Risk-based Ranking Research Articles

An index-based multi-hazard risk assessment method for prioritisation of existing bridge portfolios

In recent years, several catastrophic collapses of existing bridges have highlighted the need for rapid risk analysis methods aimed at supporting infrastructure managers in the prioritisation of detailed assessments and, if any, risk mitigation actions. A large percentage of existing road bridges were built between the 1960s and 1980s, having thus already reached or even exceeded their design lifetime. Several studies have also shown that bridges often collapse due to either natural or human-related events, such as floods, collisions or overloading that, in addition to earthquakes, should be duly considered in risk assessment. This calls for multi-hazard approaches that provide an integrated perspective of the risk of bridge portfolios, to identify critical structures to support decision-makers. This study proposes a multi-hazard risk-based prioritisation methodology for application to a large number of bridges under limited level of knowledge. Specifically, the risk level is quantified through indices, accounting for uncertainties, that are used for comparative purposes among bridges. The methodology is applied to a highway bridge portfolio located in northern Italy, producing a risk-based ranking that is critically discussed. Analysis results are then compared with the outcome of the current Italian guidelines for safety assessment and maintenance of existing bridges.

Risk based maintenance for Swiss railway bridges: concept, implementation and first experiences

The Swiss Federal Railway (SBB) has an inventory of approx. 6,000 bridges. So far, condition classes of the individual bridges have been used for maintenance management. To improve the efficiency of the maintenance management, a long-term change from the condition class-based approach to a risk-based approach is considered. Such a risk-based approach was developed and implemented during a two-stage process into software (Excel and Python). The software was also linked to the SBB databases to access the relevant data. The Python software now includes 12 parameters to adapt the initial failure probability to specific bridge conditions and 15 damage parameters. So almost 30 parameters are used to compute the risk for each bridge. The software is also linked to geographical maps to show the location of the bridges. Besides the development of the approach, also the first experience of the application of this methodology will be discussed. For example, the risk-based ranking of the bridges clearly showed that specific bridge types are dominating. Also, some original ideas and concepts were not applicable due to difficulties in providing the required input data. However, currently the risk-based bridge ranking complies well with former investigations carried out by hand.

Risk-based prioritization of organic substances in the Canadian National Pollutant Release Inventory using an evaluative regional-scale multimedia mass balance model.

The National Pollutant Release Inventory (NPRI) is a Canadian inventory of facility-reported data on releases, transfers, and disposals of over 300 pollutants, but it does not contain information on chemical properties or other characteristics critical to understanding environmental and human health risks. To reconcile this gap, we use the Risk Assessment IDentification And Ranking (RAIDAR) model to integrate NPRI release data with chemical property information in a multimedia mass balance model to combine exposure estimates with toxicity hazard data yielding an estimate of risk for 198 NPRI organic substances reported in 2010-2019. The presented case study further corroborates the hypothesis that risk-based ranking gives rise to different chemical priorities versus ranking based on release quantity alone. Chemicals like propane and hexane (except n-hexane) are in the top 10 highest-ranked organic substances based on emission quantities reported to NPRI but are ranked outside the top 10 based on corresponding regional-scale risk estimates. On the contrary, dioxins and furans are ranked very low based on emissions quantities reported to NPRI but are ranked higher based on corresponding risk estimates. The results also suggest that although quantities of some NPRI organic pollutant releases change over time, the ensuing risk estimates are not always directly proportional to these changes. This can be explained by changes in mode of entry to the environment that can influence the overall fate and exposure of the same chemicals, highlighting the complex dynamics that can occur when simulating fate and risk as opposed to quantity alone. Limitations are discussed and recommendations are provided for improving the priority setting methods, including reducing the uncertainty of the NPRI data and the need for multimedia models to address point source emissions. Integr Environ Assess Manag 2022;18:1722-1732. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

AN AHP APPLICATION FOR FAILURE RISK-BASED RANKING OF ELECTRIC VEHICLE PROJECTS

In order to address challenges in the sustainable development of transportation, economy, and environment, governments along with conventional automobile manufacturers and consumers are extremely interested in the development of the electric vehicle (EV) manufacturing industry and market. However, many manufacturers are worried about entering the EV market because of some of the limitations of EVs and government economic policies. A framework for failure risk-based ranking of EV projects is proposed that applies the Analytic Hierarchy Process (AHP) as a method of ranking. The hierarchy structure of the AHP is created with the risk categories, risk factors, and EV project candidates at different levels of the decision. By specifying the failure risk categories and failure risk factors, the ranking of EV project failure risks and the EV projects are accomplished via the pairwise comparison in the AHP. The results from the ranking provide useful information for planning and decision making. In fact, the results of the proposed method make it possible to specify the EV projects that are feasible to carry out and to compare the various projects at the technical and economic level.

Cyberthreat risk identification based on constructing entity-event ontologies from publicly available texts

Aim . Out of the currently used methods of ensuring cyber security the most productive ones are traffic analysis, malware detection, denial of unauthorized access to internal networks, incident analysis and other methods of corporate perimeter protection. The efficiency of such methods however depends on the timeliness and quality of threat data. The Aim of the paper is to study the ways of improving the cyber threat awareness and capabilities to analyze texts in open sources for the purpose of cyberattack prediction, identification and monitoring of new threats, detection of zero-day vulnerabilities before they are made public and leaks are discovered. Methods. Publicly available knowledge on cyber security is acquired through continuous collection of data from the Internet (including fragments of its non-indexed part and specialized sources) and other public data networks (including a large number of specialized resources and sites in the TOR network). The collected texts in various languages are analyzed using methods of natural language processing for the purpose of extracting entities and events that are then grouped into canonical entities and events, and all of that information is used for continuous updating of a subject-matter event-entity ontology. It includes general forms of entities and events required for the context and specialized forms of events and entities for purposes of cyber security (technical identifiers, attack vectors, attack surfaces, hashes, identifiers, etc.) Such ontology can function as a knowledge base and be used for structured queries by cyber security analysts. Results. The proposed method and the system based upon it can be used for analyzing computer security information, monitoring, detection of zero-day vulnerabilities before they are made public and leaks are discovered. The information retrieved by the system can be used as highly informative features in statistical models. The latter served as the basis for a classifier that defines the risk of exploits for a specific vulnerability, as well as an IP address scoring system that can be used for automatic blocking. Additionally, a method was developed for risk-based ranking of events and entities associated with cyber threats that allows identifying – within the abundance of available information – the entities and events that require special attention, as well as taking timely and appropriate preventive measures. Conclusion. The proposed method is of direct practical value as regards the problems of analytics, risk-based ranking and monitoring of cyber threats, and can be used for the analysis of large volumes of text-based information and creation of informative features for improving the quality of machine learning models used in computer security.

Development of a Risk Assessment Module for Bridge Management Systems in New Jersey

Bridges are critical for the mobility of our society and its economic growth. Available funds for bridge repair, maintenance, and rehabilitation are limited. The Moving Ahead for Progress in the 21st Century Act (MAP-21) introduced several new parameters for improving the management of bridge assets, such as bridge element evaluation, life-cycle analysis, and risk-based performance indicators. Risk-based methods account for the uncertainties embedded into engineering variables and long-term evaluations. The objective of this paper is to identify, assess, and quantify structural risk components to bridges using probabilistic risk methodologies and data from the National Bridge Inventory database. The aim is to simplify the implementation of risk-based ranking procedures into bridge management system packages according to the MAP-21 vision. Therefore, machine learning techniques are employed to facilitate the introduction of probabilistic risk methods into bridge management systems. The procedure is described for seven hazards that are pertinent to bridges in New Jersey: overloading, fatigue, seismic, flooding, scour, vehicle and vessel collision. Risk values are computed in monetary terms to homogenize the comparison among bridges for different hazards. The analysis is performed on 5,534 bridges, showing that seismic events and fatigue resulting from truck overloading are the most dominant hazards in New Jersey, for which about 97.0% and 29.0% of bridges show some level of risk. The main limitation of the proposed framework is the lack of accurate data from bridge inventories necessary to thoroughly perform a fully structural probabilistic analysis of bridges and to minimize engineering judgment.

Risk-based approach to develop a national residue program: prioritizing the residue control of veterinary drugs in fishery products

Veterinary drugs are widely used to protect production-related diseases and promote the growth of farmed fish. The use of large amounts of veterinary drugs may have potential risk and cause adverse effects on both humans and the environment. In this study, we developed risk-based ranking based on a scoring system to be applied in the national residue program. In this approach, the following three factors of veterinary drugs that may occur as residues in fishery products were considered: potency (acceptable daily intake), usage (number of dose and withdrawal period), and residue occurrence. The overall ranking score was calculated using the following equation: potency × usage (sum of the number of sales and withdrawal period) × residue occurrence. The veterinary drugs that were assigned high score by applying this approach were enrofloxacin, amoxicillin, oxolinic acid, erythromycin, and trimethoprim. The risk-based approach for monitoring veterinary drugs can provide a reliable inspection priority in fishery products. The developed ranking system can be applied in web-based systems and residue-monitoring programs and to ensure safe management of fishery products in Korea.

Risk-based quality accident ranking approach using failure mechanism and Axiomatic domain mapping

Manufacturers face numerous product quality accidents and thus require an effective and general approach to rank the occurred quality accidents. Therefore, a universal risk-based quality accident ranking method emphasising the importance of the accident formation mechanism is proposed in this paper to rank, analyse, and prevent occurred quality accidents comprehensively. First, the risk formation mechanism of quality accident is proposed emphatically using two typical failure mechanisms and Axiomatic domain mapping, and the structure of big quality accident data is presented on the basis of big product lifecycle data in order to provide data support for the subsequent risk analysis. Second, the accident failure cost, probability, and detectivity are adopted as the critical risk factors, which are computed using the failure cost model, extended fault tree and Axiomatic relevance tree respectively. Third, a modified risk importance index considering the relative weight of risk factors is presented to measure and rank the risk priority of quality accidents. Finally, a case study on a computer control panel accident risk analysis is conducted to verify the effectiveness of the proposed approach. Results show that the proposed method can objectively reflect the accident risk degree and provide necessary reference for accident risk mitigation.

Risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions.

Agricultural regions are a significant source of groundwater pesticide pollution. To ensure that agricultural regions with a significantly high risk of groundwater pesticide contamination are properly managed, a risk-based ranking method related to groundwater pesticide contamination is needed. In the present paper, a risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions was established. The method encompasses 3 phases, including indicator selection, characterization, and classification. In the risk ranking index system employed here, 17 indicators involving the physicochemical properties, environmental behavior characteristics, pesticide application methods, and inherent vulnerability of groundwater in the agricultural region were selected. The boundary of each indicator was determined using K-means cluster analysis based on a survey of a typical agricultural region and the physical and chemical properties of 300 typical pesticides. The total risk characterization was calculated by multiplying the risk value of each indicator, which could effectively avoid the subjectivity of index weight calculation and identify the main factors associated with the risk. The results indicated that the risk for groundwater pesticide contamination from agriculture in a region could be ranked into 4 classes from low to high risk. This method was applied to an agricultural region in Jiangsu Province, China, and it showed that this region had a relatively high risk for groundwater contamination from pesticides, and that the pesticide application method was the primary factor contributing to the relatively high risk. The risk ranking method was determined to be feasible, valid, and able to provide reference data related to the risk management of groundwater pesticide pollution from agricultural regions. Integr Environ Assess Manag 2017;13:1052-1059. © 2017 SETAC.

Environmental risk-based ranking of solvents using the combination of a multimedia model and multi-criteria decision analysis

The environmental distribution and risk ranking of commonly applied solvents were performed to assess their greenness.

Risk-Based Prioritization Method for the Classification of Groundwater Pollution from Hazardous Waste Landfills

Hazardous waste landfill sites are a significant source of groundwater pollution. To ensure that these landfills with a significantly high risk of groundwater contamination are properly managed, a risk-based ranking method related to groundwater contamination is needed. In this research, a risk-based prioritization method for the classification of groundwater pollution from hazardous waste landfills was established. The method encompasses five phases, including risk pre-screening, indicator selection, characterization, classification and, lastly, validation. In the risk ranking index system employed here, 14 indicators involving hazardous waste landfills and migration in the vadose zone as well as aquifer were selected. The boundary of each indicator was determined by K-means cluster analysis and the weight of each indicator was calculated by principal component analysis. These methods were applied to 37 hazardous waste landfills in China. The result showed that the risk for groundwater contamination from hazardous waste landfills could be ranked into three classes from low to high risk. In all, 62.2 % of the hazardous waste landfill sites were classified in the low and medium risk classes. The process simulation method and standardized anomalies were used to validate the result of risk ranking; the results were consistent with the simulated results related to the characteristics of contamination. The risk ranking method was feasible, valid and can provide reference data related to risk management for groundwater contamination at hazardous waste landfill sites.

Farms at risk of chemical contamination: a proposal for a risk-based ranking method

Introduction: Public health concern arises from farming activities carried out near to sources of chemical contamination. In Italy, feed and food control for contaminants is performed randomly, in some cases based on the distance from the source of pollution. The aim of the present work was to implement a scoring methodology aimed to provide a risk based ranking of farms for official controls planning. Methods: A “Source-Pathways-Receptors” conceptual model was adopted, starting from of a single terrestrial source of chemical pollution and a single route of dispersion. Environmental and farm-specific variables were selected as potential risk predictors, directly or indirectly linked to the suitability of the soils as drivers of the contamination to the animals: 1) Distance from primary [D1] and secondary [D2] source of pollution; 2) Elevation [Q]; 3) Natural barriers [B]; 4) Land aptitude to floods [A]; 5) Depth of water [F]; 6) hydrogeological vulnerability [Vu]; 7) On-farm feed management [P]. All the environmental information were managed in a GIS project, the scoring criteria were selected and an algorithm was implemented for the calculation of the absolute risk (Rcfarm=D1*D2*[Q+F+Vu+A+B+P]) and the relative risk (VRR= RcfarmRc max) for each farm. The Rcs and VRRs values were plotted in a map and compared with two susceptibility maps from different modelling systems, describing the real spread of a β-HCH contamination in a previously studied area. Results: An high level of accordance was observed between the predictive evaluation of the risk according to the proposed scoring system and the susceptibility maps based on the real data. Conclusion: The proposed risk based scoring methodology could be an useful tool for decision makers to address two different goals: a) risk based monitoring/surveillance of farms in cases of chemical contamination; b) preventive risk evaluation for farming activities approval in proximity of industrial areas or waste dumps.I

An innovative approach to mitigating horizon mismatch

Purpose This paper aims to propose an innovative approach to risk measurement for the abolition of selection bias arising from the specious selection of different horizons for investment and risk computation of equity-linked-saving schemes (ELSS). Design/methodology/approach ELSS has a lock-in period of three years, but shorter horizons’ (daily/weekly/monthly) return data are preferred, in practice, for risk computation. This results in horizon mismatch. This paper studies the consequences of this mismatch and provides a noble solution to diminish its effect on investors’ decision-making. To accomplish this objective, the paper uses an innovative methodology, maximal overlap discrete wavelet transformation, to segregate the price movements across different horizons. Risk across all horizons is measured using Cornish-Fisher expected shortfall and Cornish-Fisher value-at-risk methods. Findings The degree of consistency of risk-based rankings across horizons is examined by means of the Spearman and Kendall’s rank correlation tests. The risk-based ranking of ELSS is found to vary significantly with the change in investor’s horizon. Precisely, the rankings formulated using daily net asset values are significantly different from the rankings developed using fluctuations over longer horizons (two-four and four-eight years). Originality/value This finding indicates that the ranking exercise may mislead investors if horizon correction is not done while developing such rankings.

Horizon heterogeneity, institutional constraint and managerial myopia: a multi-frequency perspective on ELSS

In the context of equity linked saving schemes (ELSS), the study elucidates how horizon heterogeneity, arising because of institutional constraint, can instigate managerial myopia which inevitably culminates into adverse asset-selection decision. Horizon heterogeneity is found to be induced by the 'lock-in-period provision' of ELSS which results in a mismatch between 'long term capital appreciation' objective of the investors and 'short term value augmentation' motivation of the managers. To empirically ascertain the amplitude of this scale-mismatch the paper employs the noble methodology of wavelet transformation wherein fluctuations of the price movement across multiple scale are segregated without any generic loss of information. The time-frequency analysis reveals that relative risk of ELSS changes with the change in frequency intervals. The shorter horizon (daily frequency) downside-risk-based ranking of ELSS is found to be exorbitantly different from the ranking devised over longer horizons (two to four years). The findings explicates that risk-based ranking exercise may mislead investors if horizon correction is not done while developing such rankings. The study further urges that the Security and Exchange Board of India (SEBI) should contemplate reviewing the Mutual Fund Regulation Act 1996 in the light of this discovery so as to abide by the policy of true and fair risk disclosure.

Incorporating High-Throughput Exposure Predictions With Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing.

We previously integrated dosimetry and exposure with high-throughput screening (HTS) to enhance the utility of ToxCast HTS data by translating in vitro bioactivity concentrations to oral equivalent doses (OEDs) required to achieve these levels internally. These OEDs were compared against regulatory exposure estimates, providing an activity-to-exposure ratio (AER) useful for a risk-based ranking strategy. As ToxCast efforts expand (ie, Phase II) beyond food-use pesticides toward a wider chemical domain that lacks exposure and toxicity information, prediction tools become increasingly important. In this study, in vitro hepatic clearance and plasma protein binding were measured to estimate OEDs for a subset of Phase II chemicals. OEDs were compared against high-throughput (HT) exposure predictions generated using probabilistic modeling and Bayesian approaches generated by the U.S. Environmental Protection Agency (EPA) ExpoCast program. This approach incorporated chemical-specific use and national production volume data with biomonitoring data to inform the exposure predictions. This HT exposure modeling approach provided predictions for all Phase II chemicals assessed in this study whereas estimates from regulatory sources were available for only 7% of chemicals. Of the 163 chemicals assessed in this study, 3 or 13 chemicals possessed AERs < 1 or < 100, respectively. Diverse bioactivities across a range of assays and concentrations were also noted across the wider chemical space surveyed. The availability of HT exposure estimation and bioactivity screening tools provides an opportunity to incorporate a risk-based strategy for use in testing prioritization.

An anomaly analysis framework for database systems

Anomaly detection systems are usually employed to monitor database activities in order to detect security incidents. These systems raise an alert when anomalous activities are detected. The raised alerts have to be analyzed to timely respond to the security incidents. Their analysis, however, is time-consuming and costly. This problem increases with the large number of alerts often raised by anomaly detection systems. To timely and effectively handle security incidents, alerts should be accompanied by information which allows the understanding of incidents and their context (e.g., root causes, attack type) and their prioritization (e.g., criticality level). Unfortunately, the current state of affairs regarding the information about alerts provided by existing anomaly detection systems is not very satisfactory. This work presents an anomaly analysis framework that facilitates the analysis of alerts raised by an anomaly detection system monitoring a database system. The framework provides an approach to assess the criticality of alerts with respect to the disclosure of sensitive information and a feature-based classification of alerts according to their associated type of attack. The framework has been deployed as a web-based alert audit tool that provides alert classification and risk-based ranking capabilities, significantly easing the analysis of alerts. We validate the classification and ranking approaches using synthetic data generated through an existing healthcare management system.

Risk-based prioritization methodology for the classification of groundwater pollution sources

Water management is one of the EU environmental priorities and it is one of the most serious challenges that today's major cities are facing. The main European regulation for the protection of water resources is represented by the Water Framework Directive (WFD) and the Groundwater Directive (2006/118/EC) which require the identification, risk-based ranking and management of sources of pollution and the identification of those contamination sources that threaten the achievement of groundwater's good quality status. The aim of this paper is to present a new risk-based prioritization methodology to support the determination of a management strategy for the achievement of the good quality status of groundwater. The proposed methodology encompasses the following steps: 1) hazard analysis, 2) pathway analysis, 3) receptor vulnerability analysis and 4) relative risk estimation. Moreover, by integrating GIS functionalities and Multi Criteria Decision Analysis (MCDA) techniques, it allows to: i) deal with several sources and multiple impacted receptors within the area of concern; ii) identify different receptors' vulnerability levels according to specific groundwater uses; iii) assess the risks posed by all contamination sources in the area; and iv) provide a risk-based ranking of the contamination sources that can threaten the achievement of the groundwater good quality status. The application of the proposed framework to a well-known industrialized area located in the surroundings of Milan (Italy) is illustrated in order to demonstrate the effectiveness of the proposed framework in supporting the identification of intervention priorities. Among the 32 sources analyzed in the case study, three sources received the highest relevance score, due to the medium-high relative risks estimated for Chromium (VI) and Perchloroethylene. The case study application showed that the developed methodology is flexible and easy to adapt to different contexts, thanks to the possibility to introduce specific relevant parameters identified according to expert judgment and data availability.

Quantitative risk-based ranking of chemicals considering hazardous thermal reactions

In this study, a simplified kinetic model was developed for sulphide mineral ores for assessing thermal hazards and tested with Differential Scanning Calorimeter experiments using AKTS software. In addition, a methodology was developed for assessing the severity of thermal hazards associated based on the core parameters and measured responses such as the constituents and reactive interaction effects i.e. mineralogy, particle size distribution, and moisture content. The likelihood of thermal hazards was quantified using the core parameters of the global thermal reactions via probabilistic analysis while the severity of the hazards was evaluated on the strength of accompanied enthalpy of reactions. The associated risks were then determined as product of these probability and severity. Finally, a risk ranking method was proposed on a predefined scale which was used to rank and interpret the obtained results. The quantitative risk assessment of thermal hazards for self-heating sulphide containing mineral was thus assessed. It was observed that the ores were of medium risk of runaway reaction.

Probabilistic risk assessment of veterinary medicines applied to four major aquaculture species produced in Asia

Aquaculture production constitutes one of the main sources of pollution with veterinary medicines into the environment. About 90% of the global aquaculture production is produced in Asia and the potential environmental risks associated with the use of veterinary medicines in Asian aquaculture have not yet been properly evaluated. In this study we performed a probabilistic risk assessment for eight different aquaculture production scenarios in Asia by combining up-to-date information on the use of veterinary medicines and aquaculture production characteristics. The ERA-AQUA model was used to perform mass balances of veterinary medicinal treatments applied to aquaculture ponds and to characterize risks for primary producers, invertebrates, and fish potentially exposed to chemical residues through aquaculture effluents. The mass balance calculations showed that, on average, about 25% of the applied drug mass to aquaculture ponds is released into the environment, although this percentage varies with the chemical's properties, the mode of application, the cultured species density, and the water exchange rates in the aquaculture pond scenario. In general, the highest potential environmental risks were calculated for parasitic treatments, followed by disinfection and antibiotic treatments. Pangasius catfish production in Vietnam, followed by shrimp production in China, constitute possible hot-spots for environmental pollution due to the intensity of the aquaculture production and considerable discharge of toxic chemical residues into surrounding aquatic ecosystems. A risk-based ranking of compounds is provided for each of the evaluated scenarios, which offers crucial information for conducting further chemical and biological field and laboratory monitoring research. In addition, we discuss general knowledge gaps and research priorities for performing refined risk assessments of aquaculture medicines in the near future.

Finding the needle: A risk-based ranking of product listings at online auction sites for non-delivery fraud prediction

Non-delivery fraud is a recurring problem at online auction sites: false sellers that list nonexistent products just to receive payments and afterwards disappear, possibly repeating the swindle with another identity. In our work we identified a set of publicly available features related to listings, sellers and product categories, and built a machine learning system for fraud prediction taking into account the high class imbalance of real data and the need to control the false positives rate due to commercial reasons. We tested the proposed system with data collected from a major Brazilian online auction site, obtaining good results on the identification of fraudsters before they strike, even when they had no previous historical information. We also evaluated the contribution of category-related features to fraud detection. Finally, we compared the learning algorithm used (boosted trees) with other state-of-the-art methods.