Hasil untuk "Bridge engineering"

Ruibo Cui, Xiaodong Shi, Yanghua Cui et al.

Spatial efficiency drives the adoption of integrated station–bridge structures in maglev transit, yet the rigid coupling between track and station poses inherent challenges to vibration serviceability. This study isolates the impact of support constraints, specifically contrasting rigid connections with pinned supports, on the dynamic performance of a five-story maglev station. Using a unified, high-fidelity 3D coupled model that incorporates electromagnetic suspension nonlinearity, we evaluated structural responses under train speeds of 60–120 km/h. Simulations identify a critical operational threshold: while the waiting hall remains compliant with standard comfort criteria (DIN 4150-3), the platform floor exceeds the 1.5% g acceleration limit during dual-track operations at speeds ≥ 100 km/h. Beyond standard safety checks, the main scientific innovation of this study is revealing the mechanical transmission paths of structure-borne vibrations at the track-frame interface. The results demonstrate that rigid connections create full mechanical coupling, directly passing train-induced bending moments into the station frame. Conversely, pinned supports release the rotational degrees of freedom, which physically cuts off the primary energy transmission route. By explaining this structural decoupling mechanism, this work moves beyond a specific engineering case study to provide a fundamental theoretical framework for vibration control in complex maglev hubs.

Dhyaa A.H. Abualghethe, Baogang Mu, Guoliang Dai et al.

Accurate and efficient estimation of soil parameters is critical for the safe and successful construction of super-large caisson foundations, which are increasingly utilized in major infrastructure projects. Conventional in situ and laboratory methods are often slow, costly, and unable to capture dynamic soil–structure interactions during the sinking process. This study proposes a novel hybrid framework that integrates 3D finite element modeling (FEM), Uniform Design theory, and advanced machine learning (ML) for high-precision back-analysis of soil parameters. The approach is validated using the south anchorage of the super-large rectangular caisson in the Nanjing Longtan Yangtze River Bridge project. A total of 550 FEM simulations were conducted under varying soil parameter scenarios, generating corresponding stress responses. These stress–parameter pairs trained ML models to predict soil parameters from new stress data, enabling efficient back-analysis. The dataset was further augmented to 1550 samples using an ML-based synthetic data generation scheme that preserved key parameter correlations. Eighteen ML algorithms were compared; Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Target-Specific Extra Trees (TSET) achieved the highest predictive accuracy (R² ≥ 0.98), with LightGBM performing best (R² = 0.987, MAPE = 1.68%, RSR = 0.016, VAF = 98.66%). The framework successfully captured the complex nonlinear relationships between stress responses and underlying soil properties, yielding results that aligned closely with independent geotechnical investigation reports. This validated approach provides a powerful tool for the proactive failure analysis of design assumptions, offering significant practical implications for risk assessment, failure prevention, and risk mitigation in large-scale foundation engineering.

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

CUI Bing, CHEN Kun

To improve the industrial construction level of steel bridges, a prefabricated continuous steel box girder bridge was designed in this study, which is suitable for bidirectional four lanes and six lanes with spans ranging from 40 m to 80 m. The overall dimensions and local structures of the steel box girder bridge were determined according to the specifications and engineering experience, and the load-bearing capacity was checked. Modular industrial products were designed by defining the vertical and horizontal modular divisions for the steel box girders, resulting in 21 standardized modular components. Based on the concept of life cycle, an economic analysis of the prefabricated steel box girder bridge was carried out. Research results show that the overall dimensions and local structures of the prefabricated steel box girder bridge are reasonable. The modular product design improves the versatility of steel box girder bridges across different spans and lanes, greatly simplifying the construction process to meet industrial construction requirements. When considering the whole life cycle and the recovery of steel materials, the average annual cost of prefabricated steel box girder bridges is only about 30% of that of concrete girder bridges. Additionally, its carbon emissions during the construction phase are lower than those of concrete girder bridges, providing a significant economic advantage over the entire life cycle.

Honglin Ran, Lin Li, Yi Wei et al.

To address the challenge of repairing the damage to concrete box girder bridge piers on mountainous highways caused by falling rocks, this paper proposes an active underpinning technique that integrates a “井”-shaped cap system, graded preloading of the foundation, and synchronized beam body correction. The technique utilizes lateral beam preloading (to eliminate the inelastic deformation of the new pile foundation) and longitudinal beam connections (to form overall stiffness). The method involves building temporary and permanent support systems in stages. Through the two-stage temporary support system transition, the removal and in situ reconstruction of the old piers, a smooth transition from the pier–beam consolidation system to the basin-type bearing system is achieved while simultaneously performing precise correction of beam torsion. The structural safety during the construction process was verified through finite element simulations and dynamic monitoring. Monitoring results show that the beam torsion recovery effect is significant (maximum lift of 5.2 mm/settlement of 7.9 mm), and the pier strain (−54.5~−51.3 με) remains within a controllable range. Before the bridge was opened to traffic, vehicle load and impact load tests were conducted. The actual measured strength and vertical stiffness of the main beam structure meet the design requirements, with relative residual deformation less than 20%, indicating that the structure is in good, elastic working condition. The vehicle running and braking dynamic coefficients (μ = 0.058~0.171 and 0.103~0.163) are both lower than the theoretical value of 0.305. The study shows that this technique enables the rapid and safe repair of bridge piers and provides important references for similar engineering projects.

Shefeng Guo, Jin Wu, Haoxiang Luan et al.

With global sustainable construction growth, fully recycled coarse aggregate concrete (RCAC)—eco-friendly for cutting construction waste and reducing natural aggregate over-exploitation—has poor durability in seasonally freezing saline-soil regions (e.g., Tumushuke, Xinjiang): freeze-thaw and salt ions (NaCl, Na₂SO₄) cause microcracking, faster performance decline, and shorter service life, limiting its use and requiring better salt freeze resistance. To address this, a field survey of Tumushuke’s saline soil was first conducted to determine local salt type and concentration, based on which a matching 12% NaCl + 4% Na₂SO₄ mixed salt solution was prepared. RCAC specimens modified with fly ash (FA), silica fume (SF), and polypropylene fiber (PPF) were then fabricated, cured under standard conditions (20 ± 2 °C, ≥95% relative humidity), and subjected to rapid freeze-thaw cycling in the salt solution. Multiple macro-performance and microstructural indicators (appearance, mass loss, relative dynamic elastic modulus (RDEM), porosity, microcracks, and corrosion products) were measured post-cycling. Results showed the mixed salt solution significantly exacerbated RCAC’s freeze-thaw damage, with degradation severity linked to cycle count and admixture dosage. The RCAC modified with 20% FA and 0.9% PPF exhibited optimal salt freeze resistance: after 125 cycles, its RDEM retention reached 75.98% (6.60% higher than the control), mass loss was only 0.28% (67.80% lower than the control), and its durability threshold (RDEM > 60%) extended to 200 cycles. Mechanistic analysis revealed two synergistic effects for improved performance: (1) FA optimized pore structure by filling capillaries, reducing space for pore water freezing and salt penetration; (2) PPF enhanced crack resistance by bridging microcracks, suppressing crack initiation/propagation from freeze-thaw expansion and salt crystallization. A “pore optimization–ion blocking–fiber crack resistance” triple synergistic protection model was proposed, which clarifies admixture-modified RCAC’s salt freeze damage mechanism and provides theoretical/technical guidance for its application in extreme seasonally freezing saline-soil environments.

Xiang Jiao, Xiaozhi Fu, Qishuang Li et al.

Abstract Protoberberine alkaloids and benzophenanthridine alkaloids (BZDAs) are subgroups of benzylisoquinoline alkaloids (BIAs), which represent a diverse class of plant-specialized natural metabolites with many pharmacological properties. Microbial biosynthesis has been allowed for accessibility and scalable production of high-value BIAs. Here, we engineer Saccharomyces cerevisiae to de novo produce a series of protoberberines and BZDAs, including palmatine, berberine, chelerythrine, sanguinarine and chelirubine. An ER compartmentalization strategy is developed to improve vacuole protein berberine bridge enzyme (BBE) activity, resulting in >200% increase on the production of the key intermediate (S)-scoulerine. Another promiscuous vacuole protein dihydrobenzophenanthridine oxidase (DBOX) has been identified to catalyze two-electron oxidation on various tetrahydroprotoberberines at N7-C8 position and dihydrobenzophenanthridine alkaloids. Furthermore, cytosolically expressed DBOX can alleviate the limitation on BBE. This study highlights the potential of microbial cell factories for the biosynthesis of a diverse group of BIAs through engineering of heterologous plant enzymes.

Akinola Gbemisola Ajoke, Fagbenle Olabosipo Ishola, Ogunde Ayodeji Olubunmi

In the face of limited financial resources, public tertiary institutions are pressured to optimize expenditure on educational building projects. Effective cost reduction techniques can help bridge the gap between limited budgets and the need for quality infrastructure. This research investigates cost reduction techniques implemented on educational building projects in public tertiary institutions in southwestern Nigeria and its relationship with the type of tertiary institution. A quantitative research method was employed in the study using a questionnaire survey. The building projects considered were those completed between the years 2012-2022. 133 projects from 15 public tertiary institutions in southwestern Nigeria were surveyed using purposive sampling techniques. The mean item score and the Kruskal-Wallis test were employed for data analysis. The findings showed that amongst the 16 various cost reduction techniques investigated, value analysis/engineering, supply chain management, target value design, and budget control were top-ranked and used on many elements of the projects. At the same time, automation and circular economy were the least ranked cost reduction techniques used. The study further showed significant differences in implementing 7 of the techniques in the various tertiary institutions. It is concluded that integrating cost reduction techniques into existing policies and guidelines will facilitate the development of a standardized framework for their implementation across public tertiary institutions, promoting broad adoption and ensuring consistency in their application.

Cheng-Long Zhou, Yu-Chen Peng, Yong Zhang et al.

We explore a novel approach to achieving anisotropic thermal photon tunneling, inspired by the concept of parity-time symmetry in quantum physics. Our method leverages the modulation of constitutive optical parameters, oscillating between loss and gain regimes. This modulation reveals a variety of distinct effects in thermal photon behavior and dispersion. Specifically, we identify complex tunneling modes through gain-loss engineering, which include thermal photonic defect states and Fermi-arc-like phenomena, which surpass those achievable through traditional polariton engineering. Our research also elucidates the laws governing the evolution of radiative energy in the presence of gain and loss interactions, and highlights the unexpected inefficacy of gain in enhancing thermal photon energy transport compared to systems characterized solely by loss. This study not only broadens our understanding of thermal photon tunneling but also establishes a versatile platform for manipulating photon energy transport, with potential applications in thermal management, heat science, and the development of advanced energy devices.

D.K. MacKenzie

Nianchun Deng, Haitang Zhang, Feng Ning et al.

As an important component connecting the upper and lower structures of a bridge, bridge bearings can reliably transfer vertical and horizontal loads to a foundation. Bearing capacity needs to be monitored during construction and maintenance. To create an intelligent pot bearing, a portable small spot welding machine is used to weld pipe-type welding strain gauges to the pot bearing to measure strain and force values. The research contents of this paper include the finite element analysis of a basin bearing, optimal arrangement of welding strain gauges, calibration testing, and temperature compensation testing of the intelligent basin bearing of the welding strain gauges. Polynomial fitting is used for the fitting and analysis of test data. The results indicate that the developed intelligent pot bearing has a high-precision force measurement function and that after temperature compensation, the measurement error is within 1.8%. The intelligent pot bearing has a low production cost, and the pipe-type welding strain gauges can be conveniently replaced. The novelty is that the bearing adopts a robust pipe-type welding strain gauge and that automatic temperature compensation is used. Therefore, the research results have excellent engineering application value.

Juliusz Engelhardt

Abstract: The subject of the article is the development of the railway infrastructure of the Zachodniopomorskie Voivodeship in the period of socio-economic transformation. After an introduction containing a short historical outline, a significant regression of the railway infrastructure in Western Pomerania in the first phase of the transformation period covering the years 1990 - 2004 was indicated. the EU budget perspective 2007-2013 and investments implemented in this period under the Regional Operational Programme. The basic documents concerning the transport policy of the Zachodniopomorskie Voivodship from 2002 and 2010, which defined the needs of the region in the development of railway infrastructure, were also indicated. In the summary of the article, the advantages and disadvantages of the existing railway network in Western Pomerania were pointed out, with the disadvantages clearly determining the directions of past investments in this area of transport infrastructure. Keywords: Transport policy; Railway infrastructure; Line and station investments

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza et al.

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real et al.

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.

Neil A. Ernst, Maria Teresa Baldassarre

Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including Empirical Software Engineering. We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.

Vilma Jasiūnienė, Aja Tumavičė

The rapidly growing popularity of electric scooters in recent years has allowed the road user to choose another alternative mode of transportation. On the one hand, it is an ecological means of transportation in the city, allowing you to quickly reach your destination; on the other hand, it is a vehicle that causes risk to road safety. Although this is a fairly new mode of transport, it is already of great concern for road safety authorities. E-scooter accidents are recorded with all road users – pedestrians, bicyclists, motor vehicles, other e-scooter riders, or even alone. In this article, the analysis made according to the accident data of 2019–2020 showed that the highest number of accidents occurred between e-scooters and vehicles. Most e-scooter accidents with motor vehicles occur in the intersection zone or during a vehicle turning manoeuvre to (or from) side streets and exit lanes. A descriptive statistical analysis showed that the proportions of the distribution of road accidents between accident participants changed significantly during the analysis period – the number of road accidents between e-scooters and bicycles increased, while the number of accidents between e-scooters and pedestrians decreased. The road accidents between e-scooters and other vulnerable road users are usually caused by sudden, unexpected manoeuvring of road users. Identification of accident schemes and locations is an additional tool for traffic organisation specialists and road safety professionals to prevent accidents, injuries, and fatalities.

Jonathan Álvarez Ariza, Heyson Baez

In the last decade, Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels. Several factors such as the versatility, the low-cost, and the possibility to enhance the learning process through technology have contributed to the educators and students usually employ these devices. However, the implications, possibilities, and constraints of these devices in engineering and Computer Science (CS) education have not been explored in detail. In this systematic literature review, we explore how the SBCs are employed in engineering and computer science and what educational results are derived from their usage in the period 2010-2020 at tertiary education. For that, 154 studies were selected out of n=605 collected from the academic databases Ei Compendex, ERIC, and Inspec. The analysis was carried-out in two phases, identifying, e.g., areas of application, learning outcomes, and students and researchers' perceptions. The results mainly indicate the following aspects: (1) The areas of laboratories and e-learning, computing education, robotics, Internet of Things (IoT), and persons with disabilities gather the studies in the review. (2) Researchers highlight the importance of the SBCs to transform the curricula in engineering and CS for the students to learn complex topics through experimentation in hands-on activities. (3) The typical cognitive learning outcomes reported by the authors are the improvement of the students' grades and the technical skills regarding the topics in the courses. Concerning the affective learning outcomes, the increase of interest, motivation, and engagement are commonly reported by the authors.

Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis et al.

In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show capabilities' potential for reflecting model generalizability, which can provide guidance for ML engineering process. We discuss challenges and opportunities for capabilities' integration into ML engineering.

Ahmad Dalvand, Hossein Hatami, Arezo Seyedi Chegini

The nature of dynamic loading is different due to the high force in a few milliseconds with static loading. The amount of energy absorption and energy loss in composite materials is a suitable measurement to evaluate the performance against impact loads. On the other hand, the use of self-compacting composites due to its unique properties has attracted the attention of researchers. High compressive and tensile strength, high flexural strength, has attracted more attention from researchers to this kind of cement composites. In this research, in the form of a comprehensive laboratory work, using four basement mixing designs, 64 rectangular composite panels were constructed in two groups of 100*100 mm with four thicknesses of 30, 45, 60 and 75 mm and tested under dynamic loading. Tensile and flexural strength tests were made on all four mixing designs. Steel fibers with percentages of 0, 0.25, 0.5 and 0.75 with length of 25 mm were used for the construction of cement composites. The drop hammer test machine with weighs 180 kg and the power of 7500 J is used. According to laboratory results, the combined use of steel and fiber reinforced steel sheets increased the energy absorption considerably. Also, the initial peak force increased and the deformation length decreased.

Mohammad Kasra Habib, Stefan Wagner, Daniel Graziotin

Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.