Hasil untuk "Bridge engineering"

Die Liu, Jianxi Yang, Lihua Chen et al.

Long-term data loss resulting from sensor malfunctions, communication interruptions, and other factors in Structural Health Monitoring (SHM) significantly undermines the reliability of damage identification and safety assessment. Existing methods—ranging from statistical approaches and low-rank matrix completion to traditional machine learning and deep learning imputation techniques—often suffer from either limited accuracy or excessive model size and slow inference, making deployment in resource-constrained scenarios difficult. To address these challenges, this paper proposes TEFN–Imputation, a lightweight and efficient time-series imputation model. This model utilizes observation-driven non-stationary normalization to mitigate the impact of time-varying characteristics and dimensional discrepancies. It employs linear projection for temporal length alignment and constructs BPA-style mass representations from dual perspectives of time and channel. Furthermore, it replaces strict Dempster–Shafer belief combination with an expectation-based evidential aggregation (readout), thereby significantly reducing computational overhead while enabling uncertainty-aware evidential indicators for interpretation rather than claiming a direct accuracy gain from uncertainty modeling. The observed accuracy and robustness improvements are primarily attributed to the normalization and dual temporal–channel modeling design under the same lightweight readout. Systematic experiments on two real-world bridge monitoring datasets, Z24 and Hell Bridge, demonstrate that TEFN consistently maintains low Mean Absolute Error (MAE) and minimal volatility across various combinations of training and testing missing rates, exhibiting high robustness against variations in missing rates and train–test mismatches. Concurrently, compared to RNN and large-scale Transformer baselines, TEFN reduces parameter count and CPU inference time by one to two orders of magnitude. Thus, it achieves a superior trade-off among accuracy, efficiency, and model scale, making it highly suitable for online SHM and imputation tasks in practical engineering applications. Across the settings on Z24, TEFN achieves a mean MAE of 0.218 with a standard deviation of 0.002, while using only 0.02 MB parameters and 2.73 ms per batch CPU inference.

Tab, Zhang, Bjorn De Sutter et al.

Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.

Esteban Parra, Sonia Haiduc, Preetha Chatterjee et al.

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Zeyu Zhang, Shanwu Li, Hui Feng et al.

LI Jinlong, WANG Xinnan, LIU Dongsheng et al.

At present, the highway bridge scheme requires continuous adjustments and upgrading due to iterative updates of design data and refined investigation of the surrounding environment of the bridge site. This paper upgraded the traditional route design software, developed a GIS-integrated platform, built a three-dimensional environment with design data as the core, and created a bridge scheme model through parametric intelligent design. It also evaluated and optimized the bridge scheme based on the integration of the bridge model and the surrounding environment. The results show that by using BIM technology, the forward design of a bridge scheme based on a three-dimensional environment is realized. This approach automates the scale statistics of bridge construction, the positioning and identification of high piers, and the transmission of design data to downstream professionals for further use. The bridge scheme design based on BIM technology can significantly improve the design efficiency, enhance the intuitiveness of bridge scheme evaluation, and increase the practical value of design data.

ZHENG Junqiu, MA Hui, QIAO Yuelai et al.

To accurately assess the impact of structural cracks on the strength of pavement structures and establish corresponding evaluation indicators based on deflection response, field tests were conducted on multiple sections of expressways in Jiangsu Province. A falling weight deflectometer was used to test a total of 15 measurement points within a 3-meter range on both sides of each crack. The center deflection values at each measurement point were plotted into curves, and three evaluation indicators for cracks, namely deflection range, maximum influence distance on one side of the crack, and influence area, were proposed based on the characteristics of the curves. Core samples were taken directly above the selected cracks within the test sections to verify the crack types and development layers. According to the distribution and cracking conditions of the cracks in the core samples, six types of structural cracks were classified. The results show that the proposed indicators can effectively distinguish fatigue cracks from structural cracks. The coefficient of determination (R2) between deflection range and influence area is 0.75, and the Pearson correlation coefficient is 0.88. Both indicators have a good correspondence with the various stages of crack development and can be used in combination to evaluate the severity of cracks, providing practical guidance for pavement maintenance and construction.

Zihan Jiang, Zhiwen Zhu, Giuseppe Lacidogna

The UHPC deck layer may be susceptible to cracking during construction, raising concerns for bridge engineering utilizing this advanced material. Addressing the issue of drying shrinkage cracking observed on the UHPC deck layer of a cable-stayed bridge, a real-time investigation into crack evolution was conducted. This study employed acoustic emission (AE) technique with in-situ data processing, focusing on AE time series analysis. Additionally, triangulation techniques were utilized to determine the AE source positions of active cracks. The results showed continuous crack evolution on the UHPC deck layer, mainly due to construction vehicles, with two major instances of crack propagation and arrest. AE signals correlated with measured crack propagation, with two major AE events matching recorded crack jumps. Later AE sources indicated a step-by-step crack tip advancement. This paper underscores the effectiveness of the AE technique for crack identification and real-time monitoring of in-service bridges.

MAO Quan, ZHENG Junqiu, MA Hui et al.

Asphalt pavement is prone to fatigue cracking during long-term service, which can lead to secondary diseases and weaken the structural durability. For in-depth revelation of the fatigue damage behavior of asphalt mixtures, in this paper, a semi-circular bending (SCB) mesoscopic model was established based on the discrete element method. The fatigue fracture behavior of three typical asphalt mixtures in Jiangsu Province’s expressways, including SMA-13, SUP-20, and SUP-25, was simulated under different stress ratio conditions. The fatigue damage mechanism and evolution law of asphalt mixtures were analyzed from the aspects of fatigue life, crack propagation, and residual strength. The results show that all three mixtures exhibit typical three-stage fatigue damage characteristics, and the increase of stress ratio will significantly shorten the fatigue life and accelerate the crack development. SMA-13 shows excellent fatigue resistance due to its dense skeleton and modified asphalt mortar, followed by SUP-20, while SUP-25 performs the worst due to insufficient skeleton constraint and low mortar toughness. Meanwhile, the crack propagation paths and residual strength attenuation laws of different mixtures are significantly different, further revealing the key role of skeleton structure and asphalt mortar performance in the fatigue damage mechanism.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Mugeng Liu, Siqi Zhong, Weichen Bi et al.

Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.

Guojun Tang, Carylyne Chan, Ning Nan et al.

Bitcoin's limited scripting capabilities and lack of native interoperability mechanisms have constrained its integration into the broader blockchain ecosystem, especially decentralized finance (DeFi) and multi-chain applications. This paper presents a comprehensive taxonomy of Bitcoin cross-chain bridge protocols, systematically analyzing their trust assumptions, performance characteristics, and applicability to the Artificial Intelligence of Things (AIoT) scenarios. We categorize bridge designs into three main types: naive token swapping, pegged-asset bridges, and arbitrary-message bridges. Each category is evaluated across key metrics such as trust model, latency, capital efficiency, and DeFi composability. Emerging innovations like BitVM and recursive sidechains are highlighted for their potential to enable secure, scalable, and programmable Bitcoin interoperability. Furthermore, we explore practical use cases of cross-chain bridges in AIoT applications, including decentralized energy trading, healthcare data integration, and supply chain automation. This taxonomy provides a foundational framework for researchers and practitioners seeking to design secure and efficient cross-chain infrastructures in AIoT systems.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Zhenpeng Chen, Chong Wang, Weisong Sun et al.

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

Krishna Ronanki, Simon Arvidsson, Johan Axell

The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Weichuan Zhang, Mingxian Zhao, Zhongyan Yang et al.

A significant amount of waste is generated during the sandstone mining process. This study explores the use of red sandstone waste as a supplementary cementitious material to address environmental concerns in cement production. The experimental methods include isothermal calorimetry, compressive strength testing, thermogravimetric analysis, X-ray diffraction, infrared spectroscopy, scanning electron microscopy, high-temperature damage testing (200, 600, and 900 °C), and mesoscopic image analysis. The experimental findings reveal the following: (1) Red sandstone powder enhanced cement hydration and optimized the samples' microstructure. (2) There is no significant difference in the effects of calcined and uncalcined red sandstone powders at 800 °C. (3) At 200 °C, the high-temperature damage of the samples containing red sandstone and limestone powders was reduced. This study is expected to positively impact the utilization of red sandstone waste and contribute to the reduction of carbon emissions in cement production.

Essam Althaqafi, Tariq Ali, Muhammad Zeeshan Qureshi et al.

Abstract The major challenge for the construction industry is to design and produce sustainable construction materials that are efficient in their performance and can be affordable for construction projects. The objective of this study is to determine the viability of incorporating waste products such as sugarcane bagasse ash (SCBA) to produce concrete with polypropylene (PP) fibers. SCBA is an industry waste that has certain pozzolanic properties, however, it shows limited mechanical properties when used as a cement replacement. To improve the mechanical and durability performance, metakaolin (15%) and PP fiber (0.5%, 1%, and 1.5%) were incorporated in the SCBA blend as a ternary additive. Some of the important characteristics explored include compressive strength, tensile strength, density, water absorption, acid resistance, and sorptivity. The study reveals that the incorporation of 15% metakaolin in the composite enhanced the compressive strength by 7%, 8.2%, and 9.1% for PP fiber additions of 0.5%, 1%, 1.5%, while the acid resistance enhanced by 4%, 6% and 8% relative to the control mix for the same value of pp fiber. Furthermore, cost evaluation confirms that the overall costs of concrete with 15% metakaolin and 5% SCBA were 12.2% less than the control concrete, thus making this option economically feasible. This research proves that the inclusion of SCBA, metakaolin, and PP fibers into the concrete mixture brings a sustainable approach that enhances mechanical properties and durability while assisting sugar manufacturing plants in the proper disposal of wastes.

Yangyang Xia, Chenming Shen, Haizeng Yang et al.

The modification method of grafting multi-walled carbon nanotubes (MWCNT, abbreviated as CNT in this paper) on the surface of flax fibers was investigated, i.e. CNT were grafted onto the surface of flax fibers by silane coupling agent under the action of ultrasonic waves to form covalent bonding. The tensile strength of CNT grafted flax fiber is 22% higher than that of untreated flax fiber monofilament. The tensile strength of FFRP composites after CNT grafting treatment increased by 14.2%; however, the tensile modulus of single fiber and composites did not show a significant increase, the interfacial shear strength of the fiber-resin is 38.3% higher than that of untreated filament. The improvement of the contact angle after grafting was investigated by observing the surface morphology, and the surface of flax filament was characterized by scanning electron microscopy and atomic force microscopy. Also the elemental changes of the flax single fiber surface before and after the treatment were analyzed using X-ray photoelectron spectroscopy. The results showed that the tensile strength of flax single fibers, its composites, and interfacial shear strength of fiber-matrix was improved after CNT grafting treatment but the increase of modulus was not obvious.

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee et al.

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.