Hasil untuk "Structural engineering (General)"

Katrine T. Schjoldager, Yoshiki Narimatsu, H. Joshi et al.

D. Broek, J. Rice

P. Marcelino, Maria de Lurdes Antunes, E. Fortunato et al.

ABSTRACT In recent years, there has been an increasing interest in the application of machine learning for the prediction of pavement performance. Prediction models are used to predict the future pavement condition, helping to optimally allocate maintenance and rehabilitation funds. However, few studies have proposed a systematic approach to the development of machine learning models for pavement performance prediction. Most of the studies focus on artificial neural networks models that are trained for high accuracy, disregarding other suitable machine learning algorithms and neglecting the importance of models’ generalisation capability for Pavement Engineering applications. This paper proposes a general machine learning approach for the development of pavement performance prediction models in pavement management systems (PMS). The proposed approach supports different machine learning algorithms and emphasizes generalisation performance. A case study for prediction of International Roughness Index (IRI) for 5 and 10-years, using the Long-Term Pavement Performance, is presented. The proposed models were based on a random forest algorithm, using datasets comprising previous IRI measurements, structural, climatic, and traffic data.

Noga Chemo, Yaniv Mordecai, Yoram Reich

We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.

Xu‐Yang Cao, D. Shen, D. Feng et al.

Earthquakes cause serious damage to buildings and result in heavy losses to society, therefore, it is necessary to enhance the seismic capacity of existing buildings via structural retrofitting. The traditional retrofitting approaches are based on the component-level, but their improvement effect for the overall structure is not obvious. The ultimate goal of seismic retrofitting is to improve the overall seismic performance of the whole structure, thus a variety of external sub-structure retrofitting methods have been developed at home and abroad since the 1970s. The external sub-structure is connected with the existing structure as a whole on the structural-system-level, and it is of great significance for lifeline projects or non-interrupted buildings. At this stage, the external sub-structure retrofitting technology has received wide attention in the seismic community and is still developing in bloom. This paper gives a state of the art review of the advances and research interests of the external sub-structure retrofitting technology. First, the general concepts of the external sub-structure retrofitting technology are given, including (1) retrofitting principle and (2) retrofitting superiority. Then, the typical types of the external sub-structure retrofitting technology are summarized, including (1) external frame sub-structures, (2) external frame-brace sub-structures, (3) exter-nal wall sub-structures and (4) other external sub-structures. Finally, some critical issues of the external sub-structure retrofitting technology are extracted, including (1) interfacial shear transferring mechanism, (2) joint property and connection performance, (3) combination with precast-assembly technology, (4) combination with prestress technology, (5) numerical approach and assessment indicators, (6) optimization strategy and design procedure, (7) environment interaction and maintenance cost, and (8) application in practical engineering. The future perspectives of the external sub-structure retrofitting technology are also pointed out, and the contents can provide some reference for the subsequent research as well as the developing trend in the future.

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

M. K. Yazdi, V. Vatanpour, Ali Taghizadeh et al.

Hydrogel membranes (HMs) are defined and applied as hydrated porous media constructed of hydrophilic polymers for a broad range of applications. Fascinating physiochemical properties, unique porous architecture, water-swollen features, biocompatibility, and special water content dependent transport phenomena in semi-permeable HMs make them appealing constructs for various applications from wastewater treatment to biomedical fields. Water absorption, mechanical properties, and viscoelastic features of three-dimensional (3D) HM networks evoke the extracellular matrix (ECM). On the other hand, the porous structure with controlled/uniform pore-size distribution, permeability/selectivity features, and structural/chemical tunability of HMs recall membrane separation processes such as desalination, wastewater treatment, and gas separation. Furthermore, supreme physiochemical stability and high ion conductivity make them promising to be utilised in the structure of accumulators such as batteries and supercapacitors. In this review, after summarising the general concepts and production processes for HMs, a comprehensive overview of their applications in medicine, environmental engineering, sensing usage, and energy storage/conservation is well-featured. The present review concludes with existing restrictions, possible potentials, and future directions of HMs.

Alexandros Gazis, Ioannis Papadongonas, Athanasios Andriopoulos et al.

This article provides a comprehensive overview of sensors commonly used in low-cost, low-power systems, focusing on key concepts such as Internet of Things (IoT), Big Data, and smart sensor technologies. It outlines the evolving roles of sensors, emphasizing their characteristics, technological advancements, and the transition toward "smart sensors" with integrated processing capabilities. The article also explores the growing importance of mini-computing devices in educational environments. These devices provide cost-effective and energy-efficient solutions for system monitoring, prototype validation, and real-world application development. By interfacing with wireless sensor networks and IoT systems, mini-computers enable students and researchers to design, test, and deploy sensor-based systems with minimal resource requirements. Furthermore, this article examines the most widely used sensors, detailing their properties and modes of operation to help readers understand how sensor systems function. The aim of this study is to provide an overview of the most suitable sensors for various applications by explaining their uses and operations in simple terms. This clarity will assist researchers in selecting the appropriate sensors for educational and research purposes or understanding why specific sensors were chosen, along with their capabilities and possible limitations. Ultimately, this research seeks to equip future engineers with the knowledge and tools needed to integrate cutting-edge sensor networks, IoT, and Big Data technologies into scalable, real-world solutions.

Christoph Treude, Margaret-Anne Storey

The adoption of large language models (LLMs) and autonomous agents in software engineering marks an enduring paradigm shift. These systems create new opportunities for tool design, workflow orchestration, and empirical observation, while fundamentally reshaping the roles of developers and the artifacts they produce. Although traditional empirical methods remain central to software engineering research, the rapid evolution of AI introduces new data modalities, alters causal assumptions, and challenges foundational constructs such as "developer", "artifact", and "interaction". As humans and AI agents increasingly co-create, the boundaries between social and technical actors blur, and the reproducibility of findings becomes contingent on model updates and prompt contexts. This vision paper examines how the integration of LLMs into software engineering disrupts established research paradigms. We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments. Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.

Alex Gu, Naman Jain, Wen-Ding Li et al.

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.

Thomas J. Misa

The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both. This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders; it then examines the field's recent attention to gender issues and gender bias. It next quantitatively analyzes women's participation as research authors in the field's leading International Conference of Software Engineering (1976-2010), finding a dozen years with statistically significant gender exclusion. Policy dimensions of research on gender bias in computing are suggested.

L. Beranek, I. Vér, L. R. Quartararo

ZHANG Huipeng, ZHANG Tao, SHI Yufeng et al.

Objective To effectively prevent lining damage and water leakage in tunnels, research is carried out on minimizing lining structure bending moment for tunnel cross-section in upper-soft lower-hard composite strata. Method Soft soil-hard soil and soil-rock two types of loading modes for composite strata tunnel lining structures are proposed based on composite strata tunnel surrounding rock pressure characteristics and reasonable assumptions. Following the ideal optimization approach for a zero bending moment composite strata tunnel, a semi-structural mechanics model of tunnel cross-section is established. The rational axis equation of the zero bending moment tunnel cross-section and the analytical expressions of diameters at both composite strata boundary lines and the horizontal tunnel cross-section mid-line are calculated. The process of minimizing bending moment in composite strata tunnel cross-section is outlined briefly. Result & Conclusion The rational axis of the zero bending moment cross-section in soft soil-hard soil tunnel is pear-shaped, while in soil-rock tunnel, it is horseshoe-shaped. In practical engineering, based on the rational axis equation of tunnel cross-section, key parameters for tunnel cross-section design can be determined through comprehensive evaluation and weighted averaging method. This approach enables the design of new tunnel cross-sections in upper-soft and lower-hard strata, aiming to minimize lateral deformation in composite strata tunnels.

Guoqing Wang, Zeyu Sun, Zhihao Gong et al.

Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt engineering techniques. This paper presents an extensive empirical study that reevaluates various prompt engineering techniques within the context of these advanced LLMs. Focusing on three representative SE tasks, i.e., code generation, code translation, and code summarization, we assess whether prompt engineering techniques still yield improvements with advanced models, the actual effectiveness of reasoning models compared to non-reasoning models, and whether the benefits of using these advanced models justify their increased costs. Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models. In reasoning LLMs, the ability of sophisticated built-in reasoning reduces the impact of complex prompts, sometimes making simple zero-shot prompting more effective. Furthermore, while reasoning models outperform non-reasoning models in tasks requiring complex reasoning, they offer minimal advantages in tasks that do not need reasoning and may incur unnecessary costs. Based on our study, we provide practical guidance for practitioners on selecting appropriate prompt engineering techniques and foundational LLMs, considering factors such as task requirements, operational costs, and environmental impact. Our work contributes to a deeper understanding of effectively harnessing advanced LLMs in SE tasks, informing future research and application development.

Jin Wang, A. Khosravi, A. Vanossi et al.

A plethora of two-dimensional (2D) materials entered the physics and engineering scene in the last two decades. Their robust, membrane-like sheet permit -- mostly require -- deposition, giving rise to solid-solid dry interfaces whose bodily mobility, pinning, and general tribological properties under shear stress are currently being understood and controlled, experimentally and theoretically. In this Colloquium we use simulation case studies of twisted graphene system as a prototype workhorse tool to demonstrate and discuss the general picture of 2D material interface sliding. First, we highlight the crucial mechanical difference, often overlooked, between small and large incommensurabilities, corresponding e.g., to small and large twist angles in graphene interfaces. In both cases, focusing on flat, structurally lubric,"superlubric"geometries, we elucidate and review the generally separate scaling with area of static friction in pinned states and of kinetic friction during sliding, tangled as they are with the effects of velocity, temperature, load, and defects. Including the role of island boundaries and of elasticity, and corroborating when possible the existing case-by-case results in literature beyond graphene, the overall picture proposed is meant for general 2D material interfaces, that are of importance for the physics and technology of existing and future bilayer and multilayer systems.

D. Tiruneh, Mieke De Cock, Ataklti G. Weldeslassie et al.

Although the development of critical thinking (CT) is a major goal of science education, adequate emphasis has not been given to the measurement of CT skills in specific science domains such as physics. Recognizing that adequately assessing CT implies the assessment of both domain-specific and domain-general CT skills, this study reports on the development and validation of a test designed to measure students’ acquisition of CT skills in electricity and magnetism (CTEM). The CTEM items were designed to mirror the structural components of items identified in an existing standardized domain-general CT test, and targeted content from an introductory Electricity and Magnetism (E&M) course. A preliminary version of the CTEM test was initially piloted on three groups of samples: interviews with physics experts (N = 3), student cognitive interviews (N = 6), and small-scale paper and pencil administration (N = 19). Modifications were made afterwards and the test was administered to a different group of second-year students whose major was mechanical engineering (N = 45). The results showed that the internal consistency (Cronbach’s α = .72) and inter-rater reliability (Cohen’s kappa = .83) of the CTEM test are acceptable. The findings overall suggest that the CTEM test can be used to measure the acquisition of domain-specific CT skills in E&M, and a good basis for future empirical research that focuses on the integration of CT skills within specific subject matter instruction. A broader CT assessment framework is proposed and possible research questions that can be addressed through the CTEM test are discussed.

Pradeep Raja, Vignesh Murugan, Sindhu Ravichandran et al.

Abstract The surge towards a sustainable future in the construction industry requires the use of bio‐based insulation materials as an alternative to conventional ones for improving energy efficiency in structures. In this article, the features of bio‐based insulation materials, including their thermal conductivities, moisture buffering value, fire performance, and life cycle evaluations are examined. It is clear from the review that pre‐ and post‐treatment of the bio‐based materials used for insulation materials optimize their properties. The life cycle analysis reveals a significant reduction in global warming potential (GWP) compared to conventional foams. In addition, it is envisaged that producing bio‐based insulation materials on a larger scale will further decrease the net GWP. The article, therefore, proposes the implementation of policies that will promote the commercialization of bio‐based insulation materials.

Teraphan Ornthammarath, Amorntep Jirasakjamroonsri, Patinya Pornsopin et al.

Abstract Background The Bangkok Basin has been known from non-instrumental observations of the local population to be subject to ground motion amplification due to the deep alluvial sediments and basin geometry. This study analyzes available seismic data to confirm that basin effects are significant in the Bangkok Basin. The paper contributes to the evaluation of basin effects by characterizing the engineering ground motion parameters and HVSR curves for the Bangkok basin which produce lengthening of ground motion duration with respect to nearby rock sites, albeit with very low ground motions. For this purpose, we analyzed ground motion records from seismic stations located within the Bangkok alluvial basin from 2007 to 2021. Recorded peak horizontal ground acceleration (PGA) for seismic stations inside the basin always exceeded 1 cm/s2 during eight earthquakes with Mw ≥ 5.5. Of these, two were intraslab events and six were shallow crustal earthquakes. These recorded ground motions shook high-rise buildings in Bangkok even though their epicentral distance exceeded 600 km. Methods Several time and frequency domain analyses (such as analysis of residual, HVSR, Hodogram plots, etc.) are used on the ground motion records in the Bangkok basin to determine the frequency content of recorded ground motion and to demonstrate the significance of surface waves induced by the deep basin in altering the engineering ground motion amplitudes. In addition, centerless circular array microtremor analysis is used to determine the depth of sedimentary basin to the bedrock. Results Based on comparisons from those stations located outside the Bangkok basin, we observed the capability of alluvial deposits in the Bangkok basin to amplify ground motion records by about 3 times. We observed that there is a unique site amplification effect between 0.3 and 0.1 Hz due to local surface waves and other moderate amplifications between 2 and 0.5 Hz due to a soft layer like other deep alluvial basins in other metropolitan areas. Conclusion We noticed that there is a unique site amplification effect between 0.1 and 0.3 Hz and smaller peaks around 2 and 0.5 Hz consistent with expectations for site amplification effects associated with deep basins. Moreover, we noticed the presence of low frequencies content of the surface wave generated within the basin which deserved further studies using the 2D/3D ground motion modelling through basin topography and velocity models.

Jocelyn Ahmed Mazari, Antoine Reverberi, Pierre Yser et al.

In this work, we propose a built-in Deep Learning Physics Optimization (DLPO) framework to set up a shape optimization study of the Duisburg Test Case (DTC) container vessel. We present two different applications: (1) sensitivity analysis to detect the most promising generic basis hull shapes, and (2) multi-objective optimization to quantify the trade-off between optimal hull forms. DLPO framework allows for the evaluation of design iterations automatically in an end-to-end manner. We achieved these results by coupling Extrality's Deep Learning Physics (DLP) model to a CAD engine and an optimizer. Our proposed DLP model is trained on full 3D volume data coming from RANS simulations, and it can provide accurate and high-quality 3D flow predictions in real-time, which makes it a good evaluator to perform optimization of new container vessel designs w.r.t the hydrodynamic efficiency. In particular, it is able to recover the forces acting on the vessel by integration on the hull surface with a mean relative error of 3.84\% \pm 2.179\% on the total resistance. Each iteration takes only 20 seconds, thus leading to a drastic saving of time and engineering efforts, while delivering valuable insight into the performance of the vessel, including RANS-like detailed flow information. We conclude that DLPO framework is a promising tool to accelerate the ship design process and lead to more efficient ships with better hydrodynamic performance.

Ruochun Zhang, Bonaventura Tagliafierro, Colin Vanden Heuvel et al.

This paper introduces DEM-Engine, a new submodule of Project Chrono, that is designed to carry out Discrete Element Method (DEM) simulations. Based on spherical primitive shapes, DEM-Engine can simulate polydisperse granular materials and handle complex shapes generated as assemblies of primitives, referred to as clumps. DEM-Engine has a multi-tier parallelized structure that is optimized to operate simultaneously on two GPUs. The code uses custom-defined data types to reduce memory footprint and increase bandwidth. A novel "delayed contact detection" algorithm allows the decoupling of the contact detection and force computation, thus splitting the workload into two asynchronous GPU streams. DEM-Engine uses just-in-time compilation to support user-defined contact force models. This paper discusses its C++ and Python interfaces and presents a variety of numerical tests, in which impact forces, complex-shaped particle flows, and a custom force model are validated considering well-known benchmark cases. Additionally, the full potential of the simulator is demonstrated for the investigation of extraterrestrial rover mobility on granular terrain. The chosen case study demonstrates that large-scale co-simulations (comprising 11 million elements) spanning 15 seconds, in conjunction with an external multi-body dynamics system, can be efficiently executed within a day. Lastly, a performance test suggests that DEM-Engine displays linear scaling up to 150 million elements on two NVIDIA A100 GPUs.