Framework For Quantitative Analysis Research Articles

A BIA-Based Quantitative Framework for Built Physical Asset Criticality Analysis under Sustainability and Resilience

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios.

A New Framework of Quantitative analysis Based on WGAN

This paper follows the logic of financial investment strategies based on WGAN, one of AI algorithms. The trend prediction module and the distribution characteristics of price time series are on the basis of the WGAN. Multiple factors extraction and analysis are on the basis of natural language processing technology. Buy-sell decision module is based on DQN algorithm which is one of reinforcement learning algorithms. And a designed risk control network is used as a protector for capital of investors. A multiple feature combination is proposed to describe the stock market. In the end, four Sector ETFs were selected to make simulation experiments.

The Flash Loan Attack Analysis (FAA) Framework—A Case Study of the Warp Finance Exploitation

Decentralized finance (DeFi) has exploded in popularity with a billion-dollar market cap. While uncollateralized lending, known as a flash loan, emerged from DeFi, it has become a primary tool used by attackers to drain investment tokens from DeFi networks. The existing countermeasures seem practical, but no comprehensive quantitative analysis framework was available to test them. This paper proposes the Flash loan Attack Analysis (FAA) framework, which aids security practitioners in understanding the DeFi system’s effects on preventative methods when various factors change. The quantitative predictions can help security professionals in identifying hidden dangers and more efficiently adopting countermeasure strategies. The simulation predicts that the existing strategy, fair reserves, can fully protect the platform in a typical market environment; however, in a highly volatile market where the token price drops by 60% in a single hour, it will be broken, causing more than $8 million in damage.

Evaluation method of ecological water demand threshold of natural vegetation in arid-region inland river basin based on satellite data

The ecological water demand (EWD) of natural vegetation is a key issue in regional water resource allocation and ecosystem management. Although the evaluation of the EWD of vegetation has been emphasized in numerous studies, research on the threshold EWD of vegetation, including the minimum and optimum EWD, as well as an effective quantitative analysis framework, are still lacking. Therefore, in this study, the evapotranspiration of natural vegetation in the plain area of the Tarim River Basin over the last22 years was estimated based on remote sensing image data. Additionally, the multi-year dynamics of the EWD of natural vegetation were analyzed. In this study, the concept of “latitude” was adopted from studies of ecosystem resilience, to propose a novel analytical framework to measure the minimum and optimal EWD of natural vegetation in inland arid areas. The results show that the average minimum and optimum EWD of natural vegetation is105.45 and 135.53 mm, respectively. The average minimum EWD of woodland and high- and low-coverage grassland is 129.13, 284.38, and 111.40 mm, respectively, and the mean value of the optimum EWD is 152.96, 382.05, and 141.16 mm, respectively. The novel approach of this study is simple and easy to use, and provides an effective method for quantitative evaluation of the EWD threshold of natural vegetation.

A framework for quantitative analysis and differentiated marketing of tourism destination image based on visual content of photos

Photos shared by tourists are being generated at an unprecedented speed, creating new opportunities to study tourism destination images. Nevertheless, little research has focused on the tourist's perception of images from multiple perspectives and how to construct differentiated marketing strategies that link tourists with destinations. With the support of deep-learning technology, we propose herein a quantitative analysis strategy and differentiated marketing framework driven by photo big data which contains images of tourism destinations. We draw on photos of tourism destinations shared by tourists and analyse three aspects of perceived images – composition scene, visual aesthetic quality and visual uniqueness. We further develop a set of objective image-projection schemes that integrate multiple indicators of tourism destinations to improve destination marketing. Finally, we focus on an empirical case study of Wuyuan, China. The results of the study have methodological, theoretical and practical implications for the tourism industry.

Probabilistic fire risk analysis of process pipelines

Pool fires are considered catastrophic events in hydrocarbon liquid process pipelines. There is much research performed on fire risk analysis of liquid pipelines; however, probabilistic-quantitative methods that are based on the leak size are yet to be further developed. This paper develops a framework for quantitative fire risk analysis in liquid process pipelines based on probabilistic analysis and computational fluid dynamics. The significance of leaked volume and its dependency on the leak size, here the pipeline failure frequency is distributed into the frequencies of different leak sizes using historical data and the Bayesian network. The frequency of fire occurrence is then determined by combining the ignition probability of each leakage rate, the flammability specification of released materials, and the frequency of each leak size. In the vulnerability assessment, the thermal dose received by individuals is modified to account for the possibility of escape. The developed framework is next employed in a gasoline pipeline with a 1 km length. Results show that the pool fire scenario resulting from the rupture of the pipeline, with a frequency of 1.18 × 10−7 (km−1. y−1) and an area of exposure of 5498.4 m2, is the maximum-credible scenario. Two separate escape routes with different distances to the fire center are defined. As well, two escape speeds of 4 and 6 m/s are considered. The results show that the initial radiation and the escape speed can significantly affect the sustained damage to individuals. This study is a step forward in dynamic-quantitative individual risk analysis in pool fires of hydrocarbon liquid pipelines.

Integrating river health into the supply and demand management framework for river basin ecosystem services

Healthy rivers provide many beneficial ecosystem services (ESs) to humans in their basins, necessary to achieve high-quality human well-being. The relationship between the supply and demand of ESs in the basin can reflect the coordination level of the basin's ecology and economy. A clear understanding of the relations between ESs supply and demand is the foundation for sustainable river basin ecosystem management. However, there are differences in the health state of different rivers. Unhealthy rivers may be unable to effectively meet the needs of ecosystem services in their basins, thus threatening the ecological health state and sustainable economic development in the basin. Therefore, in this study, a quantitative analysis framework for the supply and demand of river basin ecosystem services (RBES) based on river health states was developed to achieve the basin's rational utilization and sustainable development. Guangdong Province of China was selected, the supply and demand of four ESs relevant to multi-stakeholders (water retention, soil retention, carbon storage, and water purification) in four representative sub-basins were quantified. The ecological supply-demand ratio (ESDR) was introduced to reflect the surplus or shortage of ESs at different river health states. The results showed that the study area exhibited a spatial mismatch between ESs supply and demand. The ESDR of water retention, soil retention, and water purification services in healthy sub-basins were higher than sub-healthy ones, indicating a certain synergy between the health state of the river and its supply and demand state of ES. Implementing targeted spatial management and control is an important measure to ensure the sustainable development of river basins. This study can provide references for sustainable management of river basins and targeted governance under different river health states within and beyond China.

Ecological and environmental effects of land-use changes in the Loess Plateau of China

<p indent="0mm">The Loess Plateau (LP) is a typical ecologically fragile area in China, with severe water shortage and soil erosion. Understanding the interactions between land use structure and ecological processes is the basis for effective soil and water conservation. Since the implementation of large-scale vegetation restoration programs, changes in carbon and water services, as well as trade-offs in these services have become a new scientific challenge in this region. A comprehensive analytical paradigm of “pattern–process–service” was developed by our research group to determine the ecological and environmental effects of land-use changes and support ecological restoration practices. First, by combining landscape pattern, ecological process, and scale, we studied the effects of land-use changes on soil moisture, nutrients, and erosion at multiple spatial scales of plot, transect, catchment, and region, and illustrated the mechanisms of the interactions between land-use structure and ecological processes in the LP systematically. These works have revealed the relationships among ecosystem structure, land-use structure, vegetation functional traits, vegetation biomass, ecological processes (e.g., hydrological processes and nutrient cycling) and ecosystem services. They have also identified the key roles of soil water content and understory plant diversity in ecosystem service maintenance in dryland ecosystems. Second, by linking ecological processes and ecosystem services, we studied the formation and interaction mechanisms of ecosystem services. A sediment attribution diagnostic method focusing on soil retention services was developed to diagnose the contributions of multiple factors to changes in the sediment loads of rivers. Application of this method quantitatively revealed the drivers (climate change, vegetation restoration, and ecological engineering) and mechanisms underlying the drastic reduction in the sediment load of the Yellow River over the past <sc>60 years.</sc> The trade-off between carbon sequestration and water yield was quantitatively revealed, and its temporal and spatial variations during vegetation restoration were detected. The threshold of forest restoration potential within the limit of annual precipitation, as well as the turning point from soil carbon source to soil carbon sink following farmland abandonment, was identified. By considering water consumption for both vegetation growth and socioeconomic uses, a quantitative coupling analysis framework for the sustainable use of water resources in the natural–social–economic system of the LP was constructed, and regional water-resource carrying capacities for vegetation restoration in the region under various scenarios were determined; the results suggest that current vegetation restoration is approaching sustainable water resource limits. Third, on the basis of an understanding of the relationships between ecosystem processes and services at multiple scales, a spatially explicit assessment and optimization tool for regional ecosystem services was developed to reveal the spatiotemporal dynamics of ecosystem services on the LP, including water yield, soil retention, carbon sequestration, and grain production. By combining scenario analysis and ecosystem service quantification models with a spatial optimization algorithm, this new decision-supporting tool is capable of spatially optimizing multiple ecosystem services. This has contributed crucially to the advancement of integrated research into regional ecosystem services with the aim of quantifying, modeling, and supporting the rational planning and effective implementation of ecological projects. These findings have promoted the development of physical geography from qualitative structure description to quantitative process research. They have also led ecosystem services research into ecological processes and mechanisms and their comprehensive integration and have provided a key scientific basis for ecological restoration of the LP.

Unraveling multi-state molecular dynamics in single-molecule FRET experiments. II. Quantitative analysis of multi-state kinetic networks.

Single-molecule Förster Resonance Energy Transfer (smFRET) experiments are ideally suited to resolve the structural dynamics of biomolecules. A significant challenge to date is capturing and quantifying the exchange between multiple conformational states, mainly when these dynamics occur on the sub-millisecond timescale. Many methods for quantitative analysis are challenged if more than two states are involved, and the appropriate choice of the number of states in the kinetic network is difficult. An additional complication arises if dynamically active molecules coexist with pseudo-static molecules in similar conformational states with undistinguishable Förster Resonance Energy Transfer (FRET) efficiencies. To address these problems, we developed a quantitative integrative analysis framework that combines the information from FRET-lines that relate average fluorescence lifetimes and intensities in two-dimensional burst frequency histograms, fluorescence decays obtained by time-correlated single-photon-counting, photon distribution analysis of the intensities, and fluorescence correlation spectroscopy. Individually, these methodologies provide ambiguous results for the characterization of dynamics in complex kinetic networks. However, the global analysis approach enables accurate determination of the number of states, their kinetic connectivity, the transition rate constants, and species fractions. To challenge the potential of smFRET experiments for studying multi-state kinetic networks, we apply our integrative framework using a set of synthetic data for three-state systems with different kinetic connectivity and exchange rates. Our methodology paves the way toward an integrated analysis of multiparameter smFRET experiments that spans all dimensions of the experimental data. Finally, we propose a workflow for the analysis and show examples that demonstrate the usefulness of this toolkit for dynamic structural biology.

Investigation of Pre-service Teachers’ Readiness Levels for Online Learning and Engagement Levels in the Online Environment

The aim of this study was to examine pre-service teachers’ readiness levels for online learning and engagement levels in the online environment in terms of various variables and to determine the relationship between readiness levels for online learning and engagement levels in the online environment. The design of the research is the correlational survey model, one of the quantitative research designs. The study group of the research consisted of pre-service teachers who were studying in the faculty of education at a state university in Turkey. In the study, a personal information form, the “Online Learning Readiness Scale” and the “Student Engagement Scale” were used as measurement tools. Within the framework of quantitative data analysis, descriptive statistical techniques, Mann-Whitney U test, Kruskal-Wallis H test and Pearson correlation technique were used. When the research findings were examined, it was seen that the pre-service teachers’ readiness for online learning was high in all dimensions; when it comes to engagement in the online environment, it was observed that they obtained high mean scores in the total scale, and the behavioral engagement and cognitive engagement dimensions, while they obtained a moderate mean score in the affective engagement dimension. In addition, it is one of the important results of the study that there was a medium-level, positive and significant relationship between pre-service teachers’ readiness levels for online learning and engagement levels in the online environment.

A framework for quantitative analysis of the causation of grounding accidents in arctic shipping

The melting of Arctic Sea ice has significantly facilitated Arctic shipping. However, such increased shipping has brought about higher maritime accidents in Arctic waters, especially for grounding and fire/explosion accidents. The paper presents a framework for quantitative analysis of the causation of grounding accidents in Arctic shipping by developing an accident map (AcciMap) - Bayesian network (BN) model. First, the potential risk factors for grounding accidents in the Arctic shipping were identified according to 322 maritime accident investigation reports (MAIRs) - 299 global MAIRs of grounding accidents (including 5 in Arctic waters) and 23 MAIRs (except grounding accidents) in Arctic waters and related literature. Consequently, an AcciMap model is developed for describing the evolution of grounding accident scenarios and reflecting the interdependency of the identified risk factors. Then, a probabilistic model is proposed to evaluate the probability and severity of the grounding accident for presenting a convincing justification for risk control options (RCOs). The framework is applied for the quantitative analysis of a cruise ship grounding accident in Arctic waters. Results demonstrate (1) improved understanding of cruise ship grounding risk factors related to government supervision, shipping company management, technical and operational management, unsafe incidents and behaviors, and environmental conditions; (2) quantitative analysis of the evolution of grounding accident and better identification of the critical risk factors; (3) determination of RCOs for risk management in Arctic shipping.

Enhancing decision knowledge by developing a quantitative framework for holistic exploratory acquisition policy analysis

An acquisition policy's diverse stakeholders may have heterogeneous needs. Furthermore, stakeholders may be unsure of their preferences due to a highly qualitative analysis of alternatives in the early stages of the acquisition policy development process. Lacking system knowledge and evidence, stakeholders may push back policy priorities or delay decisions. In such cases, it is necessary to increase stakeholders’ knowledge about the acquisition policy options’ impacts. This study proposes a systematic and quantitative framework development process and outlines a holistic exploratory policy analysis. Stakeholders’ needs are systematically identified and measurement areas and elements are derived from those needs. Evaluation methods are then created through a sensible quantification process to measure the impact of introducing each alternative. The framework provides information on the effects of various policy options, an analysis of each alternative's most crucial policy option, and other valuable analysis results. Finally, we present a case study to illustrate the development process and application of the framework when introducing a new military training system such as Live, Virtual, and Constructive synthetic training systems.

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

Correlation between land use spatial and functional transition: a case study of Shaanxi Province, China

Transitioning to a more optimal land use morphology contributes to the sustainable use of land resources. Land use transition refers to the land use morphology changes from one to another, which should be portrayed from both correlated spatial morphology and functional morphology. However, the current studies have the problem of separating the land use spatial and functional transition, which causes the degree and the mechanism of the correlation between them to be still unclear. Taking Shaanxi Province as a case study, based on the land use data and socio-economic data, this study used the sustainability evaluation model of land use spatial transition, the multifunctional coupling coordination model of land use, and the grey correlation model to quantify the correlation between the land use spatial and functional transition. The results showed that the correlation between land use spatial and functional transition was high in Shaanxi Province during 1980–2018, and the change in correlation had an interdecadal cycle, which was embodied in the correlation reached extremely high when a positive land use functional transition occurred. The correlation between land use spatial transition and the change of land use ecological function was extremely high, and the correlation with both production and living function was high. The high correlation between the land use spatial and functional transition was essentially a strong driving force, bound by the vulnerability of land ecology and strengthened by the rapidly changing demands of production and living. It can drive the land use spatial morphology transition to another that can give full play to the multifunctionality of land use. The study established a quantitative analysis framework for the correlation between land use spatial and functional transition, which provided quantitative evidence for the regional land use transition theory and contributed to formulating rational land use policies to achieve regional sustainable development.

A quantitative analysis framework for water-food-energy nexus in an agricultural watershed using WEAP-MODFLOW

The challenges of securing safe water supply, sustainable food production and energy conservation make quantitatively analysis of the water-food-energy (WFE) nexus in agricultural watersheds a very important issue. At present, understanding of the complex interactions among the WFE nexus in agricultural watersheds considering the conjunctive management of surface water and groundwater are limited. Based on the WEAP-MODFLOW, this paper proposed a quantitative analysis framework to analyze the interactions among WFE nexus in agricultural watersheds, which considers various scenarios such as water-saving irrigation and drought. The results indicated that this framework can sensitively capture the existing trade-offs and synergies to identify possible comprehensive measures to ensure safe access to water, food, and energy in agricultural watersheds especially in drought conditions. It could also provide valuable assistance for managers and policymakers to think beyond sectors to increase the resilience of WFE sectors under changing environment. Our results show that the impacts of drought have reduced the amount of surface water irrigation due to limited surface water supply, resulting in more than doubled groundwater extraction which may pose a crisis for sustainable use of groundwater, and an increase of 47% in energy consumption for pumping. After integrated management measures, food production rose by 6% while the groundwater level was restored by 0.48 m compared to that under drought, and energy consumption was reduced by 3% compared with water-saving irrigation only. The sustainability index results under different scenarios also reflect the higher sustainability of integrated measures management under drought impacts (SI = 0.13).

Merit Sticks to Men: Gender Pay Gaps and (In)equality at UK Russell Group Universities

Academic studies of gender pay gaps within higher education institutions have consistently found pay differences. However, theory on how organisation-level factors contribute to pay gaps is underdeveloped. Using a framework of relational inequalities and advanced quantitative analysis, this paper makes a case that gender pay gaps are based on organisation-level interpretations and associated management practices to reward ‘merit’ that perpetuate inequalities. Payroll data of academic staff within two UK Russell Group universities (N = 1,998 and 1,789) with seeming best-practice formal pay systems are analysed to determine causes of gender pay gaps. We find marked similarities between universities. Most of the variability is attributed to factors of job segregation and human capital, however we also delineate a set of demographic characteristics that, when combined, are highly rewarded without explanation. Based on our analysis of the recognition of ‘merit,’ we extend theoretical explanations of gender pay gap causes to incorporate organisation-level practices.

A methodological framework for quantitative risk analysis in container shipping operations

PurposeContainer shipping is a crucial component of the global supply chain that is affected by a large range of operational risks with high uncertainty, threatening the stability of service, manufacture, distribution and profitability of involved parties. However, quantitative risk analysis (QRA) of container shipping operational risk (CSOR) is being obstructed by the lack of a well-established theoretical structure to guide deeper research efforts. This paper proposes a methodological framework to strengthen the quality and reliability of CSOR analysis (CSORA).Design/methodology/approachFocusing on addressing uncertainties, the framework establishes a solid, overarching and updated basis for quantitative CSORA. The framework consists of clearly defined elements and processes, including knowledge establishing, information gathering, aggregating multiple sources of data (social/deliberative and mathematical/statistical), calculating risk and uncertainty level and presenting and interpreting quantified results. The framework is applied in a case study of three container shipping companies in Vietnam.FindingsVarious methodological contributions were rendered regarding CSOR characteristics, settings of analysis models, handling of uncertainties and result interpretation. The empirical study also generated valuable managerial implications regarding CSOR management policies.Originality/valueThis paper fills the gap of an updated framework for CSORA considering the recent advancements of container shipping operations and risk management. The framework can be used by both practitioners as a tool for CSORA and scholars as a test bench to facilitate the comparison and development of QRA models.

DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative.

Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.

Effects of duty cycles on passive acoustic monitoring of southern resident killer whale (Orcinus orca) occurrence and behavior

Long-term passive acoustic monitoring of cetaceans is frequently limited by the data storage capacity and battery life of the recording system. Duty cycles are a mechanism for subsampling during the recording process that facilitates long-term passive acoustic studies. While duty cycles are often used, there has been little investigation on the impact that this approach has on the ability to answer questions about a species' behavior and occurrence. In this study, the effects of duty cycling on the acoustic detection of southern resident killer whales (SRKW) (Orcinus orca) were investigated. Continuous acoustic data were subsampled to create 288 subsampled datasets with cycle lengths from 5 to 180 min and listening proportions from 1% to 67%. Duty cycles had little effect on the detection of the daily presence of SRKW, especially when using cycle lengths of less than an hour. However, cycle lengths of 15–30 min and listening proportions of at least 33% were required to accurately calculate durations of acoustic bouts and identify those bouts to ecotype. These results show that the optimal duty cycle depends on the scale of the research question and provide a framework for quantitative analysis of duty cycles for other marine species.

Quantifying the evolutionary mechanism of COVID-19 impact on international construction multi-projects: a risk driver perspective

Purpose The purpose of this paper is to quantify the evolutionary mechanism of coronavirus disease 2019 (COVID-19) impact on international construction multi-projects.Design/methodology/approach From three concepts of complexity, stressor and detractor risks, a multi-project simulation model under COVID-19 outbreak is proposed to study the characteristics of contingency with three peaks of ordered and disordered states. Specially, COVID-19 brings forth mitigation fee, epidemic prevention fee, holdup fee and schedule delay fee of multi-projects. By integrating parametric model, Monte Carlo and chaos theory, a comparative analysis of its contingency with or without COVID-19 is conducted. Summarizing the simulated results, their total contingencies at certain risk tolerance are obtained at two status of static at one-time point and dynamic over time. Meanwhile, some major risks including detractors, complexities and stressors are screened out for mitigation, especially for epidemic prevention and control. Eventually, the real case is illustrated to demonstrate its validity.Findings It provides a quantitative analysis framework for the impact of epidemic, a once-in-a-century black swan event with a long tail, on construction multi-projects.Practical implications It conduct an effective model to quantify impacts of COVID-19 on international construction multi-projects for implementing effective counter-measures, which lay foundation for claims among different stakeholders.Originality/value The term of detractor risk is applied to describe COVID-19 and quantify its impact upon international construction multi-projects. Further, a hybrid model by integrating parametric model and Monte Carlo in type I/II model is proposed to simulate their contingencies at disordered states. Finally, the simulated outcomes of these models are used to guide effective risk control to meet the requirements by the client.