Hasil untuk "Disasters and engineering"

R. Albuquerque, Y. Koskinen, Chendi Zhang

This paper presents an industry equilibrium model where firms can choose to engage in corporate social responsibility (CSR) activities. We model CSR activities as an investment in customer loyalty and show that CSR decreases systematic risk and increases firm value. These effects are stronger for firms producing differentiated goods and when consumers' expenditure share on CSR goods is small. We find supporting evidence for our predictions. In our empirical tests, we address a potential endogeneity problem by instrumenting CSR using data on the political affiliation of the firm's home state, and data on environmental and engineering disasters and product recalls.

S. Temmerman, P. Meire, T. Bouma et al.

F. Guzzetti, A. Carrara, M. Cardinali et al.

Weichen You

Abstract Reinforced concrete is susceptible to erosion from natural disasters such as earthquakes, floods, and strong winds, as well as its own aging and environmental factors. If minor structural damage is not detected in a timely manner, continuous development may lead to damage to structural components, further causing sudden failure of overall structural stability, resulting in major accidents and causing significant losses to life and property. Therefore, identifying the health status of reinforced concrete structures and promptly identifying initial minor damages has important engineering significance. Intelligent optimization algorithms are a type of efficient optimization algorithm, and using intelligent optimization algorithms to optimize hyperparameters of neural network models has become one of the research hotspots. Cuckoo Search Algorithm (CSA), as a new type of intelligent optimization algorithm, has the characteristics of simple implementation, few parameters, strong optimization ability, and good performance in complex optimization problems. It has the potential to be applied to hyperparameter optimization of neural network models. Therefore, in order to improve the performance of damage identification in reinforced concrete structures, this paper constructs a model based on Improved Cuckoo Search Algorithm (ICSA) to optimize Gaussian Weighted Extreme Learning Machine (GWELM). By leveraging the powerful feature extraction capability of Convolutional Neural Network (CNN), a CNN integrated CNN-ICSA-GWELM model is proposed and applied to damage identification of reinforced concrete structures. The experimental results showed that CNN-ICSA-GWELM achieved an accuracy of 99.81%, a precision of 99.74%, a recall rate of 99.53%, and an F1 score of 0.9960 for identifying damage test sets in reinforced concrete structures. Therefore, the performance of CNN-ICSA-GWELM in identifying damage to reinforced concrete structures is superior to CNN-CSA-GWELM, CNN-GWELM, ELM, and Support Vector Machine (SVM).

Eduard Enoiu, Jean Malm, Gregory Gay

We explore the concept of folklore within software engineering, drawing from folklore studies to define and characterize narratives, myths, rituals, humor, and informal knowledge that circulate within software development communities. Using a literature review and thematic analysis, we curated exemplar folklore items (e.g., beliefs about where defects occur, the 10x developer legend, and technical debt). We analyzed their narrative form, symbolic meaning, occupational relevance, and links to knowledge areas in software engineering. To ground these concepts in practice, we conducted semi-structured interviews with 12 industrial practitioners in Sweden to explore how such narratives are recognized or transmitted within their daily work and how they affect it. Synthesizing these results, we propose a working definition of software engineering folklore as informally transmitted, traditional, and emergent narratives and heuristics enacted within occupational folk groups that shape identity, values, and collective knowledge. We argue that making the concept of software engineering folklore explicit provides a foundation for subsequent ethnography and folklore studies and for reflective practice that can preserve context-effective heuristics while challenging unhelpful folklore.

WANG Tao1, 2, 3, 4 , FAN Hong2, 4, WANG Kangren2, 4, ZHOU Guoqing1, 3, WANG Liangliang1

The constitutive model for warm frozen soil is crucial for accurately calculating the stress and deformation of frozen soil layers. Based on the modified Cambridge model and the dual-yield surface theory, taking into account the influences of cohesion and internal friction angle of warm frozen soil, the deformation characteristics of the specimens are described by the overall deformation curve εv-lnp. The hardening parameters of the current yield surface and reference yield surface are modified by stress path correlation factors. A unified constitutive model of dual-yield surface for warm frozen soil is proposed. The incremental form of the stress-strain relationship is obtained based on the elastic-plastic theory. A convenient method for determining the model parameters is provided. The consolidation parameters and potential strength parameters that reflect the current state of warm frozen soil are defined. An analysis of the dynamic cyclic relationship and interdependence between these parameters and hardening parameters is presented. The experimental data are used to validate the constructed constitutive model, and the results show that the proposed model can accurately predict the stress-strain behavior of warm frozen soil under conventional triaxial stress conditions.

Md. Danesh Miah, Sayeeda Subah, Yaqub Ali

In the 21st century, climate change has exacerbated global instability, leading to a rise in landslide occurrences. In Bangladesh, mountainous areas such as Bandarban experience significant landslides during the monsoon season. This study seeks to evaluate landslide susceptibility in Bandarban and identify hotspots for optimal landslide hazard mitigation. This study examined landslide susceptibility using the analytical hierarchy process (AHP) and spatial weighted overlay (SWO). Ten conditioning factors were considered, with AHP based on responses from 100 key respondents. Using field surveys and high-resolution satellite images, 280 landslide occurrence samples were collected to rank the subfactors. Using AHP-derived weights of factors and subfactors, the SWO approach was used to create the landslide susceptibility map (LSM). The Getis-Ord (Gi∗) spatial statistics was then used to generate landslide susceptibility hotspots. The result showed that human influence weight 17.02%, making it the most crucial factor in landslide susceptibility. AHP-derived weights were reliable because their consistency ratio was <0.1. According to the study, 59.86% of the area is moderately susceptible, 20.06% is high, and 4.31% is very high. The validation of LSM by ROC curve found excellent performance (AUC ​= ​0.93) of the approaches. Specifically, 63.8% of very high susceptibility areas and 33.26% of high susceptibility areas were found within the hotspot zones with 99% confidence. The research showed the combined use of field samples and remote sensing-based spatial variables improved the accuracy of LSM. These findings can be useful for ensuring proper land use planning and implementation of landslide hazard mitigation measures.

Yongping WU, Shenghu LUO, Zhuangzhuang YAN et al.

Rock burst is one of the main disasters in deep coal mining. Revealing the mechanism of rock burst is the premise and foundation of its disaster evaluation, prediction and prevention. In this paper, theoretical analysis, numerical calculation, simulation experiment and engineering field test mutual feedback research methods are comprehensively used. Based on comprehensively determining the internal relationship between the spatial structure of overlying strata and its mechanical response, the mechanical response of coal and rock and the mechanism of rock burst under structural transient excitation are systematically studied. The results show that the mechanical properties and mechanical environment of the spatial structure of overlying strata are the internal and external causes of different mechanical responses of coal and rock in the mining area. The instantaneous instability of subsystems such as hard rock breaking, which will lead to the transient of the large system of the overlying rock space structure, and then cause the occurrence of the dynamic mechanical response (mine earthquake) and the step change of the static mechanical response of the coal rock mass in the stope. After the transient of the structure, in addition to the strain energy at the source, the mechanical response of the stress field, displacement field, strain energy field and gravitational potential energy field of the coal and rock in a larger range will also be transient. The instantaneous subsidence of overlying strata above the structural transient region, the sudden reduction of stress, and the release characteristics of strain energy and gravitational potential energy are obvious. The coal rock mass in the deep area outside the structural transient region sinks slightly, the stress increases suddenly, the strain energy aggregation characteristics are obvious, and the gravitational potential energy is slightly released. The total potential energy of coal rock mass in the stope decreases instantaneously, in which the strain energy of coal rock mass increases instantaneously, while the gravitational potential energy decreases instantaneously. At the same time, due to the influence of unloading, rebound and subsidence caused by the fracture of hard rock strata, When the mine earthquake occurs, not all the coal and rock mass in the area will be instantaneously loaded, and the coal mass near the structural transient area will be instantaneously unloaded. The instantaneous fracture of the hard rock stratum behind the working face will make the strike abutment pressure ‘Move forward instantaneously’, resulting in the stress state of ‘high static load + negative dynamic load’ in the high static load area of the coal body in front of the working face, while the stress state of ‘high static load + positive dynamic load’ appears in the high static load area of the coal body in the mining roadway, resulting in a phenomenon that the probability of rock burst in the mining roadway is much larger than that in the working face. In practical engineering, targeted prevention and control measures should be established based on the internal relevance between coal rock structure and its dynamic and static response, as well as the concept of “adjusting structure and controlling response”.

Changcheng Liu, Xue Bai, Qian Cao et al.

Phase change materials (PCMs) have been limited in their energy storage applications due to inherent defects, such as leakage and low thermal conductivity. Consequently, the development of highly efficient adsorptive PCMs has become a focal point of research. By constructing a three-dimensional (3D) porous aerogel support skeleton, a new composite PCM (CPCM) with a high loading rate (more tham 98%), leakage prevention, and multifunctional properties has been successfully developed. The system innovatively integrates a 3D MXene framework, constructed via the ice template method, which enhances the thermal conductivity from 0.374 W/(m·K) to 1.388 W/(m·K) and achieves 43 dB electromagnetic shielding efficiency in the Ku-band (12–18 GHz). Owing to the synergistic design, the material exhibits significant electro-thermal conversion, with a local temperature rise of 91 °C at an input voltage of 15 V. Additionally, its high energy storage density (latent heat value more than 175 J·g−1) and easy-to-shape characteristics offer potential for multi-scenario applications in electronic thermal safety management and smart energy storage systems.

Wen Zhang, Jianglong Cui, Weike Yao et al.

Abstract Climate change and rapid urbanization are intensifying flood vulnerability in highly urbanized delta regions. This study develops a Bayesian network model to assess flood control infrastructure vulnerability (including flood susceptibility and critical failure nodes) and cascading failures in the Pearl River Delta (PRD) under future climatic scenarios. To evaluate future flood hazards, downscaled climate projections from global climate models and a stochastic weather generator were employed to simulate extreme precipitation patterns. Results indicate that in certain PRD areas, 100-year return period design rainstorm values may double by the 2050 s under high-emission scenarios and the central/southeastern PRD face highest cascading flood failures due to dense hydrological interconnectivity and topographic constraints. These findings underscore the urgent need for climate-adaptive infrastructure planning, enhanced early-warning systems, and integrated watershed management. This study offers a systematic, data-driven framework to support resilient urban flood governance in deltaic megacities facing compounding environmental risks.

Martin Obaidi, Marc Herrmann, Elisa Schmid et al.

Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic emotions, based on the emotion model by Shaver et al., by four German-speaking computer science students. Evaluation of the annotation process showed high interrater agreement and reliability. These results indicate that the dataset is sufficiently valid and robust to support sentiment analysis in the German-speaking software engineering community. Evaluation with existing German sentiment analysis tools confirms the lack of domain-specific solutions for software engineering. We also discuss approaches to optimize annotation and present further use cases for the dataset.

Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski et al.

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.

Roberto Verdecchia, Justus Bogner

While mastered by some, good scientific writing practices within Empirical Software Engineering (ESE) research appear to be seldom discussed and documented. Despite this, these practices are implicit or even explicit evaluation criteria of typical software engineering conferences and journals. In this pragmatic, educational-first document, we want to provide guidance to those who may feel overwhelmed or confused by writing ESE papers, but also those more experienced who still might find an opinionated collection of writing advice useful. The primary audience we had in mind for this paper were our own BSc, MSc, and PhD students, but also students of others. Our documented advice therefore reflects a subjective and personal vision of writing ESE papers. By no means do we claim to be fully objective, generalizable, or representative of the whole discipline. With that being said, writing papers in this way has worked pretty well for us so far. We hope that this guide can at least partially do the same for others.

Yang Li, Yang Li, Jun Li et al.

The geological structure of the reservoir bank landslide is complex and intricate. After deformation and damage, it causes river blockage, surges, and loss of people’s lives and property, posing a huge threat. At present, in academia and engineering applications, a large number of techniques such as inclinometers, rain gauges, and surface GNSS deformation monitoring are still used for monitoring landslides on reservoir banks. This type of monitoring method has the problem of “point to surface” which can easily lead to missed detection and reporting in some areas, some disaster points are close to water and steep, difficult to reach, and equipment installation is difficult. This work designs and implements a non-contact video quantitative monitoring system for surface deformation of geological disaster. By constructing a deep learning neural network, deformation area recognition and displacement quantitative calculation are achieved; By obtaining continuous images for a long time, draw the surface displacement-time curve, and output the surface deformation data and landform changes of the disaster. Meanwhile, this work explores the impact of different lighting conditions on the recognition results of target areas. This work can provide non-contact monitoring methods and dynamic warning support for large-scale monitoring of geological disasters.

Mahsum Bozdoğan, Ergin Canpolat

As an inevitable consequence of population growth, human beings occupying more space on the earth's surface are more likely to encounter and be damaged by natural events. In order to minimize these damages, planning is required. Risk modelling studies are an important parameter in planning. Models can be made with higher accuracy with the help of Geographic Information Systems (GIS) and Remote Sensing (RS) technologies, which have been used intensively in multidisciplinary fields in recent years. In this study, the flood risk potential of the Delibekirli Stream Basin, which is located in the central part of the Amanos Mountains and has flood records in 2014 and 2015, was revealed by statistical and deterministic methods. Using various statistical parameters and distribution functions, the unit hydrograph peak flow and 2, 5, 10, 25, 50, 100, 500 and 1000-year flood recurrence flows of the basin were calculated. Using these values, a 1D hydraulic model was created in steady flow conditions using the River Analysis System (HEC-RAS v.6.1) software developed by the US Army Corps of Engineers, Hydrologic Engineering Center (USACE-HEC). In the city of Kırıkhan, which was built on the accumulation fan formed by the Delibekirli Stream, the channel cross-sections were narrowed due to the construction and various interventions were made to the river bed. As a result of the studies and evaluations carried out, it was concluded that in order to prevent the floods that may occur due to the Delibekirli Stream in Kırıkhan from causing loss of life and property, it was concluded that the works for the river channel should be designed based on at least 500-year flood flow (124,36 m3/s).

Xiaoliu Yang, Xiaochen Qin, Xiang Zhou et al.

Typhoon disasters are the most frequent and severe natural disasters in China’s southeastern coastal region. The strong wind and rainstorms during typhoons result in secondary disasters, such as storm surges, floods, and landslides. This phenomenon is referred to as a typhoon disaster chain that causes significant loss of life and property every year. Accurately assessing the disaster mitigation capacity and reducing limitations are crucial for improving typhoon disaster risk prevention. However, assessing the capacity of mitigating typhoon disaster chains requires further study. In this study, we select Fujian Province, China, as a case study to identify typical typhoon disaster chains, drawing on historical disasters and the sensitivity of the disaster-forming environment. We establish a county-based framework to assess the disaster mitigation capabilities for typhoon disaster chains. The mitigation capacities of 84 counties within Fujian Province are evaluated using the Random Forest (RF) algorithm, which determines the weights of various indicators. The results show the following. 1) The dominant types of typhoon disaster chains in Fujian Province are typhoon-rainstorm-urban waterlogging (TRU), typhoon-rainstorm-flooding (TRF), typhoon-rainstorm-landslide (TRL), and typhoon-strong wind-storm surge (TWS). Significant spatial differences are observed. 2) The assessment framework, which includes disaster prevention, disaster relief, and government management capabilities, accurately reflects the spatial differences in the disaster chain. The results can be extended to other regions or other disaster chains. 3) Significant spatial heterogeneity is observed in the disaster prevention, relief, and disaster mitigation capacity for typhoon disaster chains in the counties in Fujian Province. The eastern coastal areas have high mitigation capacity, whereas the northwest has low capacity. The disaster prevention capacity is very high in Changle City and Xiuyu District (2.4 %), and the disaster relief capacity is very high in Gulou District, Taijiang District, Changle City, and Huli District (4.8 %). The government disaster management capacity is very high in 7 counties (e.g., Longhai City, Minhou County, and Hui’an City (8.3 %)). The comprehensive disaster mitigation capabilities are very high in Changle City, Longhai City, and Xiuyu County (3.6 %). This study provides a scientific reference for assessing disaster chain mitigation capabilities and enhancing grassroots disaster mitigation efforts.

Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää et al.

In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they did in the beginning decided to apply to study software engineering. We were also interested in learning if there could be some signs that would help us in marketing to get more women into tech. We approached the topic via an online survey (N = 78) sent to the university students of software engineering in Finland. Our results show that, on average, women apply later to software engineering studies than men, with statistically significant differences between genders. Additionally, we found that marketing actions have different impacts based on gender: personal guidance in live events or platforms is most influential for women, whereas teachers and social media have a more significant impact on men. The results also indicate two main paths into the field: the traditional linear educational pathway and the adult career change pathway, each significantly varying by gender

Jieke Shi, Zhou Yang, David Lo

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the LLM-based software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.