Disaster Evaluation Research Articles

A Hierarchical Decoder Architecture for Multilevel Fine-Grained Disaster Detection

As a cutting-edge challenge in the field of disaster evaluation, the detection of disasters in remote sensing images is crucial. However, most existing approaches to disaster detection simply solve the problem as a naive multi-class change detection, lacking accurate damage-level classification. In this paper, we propose a new approach to disaster detection called multi-level disaster detection (MLDD) that focuses on fine-grained damage-level classification. Our proposed approach tackles MLDD through hierarchical-correlation modeling and presents a universal disaster detection architecture. Specifically, we summarize two existing applicative methods, one-step training and pre-training, which are compatible with our proposed architecture. In addition, we propose two novel hierarchical approaches, namely the multi-task (MT) based and graph-encoding (GE) based approaches. The MT approach resolves MLDD through layer-wise learning in a progressive manner, building explicit multi-stage and implicit joint models to probe into the coarse-to-fine correlation for damage-level evaluation. The GE approach enhances hierarchical relationships by encoding multifold messaging directions and probabilities using a graph neural network. Furthermore, all four hierarchical paradigms can be embedded in our hierarchical MLDD architecture, which outperforms state-of-the-art methods on the xBD dataset, particularly in fine-grained damage-level classification. Overall, our proposed approach represents a significant improvement over existing disaster detection methods and has the potential to advance the field of disaster evaluation.

Typical advances of artificial intelligence in civil engineering

Artificial intelligence (AI) provides advanced mathematical frameworks and algorithms for further innovation and vitality of classical civil engineering (CE). Plenty of complex, time-consuming, and laborious workloads of design, construction, and inspection can be enhanced and upgraded by emerging AI techniques. In addition, many unsolved issues and unknown laws in the field of CE can be addressed and discovered by physical machine learning via merging the data paradigm with physical laws. Intelligent science and technology in CE profoundly promote the current level of informatization, digitalization, autonomation, and intellectualization. To this end, this paper provides a systematic review and summarizes the state-of-the-art progress of AI in CE for the entire life cycle of civil structures and infrastructure, including intelligent architectural design, intelligent structural health diagnosis, intelligent disaster prevention and reduction. A series of examples for intelligent architectural art shape design, structural topology optimization, computer-vision-based structural damage recognition, correlation-pattern-based structural condition assessment, machine-learning-enhanced reliability analysis, vision-based earthquake disaster evaluation, and dense displacement monitoring of structures under wind and earthquake, are given. Finally, the prospects of intelligent science and technology in future CE are discussed.

An empirical study of the key factors affecting herders' purchasing decision on weather index insurance—A case study from inner Mongolia autonomous region, China

The weather index insurance for mutton sheep (WIMS) is the first weather index insurance product that addresses the increased feeding cost-related risk of grassland animal husbandry herders in China. In 2019, the WIMS was implemented as a pilot project in 11 county regions of Xilin Gol League, Inner Mongolia, China. Since the implementation of this product, the purchasing rate has varied from region to region, as the highest purchasing rate was 84.3%, while the lowest was only 1.8%. This research uses the double hurdle model to identify the factors affecting herders' purchasing behavior. This paper reports a survey study of 535 participants herders in the Xilin Gol League. The result indicates that herders' age, disaster risk evaluation, insurance awareness, pasture area, and neighborhood purchasing decision significantly impact herders' purchasing propensity. Disaster evaluation, insurance awareness, pasture area and location significantly impact the purchasing degree of herders. The study suggests that young herders and herders with large pasture areas should be motivated to purchase insurance. In addition, new methods should be applied for the publicity of this insurance product; Investments should be focused on the automatic weather monitoring station projects in pastoral areas to reduce the basis risk of the insurance.

RESEARCH STATUS AND DEVELOPMENT TREND OF PHOTOGRAMMETRY AND REMOTE SENSING IN URBAN FLOOD DISASTERS

Abstract. In recent years, urban floods occur frequently in China, and urban flood disaster has become one of the important reasons for the sustainable development of cities. Photogrammetry and remote sensing technology has the advantages of large observation range, large amount of information, fast speed, good real-time performance and strong dynamic. It plays an increasingly important role in flood control and disaster reduction, and has become an important support means for flood control and flood reduction in China. This paper mainly reviews the research status and development trend of photogrammetry and remote sensing technology in urban floods, summarizes the monitoring and evaluation of flood disasters, numerical simulation of flood disasters, and acquisition of hydrological characteristics, and prospects the future development of photogrammetry and remote sensing technology in flood disaster management in China.

Development and Application of Carbonate Dissolution Test Equipment under Thermal-Hydraulic-Chemical Coupling Condition.

The latest continuous flow micro reaction technology was adopted to independently develop carbonate rock dissolution test equipment. Carbonate rock dissolution tests were conducted under different temperatures, flow rates, and dynamic water pressure conditions to study the dissolution process of carbonate rocks under the coupling of heat-water-chemistry. The dissolution effect and development law of carbonate rocks were explored by quantitatively studying carbonate rock dissolution rate and chemical composition of karst water. The results showed that the self-designed dissolution test equipment has obvious advantages. After dissolution, carbonate rock specimens were damaged to varying degrees. The dissolution rate was proportional to water velocity and hydrodynamic pressure, with the velocity effect being greater than the hydrodynamic pressure effect. The pH value, conductivity, and Ca2+ ion content of the reaction solution gradually increased after dissolution. The development and application of the equipment have proved that, at low dynamic water pressures (2 MPa), the water flow velocity effect on the dissolution velocity was 1.5 times that when the dynamic water pressure was high (6 MPa); at a low water flow velocity of 15 mL/min, the dynamic water pressure effect on the dissolution velocity was three times that when the water flow velocity was high (75 mL/min). The development process is gradually becoming strong and stable. Its research has important theoretical significance and engineering application value to provide technical means and guarantee for the early identification, karst development, and safety evaluation of karst geological disasters.

The formation mechanism and failure mode of a talus slope induced by rockfalls in Nayong County, Southwest China

Hillslope processes and mass movement are key issues in the analysis and evaluation of geological disasters in mountainous regions. A rockfall-dominated talus slope exhibits a typical outcrop–talus slope system as the product of detached boulders and rock fragments. The Zongling rockfall zone is one of the most active outcrop–talus slope systems in southwest China, and it provides a representative case study on the assessment of rockfall hazards at the base of talus slopes. In this article, the formation mechanism and failure mode of this rock-talus system were studied using field investigation, remote sensing image analysis, and numerical simulation. The findings reveal that the lithology and rock mass structure of the study site are controlling factors for outcrop retreat and the progressive development of talus deposits. This process is intensified by rainfall and mining activities. Boulder accumulation on the platform at the middle section of the talus slope serves as top loading for the slope mechanical system. During the boulder–ground interaction, the rockfall impact acts as toe cutting to change the geometry and mechanical balance of the talus slope. It was found that toe cutting significantly influenced the slope stability, which led to a decrease in the antisliding force of the slope. The slope failure induced by rockfalls occurred with the combined effect of top loading and toe cutting on this talus slope. During rockfall prevention and mitigation in this region, the government and residents should consider the geodisaster chain, as this relates to the impact of rockfall on talus slopes, in addition to the risk of damage due to the rockfall trajectory.

Research on Identification of Natural and Unnatural Earthquake Events Based on AlexNet Convolutional Neural Network

Accurately and quickly identifying the types of natural and unnatural earthquake events is the basic premise of monitoring, prediction, early warning, and other study in the field of seismology, which is of great significance to the prevention, evaluation, emergency rescue, and other work of earthquake disasters. Convolutional neural network model is a representative artificial intelligence deep learning algorithm, which has been widely used in computer vision, natural language processing, object type identification, and other fields in recent years. In this study, AlexNet convolutional neural network model is selected to study the type identification of 1539 earthquake event waveform records in and around Ningxia Hui Autonomous Region, China. Earthquake event waveform records contain three types: natural earthquake, explosion, and collapse, in which both explosion and collapse are unnatural earthquakes. MATLAB software is used to build the training module and test module for AlexNet convolutional neural network model, and the earthquake event waveform record is transformed into an image format file of 224 times 224 pixels as input parameters. Finally, AlexNet convolutional neural network model has the ability of automatic identification of earthquake event types. The results of this study show that the identification accuracy of earthquake event type in training module is 99.97%, the average value of loss function is 0.001, the identification accuracy of earthquake event type in test set is 98.51%, and the average value of loss function is 0.059. After training and testing, 60 different types of earthquake event waveform records were randomly selected, and AlexNet convolutional neural network model was used to identify them automatically. The automatic identification accuracy of natural earthquakes, explosions, and collapses was 90%, 80%, and 85%, respectively. After training AlexNet convolutional neural network model with earthquake event waveform records, it can have accurate and fast automatic identification ability. The accuracy of automatic identification is comparable to that of professional seismic workers, and the time of automatic identification is greatly reduced compared with that of professional seismic workers. This study can provide an implementation idea of deep learning based on artificial intelligence for the identification of earthquake event types and make contributions to the cause of earthquake prevention and disaster reduction.

PENANGANAN KEGAWATDARURATAN SEHARI-HARI DAN KESIAPSIAGAAN BENCANA BANJIR DI DESA TOUNELET KABUPATEN MINAHASA

Emergency events usually occur quickly and suddenly, making it difficult to predict when they will happen. The best step for this situation is to be alert and make concrete efforts to anticipate it. A form of assistance mechanism must be considered for victims from the beginning of the scene, during the journey to health facilities, and assistance at health facilities until after the injury incident. The achievement of the patient's quality of life at the end of the aid must remain the goal of the entire series of assistance provided. Meanwhile, flood preparedness can be done by simulating a flood disaster evaluation to the community so that people know where to go when a flood occurs. With this handling activity, it is hoped that it can help overcome problems in Tounelet Village, West Langowan District, and Minahasa Regency.

Evaluation of Shear Strength Parameters in Unreinforced and Shotcrete Reinforced Coal through Experimental and Numerical Methods

The shear strength of coal mines forms the most significant aspect in the evaluation of mining disasters. A series of direct shear strength tests were conducted on intact coal specimens and coal-shotcrete specimens to evaluate the shear strength characteristics of the coal and coal-shotcrete interface. The tests were carried out under 1, 2 and 3 MPa of normal pressure respectively. The peak shear strength in intact coal specimen reaches its maximum value of 3.96 MPa under 3MPa of normally applied pressure while the shotcrete-coal interface has an estimated peak shear strength of 0.73, 0.80 and 0.86 MPa under the normal pressure of 1, 2 and 3 MPa respectively. Mohr-Coulomb model parameters such as adhesion strength and angle of adhesion were further determined that were used for the design and evaluation of shear strength of in situ coal mines. Numerical simulations of coal-shotcrete and intact coal specimens were performed using the Mohr-Coulomb model in Abaqus software and the numerical results obtained were found in good agreement with experimental results exhibiting peak variation of less than 15%. Further, ultrasonic pulse velocity test measurements were performed on coal specimens to evaluate the dynamic shear strength parameters and the parameters obtained were found to be enhanced compared to static strength parameters.

Mainstreaming resilience analytics: 10 years after the Fukushima disaster.

Multiple events over the last decade, including the ongoing COVID-19 pandemic, demonstrate a global lack of preparedness for low probability but high consequence events. Following the evaluation of the Fukushima Daiichi nuclear disaster, these authors called for a change from a risk-oriented approach to a resilience-focused framework for managing such disruptions. Over the past five years, the field of resilience analytics has conceptualized further resilience frameworks within the context of infrastructure development; however, the practice of resilience planning is still lagging behind the theories developed in the literature. In this article, we consider the lessons learned from the Fukushima nuclear accident through the lens of newly developed resilience analytics and the ongoing COVID-19-related challenges.Integr Environ Assess Manag 2022;18:1551-1554. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Fuzzy Comprehensive Safety Evaluation of Pipeline Disaster in China-Russia Crude Oil Permafrost Region Based on Improved Analytic Hierarchy Process-Entropy Weight Method

Considering the difficulty in evaluating pipeline safety in permafrost region, the pipeline disaster safety evaluation index system in permafrost region is analyzed and established. A comprehensive weight determination method based on improved fuzzy analytic hierarchy process, entropy weight method and Lagrange algorithm is proposed, and a fuzzy comprehensive evaluation model of permafrost pipeline safety is established. Taking the disaster safety evaluation of pipeline in the China-Russia crude oil permafrost region as an example, the proposed method is used to determine the comprehensive weight of disaster safety index of permafrost pipeline and carry out comprehensive safety evaluation. The rationality of the comprehensive weight determination method is verified by comparing with the performance of the traditional AHP evaluation method.

Urban open space resilience evaluation of air pollution disasters based on monitoring data of national monitoring stations in Hangzhou

Using air quality monitoring data of 2018, 2020 and 2021 from national monitoring stations in Hangzhou, the disaster cycles, with levels of ozone (O3), fine particulate matter (PM2.5), and coarse particulate matter (PM10) were determined. The damage to human health from pollutants, which represents the loss of open space functionality, was determined by a review of the literature. Using the theory of resilience evaluation, a correlation was established between pollutant concentrations and system functions, and resilience performance curves of different types of open spaces were constructed for the urban open space pollutant disaster cycle to evaluate the resilience and related properties for open space responses to O3, PM2.5, and PM10 pollution disasters. The results indicate that open spaces in scenic and cultural areas are more resilient to pollution than residential areas and commercial transportation residential mixed zones. All open spaces were the most robust to PM10 and had the highest redundancy for O3 and PM10, while had the lowest redundancy for PM2.5. All open spaces responded most rapidly to PM2.5, partly because of larger scale meteorological changes in cities. All open spaces had the strongest resilience to PM10 disasters, followed by O3 disasters, with the worst resilience to PM2.5 disasters, with a resilience index range of [0.5, 1]. The results and methods can help enhance urban resilience by bringing new ideas and broadening urban resilience theory and research. The strategy of enhancing greenery in urban open spaces was found to significantly reduce the impact of air pollution disasters, thereby reducing human health damage to users of open spaces. This is significant for enhancing urban resilience and building urban health infrastructure.

A qualitative content analysis for determining indexes and factors affecting for evaluation of disaster exercises immediate feedback stage.

BACKGROUND:Taking exercise in health sector is one of the important steps to implement the disaster risk management programs, especially preparedness phase. The present study aimed to identify indexes and factors affecting successful evaluation of disasters preparedness exercises in hot wash stage.MATERIALS AND METHODS:This study was a qualitative content analysis. Data were collected by purposeful sampling through in-depth and semi-structured individual interviews with 25 health professionals in the field of disasters. The data were analyzed using directed content analysis method by which the initial codes were extracted after transcribing the recorded interviews and immersing them in the data analysis. The initial codes were reviewed, classified, and subdivided into several stages to determine the main classes.RESULTS:The data analysis resulted in the production of 24 initial codes, 5 subcategories, 2 main categories of “evaluation and exercise debriefing” and “modification of programs and promotion of exercise operational functions” under the original theme of “exercise immediate feedback.”CONCLUSION:This study can be considered a suitable standard guide for health care organizations to evaluate successfully disasters exercises in hot wash stage, maintain and promote their preparedness, and properly respond to disasters.

Geotechnical Management of Isolated Sustainable Alpine Communities

The Albanian Alps are situated in a mountainous block in the Northern Albania region, in the counties of Shkodër (also known as Shkodra or Gegëria) and Kukës (Kukësi). The nature of the mountainous terrain formation has led to the creation of isolated communities. The need for integrating these scattered communities into a cohesive co-operating community for area sustainability is now possible by using the Internet to link them all onto an online system. To deal with natural catastrophes, disaster management cells will be created which will serve as hubs. These hubs will be located at geographically strategic positions that will enable a predetermined geofenced region for evaluation of different disasters viz. forest fires, landslide, flooding, avalanches, the burial of villages under heavy snowfalls, etc. These cells will connect the particular case with the most appropriate disaster relief, rescue service and EMR (Emergency Medical Responder), first aid services (e.g. Green Crescent/Red Cross) and EMT (Emergency Medical Technician) personnel. The cells shall be managed by locally trained human resources with the necessary equipment to provide the monitoring/analyses and first aid assistance in case of need. The technology needed for the monitoring and geotechnical management of the isolated Alpine communities will be described. The socio-economic impact of the deployment of these technologies aiding in the sustainability of these vulnerable communities will conclude the research.

Comprehensive vulnerability evaluation of urban earthquake disaster based on RAGA-PPE

In view of the multi factor influence problem in the comprehensive vulnerability assessment of urban earthquake disasters combined with the complex internal characteristics of urban system based on the pressure state response model, the comprehensive vulnerability index system of urban earthquake disasters is constructed from seven aspects: natural factors, human factors, lifeline system, social status, economic status, engineering seismic capacity and recovery capacity. The urban vulnerability is evaluated by establishing the comprehensive vulnerability evaluation model of urban earthquake disaster, and the best projection direction of projection pursuit evaluation model is sought by using accelerated genetic algorithm. Combined with the evaluation index value, the projection characteristic index value of the evaluation sample is determined. Taking 12 cities in Gansu Province as an example, the comprehensive vulnerability state of cities is determined by the above model, and the main factors influencing the earthquake vulnerability of cities are analysed according to the results, which provides the basis for decision makers to make earthquake prevention and disaster reduction planning.

Failure analysis of a buried large-diameter prestressed concrete cylinder pipeline subjected to strike-slip fault displacement

In this paper, the structural responses and failure analysis of a buried prestressed concrete cylinder pipeline (PCCP) subjected to strike-slip fault displacement are numerically investigated considering soil-pipe interactions. ABAQUS software was used to build a detailed pipeline model based on the optimal model length. The structural response and damage failure performance of the pipe barrels and joints were analyzed and discussed. The results showed that the main type of damage to the PCCP was damage failure in each layer of the pipe barrel caused by axial forces, pull-out leakage of the joints caused by the relative rotation angle, and compression and stacking. With increasing fault displacement, the scope and extent of pipeline damage expanded, and the pipes gradually reached the limit states. This study focused on a large-diameter PCCP to predict pipeline damage under the condition of strike-slip faults crossing the middle of a pipe barrel, and the findings of this research contribute to understanding the failure modes, damage process and disaster evaluation of PCCPs subjected to strike-slip faults.

A physical model-free ant colony optimization network algorithm and full scale experimental investigation on ceiling temperature distribution in the utility tunnel fire

To advance understanding of the ceiling temperature characters in tunnel fires, a physical model-free ant colony optimization network algorithm is developed. Compared to the traditional physical model-based methods, the algorithm is not limited to the specific fire conditions and the structure of tunnels. The main advantage and contribution of the algorithm is that a novel ant colony optimization (ACO) network is constructed and firstly used to predict the ceiling temperature distribution in the tunnel fire as well as the maximal ceiling temperature based on only some sensors data. In order to verify the effectiveness of the algorithm, full scale burning tests were investigated in the largest fire experiment platform of the utility tunnel at the Tianjin Fire Research Institute, China. In addition, the developed ACO network algorithm has excellent performance by contrast with the commonly used back propagation (BP) neural network algorithm. By compared with the experimental results and the results obtained from the BP neural network algorithm, the ability and the effectiveness of the algorithm were supported. The algorithm can be used to predict the ceiling temperature in the tunnel fires for rapid and efficient fire disaster evaluation.

Effectiveness of Nurse Managers’ Political Skills on Preparedness and Response Activities during Coronavirus Disease 2019 Outbreak

Background: The pandemic of coronavirus disease 2019 placed multifaceted challenges for nurse managers’ achievement of their roles’ activities efficiently. Political skills are the essential tool for communicating successfully, influencing nursing staff’s work, and responding to emergency events and obstacles during crises. Aim: This study aimed to determine the effectiveness of nurse managers’ political skills on preparedness and response activities during coronavirus disease 2019 outbreak. Design: It was utilized a comparative, correlational design. Setting: It was conducted at El Mahalla El Kobra General Hospital in Intensive Care Units, operation, isolation, and inpatient wards. Subjects: It involved all nurse managers (51) and 203 nursing staff. Tools: The study’s questionnaire consisted of three parts; involving demographic data, assessment of preparedness and response activities of COVID-19, and political skills inventory. Results: The majority of NMs had high levels of political skills and were satisfied with their activities during the preparedness and response of Covid-19. Furthermore, the nursing staff was satisfied with nurse managers’ activities of communication & information sharing and education, ethical and legal practice, incident management, and intervention, but not with the activities of preparation and planning, safety & security, and psychological support. Conclusion: Nurse Managers need to possess strong political skills to have the social power of attaining their roles’ activities during the COVID-19 crisis. Recommendations: Developing a periodic in-service training program on political skills, as well preparedness, responses, recovery, and evaluation of disasters.

Study on the Evolution Law of Sandstone Porosity under Soaking-Drying Cycles

In view of the problem whereby the rock masses in water-level fluctuation zones of reservoir banks are susceptible to deterioration due to soaking-drying cycles, the sandstone of a typical landslide in the Three Gorges Reservoir area is selected as the test object to determine the ion concentration of an immersion solution and rock sample via periodic water-rock microscopic images, quantitative back analysis of the porosity changes caused by the dissolution of rock minerals under soaking-drying cycles, and evaluation of the sandstone porosity evolution. The results show that, 1) in the first two soaking-drying cycles, the generation rate of each ion is relatively high, and after 6 soaking-drying cycles, the ion generation rate tends to remain stable. 2) The volume reduction in calcite is the largest, followed by that in albite and potash feldspar. 3) Small porosity changes result in a considerable decrease in the compressive strength. 4) Sandstone porosity increase leads to an increase in rock pores, which is manifested as a deterioration of the macroscopic rock mechanical properties. Relevant research results could provide a reliable data basis for the study of large-scale reservoir bank slope protection and geological disaster evaluation and prediction.

A novel comprehensive quantitative method for various geological disaster evaluations in underground engineering: multidimensional finite interval cloud model (MFICM)

The risk prediction of geological disasters exhibits extreme uncertainty and complexity due to the random distribution of evaluation indexes within a finite interval. To improve the accuracy of disaster prediction results, a new evaluation method is in this study suggested to perform a risk evaluation of geological disasters based on multidimensional finite interval cloud model (MFICM) and combination weighting. The MFICM with a transformation between qualitative concept and quantitative data depicts uncertainties and actual distribution features of indexes in the finite interval. Analytic hierarchy process and principal component analysis are adopted to determine the subjective and objective weights of evaluation indexes, respectively, and the combination weight is calculated by a linear method to reduce the influence of subjective factors. The numerical characteristic parameters of each indexes belonging to various risk levels are first calculated based on established evaluation index system. Subsequently, a multi-dimensional finite interval cloud is generated from a forward cloud generator using MATLAB software. Finally, the comprehensive certainty degrees relative to different levels for each sample are determined combined with combination weight, which achieves a mapping of uncertainty between semantic variables and index values. The proposed method is applied to engineering cases regarding three geological disasters, i.e., water inrush, rock burst and collapse. The obtained results with accuracy and results compared with reference methods show that the MFICM is verified to be practical and universal for the risk evaluation of geological disasters, which improves and enriches the theoretical framework of geotechnical engineering disaster risk evaluation.