Hasil untuk "Structural engineering (General)"

C. Calladine, J. L. Sanders

Idriss El-Thalji

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing.

Zeyu Kang, Yi Cao, Lu Liu et al.

Abstract Precise tuning of dielectric constants (εr) in oxide glasses is critical for high‐frequency devices in 5G/6G systems, where εr directly governs signal propagation efficiency. A machine learning framework combining data augmentation and physicochemical descriptor integration is developed to address data scarcity. Validated pseudo‐labels are generated via ensemble learning, expanding the dataset from 1503 to 11,029 compositions without distributional shift. The XGBoost model trained on the augmented dataset achieved superior accuracy, with an R2 of 0.96 and an MSE of 0.14. For prediction tasks on unseen data, it reduced the error rate by 48% compared to the non‐augmented model and improved generalization performance by 43% over GlassNet. B2O3 and SiO2 are identified as εr suppressors and BaO and TiO2 as enhancers through SHAP analysis, aligning with network former/modifier roles. Cation‐specific polarizabilities are derived via Clausius–Mossotti regression (R2 = 0.909). Integration of physicochemical descriptors (coordination number and bond strength) enables transferable predictions for Y2O3 and La2O3 containing glasses, with mean deviation 2.46%–4.76%. Crucially, structural descriptors dominate polarizability with 69.9% feature importance, establishing network engineering as the optimal design paradigm. A data‐driven pathway for rational dielectric glass development is thus established.

Carolina Schillaci, Daniela Pilone, Filippo Berto et al.

The present study of Ti6Al4V alloy production via Electron Beam Melting (EBM) represents a cutting-edge research topic impacting different strategic engineering applications. This can be attributed to the widespread use of this alloy and by the unique characteristics of the EBM process. Operating under vacuum and with powder pre-heating, EBM enables the fabrication of components with higher density and reduced residual stress compared to other additive manufacturing techniques. The research reported in this paper analyses the effect of process parameters used in the manufacturing process on defect formation and then on mechanical properties. The results highlighted that the presence of lack of fusion defects leads to a markedly anisotropic behavior of the alloy. This is due to the different morphology of the defects in the different considered directions and to their effect in concentrating stresses.

Jinlong Liang, Yipeng Wu, Zhiwei Huang et al.

Abstract Bones are vital components of the body that provide soft internal organs and tissues with structure, motion, and safety. Damage to bones can make life challenging. In repairing and regenerating damaged bone, polymeric composite materials significantly contribute to bone tissue engineering. In this article, we described developing porous scaffolds using the freeze-drying process from sodium alginate (SA), polyvinyl alcohol (PVA), and graphene oxide (GO) incorporated with zinc (Zn). These scaffolds were characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and universal testing machine (UTM) to investigate their structural analysis, surface morphology, and mechanical behavior, respectively. These scaffolds also exhibited less swelling in phosphate buffer saline (34.73–64.27%) than in aqueous media (42.36–75.92%) with controlled degradation. These scaffolds have potential biomineralization activities, hemocompatibility, and antibacterial activities, which were increased by increasing the Zn amount. All the scaffolds have biocompatibility as they have shown cell viability with mature cell morphology against preosteoblast and HEK-293 cell lines under standard in vitro conditions. The enhanced biological activities of the scaffolds were found by increasing the Zn amount. Thus, newly designed polymeric composite scaffolds would be promising materials to repair and regenerate fractured bone tissue.

P. Morato, C. Andriotis, K. Papakonstantinou et al.

In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies.

Kun Wei, Jidong Deng, Li Yang et al.

Investigation and optimization of the buried interfaces are crucial for further improving the efficiency and stability of perovskite solar cells (PSCs). In this work, a general route to modify the interfaces of electron conductor is strategically developed via introducing a well‐designed core@dual–shell structure based on SnO2 nanoparticles grafted by potassium thiocyanate (KSCN) and polyethylene oxide (PEO). This graded bimolecular strategy is desired as it efficiently decouples the processes of defect healing and crystallization engineering. Synergistic effects of KSCN and PEO lead to superior structural uniformity at the buried interfaces, enhanced charge collection, as well as the suppressed carrier recombination. Consequently, a significant increase of efficiency from 20.0% to 23.01% is achieved, accompanied by a remarkable open‐circuit voltage of 1.19 V and extremely low energy losses down to 0.4 eV. Moreover, this interfacial configuration enables the unencapsulated devices to have greatly improved performance longevity by retaining 87% of initial power after 5112 h storage in air, as well as strong mechanical endurance by maintaining over 80% of initial efficiency after 4700 bending cycles at a curvature radius of 5 mm for flexible devices. This work offers an effective and generally applicable approach for engineering the nanostructured interface to realize stable and efficient PSCs.

P. Kossakowski, W. Wciślik

In this review, we discuss the basic issues related to the use of FRP (fiber-reinforced polymer) composites in bridge construction. This modern material is presented in detail in terms of the possibility of application in engineering structures. A general historical outline of the use and development of modern structural materials, such as steel and concrete, is included to introduce composites as a novel material in engineering, and the most important features and advantages of polymers as a construction material are characterized. We also compare FRP to basic structural materials, such as steel and concrete, which enables estimation of the effectiveness of using of FRP polymers as structural material in different applications. The first bridges made of FRP composites are presented and analyzed in terms of applied technological solutions. Examples of structural solutions for deck slabs, girders and other deck elements made of FRP composites are discussed. Particular attention is paid to the systems of deck slabs, especially those composed of pultruded profiles, sandwich panels and hybrid decks. The disadvantages of composites, as well as barriers and limitations in their application in engineering practice, are presented. Exemplary analyses of the costs of construction, maintenance and demolition of FRP composite bridges are presented and compared with the corresponding costs of concrete and steel bridges. The directions of development of composite bridge structures and the greatest challenges facing engineers and constructors in the coming years are discussed.

赵秋, 游鹏飞, 杨艳

正交异性钢桥面板受力行为复杂，基于子系统可以更加有效地分析钢桥面板的疲劳受力行为，以钢桥面板子系统1为研究对象，分析轮载作用面积和轮载作用位置对子系统1疲劳受力行为的影响，在此基础上分析了盖板厚度和U肋厚度对U肋-盖板构造细节处3个弯矩的影响。同时基于等效结构应力法，对子系统1的主导疲劳开裂模式进行研究。研究结果表明：轮载作用面积为600 mm×200 mm且轮载作用在U肋间时，子系统1中U肋-盖板构造细节的受力最不利；子系统1以U肋外侧弯矩<italic>M</italic>_r大于U肋内侧弯矩<italic>M</italic>_l的情况为主；子系统1焊根开裂模式的主导疲劳开裂模式均为开裂模式Ⅱ，增大盖板厚度能有效降低焊缝焊根开裂的风险；焊趾开裂模式的主导疲劳开裂模式与盖板和U肋厚度有关，增大盖板厚度能减小开裂模式Ⅰ发生的概率，增大U肋厚度能减小开裂模式Ⅲ发生的概率；在焊根和焊趾的主<italic>S</italic>-<italic>N</italic>曲线相同的情况下，子系统1的主导开裂模式为焊趾开裂模式。

Ayele Tesema Chala, Richard Ray

Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification.

Ming Yuan, Z. Cao, Jun Luo et al.

Acoustic energy is a type of environmental energy source that can be scavenged and converted into electrical energy for small-scale power applications. In general, incident sound power density is low and structural design for acoustic energy harvesting (AEH) is crucial. This review article summarizes the mechanisms of AEH, which include the Helmholtz resonator approach, the quarter-wavelength resonator approach, and the acoustic metamaterial approach. The details of recently proposed AEH devices and mechanisms are carefully reviewed and compared. Because acoustic metamaterials have the advantages of compactness, effectiveness, and flexibility, it is suggested that the emerging metamaterial-based AEH technique is highly suitable for further development. It is demonstrated that the AEH technique will become an essential part of the environmental energy-harvesting research field. As a multidisciplinary research topic, the major challenge is to integrate AEH devices into engineering structures and make composite structures smarter to achieve large-scale AEH.

Christian Munk, Eshita Mutt, Vignir Ísberg et al.

Chen Qu, Liu Yang, W. Bruce Croft et al.

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.

Baolin Ge, Chunyu Hou, Bin Bao et al.

Fish collagen has been widely used in tissue engineering (TE) applications as an implant, which is generally transplanted into target tissue with stem cells for better regeneration ability. In this case, the success rate of this research depends on the fundamental components of fish collagen such as amino acid composition, structural and rheological properties. Therefore, researchers have been trying to find an innovative raw material from marine origins for tissue engineering applications. Based on this concept, collagens such as acid-soluble (ASC) and pepsin-soluble (PSC) were extracted from a new type of cartilaginous fish, the blacktip reef shark, for the first time, and were further investigated for physicochemical, protein pattern, microstructural and peptide mapping. The study results confirmed that the extracted collagens resemble the protein pattern of type-I collagen comprising the α₁, α₂, β and γ chains. The hydrophobic amino acids were dominant in both collagens with glycine and hydroxyproline as major amino acids. From the FTIR spectra, α helix (27.72 and 26.32%), β-sheet (22.24 and 23.35%), β-turn (21.34 and 22.08%), triple helix (14.11 and 14.13%) and random coil (14.59 and 14.12%) structures of ASC and PSC were confirmed, respectively. Collagens retained their triple helical and secondary structure well. Both collagens had maximum solubility at 3% NaCl and pH 4, and had absorbance maxima at 234 nm, respectively. The peptide mapping was almost similar for ASC and PSC at pH 2, generating peptides ranging from 15 to 200 kDa, with 23 kDa as a major peptide fragment. The microstructural analysis confirmed the homogenous fibrillar nature of collagens with more interconnected networks. Overall, the preset study concluded that collagen can be extracted more efficiently without disturbing the secondary structure by pepsin treatment. Therefore, the blacktip reef shark skin could serve as a potential source for collagen extraction for the pharmaceutical and biomedical applications.

Giulia Delo, Marco Civera, Erica Lenticchia et al.

In recent years, the use of interferometric satellite data for Structural Health Monitoring has experienced a strong development. The urban environment confirms its fragility to adverse natural events, made even more severe by climate change. Hence, the need to carry out continuous monitoring of structures and artefacts appears increasingly urgent. Furthermore, satellite data could considerably increase the feasibility of traditional Structural Health Monitoring (SHM) approaches. This study aims to explore this remote sensing approach, focusing on the representation techniques that can be adopted to highlight their advantages and provide an interpretation of the results. In particular, the study analyzes records from the urban area of Rome (Italy), subject to the construction of a new subway line. These data are exploited to create a velocity map to highlight the possible subsidence phenomenon induced by excavations. Then, the paper focuses on single buildings or building complexes through the entropy-energy representation. Beyond the different limitations caused by the input data, a correlation is identified between the results of the two representation techniques. Accordingly, the effects of excavation on the urban area are demonstrated, and the methodologies are validated.

Zaheer Qasim, Yonggang Tan, Qamar Furqan

The structural development in bridge engineering along with efficiency have got much attention in few decades. Leading to the development, Optimization of structure established on mathematical analysis emerged mostly employed strategies for productive and sustainable design in the bridge engineering. Despite the widespread knowledge, there has yet to be a rigorous examination of recent structural optimization exploration development. Thus, the primary objectives of this paper are to critically review previous structural optimization research, provide a detailed examination of optimization goals and outline recent research field limitations and provide guidelines for future research proposal in the field of bridge engineering structural optimization. This article begins by outlining the relevance of efficiency and sustainability in the bridge construction, as well as the work done required for this review. Suitable papers are gathered and followed by a statistical analysis of the selected publications. Following that, the selected papers are evaluated in terms of the optimization targets as well as their spatial patterns. Structure's optimization four key steps, including modeling, optimization techniques, formulation of optimization concerns and computational tools, are also researched and examined in depth. Finally, research gaps in contemporary works are identified, as well as suggested guidance for future works.

Chung Kwan Lo

In recent years, there has been increasing emphasis on integrated STEM education, reflecting the fact that the four STEM disciplines (i.e., science, technology, engineering, and mathematics) are often integrated in real-world applications. However, most K-12 teachers are trained within their own subject discipline and may not be capable of implementing an integrated approach to STEM education. There is therefore a need to develop teacher professional development (TPD) programs that can provide high-quality learning opportunities and support for teachers. The overarching goal of this research synthesis is to develop a set of design principles for effective TPD for integrated STEM education. To this end, this paper reviews 48 empirical studies and identifies the elements of effective TPD and potential challenges to implementing integrated STEM education. Content knowledge, pedagogical content knowledge, and sample STEM instructional materials are the three most frequently reported elements of effective TPD programs. However, even with TPD, teachers encounter various obstacles to the implementation of integrated STEM education, including pedagogical challenges (e.g., teachers’ limited STEM knowledge) and structural challenges (e.g., teachers’ lack of preparation time and resources). Based on the findings of this review, a set of design principles (e.g., allocate TPD time for teachers’ micro-teaching) is proposed. This review contributes to the design and implementation of TPD programs by leveraging studies of the effective elements of TPD and addressing the potential challenges to integrated STEM education.

Y. Zhang, Weihua Wang, A. L. Greer

Long Ngoc TRAN, The Truyen TRAN, Manh Hung NGUYEN et al.

This paper presents the results of an experimental study to measure the shortening of reinforced concrete (RC) columns under long-term maintaining concentric axial load. Long-term axial deformation due to shrinkage and creep of the concrete were recorded beside deformation due to mechanical load. Eight RC cylinder - columns (content of reinforcement 1.5% and 2%) with diameter of 150 mm and height of 600 mm were tested during the period of 600 days to determine their shortening. The experimental results showed that the long-term deformation of RC columns occurs primarily during the first year of loading. The deformation creep of concrete is much greater than the shrinkage deformation. The reinforcement content has a significant effect on the long-term deformation of concrete columns.