Hasil untuk "Structural engineering (General)"

Daniel N. Wilke

This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.

Florian Felten, Gabriel Apaza, Gerhard Bräunlich et al.

Engineering design optimization seeks to automatically determine the shapes, topologies, or parameters of components that maximize performance under given conditions. This process often depends on physics-based simulations, which are difficult to install, computationally expensive, and require domain-specific expertise. To mitigate these challenges, we introduce EngiBench, the first open-source library and datasets spanning diverse domains for data-driven engineering design. EngiBench provides a unified API and a curated set of benchmarks -- covering aeronautics, heat conduction, photonics, and more -- that enable fair, reproducible comparisons of optimization and machine learning algorithms, such as generative or surrogate models. We also release EngiOpt, a companion library offering a collection of such algorithms compatible with the EngiBench interface. Both libraries are modular, letting users plug in novel algorithms or problems, automate end-to-end experiment workflows, and leverage built-in utilities for visualization, dataset generation, feasibility checks, and performance analysis. We demonstrate their versatility through experiments comparing state-of-the-art techniques across multiple engineering design problems, an undertaking that was previously prohibitively time-consuming to perform. Finally, we show that these problems pose significant challenges for standard machine learning methods due to highly sensitive and constrained design manifolds.

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn et al.

Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retrieval mechanisms with generative capabilities, resulting in more accurate, contextually relevant responses that enhance user experience. In particular, Transurban, a road operation company, is replacing its rule-based virtual assistant (VA) with a RAG-based VA (RAGVA) to offer more flexible customer interactions and support a wider range of scenarios. In this paper, drawing from the experience at Transurban, we present a comprehensive step-by-step guide for building a conversational application and how to engineer a RAGVA. These guides aim to serve as references for future researchers and practitioners. While the engineering processes for traditional software applications are well-established, the development and evaluation of RAG-based applications are still in their early stages, with numerous emerging challenges remaining uncharted. To address this gap, we conduct a focus group study with Transurban practitioners regarding developing and evaluating their RAGVA. We identified eight challenges encountered by the engineering team and proposed eight future directions that should be explored to advance the development of RAG-based applications. This study contributes to the foundational understanding of a RAG-based conversational application and the emerging AI software engineering challenges it presents.

Carl Philipp Hohl, Philipp Reis, Tobias Schürmann et al.

Vehicle data is essential for advancing data-driven development throughout the automotive lifecycle, including requirements engineering, design, verification, and validation, and post-deployment optimization. Developers currently collect data in a decentralized and fragmented manner across simulations, test benches, and real-world driving, resulting in data silos, inconsistent formats, and limited interoperability. This leads to redundant efforts, inefficient integration, and suboptimal use of data. This fragmentation results in data silos, inconsistent storage structures, and limited interoperability, leading to redundant data collection, inefficient integration, and suboptimal application. To address these challenges, this article presents a structured literature review and develops an inductive taxonomy for automotive data. This taxonomy categorizes data according to its sources and applications, improving data accessibility and utilization. The analysis reveals a growing emphasis on real-world driving and machine learning applications while highlighting a critical gap in data availability for requirements engineering. By providing a systematic framework for structuring automotive data, this research contributes to more efficient data management and improved decision-making in the automotive industry.

Leonhard Applis, Yuntong Zhang, Shanchao Liang et al.

The growth of Large Language Model (LLM) technology has raised expectations for automated coding. However, software engineering is more than coding and is concerned with activities including maintenance and evolution of a project. In this context, the concept of LLM agents has gained traction, which utilize LLMs as reasoning engines to invoke external tools autonomously. But is an LLM agent the same as an AI software engineer? In this paper, we seek to understand this question by developing a Unified Software Engineering agent or USEagent. Unlike existing work which builds specialized agents for specific software tasks such as testing, debugging, and repair, our goal is to build a unified agent which can orchestrate and handle multiple capabilities. This gives the agent the promise of handling complex scenarios in software development such as fixing an incomplete patch, adding new features, or taking over code written by others. We envision USEagent as the first draft of a future AI Software Engineer which can be a team member in future software development teams involving both AI and humans. To evaluate the efficacy of USEagent, we build a Unified Software Engineering bench (USEbench) comprising of myriad tasks such as coding, testing, and patching. USEbench is a judicious mixture of tasks from existing benchmarks such as SWE-bench, SWT-bench, and REPOCOD. In an evaluation on USEbench consisting of 1,271 repository-level software engineering tasks, USEagent shows improved efficacy compared to existing general agents such as OpenHands CodeActAgent. There exist gaps in the capabilities of USEagent for certain coding tasks, which provides hints on further developing the AI Software Engineer of the future.

Pierre-Emmanuel Goffi, Raphaël Tremblay, Bentley Oakes

Successfully engineering interactive industrial DTs is a complex task, especially when implementing services beyond passive monitoring. We present here an experience report on engineering a safety-critical digital twin (DT) for beer fermentation monitoring, which provides continual sampling and reduces manual sampling time by 91%. We document our systematic methodology and practical solutions for implementing bidirectional DTs in industrial environments. This includes our three-phase engineering approach that transforms a passive monitoring system into an interactive Type 2 DT with real-time control capabilities for pressurized systems operating at seven bar. We contribute details of multi-layered safety protocols, hardware-software integration strategies across Arduino controllers and Unity visualization, and real-time synchronization solutions. We document specific engineering challenges and solutions spanning interdisciplinary integration, demonstrating how our use of the constellation reporting framework facilitates cross-domain collaboration. Key findings include the critical importance of safety-first design, simulation-driven development, and progressive implementation strategies. Our work thus provides actionable guidance for practitioners developing DTs requiring bidirectional control in safety-critical applications.

Atsushi Suzuki, Ruiyu Che, Takeru Niihori et al.

ABSTRACT In general, a steel beam is connected to a concrete slab using shear connectors, forming a composite beam. This composite action shifts the neutral axis under bending, resulting in a buckling behavior that differs from that of a bare steel beam. Moreover, the structural performance of composite beams is significantly influenced by the stress transfer mechanism facilitated by the shear connectors. However, the impact of shear connector and slab properties on buckling behavior has not been fully explored. To address this gap, this research first proposes a simplified method. For modeling composite beams that accurately reflects the behavior of shear connectors. Additionally, a comprehensive parametric study is conducted using an experimentally validated finite element analysis (FEA) model, exploring the influence of various shear connectors and slab properties. Based on the analytical results, a modified evaluation index and equation are ultimately proposed to enhance the assessment of composite beam performance.

Elena Ignatova, Elena Kitaytseva

This article deals with the water consumption regime in a residential building. The study is based on data of cold and hot water hourly consumption in a multi-storey apartment building. The measurement period is one month. The study comprehensively uses statistical analysis of water consumption and data mining of group outliers. Statistical data analysis is designed to determine the distribution pattern of different data samplings. The analysis is carried out for three different samplings of apartment water consumption data. As a result, group outliers of hourly water consumption are identified.  Machine learning methods are used to identify group outliers. The task boils down to clustering the hours of the day to find hours with reduced (nighttime) water consumption. Clustering is carried out using five methods, and clustering quality is assessed by three metrics. As a result, nighttime consumption periods are determined for different samplings of water consumption data in apartment buildings. In general, comprehensive intellectual and statistical analysis of water consumption is useful for solving the tasks of designing water supply and sanitation systems, adjusting the operating modes of engineering equipment, and clarifying the calculated parameters of water consumption in apartment buildings.

Qi Peng, Shuichiro Yokoyama, Kiyotomo Ichiki

A modification to the vis-viva equation that accounts for general relativistic effects is introduced to enhance the accuracy of predictions of orbital motion and precession. The updated equation reduces to the traditional vis-viva equation under Newtonian conditions and is a more accurate tool for astrodynamics than the traditional equation. Preliminary simulation results demonstrate the application potential of the modified vis-viva equation for more complex n-body systems. Spherical symmetry is assumed in this approach; however, this limitation could be removed in future research. This study is a pivotal step toward bridging classical and relativistic mechanics and thus makes an important contribution to the field of celestial dynamics.

Syed Haider Mehdi Rizvi, Muntazir Abbas, Syed Sajjad Haider Zaidi et al.

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency.

Xiao Wang, Xiaofeng Hou, Wei Yuan et al.

The blast-induced vibration during excavation by drilling and blasting method has an important impact on the surrounding structures. In particular, with the development of tunnel engineering, the impact of blasting vibration on tunnel construction has attracted extensive attention. In this paper, the propagation attenuation characteristics of blast-induced vibration (PPV, peak particle velocity) on different tunnel structures were systematically studied based on the field monitoring data. Initially, the attenuation characteristics of blasting vibration PPV on the lower bench surface, the side wall of the excavated tunnel and the closely spaced adjacent tunnel were investigated. Subsequently, the capacity of several widely utilized empirical prediction equations to estimate the PPV on tunnel structures was examined, along with a comparative analysis of their prediction accuracy. The research findings indicate that it is feasible to predict the PPV on the tunnel structures using empirical equations. The attenuation characteristics of blasting vibration PPV are different in different structures and directions. The prediction accuracy of the empirical equations varies, while the discrepancies are minimal. The principal variation among these equations lies in the site-specific coefficients k, β, λ, highlighting the differential impact of structural and directional considerations on the predictive efficacy. Based on the empirical equation and safe PPV provided by the blasting vibration safe standards on tunnels of China (GB6722-2014), and considering the influence of all structures and directions, it is determined that the safe distance of blasting vibration in the tested tunnel project should be larger than 20.28–18.31 ​m, 18.31–16.16 ​m, and 16.16–13.75 ​m for blasting vibration frequency located in ≤10 ​Hz, 10–50 ​Hz, and >50 ​Hz.

Haoyan Wei, C. T. Wu, Wei Hu et al.

Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.

Olga Ivanova, Jose Gavaldá-Garciá, Dea Gogishvili et al.

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. The Protein DataBank (PDB) contains a wealth of structural information. In order to investigate the similarity between different proteins in this database, one can compare the primary sequence through pairwise alignment and calculate the sequence identity (or similarity) over the two sequences. This strategy will work particularly well if the proteins you want to compare are close homologs. However, in this chapter we will explain that a structural comparison through structural alignment will give you much more valuable information, that allows you to investigate similarities between proteins that cannot be discovered by comparing the sequences alone.

Qinghai Du, Fengyou Liu, Qi Lei

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to reveal the structural impact performance of PMMA materials in underwater engineering thoroughly, this paper firstly proposed a simplified plate model for a spherical shell hull under concentrative impact loading. Then, to simulate the hyper-elastic material properties of PMMA in the impact process, the Johnson–Cook constitutive model and damage failure model were adopted. And the least squares method was used to confirm accurately the J–C constitutive and damage failure model parameters of PMMA through material test data. Moreover, the dynamic process of the steel bullet impacting the PMMA plate structure was analyzed by the finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper have a good convergence, and the residual velocities at different initial velocities and thicknesses of plates are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J–C constitutive and damage failure models in this paper are verified accordingly. Finally, based on the presented finite element model, the structure response and the variation of residual velocity of the bullet with the PMMA plate thickness was analyzed in depth; that is, the results show that the residual velocity of the bullet has a certain linear relationship with the thickness, even in an underwater environment, and even in an underwater environment will increase both with a thicker structure or a higher pressure.

Andrea Chiappa, Christian Bachmann, Francesco Maviglia et al.

The DEMO tokamak exhibits extraordinary complexity due to the constraints and requirements pertaining to different fields of physics and engineering. The multidisciplinary nature of the DEMO system makes its design phase extremely challenging since different and often opposite requirements need to be accounted for. Toroidal field (TF) coils generate the toroidal magnetic field required to magnetically confine the plasma particles and support at the same time the poloidal field coils. They must bear tremendous loads deriving from electromagnetic interactions between the coil currents and the generated magnetic field. An efficient tokamak design aims at minimizing the energy stored in its magnetic field and hence at reducing the toroidal volume within the TF coils whose shape would hence ideally mimic co-centrically the shape of the plasma. In order to bear the enormous forces a D-shape is most suitable for the TF coils as it allows them to resist the very large compression on the inner side and to carry the electro-magnetic (EM) pressure mainly by membrane stresses preventing large bending to occur on the outer side. At the same time the divertor structures must fit within the TF coils and this requires adaptations of the TF coil shape in the case of so-called advanced divertor configurations (ADCs), which require larger divertor structures. This article shows the TF coils adapted to ADCs using a structural optimisation procedure applied to the reference shape. The introduced strategy takes as structural optimum the iso-stress profile associated to each coil. A continuous transformation, based on radial basis functions mesh morphing, turns the baseline finite element (FE) model into its iso-stress counterpart, with a series of intermediate configurations available for electromagnetic and structural investigations as output. The adopted strategy allowed to determine, for each of the ADC cases, a candidate shape. Static membrane stress levels during magnetization could be reduced significantly from more than 700 MPa to below 450 MPa.

J. Kabanda, C. MacDougall

Iulius Stroe, Ionel SIMION

A vertebral compression fracture is a compression of a vertebra, which occurs in the anterior portion of the vertebral body. In the treatment of compression-induced spinal fractures, there are two main directions of treatment: conservative treatment (bed rest, pharmacological treatment, orthosis) and minimally invasive surgical treatment - vertebroplasty, kyphoplasty, combined technology (uses implant and cement in the treatment method). Recently, coupled with technological advancements, the combined technology has become increasingly attractive to manufacturers of implantable medical devices. This article presents the combined technologies for minimally invasive surgical treatment that are currently available. Some of them have already been used to treat compression-induced spinal fractures for nearly a decade, sometimes being regarded as a reference for new medical devices entering the market.

Syifaul Huzni, Fikri Oktiandar, Syarizal Fonna et al.

Tibia is one of the bones that often fracture, generally occurring due to a car accident, falling from high places, work accidents, and sports injuries. Internal fixation is one of the solutions to repair broken bones. In some cases, internal fixation also failed to carry out its function, so the healing process was disturbed and did not run according to the plan. Factors that might interfere with the process can be analyzed using FEM. The objective of this study is to study the effect of the contact model used to model the connection between broken bones of the tibia, to stress distribution that occurs on fixation plate for walking conditions. Analysis was carried out by using ANSYS software with fine-sized tetrahedrons mesh. Two contact models were used. Namely, friction and bonded. The load amount used is based on the average weight of Indonesian Adults, i.e. 63 kg. The results of the analysis show that, for the friction contact model, higher stress is found in the middle area plate, adjacent to the broken location on the bone. Different results are found in the bonded contact model, larger stress occurs in the upper-end area fixation plate

Locif REDOUANI, Sarra KHILOUF, Rabah LAIDI et al.

In this paper, we have used the hot isostatic pressing HIP models previously carried out for the study of the random dense packing densification (RDP) of spherical particles of the same size in order to adapt them to the RDP of two-dimensional spherical particles. A new microscopic approach is thus developed that allows the densification parameters of two-dimensional spherical powder aggregates to be evaluated as a function of the relative density, taking into account the morphological changes of the powder particles and the porosity. The equations obtained for each parameter (coordination number, mean contact area and effective pressure) made it possible to represent the results in the form of curves. These show that our new approach is well adapted to a realistic description of the densification of powder aggregates with particles of more or less similar sizes.

Myung-Jin Choi, Jae-Hyun Kim, Bonyong Koo et al.

This paper presents a configuration design optimization method for three-dimensional curved beam built-up structures having maximized fundamental eigenfrequency. We develop the method of computation of design velocity field and optimal design of beam structures constrained on a curved surface, where both designs of the embedded beams and the curved surface are simultaneously varied during the optimal design process. A shear-deformable beam model is used in the response analyses of structural vibrations within an isogeometric framework using the NURBS basis functions. An analytical design sensitivity expression of repeated eigenvalues is derived. The developed method is demonstrated through several illustrative examples.