high reliability and durability of the products obtained from pure iron powder.

M. Shi, D. Xie

Arabidopsis thaliana is the first model plant, the genome of which has been sequenced. In general, intensive studies on this model plant over the past nearly 30 years have led to many new revolutionary understandings in every single aspect of plant biology. Here, we review the current understanding of anthocyanin biosynthesis in this model plant. Although the investigation of anthocyanin structures in this model plant was not performed until 2002, numerous studies over the past three decades have been conducted to understand the biosynthesis of anthocyanins. To date, it appears that all pathway genes of anthocyanins have been molecularly, genetically and biochemically characterized in this plant. These fundamental accomplishments have made Arabidopsis an ideal model to understand the regulatory mechanisms of anthocyanin pathway. Several studies have revealed that the biosynthesis of anthocyanins is controlled by WD40-bHLH-MYB (WBM) transcription factor complexes under lighting conditions. However, how different regulatory complexes coordinately and specifically regulate the pathway genes of anthocyanins remains unclear. In this review, we discuss current progresses and findings including structural diversity, regulatory properties and metabolic engineering of anthocyanins in Arabidopsis thaliana.

Shatha S. Hasan, Rasha H. Abd Al-Ameer, Haider A. Hassani

The use of epoxy asphalt in road paving is one of the promising solutions for long-life road pavements in service with minimal maintenance. However, the high cost still stands as an obstacle to the widespread use of this high-performance material. The use of tire rubber waste (TRW) is one of the solutions in order to reduce costs, improve the environment, and improve the performance of epoxy asphalt mixtures, in addition to alleviating the brittle behaviour that epoxy asphalt tends to. This study proposes to add TRW in improving epoxy asphalt produced in local laboratories by using phenol Novolac resin as an epoxy curing agent of the epoxy base inside asphalt binder to produce and evaluate improved epoxy asphalt. The percentage of epoxy base used was 25% of the asphalt binder mixed with a 1:1 ratio of epoxy to Novolac using potassium hydroxide (KOH) as a catalyst. Whereas the proportions of added TRW were (1%, 2%, and 3%) of the total mixture weight by using the dry mixing method. The results showed, at its best values at 2% of TRW, that there was an increase in Marshall stability by 10%, and Marshall flow remained within specification limits with a decrease in the value of air voids at the highest bulk density, and a slight decrease in indirect tensile strength by 2%, with remaining excellent resistance to moisture sensitivity at 94%, and improvement in resistance to permanent deformation (rutting) by 14%. This indicates an improvement in the improved epoxy asphalt mixtures by the addition of TRW compared to the reference epoxy asphalt mixtures.