Hasil untuk "Bridge engineering"

B. Schleich, N. Anwer, L. Mathieu et al.

S. Mathew, A. Yella, P. Gao et al.

Dye-sensitized solar cells have gained widespread attention in recent years because of their low production costs, ease of fabrication and tunable optical properties, such as colour and transparency. Here, we report a molecularly engineered porphyrin dye, coded SM315, which features the prototypical structure of a donor-π-bridge-acceptor and both maximizes electrolyte compatibility and improves light-harvesting properties. Linear-response, time-dependent density functional theory was used to investigate the perturbations in the electronic structure that lead to improved light harvesting. Using SM315 with the cobalt(II/III) redox shuttle resulted in dye-sensitized solar cells that exhibit a high open-circuit voltage VOC of 0.91 V, short-circuit current density JSC of 18.1 mA cm(-2), fill factor of 0.78 and a power conversion efficiency of 13%.

Junwon Seo, Luis Duque, J. Wacker

Abstract The field of Civil Engineering has lately gained increasing interest in Unmanned Aerial Vehicles (UAV), commonly referred to as drones. Due to an increase of deteriorating bridges according to the report released by the American Society of Civil Engineers (ASCE), a more efficient and cost-effective alternative for bridge inspection is required. The goal of this paper was to analyze the effectiveness of drones as supplemental bridge inspection tools. In pursuit of this goal, the selected bridge for inspection was a three-span glued-laminated timber girder with a composite concrete deck located near the city of Keystone in the state of South Dakota (SD). A drone, a Da-Jiāng Innovations (DJI) Phantom 4, was utilized for this study. Also, an extensive literature review to gain knowledge on current bridge inspection techniques using drones was conducted. The findings from the literature review served as the basis for the development of a five-stage drone-enabled bridge inspection methodology. A field inspection utilizing the drone was performed following the stages of the methodology, and the findings were compared to current historical inspection reports provided by the SD Department of Transportation (SDDOT). Quantified data using the drone such as a spalled area of 0.18 m2, which is identical to the measurement provided by the SDDOT (0.3 m by 0.6 m), demonstrated the efficiency of the drone to inspect the bridge. This study detailed drone-enabled inspection principles and relevant considerations to obtain optimum data acquisition. The field investigation of the bridge demonstrated the image quality and damage identification capabilities of the drone to perform bridge inspection at a lower cost when compared to traditional methods.

Jung Tae Kim, Han Su, Yu Zhong et al.

Phani Raja Bharath Balijepalli, Bulent Soykan, Veeraraghava Raju Hasti

A hybrid digital twin framework is presented for bridge condition monitoring using existing traffic cameras and weather APIs, reducing reliance on dedicated sensor installations. The approach is demonstrated on the Peace Bridge (99 years in service) under high traffic demand and harsh winter exposure. The framework fuses three near-real-time streams: YOLOv8 computer vision from a bridge-deck camera estimates vehicle counts, traffic density, and load proxies; a Lighthill--Whitham--Richards (LWR) model propagates density $ρ(x,t)$ and detects deceleration-driven shockwaves linked to repetitive loading and fatigue accumulation; and weather APIs provide deterioration drivers including temperature cycling, freeze-thaw activity, precipitation-related corrosion potential, and wind effects. Monte Carlo simulation quantifies uncertainty across traffic-environment scenarios, while Random Forest models map fused features to fatigue indicators and maintenance classification. The framework demonstrates utilizing existing infrastructure for cost-effective predictive maintenance of aging, high-traffic bridges in harsh climates.

Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo et al.

Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Lei Sha, Feng Qian, Baobao Chang et al.

Event extraction plays an important role in natural language processing (NLP) applications including question answering and information retrieval. Traditional event extraction relies heavily on lexical and syntactic features, which require intensive human engineering and may not generalize to different datasets. Deep neural networks, on the other hand, are able to automatically learn underlying features, but existing networks do not make full use of syntactic relations. In this paper, we propose a novel dependency bridge recurrent neural network (dbRNN) for event extraction. We build our model upon a recurrent neural network, but enhance it with dependency bridges, which carry syntactically related information when modeling each word.We illustrates that simultaneously applying tree structure and sequence structure in RNN brings much better performance than only uses sequential RNN. In addition, we use a tensor layer to simultaneously capture the various types of latent interaction between candidate arguments as well as identify/classify all arguments of an event. Experiments show that our approach achieves competitive results compared with previous work.

Muhammad Yasin, Nisar Khan, Muhammad Murad et al.

Platinum (Pt) based electrocatalysts remain the gold standard for the hydrogen evolution reaction (HER) in acidic environments due to their optimal hydrogen adsorption-free energy (ΔGH⁎ ≈ 0), high electrical conductivity, and superior chemical stability. However, the scarcity and high cost of Pt necessitate innovative strategies to reduce Pt loading while enhancing catalytic efficiency and long-term durability. This review systematically presents the recent advancements in Pt-based HER electrocatalysts, emphasizing mechanistic insights across the Volmer, Heyrovsky, and Tafel steps, and explores the influence of Pt’s electronic structure and nanostructuring on HER kinetics. Strategies such as alloying with transition metals (e.g., Ni, Co, Zn), developing single-atom catalysts (SACs), and engineering hybrid systems with supports like MXenes, graphene aerogels, and metal carbides are discussed in detail. These approaches optimize active site exposure, electronic modulation, and catalyst-support interactions to achieve high turnover frequencies, low overpotentials, and enhanced electrochemical stability under industrially relevant conditions. The review further highlights key performance indicators such as Tafel slope, mass activity, TOF, and stability, along with advanced synthesis methods, including atomic layer deposition and microwave-assisted reduction. Finally, current challenges in scalability, degradation resistance, and cost-performance trade-offs are evaluated, providing future directions toward sustainable, high-performance HER systems based on Pt. This comprehensive analysis aims to bridge the gap between fundamental catalyst design and practical hydrogen production technologies.

Yu C, Xu M, Pang X et al.

Chunchun Yu,1 Mengying Xu,1 Xinyue Pang,2 Yuting Zhang,3 Xinmei Cao,2 Yixin Xu,1 Shuai Huang,1 Hongjun Zhao,4 Chengshui Chen1,3,4 1Key Laboratory of Interventional Pulmonology of Zhejiang Province, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 2Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China; 3Cixi Biomedical Research Institute, Wenzhou Medical University, Wenzhou, Zhejiang, 315302, People’s Republic of China; 4Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, People’s Republic of ChinaCorrespondence: Hongjun Zhao; Chengshui Chen, Zhejiang Province Engineering Research Center for Endoscope Instruments and Technology Development, Department of Pulmonary and Critical Care Medicine, Quzhou People’s Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, People’s Republic of China, Email zhaohongjun@wmu.edu.cn; chenchengshui@wmu.edu.cnPurpose: This study aims to construct a contemporaneous symptom network of inpatients with Exacerbation of Chronic Obstructive Pulmonary Disease (ECOPD) based on the symptom cluster, identify core and bridge symptoms, and patient subgroups with different symptom clusters based on individual differences in the intensity of patient symptom experiences.Patients and Methods: This study used convenience sampling to collect demographic, symptom, auxiliary examination, and prognosis information of 208 inpatients with ECOPD from April 2022 to October 2023. The data underwent exploratory factor analysis (EFA), symptom network analysis, latent class analysis (LCA), Spearman correlation analysis, Wilcoxon signed-rank test, single-factor regression and multiple-factor stepwise regression.Results: In hospitalized patients with ECOPD, symptom network analysis revealed that loss of appetite was the core symptom, while chest distress was the bridge symptom. Through LCA analysis, two symptom subgroups were identified: a high-symptom group (53.8%) and a low-symptom group (46.2%). This suggests that there is significant heterogeneity in symptom experience among ECOPD individuals. Patients in the high-symptom group had a higher probability of experiencing symptom clusters related to nutrition-sleep.Conclusion: The combination of symptom network analysis and LCA comprehensively captures the symptom/symptom cluster characteristics and accounts for the heterogeneity of ECOPD patients from both individual and group perspectives. This study identifies core symptoms, bridge symptoms, and symptom subgroups, offering valuable insights for precision symptom management in ECOPD.Keywords: chronic obstructive pulmonary disease, COPD, exacerbation, precision care, network analysis, latent class analysis

Eneye A. Ibrahim, Dale Goff, Ali Keyvanfar et al.

Bridge fires present unique challenges due to their potential for catastrophic structural failures, leading to extensive traffic disruptions, economic losses, and, in some cases, loss of life. In the aftermath of a fire incident, assessing the structural integrity and future viability of concrete bridges has become a paramount concern for civil engineers and safety inspectors. The critical decision to rehabilitate or demolish a fire-damaged structure hinges on accurately assessing the extent of damage incurred. Enhancing the fire resilience of concrete structures is a critical endeavor within civil engineering, necessitating accurate evaluation methods to analyze conditions after fire exposure. Focusing on concrete bridges, this study aimed to establish a comprehensive review of research on the effects of fire, providing engineers with the necessary means to develop guidelines for post-fire assessment to enhance safety and operational readiness. It proposes an in-depth examination of various methods as strategic decision-making tools. The assessment involves estimating the temperature, the extent of damage to concrete, and the reduction in the strength of both concrete and reinforcement. To achieve this, a detailed review of the existing literature on the impact of fire on concrete and its steel reinforcements is conducted. Current post-fire assessment tools have also been evaluated to improve the efficiency of the evaluation process. This study establishes a systematic post-fire assessment review framework that incorporates assessment information domains (including non-destructive testing, destructive testing, advanced computational modeling, and digital-twin technology) to provide a practical solution for accurately determining the safety and operational readiness of fire-damaged concrete bridges.

Hongliang Fang, Qiuwei Yang, Jiwei Ma et al.

When the chloride ion concentration within concrete reaches a certain threshold, it triggers corrosion of the reinforcing steel bars, severely compromising the durability of reinforced concrete structures. Accurately assessing how the chloride ion concentration in concrete evolves over time is crucial for ensuring structural safety and evaluating the remaining service life. This work first analyzes the advantages and disadvantages of several existing time-dependent models for chloride ion diffusion coefficients. Based on this foundation, a new time-varying model is proposed to more accurately predict the variation of chloride ion diffusion coefficient with service time. The newly proposed model can be regarded as a variant of the square-root model, incorporating only two fitting parameters. It can be readily transformed into a linear regression model for solving the fitting parameters, rendering it highly convenient to use. Using 11 sets of experimental data from the existing literature as examples, the new model consistently demonstrates the lowest mean fitting error and the highest coefficient of determination across all scenarios, showcasing its superior generality. This new model likely reflects the fundamental physical law governing the temporal variation of chloride ion diffusion coefficients.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.

D. Frangopol, You Dong, S. Sabatino

Abstract The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.

G. Calvi, M. Moratti, G. O’Reilly et al.

Abstract On 14 August 2018 at 11:35 AM, a relevant portion (about 243 m) of the viaduct over the Polcevera river in Genoa collapsed, killing 43 people. The bridge was designed in the early 1960s by Riccardo Morandi, a well-known Italian engineer, and opened to the public in 1967. The collapsed part of the bridge essentially comprised an individual self-standing structure spanning 171 m and two simply-supported connecting Gerber beam systems, each spanning 36 m from the self-standing structure to the adjacent portions of the bridge. This paper aims to reminisce the complete story of the bridge, from the Italian construction boom in the 1960s to some of the issues that soon arose thereafter: the strengthening intervention in the 1990s, the subsequent structural monitoring and, finally, the strengthening project never brought to fruition. Potential reasons for the collapse are discussed, together with some of the possible inadequacies of the bridge, its maintenance and loading history based on critical reflection, comparison with specific features of bridge construction practice today and results obtained using numerical models with different levels of refinement. Since the entire matter (specifically the debris) was considered classified by the investigating magistrate in the immediate aftermath of the bridge collapse, this work is based entirely on publicly available material.

Baichang Zhong, Xiaofan Liu, Xinwei Li

In K-12 STEM education, engineering design is emphasized, as demonstrated by the bridge-design project. Due to the iterative nature of engineering design, engineering practice is frequently complicated and requires pedagogical guidance. As an emerging pedagogy in STEM education, REP (Reverse Engineering Pedagogy) is showing, but not enough, some benefits in several cases. This paper aims to explore the effects of REP in a bridge-design course. A comparison experiment, REP versus PBL (Project-Based Learning), was conducted by randomly forming two groups of fourth-grade students from a primary school in China. Results indicated that REP was more advantageous than PBL in terms of decreasing students' cognitive load, boosting their scientific knowledge level and engineering design skills. However, REP and PBL have the same effect on the students’ learning attitude and engagement. The key findings, possible reasons, and suggestions for practice are also discussed.

Woqin Luo, Ye Xia, Tiantao He

In recent years, the global upswing in vessel-bridge collisions underscores the vital need for robust vessel track identification in accident prevention. Contemporary vessel trajectory identification strategies often integrate target detection with trajectory tracking algorithms, employing models like YOLO integrated with DeepSORT or Bytetrack algorithms. However, the accuracy of these methods relies on target detection outcomes and the imprecise boundary acquisition method results in erroneous vessel trajectory identification and tracking, leading to both false positives and missed detections. This paper introduces a novel vessel trajectory identification framework. The Co-tracker, a long-term sequence multi-feature-point tracking method, accurately tracks vessel trajectories by statistically calculating the translation and heading angle transformation of feature point clusters, mitigating the impact of inaccurate vessel target detection. Subsequently, vessel trajectories are predicted using a combination of Long Short-Term Memory (LSTM) and a Graph Attention Neural Network (GAT) to facilitate anomaly vessel trajectory warnings, ensuring precise predictions for vessel groups. Compared to prevalent algorithms like YOLO integrated with DeepSORT, our proposed method exhibits superior accuracy and captures crucial heading angle features. Importantly, it effectively mitigates the common issues of false positives and false negatives in detection and tracking tasks. Applied in the Three Rivers area of Ningbo, this research provides real-time vessel group trajectories and trajectory predictions. When the predicted trajectory suggests potential entry into a restricted zone, the system issues timely audiovisual warnings, enhancing real-time alert functionality. This framework markedly improves vessel traffic management efficiency, diminishes collision risks, and ensures secure navigation in multi-target and wide-area vessel scenarios.

Xuewen Rong, Shuo Deng, Baozhen Liang et al.

The structural properties of loess are susceptible to change when subjected to external loads and complex environments, leading to various geological disasters. To investigate the mechanical behavior and strengthening mechanism of loess stabilized with biopolymers such as xanthan gum and guar gum, especially for soils with low bearing capacity and stability in engineering applications, we conducted research on the improvement of soil with xanthan gum and guar gum, tests including unconfined compressive strength, disintegration, direct shear, and microstructure tests were conducted. Among the four different dosages of biopolymers (0%, 0.5%, 1%, 2%) and four different curing ages (1 day, 3 days, 7 days, 14 days), the 2% content of biopolymer and 14 days had the greatest impact on the mechanical properties of loess, Both the compressive and shear strength, as well as the water stability of solidified loess, improve with higher content of xanthan gum and guar gum or prolonged curing time; however, the disintegration rate decreases. Microscopic analysis indicates that the biopolymers effectively fill the gaps between soil particles and attach to the particle surfaces, forming fibrous and reticular structures that improve the interparticle bonding and ultimately increase the strength and water stability of the loess. Xanthan gum and guar gum biopolymers can improve the mechanical properties and water stability of loess, enhance the erosion resistance and improve the water-holding capacity. These outcomes suggest that guar gum and xanthan gum biopolymers have the potential to serve as environmentally sustainable alternatives to conventional soil stabilizers.