Hasil untuk "Disasters and engineering"

K. Barker, J. Ramírez-Márquez, Claudio M. Rocco Sanseverino

Shuyun Yuan, Ying Li, Fangwen Bao et al.

Basic monitoring of the marine environment is crucial for the early warning and assessment of marine hydrometeorological conditions, climate change, and ecosystem disasters. In recent years, many marine environmental monitoring platforms have been established, such as offshore platforms, ships, or sensors placed on specially designed buoys or submerged marine structures. These platforms typically use a variety of sensors to provide high-quality observations, while they are limited by low spatial resolution and high cost during data acquisition. Satellite remote sensing allows monitoring over a larger ocean area; however, it is susceptible to cloud contamination and atmospheric effects that subject the results to large uncertainties. Unmanned vehicles have become more widely used as platforms in marine science and ocean engineering in recent years due to their ease of deployment, mobility, and the low cost involved in data acquisition. Researchers can acquire data according to their schedules and convenience, offering significant improvements over those obtained by traditional platforms. This study presents the state-of-the-art research on available unmanned vehicle observation platforms, including unmanned aerial vehicles (UAVs), underwater gliders (UGs), unmanned surface vehicles (USVs), and unmanned ships (USs), for marine environmental monitoring, and compares them with satellite remote sensing. The recent applications in marine environments have focused on marine biochemical and ecosystem features, marine physical features, marine pollution, and marine aerosols monitoring, and their integration with other products are also analysed. Additionally, the prospects of future ocean observation systems combining unmanned vehicle platforms (UVPs), global and regional autonomous platform networks, and remote sensing data are discussed.

Fatemeh Abbasi, Naser Hasheminejad, Faezeh Makki et al.

Background: Bus drivers are vulnerable due to prolonged sitting. A sedentary lifestyle can cause many health problems for them. This study was performed with the aim of investigating sedentary behaviour and its relationship with musculoskeletal disorders (MSDs) and depression among bus drivers.Methods: This is a descriptive-analytical cross-sectional study on 300 professional drivers selected via available sampling method. Data were collected through Demographic Questionnaire, International Physical Activity Questionnaire, Nordic Musculoskeletal Questionnaire (NMQ), and Beck Anxiety Inventory (BAI). Data analysis was performed using SPSS version 25 software, and path analysis was conducted using AMOS version 18.Results: The findings showed that 86.7% of the participants had very little physical activity. Most reported musculoskeletal discomfort in lower back (77.3%), neck (77%), and back (60.7%). 70.3% of drivers did not show depression or had mild depression. Path analysis showed that age (P=0.477), education (P=0.416), and marital status (P=0.271) did not affect sedentary behaviours. A two-way relationship existed between pain and physical activity (P=0.001). In the group with depression, low mobility existed, but no significant relationship was found between depression and inactivity (P=0.948).Conclusion: The results indicated that inactivity among bus drivers was almost high and significantly related to the prevalence of MSDs. While inactivity was not significantly related to depression, the rate of depression was higher in the inactive group. It is recommended that implement intervention programs, such as educational initiatives, should be held to increase physical activity and alter the lifestyle of these individuals. Increasing physical activity may reduce the prevalence of MSDs in this occupational group.

Zongheng Xu, Yun Zeng, Linglong Zha

Abstract Background The Chenghai-Binchuan fault zone in northwestern Yunnan Province, China, is a tectonically active structure with frequent paleo-landslides, largely controlled by normal faulting and left-lateral strike-slip motion. Methods To systematically investigate their formation mechanisms, and chronological evolution, To examine their formation and chronology, 284 landslides across representative zones were analyzed, with selected cases field-investigated to characterize morphology and classify failure mechanisms. Field investigations combined with OSL and ESR dating were used to estimate the timing of the major landslide events. Results Field observations show that paleo-landslides are strongly concentrated along active fault traces, and that some of them exhibit large, coherent accumulations with well-preserved stratigraphy, long horizontal runouts with limited vertical displacement, whereas others display pronounced creep deformation characteristics, together with steep rear scarps and well-developed tensile fractures. Their kinematic features include bedding-controlled creep, tensile-fracture sliding, ejection, and widely distributed shallow instability or collapse. Chronological analyses reveal that large paleo-landslides occurred between 485 ± 32 ka and 13.0 ± 0.7 ka, accompanied by long-lived landslide-dammed lakes. The integrated evidence indicates that strong fault–slope coupling, expressed through long-term creep weakening and abrupt seismic acceleration, exerts the primary control on the initiation, evolution, and spatial clustering of major paleo-landslides. Conclusion These findings provide new insights into the mechanisms and temporal evolution of fault-controlled paleo-landslides and their implications for landscape development in seismically active regions.

S. Argyroudis, S. Mitoulis, M. Winter et al.

Vulnerability is a fundamental component of risk and its understanding is important for characterising the reliability of infrastructure assets and systems and for mitigating risks. The vulnerability analysis of infrastructure exposed to natural hazards has become a key area of research due to the critical role that infrastructure plays for society and this topic has been the subject of significant advances from new data and insights following recent disasters. Transport systems, in particular, are highly vulnerable to natural hazards, and the physical damage of transport assets may cause significant disruption and socioeconomic impact. More importantly, infrastructure assets comprise Systems of Assets (SoA), i.e. a combination of interdependent assets exposed not to one, but to multiple hazards, depending on the environment within which these reside. Thus, it is of paramount importance for their reliability and safety to enable fragility analysis of SoA subjected to a sequence of hazards. In this context, and after understanding the absence of a relevant study, the aim of this paper is to review the recent advances on fragility assessment of critical transport infrastructure subject to diverse geotechnical and climatic hazards. The effects of these hazards on the main transport assets are summarised and common damage modes are described. Frequently in practice, individual fragility functions for each transport asset are employed as part of a quantitative risk analysis (QRA) of the infrastructure. A comprehensive review of the available fragility functions is provided for different hazards. Engineering advances in the development of numerical fragility functions for individual assets are discussed including soil-structure interaction, deterioration, and multiple hazard effects. The concept of SoA in diverse ecosystems is introduced, where infrastructure is classified based on (i) the road capacity and speed limits and (ii) the geomorphological and topographical conditions. A methodological framework for the development of numerical fragility functions of SoA under multiple hazards is proposed and demonstrated. The paper concludes by detailing the opportunities for future developments in the fragility analysis of transport SoA under multiple hazards, which is of paramount importance in decision-making processes around adaptation, mitigation, and recovery planning in respect of geotechnical and climatic hazards.

Zhice Fang, Yi Wang, Ling Peng et al.

Abstract Identifying floods and producing flood susceptibility maps are crucial steps for decision-makers to prevent and manage disasters. Plenty of studies have used machine learning models to produce reliable susceptibility maps. Nevertheless, most studies ignore the importance of developing appropriate feature engineering methods. In this study, we propose a local spatial sequential long short-term memory neural network (LSS-LSTM) for flood susceptibility prediction in Shangyou County, China. Three main contributions of this study are summarized as follows. First, it is a new perspective that the deep learning technique of LSTM is used for flood susceptibility prediction. Second, we integrate an appropriate feature engineering method with LSTM to predict flood susceptibility. Third, we implement two optimization techniques of data augmentation and batch normalization to further improve the performance of the proposed method. The LSS-LSTM method can not only capture both attribution information of flood conditioning factors and local spatial information of flood data, but also retain the powerful sequential modelling capability to deal with flood spatial relationship. Experimental results demonstrate that the LSS-LSTM method achieves satisfying prediction performance (93.75% and 0.965) in terms of accuracy and area under the ROC curve.

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.

Taorui Zeng, Xuenong Wu, Yuanming Lai et al.

Abstract Background In subtropical-typhoon regions, prolonged rainfall often triggers landslides, creating challenges in predicting soil thickness and its spatial distribution due to dense vegetation and complex topography. This study aims to tackle these issues by developing a watershed-scale map of unstable layer thickness to improve predictions of debris flows and landslides. Methods We integrated geomorphological surveys with ensemble machine learning techniques. Unmanned Aerial Vehicle technology was used to create a 3D digital elevation model, identifying different Quaternary deposits in the study area. Fieldwork involved three stages: soil thickness data collection via field surveys, geophysical exploration for spatial distribution, and core drilling for geotechnical properties. Separate evaluation systems were built for eluvium and slope deposits. A machine learning model was developed on a Python platform to predict soil thickness, using data from mountainous watersheds along China’s eastern coast. Results The model accurately predicted 84.7% of eluvium soil thickness (average 0.64 m, mainly sandy clay) and 81.3% of slope deposit thickness (average 2.34 m, including sandy clay and crushed stone). For eluvium, the root mean square error was 0.148 m, and for slope deposits, it was 0.27 m. Key influencing factors were lithology for eluvium and elevation for slope deposits. Shallow landslides were most prevalent in these layers, with sliding surfaces at specific interfaces between material types. Conclusions This study demonstrates the effectiveness of combining geomorphological surveys and machine learning for precise soil thickness prediction. The methodology enhances geohazard models, offering insights into landslide behavior and supporting more accurate risk assessments. These findings provide a foundation for future research on mitigation strategies in similar regions.

Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos et al.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Minyi Xu, Song Wang, Steven L. Zhang et al.

Abstract Wave monitoring is essential for marine engineering construction, development and utilization of ocean resources, maritime safety and early warning of marine disasters. In this paper, a highly-sensitive wave sensor based on liquid-solid interfacing triboelectric nanogenerator is proposed and systematically investigated. The wave sensor is made of a copper electrode covered by a poly-tetra-fluoroethylene film with microstructural surface. The effects of substrate, wave height, frequency, and water salinity on the performance of wave sensor are systematically investigated. It is found that the output voltage increases linearly with wave height with a sensitivity of 23.5 mV/mm for the electrode width of 10 mm, implying that the wave sensor could sense the wave height in the millimeter range. The sensitivity could be further increased by widening the electrode and/or enhancing the surface hydrophobicity. In a water wave tank, the wave sensor is successfully used to monitor wave around a simulated offshore platform in real time. Therefore, the novel wave sensor could provide an alternative to monitor wave for smart marine equipment.

Pavanaditya Badida, Yakesh Balasubramaniam, J. Jayaprakash

Abstract Oil and gas sector plays a major role in a country's economy. The development of large transmission pipelines for onshore and offshore gas transport is executed rapidly. These pipelines are vulnerable to natural disasters and can have a serious impact on the environment. The Potential damage to the pipeline infrastructure may contribute to increased risk of spill and thus can have an impact on the environment. Hence, the structural integrity of these pipelines is of great interest to the oil and gas companies, governments, and various stakeholders due to the probable environmental, infrastructural and financial losses in case of a structural failure. Fault tree analysis is an important risk assessment technique which treats the failure probabilities of the components as exact values for estimating the probability of the occurrence of a top event. Due to the lack of historical data for calculating the failure rate of pipelines due to natural hazards, this study aims to analyze the probability of pipeline failure by Fuzzy Fault Tree Analysis (FFTA) with expert elicitation. Fussel-Vesely Importance measures were utilized to rank the cutsets. The proposed FFTA framework was used to analyze the occurrence of top event even in the absence of historical probability data. The results are expected to be helpful to the safety professionals while making decisions related to the risk management of oil and gas pipelines.

Zilong Zhou, Xin Cai, Wenzhuo Cao et al.

A. Arwin, Ary Kiswanto Kenedi, Y. Anita et al.

Efforts are required to enhance community resilience to disasters, especially among elementary school students who are highly vulnerable to losses caused by natural disasters. In previous research, a science, technology, engineering, and mathematics (STEM) based digital disaster learning model was developed for elementary school students, so further research is needed to determine its effect on the adaptability of elementary school students. This study aimed to assess the impact of the STEM-based digital disaster learning model on the disaster adaptation abilities of elementary school students. This research is a quasi-experimental. The data collection instrument is the disaster adaptation ability essay test questions. The data analysis process uses the help of the SPSS 26 application. The findings found an average difference in students' disaster adaptation abilities between STEM-based digital learning models and conventional learning models. This finding was also reinforced by the post-test average scores of students who studied using STEM-based digital disaster learning models, which were higher than those with conventional learning models. So overall, the STEM-based digital disaster learning model increases elementary school students' disaster adaptation abilities. The implications of this research can be used as a reference in developing elementary school students' disaster adaptation abilities.

Jing Wu, Qimei Chen, Hao Jiang et al.

In the context of 6G, airborne post-disaster emergency networks (PENs) could be resilient in calamities and offer hope for disaster recovery in the underserved disaster zone. Unmanned aerial vehicles (UAV)-enabled ad-hoc network is such a significant contingency plan for communication after natural disasters, such as typhoon and earthquake. Specially, we present possible technological solutions for PENs targets for counteracting any large-scale disasters to achieve efficient communication and rapid network deployment. To this end, in this paper we jointly take power and coverage control into account during the UAV network configuration. An innovative noncooperative game theoretical model and improved binary log-linear algorithm (BLLA) have been adopted to achieve the optimal system performance. To deal with the challenges brought by highly dynamic post-disaster circumstances, we employ the aggregative game which is able to capture the strategies updating constraint and strategy-deciding error in large-scale UAV networks. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and hence reduce strategy updating time and energy consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Superior performance gains are seen in SNR and network coverage and hence render a great network solution in emergency scenarios.

Dong Wang, Feng Ju, Juan Xu et al.

X. Qiang, Yapeng Wu, Yuhan Wang et al.

In civil engineering, beam structures such as bridges require reinforcement to increase load-bearing capacity and extend service life due to damage, aging, and capacity degradation under long-time services and disasters. The utilization of Fe-based shape memory alloys (Fe-SMA) to reinforce structures has been proven efficient and reliable, and the recovery stress of activated Fe-SMA can satisfy the reinforcement requirements. This article overviews the material characteristics and mechanical properties of Fe-SMA. Furthermore, the principle of thermal activation for reinforcing beams using Fe-SMA is described. On this basis, the joining methods between Fe-SMA members and reinforced components are reviewed, and the existing reinforcement research and applications are analyzed for steel and concrete beams. Finally, given the current shortcomings, this paper puts forward the perspectives that need to be studied to promote Fe-SMA’s reinforcement application in civil engineering.

Bicheng Zhou, Anatoly V. Brouchkov, Lidia I. Eremina et al.

Water migration behavior is the main cause of engineering disasters in cold regions, making it essential to understand its mechanisms and the resulting mechanical characteristics for engineering protection. This study examined the water migration process during soil freezing through both experimental and numerical simulations, focusing on the key mechanical outcomes such as deformation and pore water pressure. Initially, a series of controlled unidirectional freezing experiments were performed on artificial kaolin soil under various freezing conditions to observe the water migration process. Subsequently, a numerical model of water migration was formulated by integrating the partial differential equations of heat and mass transfer. The model’s boundary conditions and relevant parameters were derived from both the experimental processes and existing literature. The findings indicate that at lower clay water content, the experimental results align closely with those of the model. Conversely, at higher water content, the modeled results of frost heaving were less pronounced than the experimental outcomes, and the freezing front advanced more slowly. This discrepancy is attributed to the inability of unfrozen water to penetrate once ice lenses form, causing migrating water to accumulate and freeze at the warmest ice lens front. This results in a higher ice content in the freezing zone than predicted by the model, leading to more significant freezing expansion. Additionally, the experimental observations of pore water pressure under freeze–thaw conditions corresponded well with the trends and peaks projected by the simulation results.

Josephine U. Adekola, Robert Chia

Abstract The adoption of a stakeholder approach to public engagement within the public sector has been extensive. However, there remain critical gaps in the understanding of stakeholder participation arising from hidden disparities that contribute to unequal access to communication channels, information, and hence ultimately knowledge and decision making. The term “epistemic injustice” has been used to describe such inequality of access and consequently, the outcome that ensues. Epistemic injustice is much overlooked in stakeholder theory. This article shows how epistemic injustice can act as a barrier to effective stakeholder engagement and hence to successful public policy formulation and implementation. We use the case of vaccine hesitancy among Scotland’s African, Caribbean, and Black (ACB) communities to illustrate this problem of unequal participation. The study drew on primary data involving 85 participants and secondary data sources from extant literature and explored salient factors shaping barriers to vaccine uptake during the recent pandemic. The findings demonstrate how the failure to grasp epistemic injustice undermines the effectiveness of the stakeholder approach, even with the most well-intentioned efforts. We argue that epistemic injustice is a critical barrier to effective stakeholder approaches.