Hasil untuk "Disasters and engineering"

Lei Zhang

The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.

Alexander Korn, Lea Zaruchas, Chetan Arora et al.

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Xin Ren, Tianhu He, Pengfei He et al.

Tunnel excavation in water-rich and saturated loess layers often encounters a series of engineering disasters, including surface settlement, large deformations of surrounding rock, collapses, water inrushes, mud inrushes, and lining cracks. This paper presents an analogy of 16 cases of instability and collapse of surrounding rock during the excavation of water-rich loess tunnels in China’s loess regions. The weight of influence of various factors affecting the stability of surrounding rocks has been analyzed based on the Fuzzy Analytic Hierarchy Process (FAHP), addressing the engineering challenges encountered during the construction of the Tuanjie Tunnel. Measures such as deep well-point dewatering of the surface, reinforcement of locking foot anchors, and construction treatment with large arch feet are proposed. The effectiveness of these treatments is then monitored and analyzed. The results show that after 30 days of dewatering, the average water content of the surrounding rock decreased from 28.8% to 22.3%, transforming the surrounding rock from a soft plastic state to a hard plastic state. Phenomena such as mud inrushes at the tunnel face and water seepage through the lining are significantly reduced, and the self-stabilizing capacity of the surrounding rock is markedly improved. By optimizing the excavation method and enhancing support parameters, the construction progress rate for Grade VI surrounding rock has increased from 10–15 m per month to 40 m per month, validating the effectiveness of the proposed measures.

Xiwei Xu, Wenjun Kang, Tao Wang et al.

Abstract Utilizing the pixel offset tracking and optical satellite imagery, this study interpreted the 2025 Myanmar Earthquake(Mw7.7) distribution and segmentation of surface ruptures along the Sagaing fault. The total length of the surface rupture is estimated to be 465 ± 25 km, predominantly characterized by supershear rupture propagation. This rupture process generated distinct near-fault strong ground motion spectra, which played a critical role in the earthquake’s devastating impact.

Pooja Sharma, Vishwa Bandhu Singh Chandel, Simrit Kahlon et al.

Abstract Background The Himalayan region has witnessed a notable increase in landslide occurrences due to changing human-environment relations and rising anthropogenic pressures. These geomorphic hazards are frequently triggered by extreme weather events, such as intense monsoon rainfall and cloudbursts, resulting in loss of life, infrastructure damage, and widespread socio-economic disruptions. Himachal Pradesh, in particular, remains highly vulnerable to such events. Objectives This study aims to assess the spatial distribution and susceptibility of landslides in the Upper Ravi River Catchment, Himachal Pradesh, using a logistic regression model. The primary objective is to identify high-susceptibility landslide zones and understand the underlying geospatial factors affecting landslides in the Himalayan regions. Methods A landslide inventory of 513 events was prepared using visual interpretation of high-resolution satellite imagery from Landsat, Sentinel, PlanetScope, and Google Earth and field work from 2001-2020. A total of 22 thematic layers were generated using ArcGIS Pro 2.5 and Erdas Imagine 2014, covering topographic, hydrological, geological, and anthropogenic variables. Logistic regression modeling was implemented in the R environment. Model performance was evaluated using pseudo-R² indices (McFadden’s, Cox & Snell, and Nagelkerke) along with the Area Under the Receiver Operating Characteristic Curve (AUC) to assess predictive accuracy. Results The logistic regression model showed strong predictive capability with an AUC value of 0.855, indicating excellent model performance. Approximately 8.65% of the catchment area—equivalent to around 280 sq. km—is classified as having high to very high landslide susceptibility. The spatial analysis revealed that susceptibility is greatest in the western and central parts of the catchment, particularly along valley floors, river-adjacent slopes, and human-inhabited areas. A total of 192 villages is identified as being exposed to potential landslide risks, along with vulnerable infrastructure such as roads, agricultural lands, and residential settlements. Conclusions The study successfully maps landslide-susceptible zones using logistic regression and multi-source geospatial data. It provides actionable insights for local authorities, planners, and disaster risk managers. The findings emphasise the need for targeted interventions in highly susceptible areas to reduce hazard exposure and enhance community resilience in the Upper Ravi River Catchment. The methodology presented can be replicated in other mountainous regions facing similar challenges.

Jianlin LIU, Fei LIU, Quanxin LI et al.

Specialized drilling technology is a critical component of drilling technology system in underground coal mine. Its core lies in adopting specialized drilling method and equipment to address unique working conditions and achieve specific drilling objectives, playing a vital role in preventing major geological disasters and advancing geological transparency in underground coal mine. The typical application scenarios of specialized drilling technology are summarized, such as precise gas control in soft-fragmented coal seam, replacing tunnel with borehole for relieved gas drainage in roof, mine water detection and release, and grouting reinforcement of water-resisting layer.It delves into the key technical challenges encountered during implementation.Focusing on drilling equipment upgrade, innovative drilling method, and drilling parameter optimization, the study comprehensively reviews the current development status, critical technological advancements, and practical outcomes.The specialized drilling technology suitable for soft-fragmented coal seam, hard rock strata, and fractured and water sensitive strata are outlined, and the existing technical bottlenecks and challenges are identified.Given the evolving trends of diversified demand, equipment intelligence, and region-specific adaptation in underground specialized drilling technology, future research priorities are proposed: ① To enhance air directional drilling technology for soft-fragmented coal seam to improve formation adaptability and process compatibility. ② To advance high-efficiency drilling acceleration technology and tool for hard rock strata to boost rock-breaking efficiency and service life of impact drilling tool. ③ To overcome theoretical and technical barrier in pressure-controlled drilling under near-horizontal condition to strengthen borehole formation capability in fractured and water-sensitive strata of roof or floor. ④ To accelerate the improvement of the reliability and stability of intelligent drilling technology and equipment in underground coal mine, and promote the realization of the transformation of specialized drilling technology from equipment intelligence to engineering intelligence. ⑤ To research and constructe specialized drilling technology system of regional, mining area and mine, and promote their wide application and in-depth development in the coal mining industry.The study can provide an important reference for the technological innovation and efficiency improvement of drilling process in underground coal mine.

Xiwei Xu, Wenjun Kang, Tao Wang et al.

Davide Wüthrich, Paul A. Korswagen, Harish Selvam et al.

Abstract The July 2021 flood heavily affected many inhabitants, buildings and critical infrastructure throughout Germany, Belgium and the Netherlands. Specifically, the Ahr Valley (Germany) showcased the destructive power associated with these extreme events. Hence, this region was the focus of a field survey, aiming at describing the flood‐induced damage to buildings and assessing the possible underlying processes that led to structural failures. The field assessment revealed a close connection between building failures and (1) local flow depths and velocities, (2) building location, (3) distance from the riverbank and (4) construction type. Although it is difficult to identify the exact causes that induced failures, the detailed assessment revealed that damages mainly originated from local scour and hydraulic loads, often unevenly distributed around buildings. Importantly, many buildings were significantly affected by (large) floating debris impacts and damming, both responsible for additional loads, highlighting their importance in flood‐resistant building design. Furthermore, data showed that buildings near the riverbanks and in the upstream part of villages were more severely damaged. Altogether, data provide a better understanding of the flood processes that lead to building failures, fostering future research towards the development of safer protection measures and more effective flood risk management strategies.

Ramón García Marín, Miguel Ángel López-Sandoval

Se presenta un estudio en el que se han analizado diferentes elementos que han permitido la consecución de toda una secuencia de patrones temporales y espaciales asociados a los incendios forestales originados por rayos en la Región de Murcia, a lo largo del periodo 2000-2020. El resultado ha sido la obtención de una relación entre los incendios inducidos por rayos con una serie de patrones temporales desde el punto de vista de tendencia dentro del periodo, interanual, mensual, por quincenas y horaria, además de la consecución de toda una serie de patrones espaciales desde el punto de vista de su localización geográfica, altitud, pendiente, orientación, modelo de combustible, comunidades vegetales, humedad del material vegetal fino, condiciones meteorológicas, piso bioclimático, edafología, litología e intensidad eléctrica (Kiloamperios) de los rayos. En la investigación se han analizado los datos proporcionados por la UDIF (Unidad de Defensa contra Incendios Forestales de la Región de Murcia), obtenidos de los informes realizados por los agentes medioambientales que recogieron la información. Se concluye que la distribución de los incendios forestales originados por rayos no es aleatoria, y el riesgo de simultaneidad de incendios al que dan lugar las descargas eléctricas provocadas por tormentas hacen que este fenómeno de origen natural deba tenerse muy en cuenta por parte de las administraciones y gestores ambientales.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Max Ofsa, Taylan G. Topcu

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

Junlin Zhang, Wei Xu, Yu Qiao et al.

Abstract Growing evidence indicates that extreme heat and rain may occur in succession within short time periods and cause greater impacts than individual events separated in time and space. Therefore, many studies have examined the impacts of compound hazard events on the social-ecological system at various scales. The definition of compound events is fundamental for such research. However, there are no existing studies that support the determination of time interval between individual events of a compound rainstorm and heatwave (CRH) event, which consists of two or more potentially qualifying component heatwave and rainstorm events. To address the deficiency in defining what individual events can constitute a CRH event, this study proposed a novel method to determine the maximum time interval for CRH events through the change in CRH event frequency with increasing time interval between individual events, using southern China as a case study. The results show that the threshold identified by the proposed method is reasonable. For more than 90% of the meteorological stations, the frequency of CRH events has reached a maximum when the time interval is less than or equal to the threshold. This study can aid in time interval selection, which is an important step for subsequent study of CRH events.

Jingwei Tian, Xiao Qi, Guijun Xian

Polymer-based composites for steel structure coating are an effective strategy to improve tribological properties and service durability under hygrothermal aging conditions. In the present paper, epoxy resin was improved by mechanical reinforcement and frictional lubrication fillers, and a kind of multi-filler reinforced epoxy composite (MFREC) was successfully prepared. Water uptake behavior, mechanical and thermal property evolution, friction behavior and wear mechanism as well as micro-topography analysis of MFREC under hygrothermal aging were conducted and discussed in detail. The research results showed that the water uptake curve of MFREC after distilled water and 5 wt% NaCl water solution immersions confirmed the two-stage diffusion model, where the quasi-equilibrium water uptakes were 4.23% and 3.67%, respectively. Additionally, the fundamental reason for the degradation of the mechanical and thermal properties of MFREC was the hydrolysis of the resin and the de-bonding of the fillers/matrix interface. The interface shear strength of MFREC adhesive and steel gradually decreased with the hygrothermal aging, but did not lose the bonding property, because the uniformly dispersed multi-fillers can fill the internal defects of the matrix and prolong the crack propagation path to block the effect. Before hygrothermal aging, MFREC had excellent friction and wear resistance compared to resin matrix, especially under water lubrication conditions. The anti-wear rate of MFREC increased by up to 59.6% compared to the room temperature dry test environment due to the coupling effects of the water lubrication and anti-friction characteristics of modified fillers. Hygrothermal aging had little effect (＜12%) on the friction coefficient of MFREC, but the anti-wear properties decreased significantly, especially the Ws increased by 255.1% under the 60 °C immersion condition. Because the degradation of thermal/mechanical properties was not enough to resist the high shear stress of the steel ball, leading to severe fatigue wear.

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.

Marco Autili, Martina De Sanctis, Paola Inverardi et al.

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Yiming Cao, Hengxing Lan, Lang-ping Li

Natural hazards constantly threaten the sustainable construction and operation of railway engineering facilities, making railway disaster risk assessment an essential approach to disaster prevention. Despite numerous studies that have focused on railway risk assessment, few have quantified specific damages, such as economic losses and human casualties. Meanwhile, the mechanism of impact damage from various disasters on railway facilities and the propagation of functional failure in railway systems have not been thoroughly summarized and addressed. Thus, it is essential to conduct effective quantitative risk assessments (QRAs) to facilitate the sustainable design, construction, and operation of rail infrastructure. This paper aimed to review and discuss the systematic development of risk assessment in railway engineering facilities. Firstly, we highlighted the importance of disaster QRA for railway facilities. Next, numerous limitations of QRA methods were concluded after conducting a comprehensive review of the risk assessment research applied to railway facilities, such as bridges, tunnels, and roadbeds. Furthermore, true QRA (TQRA) application in railway engineering has faced several significant challenges. Therefore, we proposed a promising TQRA strategy for railway engineering facilities based on the integration of building information modeling (BIM) and geographic information systems (GIS). The proposed BIM+GIS technology is expected to provide sustainable future directions for railway engineering QRA procedures.