Hasil untuk "Disasters and engineering"

Y. H. M. Amran, Rayed Alyousef, R. Rashid et al.

In civil and structural engineering, building structures with robust stability and durability using sustainable materials is challenging. The current technological means and materials cannot decrease weight, enlarge spans, or construct slender structures, thus inspiring the exploration for valuable composite materials. Fiber reinforced polymer (FRP) features high-strength and lightweight properties. Using FRP motivates civil engineers to strengthen existing RC structures and repair any deterioration. With FRP, a system that can resist natural disasters, such as earthquakes, strong storms, and floods, can be developed. However, deterioration of structures has become a critical issue in modern construction industries worldwide. This paper reviews the FRP design, matrix, material properties, applications, and serviceability performance. This literature review also aims to provide a comprehensive insight into the integrated applications of FRP composite materials for improving the techniques of rehabilitation, comprising the applications toward the repair, strengthening, and retrofit of concrete structures in the construction industry today.

S. Khan, Imran Shafi, W. H. Butt et al.

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

John Dodson Maxima

From travel disruptions to natural disasters, extreme events have long captured the public’s imagination and attention. Due to their rarity and often associated calamity, they make waves in the news (Fig. 3.1) and stir discussion in the public realm: is it a freak event? Events of this sort may be shrouded in mystery for the general public, but a particular branch of probability theory, notably Extreme Value Theory (EVT), offers insight to their inherent scarcity and stark magnitude. EVT is a wonderfully rich and versatile theory which has already been adopted by a wide variety of disciplines in a plentiful way. From its humble beginnings in reliability engineering and hydrology, it has now expanded much further; it can be used to model the occurrences of records (say for example in athletic events) or quantify the probability of floods with magnitude greater than what has been observed in the past, i.e it allows us extrapolate beyond the range of available data! In this book, we are interested in what EVT can tell us about electricity consumption of individual households. We already know a lot about what regions and countries do on average but not enough about what happens at the substation level or at least not with enough accuracy. We want to consider “worst” case scenario such as an area-wide blackout or the “very bad” case scenario such as a circuit fuse blowout or a low-voltage event. Distribution System Operators (DSO) may want to know how much electricity they will need to make available for the busiest time of day up to two weeks in advance. Local councils or policy makers may want to decide if a particular substation is equipped to meet the demands of the residents and if it needs an upgrade or maintenance. EVT can definitely help us to answer some of these questions and perhaps even more as we develop and adapt the theory and approaches further. There are many ways to infer properties about a population based on various sample statistics. Depending on the statistic, a theory about how well it estimates

Jorge Melegati

The adoption of Generative AI (GenAI) suggests major changes for software engineering, including technical aspects but also human aspects of the professionals involved. One of these aspects is how individuals perceive themselves regarding their work, i.e., their work identity, and the processes they perform to form, adapt and reject these identities, i.e., identity work. Existent studies provide evidence of such identity work of software professionals triggered by the adoption of GenAI, however they do not consider differences among diverse roles, such as developers and testers. In this paper, we argue the need for considering the role as a factor defining the identity work of software professionals. To support our claim, we review some studies regarding different roles and also recent studies on how to adopt GenAI in software engineering. Then, we propose a research agenda to better understand how the role influences identity work of software professionals triggered by the adoption of GenAI, and, based on that, to propose new artifacts to support this adoption. We also discuss the potential implications for practice of the results to be obtained.

Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém et al.

The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum'' where safety and accountability are compromised. This paper presents an industry case study of the ``Maria'' platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.

Xiao-yong Cao, Ziming Liu, Chenlin Hu et al.

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

Zhicong Yin, Botao Zhou, Mingkeng Duan et al.

Climate extremes become increasingly fierce in China Zhicong Yin,1,2,3 Botao Zhou,1 Mingkeng Duan,1 Haishan Chen,1 and Huijun Wang1,2,3,* 1Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China 2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China 3Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China *Correspondence: hjwang@nuist.edu.cn Received: January 20, 2023; Accepted: February 18, 2023; Published Online: February 21, 2023; https://doi.org/10.1016/j.xinn.2023.100406 a 2023 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Citation: Yin Z., Zhou B., Duan M., et al., (2023). Climate extremes become increasingly fierce in China. The Innovation 4(2), 100406.

Tidarut Jirawattanasomkul, Le Hang, Supasit Srivaranun et al.

This study developed a digital twin (DT) and structural health monitoring (SHM) system for a balanced cantilever bridge, utilizing advanced measurement techniques to enhance accuracy. Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges, capturing low-magnitude dynamic strains during operational vibrations. 3D-LiDAR scanning and Ultrasonic Pulse Velocity (UPV) tests captured the bridge's as-is geometry and modulus of elasticity. The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions. Dynamic properties, including modal frequencies and shapes, were extracted from the strain and acceleration measurements, providing critical insights into the bridge's structural behavior. The neutral axis depth, indicating stress distribution and potential damage, was accurately determined. Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model. Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions, serving as sensitive indicators of local damage. The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations. This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance, ensuring the sustainability and safety of the bridge infrastructure, thereby enhancing its overall resilience to operational and environmental stressors.

Changbao Guo, Sanshao Ren, Yinlong Tan et al.

Abstract Loess is extensively developed on both sides of the Longwu River, a tributary of the Yellow River, Tongren County, Qinghai Province. The engineering geological characteristics are complex, and landslide disasters are highly developed. Based on field geological surveys and physical property analysis of the loess in this area, this study analyzes the influence of water content, consolidation pressure, and soil disturbance on the dynamic characteristics of loess using GDS dynamic triaxial tests. The results show that Tongren loess has strong structural properties. Its dynamic constitutive relationship conforms to the Hardin-Dinevich hyperbolic model, where the parameters a and b decrease with increasing confining pressure and increase with increasing water content. The dynamic cohesion and dynamic friction angle both decrease with increasing water content, with the dynamic cohesion significantly affected, decreasing by 77% ~ 83% when saturated. However, the dynamic friction angle is less affected by water content changes, decreasing by 18% ~ 25% when saturated. Under dynamic conditions, the dynamic friction angle of Tongren loess is only 12 ~ 16°, making slopes composed of Tongren loess highly susceptible to instability and sliding under strong earthquake conditions. The dynamic structural properties of Tongren loess are significantly influenced by water content, with increasing water content destroying the joint structural strength of intact loess. Before reaching the plastic limit water content, the joint structural strength of intact loess decreases sharply with increasing water content, and then decreases slowly. The frictional structural strength shows an opposite trend. Under the same experimental conditions, the failure dynamic stress, maximum dynamic elastic modulus, and dynamic shear strength parameters of intact loess are generally greater than those of remolded loess, and the differences between them decrease with increasing water content. This study provides insights into the dynamic structural characteristics of Tongren loess, which can serve as a reference for understanding the formation mechanisms, stability analysis, and seismic design of loess landslides in the region.

Joe Forbis, Cuong Ly

Abstract The US Army Corps of Engineers (USACE) prescribes flood control operations for reservoirs it regulates in watershed‐specific water control manuals (WCMs), which can be decades‐old and may not capture changed conditions in the watersheds or include the benefit of state‐of‐the‐science weather and streamflow prediction. Considering the specific characteristics of a reservoir, forecast‐informed reservoir operations (FIRO) may be used to enhance flood risk reduction, improve water availability, and achieve other benefits. The first FIRO pilot project at Lake Mendocino in California focused on determining if water supply reliability could be improved using FIRO without increasing flood risk. The final report concluded that FIRO concepts could indeed improve water supply reliability while enhancing flood risk reduction. Subsequently, USACE chose additional reservoir systems in California with different characteristics as additional pilot study locations to further investigate FIRO concepts. These successful FIRO efforts have provided justification to continue its expansion beyond the initial pilot sites. The lessons learned from the FIRO pilot projects are being used to inform the development of the FIRO Screening Process, a screening level framework intended to scale up the implementation of FIRO. The lessons learned could support FIRO implementation at suitable USACE reservoirs by updating WCMs.

Lei Hong, Wen Wang, Xuewen Cao et al.

Enhanced Geothermal Systems (EGS) improve geothermal energy extraction but can rapidly cool high-temperature rocks, leading to internal fractures that weaken mechanical properties and pose risks such as well collapses and seismic events. Understanding the physico-mechanical changes in dry hot rocks, particularly sandstone, when high-temperature water cooling cycles is essential. This study examines the dynamic behavior of sandstone through impact tests at varying temperatures and cycles. Results show that as temperature and cycle count increased, peak dynamic stress decreased while dynamic strain increased. A critical temperature range of 500–600 ​°C was identified, beyond which significant changes in dynamic stress and strain occurred, indicating severe damage to the specimens’ stability. High-temperature water cooling cycles enhanced energy reflectivity and dissipated energy, reducing transmittance. The study revealed that between 200 and 400 ​°C, tensile damage predominated, while between 500 and 600 ​°C, compression-shear damage was dominant. Increasing temperature and cycles led to more extensive cracking and increased rock fragmentation. These findings provide a basis for assessing the stability of sandstone and offer theoretical insights into mechanical properties, energy transfer, and crack propagation in geothermal energy extraction, aiding in the prevention of geological disasters.

Ali Pourzangbar, Peter Oberle, Andreas Kron et al.

ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a clear roadmap for the deployment of ML‐based FSM frameworks. The critical aspects of ML‐based FSM are identified, including data considerations, the model's development procedure, and employed algorithms. A comparative analysis of different ML models, alongside their practical applications, is made. Findings suggest that despite existing limitations, ML methods, when carefully designed and implemented, can be successfully utilized to determine areas at risk of flooding. We show that the effectiveness of ML‐based FSM models is significantly influenced by data preprocessing, feature engineering, and the development of the model using the most impactful parameters, as well as the selection of the appropriate model type and configuration. Additionally, we introduce a structured roadmap for ML‐based FSM, identification of overlooked conditioning factors, comparative model analysis, and integration of practical considerations, all aimed at enhancing modeling quality and effectiveness. This comprehensive analysis thereby serves as a critical resource for professionals in the field of FSM.

Qiuyang Zhang, Weidong Ma, Yuan Gao et al.

The Qinghai–Tibet Plateau is one of the most geologically active regions in the world, characterized by significant geomorphic variation and a wide range of geological hazards. The multifactorial coupling of tectonic movements, geomorphological evolution, climate variability, and lithological characteristics contributes to the pronounced spatial heterogeneity of the disaster-inducing environment. Identifying key controlling factors and their driving mechanisms is crucial for effective regional disaster prevention and mitigation. This study adopts a systematic framework based on regional disaster systems theory, integrating tectonic activity, engineering geology, topography, and precipitation to construct a multi-factor zoning system. Using the Random Forest model, we quantify factor contributions and delineate eight distinct disaster-inducing environment zones. Zones I–III (Himalayas–Hengduan Mountains–Qilian Mountains) are characterized by a dominant coupling mechanism of “tectonic fragmentation—topographic relief—precipitation erosion” and account for the majority of large-scale disasters. In contrast, Zones IV–VIII, primarily located in the central–western Plateau basins, are constrained by limited material sources, resulting in lower disaster densities. The findings indicate that geological structures and lithological fragmentation provide the material foundation for hazard occurrence, while topographic potential and hydrodynamic forces serve as critical triggering conditions. This nonlinear coupling of factors shapes a disaster geographic pattern characterized by “dense in the east and sparse in the west”. Based on these results, the targeted recommendations proposed offer valuable theoretical insights and methodological guidance for disaster mitigation and region-specific management across the Qinghai–Tibet Plateau.

Haibo Wang, Zhuolin Xu, Shin Hwei Tan

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed.

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Kunjie Tang, Dedi Liu, Shaohua Xie et al.

Cuiyun Gao, Xing Hu, Shan Gao et al.

With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models, by leveraging the power of deep learning and massive amounts of data, have demonstrated an unprecedented capacity to understand, generate, and operate programming languages. They can assist developers in completing a broad spectrum of software development activities, encompassing software design, automated programming, and maintenance, which potentially reduces huge human efforts. Integrating LLMs within the SE landscape (LLM4SE) has become a burgeoning trend, necessitating exploring this emergent landscape's challenges and opportunities. The paper aims at revisiting the software development life cycle (SDLC) under LLMs, and highlighting challenges and opportunities of the new paradigm. The paper first summarizes the overall process of LLM4SE, and then elaborates on the current challenges based on a through discussion. The discussion was held among more than 20 participants from academia and industry, specializing in fields such as software engineering and artificial intelligence. Specifically, we achieve 26 key challenges from seven aspects, including software requirement & design, coding assistance, testing code generation, code review, code maintenance, software vulnerability management, and data, training, and evaluation. We hope the achieved challenges would benefit future research in the LLM4SE field.

Umm-e- Habiba, Markus Haug, Justus Bogner et al.

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.