Hasil untuk "Bridge engineering"

Nidhal Selmi, Jean-michel Bruel, Sébastien Mosser et al.

Decision-making is a core engineering design activity that conveys the engineer's knowledge and translates it into courses of action. Capturing this form of knowledge can reap potential benefits for the engineering teams and enhance development efficiency. Despite its clear value, traditional decision capture often requires a significant amount of effort and still falls short of capturing the necessary context for reuse. Model-based systems engineering (MBSE) can be a promising solution to address these challenges by embedding decisions directly within system models, which can reduce the capture workload while maintaining explicit links to requirements, behaviors, and architectural elements. This article discusses a lightweight framework for integrating decision capture into MBSE workflows by representing decision alternatives as system model slices. Using a simplified industry example from aircraft architecture, we discuss the main challenges associated with decision capture and propose preliminary solutions to address these challenges.

Jay Lee, Hanqi Su, Dai-Yan Ji et al.

Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.

Anatoly A. Krasnovsky

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.

Wang Jue, Su Xincheng, Gao Quanqing

Carbon fiber reinforced polymer (CFRP), as an advanced composite material, has been attracting a great deal of attention from researchers because of its excellent mechanical properties and durability. CFRP reinforcements are increasingly used in the construction and strengthening engineering. In recent years, considering the application of CFRP reinforcements in civil engineering has entered a stage of rapid development, it is necessary to review the development and recent advances in civil engineering. The present paper reviews the applications of CFRP reinforcements in civil engineering, the characteristics and types of CFRP reinforcements and cables, as well as the anchoring system of CFRP cables, are summarized. Furthermore, the applications of CFRP reinforcements and cables in the construction and strengthening engineering are reviewed, especially the developments and applications in bridge engineering. The main goal of this research is to provide a comprehensive review of the application development of CFRP reinforcements in engineering structures. Moreover, the critical issues should be solved for expanding the application scope of them are discussed.

Shiwen Li, Zhihai Deng, Junguang Wang et al.

Addressing the challenges of scarce measured data for Class 3–4 bridges and strong subjectivity in manual assessments in bridge technical-condition evaluation, this study innovatively proposes a FastText+CNN evaluation model that integrates semantic features with spatial pattern recognition. By constructing a hierarchical data structure of bridge scale matrices using the analytic hierarchy process (AHP) and generating a balanced training set encompassing Class 1–5 bridges through computational code, the model overcomes the bottleneck of training under small-sample conditions. It employs N-Gram embeddings to achieve semantic representation of defect feature combinations, combines one-dimensional convolutional neural networks to capture cross-component spatial correlation patterns, and utilizes hierarchical Softmax to optimize multi-classification efficiency. Experiments show that the model achieves 92.4% accuracy on the test set, outperforming random forest and multi-layer CNN models by 15.9% and 3.7%, respectively, with recognition rates for Class 3–5 bridges rising to 85% and cross-entropy loss reduced to 0.36. Validated with data from 30 actual bridges, the model maintains 92.3% accuracy and demonstrates the ability to discover implicit patterns in cross-component defect chains, providing an intelligent solution for bridge technical condition evaluation that combines semantic understanding with spatial feature extraction.

Bahman Zarazvand, Jana Frankovska

This paper presents a comprehensive case study on the numerical analysis of stone columns as a ground improvement technique for an expressway embankment. The primary objective is to assess the effectiveness of stone columns in enhancing the performance of predominantly fine-grained soils using Finite Element Method (FEM) analysis. To achieve the objective, detailed numerical models are developed in both three-dimensional (3D) and two-dimensional (2D) plane strain configurations to simulate embankment conditions accurately. Key geotechnical parameters, including the modulus of elasticity and hydraulic conductivity of the stone column material, are incorporated to account for the improved stiffness and drainage effects. The installation process considers critical factors such as vibration-induced changes and horizontal displacement to capture the evolution of soil stress conditions. A staged construction approach is implemented to realistically simulate the sequential embankment construction process and its impact over time. To ensure model reliability, validation is performed by comparing numerical results with field measurements obtained from horizontal inclinometers installed beneath the embankment. The analysis focuses on key performance indicators such as settlement behaviour, the generation and dissipation of excess pore water pressure, and overall stability assessments. The results demonstrate a strong correlation between numerical predictions and field observations, confirming the accuracy of the developed models. This study provides valuable insights into the performance of stone column-reinforced embankments, highlighting significant improvements in load-bearing capacity, reduction in settlement, and overall ground stability. By evaluating the role of stone columns in accelerating consolidation and enhancing the stiffness, strength, and stability of fine-grained soil layers, the research contributes to the optimisation of design and construction methodologies for ground improvement. Additionally, a comparative assessment of 3D and 2D plane strain numerical models is conducted to evaluate their predictive capabilities in representing real embankment behaviour. The findings support the advancement of safer and more resilient infrastructure solutions.

YUAN Sifan, LI Tonglu, Haider Mumtaz et al.

Progressive toppling of rock slopes with fractured slab structures is quite common in the slopes of hydropower and highway engineering, posing a potential safety risk during both construction and operational phases. The field investigation found that progressive toppling mostly occurs on slopes with concentrated tectonic stress, intense river incision, and high steepness. Typically, these slopes are composed of hard, thinly-layered, and overthrusted rock mass with a fractured slab structure. From a macroscopic point of view, progressive toppling failure is neither a collapse nor a slide but a form of discontinuous deformation. The traditional limit equilibrium method for slope stability analysis is not suitable for evaluating such slopes. In this paper, by taking the progressive toppling of the slopes along the G108 Highway in the Qinling Mountains as an example and considering the uplift of the mountain and river downcutting, a discontinuous deformation analysis (DDA) method was employed to simulate the entire process of progressive toppling, and the mechanisms of its evolution were analyzed. The results show that as the river downcutting progresses, the rock mass experiences interlayer shearing along bedding planes under the influence of gravity, gradually tilting toward the river valley. Once a through-going rupture area develops, it transforms into a landslide failure. For the evaluation of such slopes, a numerical model should be used to simulate the entire deformation failure process. This allows for the estimation of the current state and development trends, providing a basis for reinforcement and management.

Aline de Campos, Jorge Melegati, Nicolas Nascimento et al.

Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.

Binnur Görer, Fatma Başak Aydemir

Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.

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.

Tom Coenye, Merja Ahonen, Skip Anderson et al.

Priority question exercises are increasingly used to frame and set future research, innovation and development agendas. They can provide an important bridge between the discoveries, data and outputs generated by researchers, and the information required by policy makers and funders. Microbial biofilms present huge scientific, societal and economic opportunities and challenges. In order to identify key priorities that will help to advance the field, here we review questions from a pool submitted by the international biofilm research community and from practitioners working across industry, the environment and medicine. To avoid bias we used computational approaches to group questions and manage a voting and selection process. The outcome of the exercise is a set of 78 unique questions, categorized in six themes: (i) Biofilm control, disruption, prevention, management, treatment (13 questions); (ii) Resistance, persistence, tolerance, role of aggregation, immune interaction, relevance to infection (10 questions); (iii) Model systems, standards, regulatory, policy education, interdisciplinary approaches (15 questions); (iv) Polymicrobial, interactions, ecology, microbiome, phage (13 questions); (v) Clinical focus, chronic infection, detection, diagnostics (13 questions); and (vi) Matrix, lipids, capsule, metabolism, development, physiology, ecology, evolution environment, microbiome, community engineering (14 questions). The questions presented are intended to highlight opportunities, stimulate discussion and provide focus for researchers, funders and policy makers, informing future research, innovation and development strategy for biofilms and microbial communities.

Srijoni Majumdar, Soumen Paul, Debjyoti Paul et al.

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects and an additional dataset generated individually by teams using large language models. Overall 56 experiments have been submitted by 17 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The labels generated from large language models increase the bias in the prediction model but lead to less over-fitted results.

Wouter Groeneveld, Laurens Luyten, Joost Vennekens et al.

Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem solving in higher education, we have included a list of implications for educational practice.

Ahmed Salih Mohammed, Wael Emad, Warzer Sarwar Qadir et al.

It is generally known that the two most crucial elements of concrete that depend on the slump value of the mixture are workability and compressive strength. In addition, slump retention is more delicate than the commonly used slump value since it reflects the concrete mixture’s durability for usage in civil engineering applications. In this study, the effect of three water-reducer additives was tested on concrete’s workability and compressive strength from 1 day to 28 days of curing. The slump of the concrete was measured at the time of adding water to the mix and after 30 min of adding water. This study employed 0–1.5% (%wt) water-reducer additives. The original ratio between water and cement (wc) was 0.65, 0.6, and 0.56 for mixtures incorporating 300, 350, and 400 kg of cement. It was lowered to 0.3 by adding water-reducer additives based on the additives type and cement content. Depending on the kind and amount of water-reducer additives, w/c, gravel content, sand content, crushed content, and curing age, adding water-reducer additives to the concrete increased its compressive strength by 8% to 186%. When polymers were added to the concrete, they formed a fiber net (netting) that reduced the space between the cement particles. As a result, joining the cement particles quickly enhanced the fresh concrete’s viscosity and the hardened concrete’s compressive strength. The study aims to establish mathematical models (nonlinear and M5P models) to predict the concrete compressive strength when containing water-reducer additives for construction projects without theoretical restrictions and investigate the impact of mix proportion on concrete compressive strength. A total of 483 concrete samples modified with 3 water-reducer additives were examined, evaluated, and modeled for this study.

Ying Wang, Yue Chen, Yuhan Yao et al.

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice.

Fu Zheng, Annan Jiang, Tengfei Jiang et al.

Rock engineering, which includes slopes, tunnels, and mines, often encounters stratified rocks. These projects are also frequently exposed to special environments of high temperatures, such as deep underground or fire-related conditions. It is of significant importance to conduct research on the damage characteristics and constitutive models of stratified rocks under high-temperature conditions to accurately reflect the influences of rock structure characteristics, geological conditions, and load effects on the damage and deformation characteristics of rock engineering. Under five temperature conditions (20, 200, 400, 600, 800 ℃), the intact sandstone rock samples and the layered sandstone samples are subjected to high-temperature treatment, followed by triaxial compression tests. Based on existing research on statistical damage constitutive models for rocks, a high-temperature layered rock statistical damage constitutive model is established by introducing the Weibull distribution function and high-temperature, bedding, and load coupling damage variables, under the condition that the microelement strength follows the Drucker-Prager (D-P) criterion. The results indicate that the peak strength, damage threshold, elastic modulus, and longitudinal wave velocity show a ''U''-shaped trend with an increasing bedding angle, with an opening upwards. As the temperature increases, the anisotropy of the rock initially increases and then decreases, with obvious ductile characteristics after the temperature reaches 600 ℃. The analysis of damage threshold, stress-strain curve, and macroscopic failure morphology shows that the 60° dipping angle sandstone is prone to undergo compressive-shear failure along the weak plane of bedding, exhibiting low toughness mechanical characteristics. Theoretical curves of the statistical damage constitutive model for high-temperature rock are in good agreement with the Triaxial shear test curve of sandstone, which indicates that the constitutive model can reflect the stress-strain process of layered sandstone after high-temperature action, and verifies the applicability of the model. This model does not include unconventional mechanical parameters and can reflect the ductility, brittleness, and strength characteristics with clear physical meanings. The findings of the study can offer theoretical support for computing and numerically modeling rock mechanics after high-temperature action.

Armin Norouzi, Saeid Shahpouri, David Gordon et al.

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (\nox) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time.

Gang Zhang, Xiaocui Zhao, Zelei Lu et al.

This paper presents an overview on fire behavior of bridge girders mainly including prestressed concrete (PC) bridge girders and steel bridge girders. The typical fire accidents occurred on bridges are illustrated and, the seriousness of posing threats to bridge structures resulted from increasing traffic fires, specially intense hydrocarbon fires generated from petrol-chemicals, is highlighted. The current researches, embracing high-temperature properties of constituent materials, prestress state, measurement in fire tests, numerical methods, structural fire resistance, and so forth, taken on coping with problems existing in fire behavior and structural fire behavior in bridge girders are reviewed and discussed. Further, strategies for enhancing fire resistance of bridge girders followed with failure criterion and mode in types of bridge structures are provided. Future research area along with emerging trends in structural fire behavior of bridge girders is also recommended for mitigating fire hazards occurred on bridge girders. Herein, it can be attained a conclusion from review and discussion that prestressed concrete bridge girders with thin webs, specially T-shaped bridge girder, are prone to unstable under fire exposure conditions. High-strength concrete utilized in prestressed concrete bridge girders is vulnerable to spalling at elevated temperature. Steel-truss bridge girder present a more significant fragility to fire exposure compared than other steel bridge girders.

Linjie Li, Fang Du, Yingying Yang et al.

The toxic high-temperature fire smoke plume in tunnel fire will seriously threaten the safety of personnel and tunnel structure. Therefore, it is very important to study the fire smoke plume movement law in tunnel fire for controlling and exhausting smoke. However, previous studies mainly focused on single fire source. In fact, most of the catastrophic fires have multiple fire sources. When multiple fires burn at the same time, the competitive entrainment air will cause the flame to tilt and even merge with each other, which will increase the burning rate and accelerate the spread of the fire. In this paper, the influence of fire heat release rate and distance between fires on fire smoke plume generation in tunnel is studied by a series of numerical simulations. The investigation results show that when the spacing is non-zero, the fire smoke plume generation rate is greater than that when the spacing is zero, because of the additional entrainment region. The fire smoke plume generation rate increases with HRR in spreading stage tunnel fire. In addition, a prediction model considering the heat release rate and burner edge spacing is proposed.

Muhammad Ammar Naufal, Asdar Asdar

Science, Technology, Engineering, Arts, and Mathematics (STEAM) saat ini diakui dan banyak digunakan sebagai metadisiplin yang menjembatani berbagai displin ilmu untuk menciptakan pengetahuan secara keseluruhan. Pembelajaran STEAM berperan penting dalam menghasilkan sebuah produk dengan mengintegrasikan lima disiplin ilmu. Namun, pembelajaran STEAM di Indonesia belum banyak diterapkan untuk mempelajari matematika dengan menghasilkan sebuah produk STEAM. Penelitian ini bertujuan untuk menginvestigasi persepsi calon guru matematika terhadap pembelajaran STEAM melalui proyek Straw Bridge (Jembatan dari Sedotan Plastik) yang diterapkan di sekolah. Metode penelitian yang digunakan adalah penelitian deskriptif kualitatif. Data dikumpulkan melalui angket tertutup yang terdiri dari empat item pertanyaan. Subjek penelitian yang digunakan dalam penelitian ini adalah 15 mahasiswa calon guru matematika yang sebelumnya telah terlibat langsung dalam pembelajaran STEAM. Analisis deskriptif dilakukan untuk mengetahui persepsi mahasiswa calon guru matematika terhadap proyek Straw Bridge (Jembatan dari Sedotan Plastik) yang diterapkan di sekolah. Hasil penelitian ini menunjukkan bahwa melalui pembelajaran STEAM, mahasiswa calon guru matematika berkeyakinan mampu meningkatkan keaktifan, kreativitas, dan konsentrasi belajar peserta didik sehingga peserta didik dapat menemukan dan mengembangkan ide-ide baru dalam mendesain dan menyelesaikan proyek STEAM-Straw Bridge (Jembatan dari Sedotan).