Hasil untuk "Bridge engineering"

Miaomiao Xu, Xiao-Yi Zhou, Jie Shen et al.

Abstract This study investigates six types of prediction methods for estimating extreme bridge traffic load effects, aiming to establish a correlation between prediction accuracy and data quality. Accurately determining the distribution functions of maximum values is crucial for assessing bridge safety under traffic loads. Methods including the Peaks Over Threshold, the block maxima approach, fitting to a Normal distribution, and the Rice formula based level crossing method, are investigated. Additionally, Bayesian Updating and Predictive Likelihood techniques, integrated with the block maxima approach, are explored. The performance of these methods is assessed using two distinct datasets. The first dataset is generated from a known distribution, allowing the estimated distribution parameters and extreme values derived from each method to be compared with the true values. The analysis is then extended to more realistic scenarios, where long-run simulations provide benchmark results for evaluating the accuracy of each method. Based on the findings, recommendations are provided for selecting the most suitable prediction method, considering factors such as sample size, time interval, and the type of load effect. This work offers practical insights for improving the reliability of extreme value prediction methods in bridge safety assessments.

WAN Lei, DONG Fuqiang, CHEN Jinzhen et al.

With the gradual shift of high-grade highways in China from construction to maintenance, higher performance requirements have been placed on asphalt binders. High-content SBS modified asphalt has become an inevitable choice for both new pavement construction and maintenance. However, conventional high-content SBS modified asphalt suffers from high energy consumption, excessive carbon emissions, and poor construction workability. In this study, a self-developed warm-mixed additive was introduced into high-content SBS modified asphalt, and the process was optimized to obtain warm-mixed SBS modified asphalt. The effects of the additive on the asphalt performance and the warm-mix efficiency were evaluated in terms of viscosity-temperature characteristics, rheological properties, and thermal properties, while the viscosity-reduction mechanism was further revealed through microstructural analysis. The results show that when the mixing ratio of additive A to B is 2% to 1%, the warm-mixed SBS modified asphalt exhibits optimal performance. The softening point increases by 1.8 ℃; the ductility at 5 ℃ improves by 5.7 cm, and the rotational viscosity at 135 ℃ decreases by 0.9 Pa·s, thereby significantly enhancing construction workability. Rheological tests demonstrate that both high- and low-temperature performance meet the PG76-22 grade requirements. Microstructural observations confirm that no new chemical substances are generated during the viscosity-reduction process; instead, the additive functions as a lubricant in the molten state to reduce viscosity through physical action and serves as a skeleton in the solid state to reinforce the binder and improve its rheological properties.

ZENG Zhuo, ZHENG Honggang, XIANG Sheng et al.

As the bridge construction goes forward to the deep-water environments, the permanent floating bridge structures have attracted more and more attention from international scholars. The project cases of worldwide representative permanent floating bridges were presented. The structural systems of the built permanent floating bridges were summarized. The research and application advances regarding the mechanical features, the construction process, and the special configurations of the permanent floating bridges were introduced. Finally, from the perspectives of the engineering economy and environment applicability, the development prospect of permanent floating bridges was analyzed. The research shows that the permanent floating bridge structure has been applied in engineering around the world and has two types of structural systems, which are the continuous pontoon system and the discrete pontoon system. The permanent floating bridges adopted in deep-water environments have shown superior engineering economy. Based on further research and verifications, the permanent floating bridge structures can be applied in deep-water crossing projects.

Tingting Sun, Jingnan Yang, Kuang Shi et al.

Understanding the interface shear behavior between clay and structures is crucial in geotechnical engineering. The mechanism of the roughness effect in the shear process between the clay and structures was studied to reveal the macroscopic and microscopic interface shear behavior. The different surface protrusion shapes of the structures were produced using a three-dimensional (3D) printer. Direct shear tests were conducted to analyze the shear failure modes and peak and residual strengths under different conditions. Subsequently, a discrete element method (DEM) numerical analysis was employed to study the contact network, soil fabric evolution, shear zone, coordination number, and void ratio variations in the interface shear. The test results indicated that the shear interfaces exhibited the same failure mode under various conditions, and the peak and residual strengths showed a strong positive correlation with roughness. The results obtained from numerical calculations match the experimental findings. The contact orientations and principal stresses shifted during the shear process, and the shear zone, coordination number, and void ratio also showed regular changes with the change of roughness. The evolution of microscopic parameters in DEM can effectively help explain the macroscopic interface shear behavior.

Haonan Cai, Chongsheng Cheng, Hong Zhang et al.

Conducting rapid quantitative debonding detection after the completion of Concrete-filled steel tube (CFST) pouring is crucial for the timely identification and repair of potential structural issues. Currently, there is a lack of an infrared detection method that can perform quantitative detection specifically during the construction phase of CFST. This study proposes a Discreteness-Based Image Preprocessing (DBIP) method, combined with Otsu’s and K-means image segmentation methods, to explore its effectiveness in detecting debonding in CFST during the hydration heat phase. A full-scale CFST model was used to simulate debonding areas of different sizes, and infrared thermal imaging data were collected. The results show that the DBIP method significantly improved detection accuracy, and the DBIP+K-means combination can effectively quantify debonding areas with a minimum side length of 126 mm (10 % debonding rate). The study also reveals that the correlation between the F1-score and thermal contrast is linear when the thermal contrast is between 0°C and 0.18°C. When the thermal contrast exceeds 0.18°C, the F1-score stabilizes at approximately 0.8. The finding clarifies the detection accuracy range under different thermal contrast conditions and suggests potential optimization directions for the quantification of CFST debonding in practical application via infrared thermography.

Siyang Gu, Jian Zhang

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management.

Xiaohua Wang, Chonghao Sun, Junjie Dong et al.

In this study, to solidify the silt in an expressway, a stabilizing agent composed of industrial wastes, such as ordinary Portland cement (OPC), calcium based alkaline activator (CAA), silicate solid waste material (SISWM) and sulfate solid waste material (SUSWM) was developed. Orthogonal experiments and comparative experiments were carried out to analyze the strength and water stability of the stabilized silt, and get the optimal proportion of each component in the stabilizing agent. A series of laboratory tests, including unconfined compressive strength (UCS), water stability (WS), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) analyses, were conducted on solidified silt samples treated with the stabilizing agent at optimal mixing ratios of OPC, CAA, SISWM, and SUSWM to elucidate the evolution of mineral composition and microstructure.

ZHENG Junlin, JIANG Youbao, ZHENG Xinhao et al.

The existing design methods for concrete arch bridge structures do not consider the random characteristics of the eccentricity generated by load effects and overlook the influence of stochastic eccentricity on structural reliability. To address this deficiency, a calculation model for eccentricity under temperature effect combinations was derived. The random characteristics of eccentricity for different sections of the main arch of concrete arch bridges and their sectional bearing capacities were analyzed under different load parameter combinations. By utilizing the Monte Carlo method, reliability indices were computed for different sections of the main arch and different load parameter combinations after considering the random characteristics of eccentricity. The results indicate that considering the random characteristics of eccentricity leads to significant variability in the eccentricity of main arch sections, and the sectional bearing capacity may be governed by tensile strength. Reliability indices vary greatly with changes in the temperature-to-load effect ratio, suggesting that when the temperature-to-load effect ratio is significant, existing designs based on fixed eccentricity may lead to potentially unsafe designs.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer et al.

Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.

Muhammed Yasin Çodur, Halis Bahadir Kasil, Emre Kuşkapan

Hot mix asphalt, which is frequently used in road pavements, contains bitumen in certain proportions. This bitumen ratio varies according to the layers in the road pavements. The bitumen ratio in each pavement is usually estimated by the Marshall design method. However, this method is costly as well as time-consuming. In this study, the Naive Bayes method, which is a machine learning algorithm, was used to estimate the bitumen ratio practically. In the study, a total of 102 asphalt concrete designs were examined, which were taken from the wearing course, binder course, and asphalt concrete base course and stone mastic asphalt wearing course layers. Each road pavement layer was divided into three different classes according to the bitumen ratios and the algorithm was trained with machine learning. Then the bitumen ratio was estimated for each data set. As a result of this process, the bitumen ratios of the layers were estimated with an accuracy between 75% and 90%. In this study, it was revealed that the bitumen ratio in the road pavement layers could be estimated practically and economically.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Diana Robinson, Christian Cabrera, Andrew D. Gordon et al.

What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better serve end users? What are the implications of this process for the future of end-user software engineering and the software development lifecycle? We discuss the research needed to bridge the gap between where we are today and these imagined systems of the future.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

Sergio Rico

Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.

Kai Qie, Zhitian Zhang, Shouying Li et al.

The trial-and-error method is complex and tedious, but often adapted to determine the cross-section sizes of core beams in the design of reduced-scale models. In this study, two optimization methods, the optimization methods in ANSYS and the genetic algorithm, are investigated to optimize the cross-section sizes of core beams of reduced-scale models, which centers around two targeted moments of inertia and a targeted torsion constant. Due to the difficulty of obtaining an analytical solution of the torsion constant, a series of numerical solutions are proposed. Then, taking a U-shaped cross section as an example, the four geometric sizes of the section are optimized by the ANSYS optimization method and the genetic algorithm, respectively. The results of both methods are in good agreement with the targeted values, but the ANSYS optimization method is prone to fall into the local optimization zone and hence could be easily affected by the initial values. The shortcomings of the ANSYS optimization method can be easily avoided by the genetic algorithm, and it is easier to reach the global optimal solution. Finally, taking a suspension bridge with a main span of 920 m as a prototype, the full-bridge aeroelastic model is designed and the genetic algorithm is used to optimize the cross-section sizes of core beams in the bridge tower and the deck. Natural frequencies identified from the aeroelastic model agree well with the target ones, indicating the structural stiffness, which is provided by the core beams, has been modelled successfully.

Michał Żochowski

Abstract: The author presented the legal status in relation to the use of balustrades on bridges. The analysis presented regulations on technical conditions on bridges and regulations on occupational health and safety. The article attempts to carry out the analysis in such a way that the conclusions are universal and can be applied to all types of objects. It has been shown that the balustrades are an element which protects against falling from a height. This type of collective protection elements should be used when there is a risk of falling from a height. Keywords: Balustrade, Bridge, Safety