Comprehensive Risk Assessment Model Research Articles

Exploring two-decadal risk variability of drought-flood abrupt alternation in a high-plateau basin

The escalating intensity and frequency of drought-flood abrupt alternation (DFAA) events, intensified by climate change, pose a significant threat to both the eco-environment and human life. Despite their evident impacts, the risk associated with DFAA events has received limited attention. In this study, we present a comprehensive risk assessment model incorporating multifold natural and anthropogenic indicators to analyze the spatiotemporal variations of DFAA risk. Utilizing a DFAA index (DFAAI) based on river flow data from seven monitoring stations spanning 1999 to 2019, we investigate DFAA events in the Dianchi Basin, the largest lake in the Yunnan-Guizhou Plateau, China. Our analysis reveals that 56% of total DFAA events occurred between June and August, with drought-to-flood (DTF) events accounting for 62.5% of DFAA occurrences in the Dianchi Basin. Notably, the intensity and frequency of DFAA events exhibit significant spatial variations across the basin. The hazard, vulnerability, exposure, and disaster defense indices further exhibit substantial spatial discrepancies, with hazard indicators being the most influential, notably the DFAA rate accounting for 72.4% of the overall impact. Spatial correlations in DFAA risk among monitoring stations were found to be low, primarily influenced by variations in meteorological conditions, underlying surface characteristics, and human activities. The findings of this study can offer valuable insights for effective water resource management in an ever-changing environment.

Research on a Model for an Empirical Analysis of Inherent Defect Insurance Based on Ruin Theory

The system of inherent defect insurance is an important measure to serve the real economy through financial means and improve the quality of construction projects, which is the future development direction of China’s construction industry. However, the related research is not perfect due to the short implementation of the insurance. This could bring risks to the promotion of insurance for companies. Insurance ruin theory is an important method in risk management theory, so adopting it to manage the risk inherent defect insurance from the perspective of insurance companies is vital. The research starts with the classic insurance ruin theory and determines the coefficient of premium collection from the perspective of claim settlement distribution expectations. Furthermore, the approximate distribution of claim settlement is deduced, and a comprehensive risk assessment model is constructed. Finally, based on the data of insurance actuarial practice in Shanghai, both the ruin probability of inherent defect insurance in each insuring term and its average required initial reserve are calculated, which provides the analyses on the risks and main subitems of inherent defect insurance as well as relevant suggestions. Finally, the sensitivity analysis is used to further analyse the risk of different insurance stages of IDI, and the relevant measures are proposed. The research can provide theoretical assistances for insurance companies to carry out effective risk management and provide model tools to make scientific decisions.

Safety Risk Assessment of Low-Volume Road Segments on the Tibetan Plateau Using UAV LiDAR Data

The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters.

Urban flood risk assessment using AHP and geospatial techniques in swat Pakistan

The rapid urbanization and changing climate patterns in Swat, Pakistan have increased the vulnerability of urban areas to flood events. Accurate assessment of flood risk is crucial for effective urban planning and disaster management. In current research study flood hazard index was developed using analytic hierarchy process (AHP) technique in combination with the geographical information system (GIS) environment in Swat, Pakistan. The study integrates various data sources, including topographic maps, land use/land cover information, rainfall data, and infrastructure data, to develop a comprehensive flood risk assessment model. The weights obtained from the AHP analysis are combined with geospatial data using a geographic information system (GIS) to generate flood risk maps. The flood hazard levels were categorized into five distinct classes: very low, low, moderate, high, and very high. Using the GIS-AHP approach, higher weights were assigned to rainfall, distance to river, elevation, and slope in comparison to NDVI, TWI, LULC, curvature, and soil type. The flood hazard map was then reclassified for each parameter. By overlaying these maps, it was determined that 5.6% of the total area is classified as very high flood risk, 52% as high risk, 39.3% as moderate risk, and 3.1% as low risk. The developed comprehensive flood risk assessment model in current study can identify high-risk areas, prioritize mitigation measures, and aid in effective urban planning and disaster management.

Spatial layout siting method for fire stations based on comprehensive forest fire risk distribution

In order to shorten the response time of forest fire (FF) rescue and improve the efficiency of forest fire prevention and control, this paper proposes a fire station spatial layout location method based on the spatial distribution of FF comprehensive risk. A comprehensive forest fire risk assessment model has been developed by three perspectives, namely the risk factors of forest fires, exposure degree of the disaster-bearing body, and vulnerability of the disaster-bearing body. The location-assignment model was introduced to scientifically plan the spatial layout of FF stations from different perspectives. Zhuhai in Guangdong Province, China, a typical “city surrounded by mountains,” was chosen as the research site for case analysis. The results show that the high-risk areas of Zhuhai are mainly concentrated in the forests of Xiangzhou District and Gaoxin District; the medium-risk areas mainly lie in the forests in the north of Gaoxin District, the forests of Hengqin Zone and some forests of Doumen District. Through a combination of the minimized facility point model and the maximized coverage model, it is calculated that 9 FF stations are the optimal choice, which can meet Zhuhai's demand for FF prevention and control with an acceptable construction cost and good coverage.

Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas.

Groundwater Risk Assessment Based on DRASTIC and Special Vulnerability of Solidified/Stabilized Heavy-Metal-Contaminated Sites

Solidification/stabilization technology is commonly used in the remediation of heavy-metal-contaminated sites, which reduces the leaching capacity of heavy metals, but the total amount of heavy metals in the soil is not reduced, there is still a risk of heavy metal re-release and contamination of groundwater, and the risk of groundwater contamination of solidified/stabilized heavy-metal-contaminated sites needs to be assessed. Through the analysis of the system structure of solidified/stabilized heavy-metal-contaminated sites, combined with the integration method of pollution sources—the vadose zone-aquifer, based on the DRASTIC model and the special vulnerability of the solidification/stabilization site, a groundwater pollution risk assessment index system including 4 influencing factors such as site hazard, pollutant hazard, aquifer vulnerability, and natural conditions and a total of 18 evaluation indexes was constructed. Each evaluation index was graded and assigned a scoring value combined with the Analytic Hierarchy Process (AHP) to calculate index weights. The comprehensive weights of site hazard, contaminant stability, aquifer vulnerability, and natural conditions were 0.1894, 0.3508, 0.3508, and 0.1090, respectively. The isometric method was used to classify the pollution risk into five risk levels (very low risk [0, 2), low risk [2, 4), medium risk [4, 6), high risk [6, 8), and very high risk [8, 10]), and a groundwater comprehensive index pollution risk assessment model was established. The model was applied to the actual site. The results showed that under the scenario of direct landfill of remediated soil, the comprehensive indexes of groundwater pollution risk for As and Cd were 4.55 and 4.58, respectively, both of which were medium risk. When the surrounding protective measures were supplemented, the comprehensive indexes of groundwater pollution risk for As and Cd were 3.98 and 4.02, respectively. Cd remained as medium risk and As as low risk. In both scenarios, the combined groundwater contamination risk index of Cd was greater than that of As because the contaminant stability of As was higher than that of Cd. The average percentage of aquifer vulnerability score reached 45.50%, which was higher than the weight of site inherent vulnerability of 35.08%, indicating that the original site hydrogeological conditions are fragile, groundwater is vulnerable to contamination, and the in situ landfill solidification/stabilization of soil is at risk. In order to further reduce the risk, the topographic slope was increased, thereby increasing the surface drainage capacity, which reduced the combined groundwater contamination risk index for As and Cd to 3.94 and 3.90, both of which were low risk. This study provides a new method for assessing the risk of groundwater contamination at solidified/stabilized heavy-metal-contaminated sites. It also has reference significance for selecting solidification/stabilization remediation parameters

Meteorological hazard risk assessment of offshore wind power grids based on CVAR method

Wind energy is a clean, renewable and green energy. Compared to land-based wind power, offshore wind power does not occupy land resources. Meanwhile offshore wind resources is much richer and its wind direction is more stable. Meteorological hazards have a major impact on offshore wind facilities. Typhoons, lightning, rainstorms and other meteorological hazards that occur during operation can not only cause fatal damage to offshore wind facilities, but often also trigger a chain reaction of marine hazards, making them more difficult to maintain. In this paper, we analyze the impact of various types of meteorological hazards on offshore wind facilities with examples and select the most important ones. A comprehensive risk assessment model CVAR for offshore wind projects is developed in combination with other types of risk elements to predict the damaging of offshore wind facilities so that managers can respond in a timely manner. In addition, A nonlinear stepwise regression (NLSR) approach is then used to forecast the meteorological elements in offshore grid facilities.

Risk Assessment of China Rapeseed Supply Chain and Policy Suggestions.

Rapeseed, as the most important oil crop in the world, not only affects national food security but also affects energy security and environmental security. It is very important to conduct a risk assessment of China's rapeseed supply chain and put forward suggestions to construct a safe, effective, and accessible supply chain. In order to accurately evaluate the safety of the rapeseed supply chain from 2010 to 2020, we applied fuzzy multiconnection theory and analytic hierarchy process model (AHP). A comprehensive risk assessment model for the rapeseed supply chain with two primary indicators and 10 secondary indicators was constructed. By establishing the rapeseed risk evaluation model, we quantitatively analyzed the risk of China's rapeseed supply chain. The domestic risk of production is still high, and the international risk under the high import dependence is alarming. We put forward risk prevention and countermeasures for China's rapeseed supply chain. The results show that China has a large demand for rapeseed products, but the increase in China production is limited and the import from other countries is unstable. The proposed suggestions are designed to optimize and enhance the stability of the rapeseed product's supply chain. It is recommended to continue to consolidate and deepen the cooperation with traditional trading partners such as Germany, Spain, the United States, and Brazil; expand other import sources to build a more diversified and efficient rapeseed product import network and extend the supply chain of rapeseed products. This research can be a basis for making decisions for promoting the sustainable and efficient development of the rapeseed supply chain.

A comprehensive multi‐hazard risk assessment model for an urban agglomeration with multiple factors

To assist the Department of Emergency Management in understanding the overall risk characteristics and situation of an urban agglomeration for a reasonable risk prevention and control strategy, this study developed a comprehensive multi-hazard risk assessment model for an urban agglomeration with multiple factors. The proposed model includes disaster probability and disaster loss sub-models. The model evaluated four types of disaster risk in urban agglomerations: natural disasters, accidental disasters, public health incidents, and social security incidents. In addition, a variety of factors were integrated into the model, including the socioeconomic foundation of urban agglomerations, the oligopoly effect of core cities, historical disaster losses, the effect of disaster chains, the ability of disaster prevention and mitigation, and intercity coordinated rescue capabilities. Finally, the risk assessment model was applied to the Beijing-Tianjin-Hebei urban agglomeration. The assessment results were compared to the distribution of the new coronavirus pneumonia epidemic in the target urban agglomeration. The results showed that after analyzing the risk characteristics and evaluating the risk levels, the model not only showed the comprehensive risk levels and distribution of urban agglomerations but also revealed the high-risk areas and the key points of risk prevention and control. More importantly, the results obtained through the model can facilitate the strategic planning of disaster prevention and mitigation for urban agglomerations.

Semi-quantitative risk assessment of adjacent track accidents on shared-use rail corridors

There are several safety questions associated with operating passenger trains and freight trains on shared-use rail corridors (SRCs). Among them are adjacent track accidents (ATA) in which derailed railroad equipment intrudes upon (“fouls”) adjacent tracks and is struck by another train on adjacent tracks. ATAs can occur in any multiple track territory, but they become more complex and potentially more hazardous on SRCs. ATAs can be broken down into three principal events: the initial derailment, an intrusion, and a train present on an adjacent track. Previous research established a foundation for addressing intrusion risk by qualitatively identifying the risk and potential mitigation measures and conducting preliminary quantitative intrusion probability analysis; however, a gap remains between current research on intrusion risk and a comprehensive risk assessment model for ATAs. This paper presents an index-based, semi-quantitative risk analysis framework to evaluate probability and consequence of ATAs. A new risk index system was developed to evaluate ATA risk by assigning levels of probability to the three principal events and the overall ATA consequence level to different track segments, thereby enabling comparison of relative ATA risk among these track segments. The levels of ATA probability and consequence are determined by various infrastructure, rolling stock, and operational factors identified in this research where each factor contributes risk scores that can be summed and converted to levels of probability and consequence. The magnitude of risk due to each factor is determined by their effect, i.e., whether it increases or decreases the probability and/or consequence. A case study based on a 320-km, modified real-world SRC is presented to demonstrate and validate the model. Higher operating speed, lack of containment or barriers, and higher initial derailment rate all significantly affect ATA risk. The model enables comparisons of the relative ATA risks among different track segments at a resolution not previously achieved. It can also be used to locate high-risk locations (risk hotspots) on a railroad corridor where ATA risk is high. This model also provides information pertinent to future improvements in quantification of ATA risk and research on mitigation measures.

GIS-based risk assessment of agrometeorological disasters of summer maize in northern mountainous area of Sichuan Basin: A case study of Wangcang County, Sichuan Province, China

Meteorological disaster is one of the main factors restricting agriculture development in China. It is important to clarify the risk of summer maize agrometeorological disaster for disaster prevention and mitigation. Based on the natural disaster risk theory, we collected meteorological data and maize yield data from 1981 to 2018 in a typical area in the northern mountainous area of Sichuan Basin (Wangcang County). The main disaster-causing factors affecting summer maize production were determined. A comprehensive agro-meteorological disaster risk assessment model for summer maize was constructed in combination with the sensitivity of pregnant environment and vulnerability of disaster bearing to evaluate the agrometeorological disasters risk of summer maize production in the northern mountainous area of Sichuan Basin. The results showed that during the study period, high temperature in mature period, rainstorm in flowering period, rainstorm in mature period, continuous rain in filling period and drought in booting stage were the main agrometeorological disasters affecting the growth and development of summer maize. The agrometeorological disaster risk of maize generally distributed in a southwest-northeast pattern, with the distribution areas of high-risk and higher-risk areas accounting for half of the total area of Wangcang County. The high-risk areas were mainly located in the southwest of the county, which was basically consistent with the high-value areas of hazard-pregnant environment sensitivity. The low-risk areas were mostly concentrated in the western part of county territory, which were also low-risk areas of high temperature in mature period, rainstorm in mature period, and rainstorm in flowering period disasters.

Decision Analysis of Multifactor Credit Risk Based on Logistic Regression and BP Neural Network

Small, medium, and micro enterprises play an important role in the development of the national economy and are of great significance in promoting technological innovation, relieving employment pressure, facilitating people’s lives, and maintaining social stability. But in China, small, medium, and micro enterprises generally exist in the phenomenon of “financing difficulties.” Therefore, we need to find a method to forecast its credit risk. By using Python, SPSS, and other software, based on a two-component logistic regression model, assisted by multievaluation model and supported by game theory, this paper establishes an innovative comprehensive credit risk assessment model for small, medium, and micro enterprises.

A MULTIMOORA-Based Risk Evaluation Approach for CCUS Projects by Utilizing D Numbers Theory

As the global climate warms, carbon emissions must be reduced in order to alleviate the human climate crisis. Carbon capture, utilization and storage (CCUS) is an emerging technology that can reduce carbon emissions. However, most of the CCUS projects have ended in failure. The reason can be attributed to insufficient risk assessment. To this end, the purpose of this study is to construct a comprehensive risk assessment model for CCUS projects. The main body of this research is divided into two parts. First, in order to evaluate the CCUS project, a risk indicator system is constructed. In what follows, a decision-making framework for risk assessment under the D numbers environment is proposed, including two stages of decision-making preparation and decision-making process. The main task of the preparation stage is to gather evaluation experts and collect decision-making information. In the decision-making stage, this paper takes the D numbers theory as the core (acting on the effective expression and fusion of subjective evaluation information), respectively, proposes the method of determining the weight of risk evaluators, the fusion method of decision-making information from different experts, and the comprehensive decision model based on the MULTIMOORA method. In order to verify the effectiveness of the constructed model, the case of CCUS project site selection in Shengli power plant is analyzed, and the results showed that the third site is the best option. This study finds the importance of a comprehensive and timely risk assessment for the successful implementation of CCUS projects, and suggests that stakeholders carry out a risk assessment of CCUS projects prior to implementation based on the method presented in this paper, so as to improve the success rate.

A comprehensive risk assessment model based on a fuzzy synthetic evaluation approach for green building projects: the case of Vietnam

PurposeRisks in implementing green building (GB) projects have emerged as a significant obstacle for GB development, especially in developing countries. In recent years, both academics and construction practitioners have paid considerable attention to the risks associated with GB. In this study, the authors aimed to create a comprehensive risk assessment model that considers three crucial risk features: impact level, probability of occurrence and risk manageability.Design/methodology/approachIn the research, authors adopted the mean scoring and fuzzy synthetic evaluation method to assess GB risks. Based on expert assessments, this model can determine the significance of risk factors, risk groups and overall risk. Notably, this research applied the proposed model to assess GB risks in Vietnam by surveying 58 GB experienced professionals.FindingsThe findings revealed that GB risks are relatively high in Vietnam, implying that risk management is essential for GB projects to succeed. The results also showed that “lack of experience of GB designers” is the most critical factor, and “human resources risk in the design phase” is the top crucial risk group.Originality/valueThis study contributes a novel and practical model to help practitioners assess risks in GB projects. In addition, this research offers detailed GB risk evaluations in Vietnam and thus could be a valuable reference for construction practitioners and future studies.

A Comprehensive Risk Assessment and Stratification Model of Papillary Thyroid Carcinoma Based on the Autophagy-Related LncRNAs.

BackgroundPapillary thyroid carcinoma (PTC) is one of the most common malignant carcinomas in the endocrine system, and it has a growing incidence worldwide. Despite the development of diagnosis and treatment modalities for thyroid carcinoma, the outcome remains uncertain. Autophagy participates in the process of cancer invasion, malignancy, metastasis, and drug resistance. Emerging research has shown that long noncoding RNAs (lncRNAs) play an important role in the process of different types of cancers. However, the interaction between the process of autophagy and lncRNA and the value of autophagy-related lncRNA for risk assessment, prediction of drug sensitivity, and prognosis prediction in PTC patients remains unknown.Materials and MethodsWe screened 1,283 autophagy-related lncRNAs and identified 144 lncRNAs with prognostic value in The Cancer Genome Atlas (TCGA) cohorts. Univariate and multivariate Cox regression analyses were used to establish the prognosis-related autophagy-related lncRNA risk classification consisting of 10 lncRNAs to indicate the level of risk, according to which the patients were grouped into high-risk group and low risk-group.ResultsThe high-risk group had dramatically worse overall survival compared with the low-risk group. Cox regression analysis was performed to confirm the independent prognostic value of the autophagy-related lncRNA risk stratification, and the time-dependent receiver operating characteristic curves of the risk stratification were 0.981 (1 year), 0.906 (3 years), and 0.963 (5 years). LncRNA CRNDE (LINC00180) is overexpressed in the tumor, and its high expression matched with poorer survival state. So, we chose it for further experiment. Finally, knockdown of the CRNDE in PTC increased the sensitivity to sorafenib.ConclusionCollectively, we successfully established a novel risk stratification for PTC based on the expression profiles of autophagy-related lncRNAs.

Towards the Development of a Comprehensive Lifecycle Risk Assessment Model for Green Roof Implementation

A green roof (GR) provides numerous social, environmental, and personal benefits through its lifespan, while exploiting these benefits is associated with several uncertainties. Since these risks to GR adoption have not yet been investigated and analyzed comprehensively, this research is aimed at developing an original risk assessment model to ensure the objectives of GR adoption are fully achieved. To this end, two novel approaches were employed, namely Monte Carlo-DEMATEL and fuzzy parsimonious analytic network process to determine the inner dependencies among the risk factors and rank them based on their relationships and importance, respectively. The findings showed that “irregular maintenance” and occurrence of “fire” are the most influential threats, while “flash flood reduction” and “achieving green building certificate award” are among the most influential opportunities. Moreover, it was shown that although “tax abatement” and “monetary loss” are the most important opportunity and threat, respectively, the ranking order of risk factors varies among an intensive GR and an extensive GR. Finally, it was concluded that with such analyses, the decision-makers have clear insights on the most influential positive and negative risks for managing purposes. The novel methods used in this research can be replicated to achieve more accurate and efficient results.

Risk assessment method combining complex networks with MCDA for multi-facility risk chain and coupling in UUS

With the acceleration of urban development, the urban underground space (UUS) begins to show the characteristics of a complex giant system. In most of the traditional risk assessment frameworks, metrics are developed by treating the object as a whole. There is a lack of applicable objective-based assessment methods when there are correlations and network structures among the internal structures of the object. Here we propose and test a method, a comprehensive risk assessment model E-CN-TOPSIS applicable to UUS, to solve this problem. First, an initial risk assessment framework with 4 primary indicators and 30 sub-indicators was proposed based on Multi-Criteria Decision Analysis (MCDA). Second, a risk chain analysis network of UUS containing 201 risk nodes based on real cases was constructed. The principal component analysis method was taken to quantify the chain-coupled risk mechanism of multi-facility within UUS to integrate the external risk assessment framework with the internal risk chain network. Then we analyzed 16 main cities in three major economic circles of China. Our analyses indicate that ‘Recovery capability’ was the priority in all evaluation indicators. The main factors influencing the UUS risk in three major economic circles are management policies. The UUS risk is highest in Greater Bay Area, followed by Jing-Jin-Ji, and Yangtze River Delta. Finally, some outlooks and suggestions are given. By applying this methodology, useful information can be provided for the fine planning and precise prevention of new complex UUS risks.

A Semi-Quantitative Risk Assessment and Management Strategies on COVID-19 Infection to Outpatient Health Care Workers in the Post-Pandemic Period.

BackgroundIn the pandemic of COVID-19, due to asymptomatic patients and high personnel fluidity in outpatient clinics, health care workers (HCWs) in outpatients were facing severe threat from infection. There is an urgent need for a risk assessment to recognize and prevent infection risks.PurposeTo establish a semi-quantitative risk assessment model on COVID-19 infections for HCWs in outpatient departments, and apply it to practices. Further to provide infection risk management strategies to reduce infection threats in the post-pandemic of COVID-19.MethodsWe used the method of Brainstorm, Literature study and Analytic Hierarchy Process (AHP) for risk factors selection and model construction, we also created corresponding indicators for each risk factors, in order to collect data in assessment practice.ResultsEighteen risk factors were recognized and selected for model construction, by scatter plot, these risk factors had been classified into four parts, spanned the scopes of diagnosis and treatment, environment, personal protection and emergency handling, with specific management suggestions provided. In the practice, outpatient clinics were divided into three risk levels, 5 clinics in high risk level, 9 in medium risk level and 11 in low risk level.ConclusionA proper comprehensive risk assessment model for COVID-19 infections has been successfully established. With the model, the ability to COVID-19 prevention in outpatients can be easily evaluated. The strategies on disinfection, surveillance and personal protection were also valuable references in the post-pandemic of COVID-19.

Dynamic safety prewarning mechanism of human–machine–environment using computer vision

PurposeThis study provides a safety prewarning mechanism, which includes a comprehensive risk assessment model and a safety prewarning system. The comprehensive risk assessment model is capable of assessing nine safety indicators, which can be categorised into workers’ behaviour, environment and machine-related safety indicators, and the model is embedded in the safety prewarning system. The safety prewarning system can automatically extract safety information from surveillance cameras based on computer vision, assess risks based on the embedded comprehensive risk assessment model, categorise risks into five levels and provide timely suggestions.Design/methodology/approachFirstly, the comprehensive risk assessment model is constructed by adopting grey multihierarchical analysis method. The method combines the Analytic Hierarchy Process (AHP) and the grey clustering evaluation in the grey theory. Expert knowledge, obtained through the questionnaire approach, contributes to set weights of risk indicators and evaluate risks. Secondly, a safety prewarning system is developed, including data acquisition layer, data processing layer and prewarning layer. Computer vision is applied in the system to automatically extract real-time safety information from the surveillance cameras. The safety information is then processed through the comprehensive risk assessment model and categorized into five risk levels. A case study is presented to verify the proposed mechanism.FindingsThrough a case study, the result shows that the proposed mechanism is capable of analyzing integrated human-machine-environment risk, timely categorising risks into five risk levels and providing potential suggestions.Originality/valueThe comprehensive risk assessment model is capable of assessing nine risk indicators, identifying three types of entities, workers, environment and machine on the construction site, presenting the integrated risk based on nine indicators. The proposed mechanism, which adopts expert knowledge through Building Information Modeling (BIM) safety simulation and extracts safety information based on computer vision, can perform a dynamic real-time risk analysis, categorize risks into five risk levels and provide potential suggestions to corresponding risk owners. The proposed mechanism can allow the project manager to take timely actions.