Risk-based Inspections Research Articles

Investigating the use of machine learning algorithms to support risk-based animal welfare inspections of cattle and pig farms.

In livestock production, animal-related data are often registered in specialised databases and are usually not interconnected, except for a common identifier. Analysis of combined datasets and the possible inclusion of third-party information can provide a more complete picture or reveal complex relationships. The aim of this study was to develop a risk index to predict farms with an increased likelihood for animal welfare violations, defined as non-compliance during on-farm welfare inspections. A data-driven approach was chosen for this purpose, focusing on the combination of existing Swiss government databases and registers. Individual animal-level data were aggregated at the herd level. Since data collection and availability were best for cattle and pigs, the focus was on these two livestock species. We present machine learning models that can be used as a tool to plan and optimise risk-based on-farm welfare inspections by proposing a consolidated list of priority holdings to be visited. The results of previous on-farm welfare inspections were used to calibrate a binary welfare index, which is the prediction goal. The risk index is based on proxy information, such as the participation in animal welfare programmes with structured housing and outdoor access, herd type and size, or animal movement data. Since transparency of the model is critical both for public acceptance of such a data-driven index and farm control planning, the Random Forest model, for which the decision process can be illustrated, was investigated in depth. Using historical inspection data with an overall low prevalence of violations of approximately 4% for both species, the developed index was able to predict violations with a sensitivity of 81.2 and 79.5% for cattle and pig farms, respectively. The study has shown that combining multiple and heterogeneous data sources improves the quality of the models. Furthermore, privacy-preserving methods are applied to a research environment to explore the available data before restricting the feature space to the most relevant. This study demonstrates that data-driven monitoring of livestock populations is already possible with the existing datasets and the models developed can be a useful tool to plan and conduct risk-based animal welfare inspection.

BUDGET PERFORMANCE RISKS ASESSMENT ISSUES IN RA

The article analyses the budget system of control implementation and applied approaches in the implementation stage of budgeting performance. The methodology of risk-based inspections in RA was surveyed, and some problematic parts were highlighted. By addressing the above-mentioned questions, it has been proposed more complete and effective approach to risk assessment, which allows more accurate assessment for the implementation of maximum effective control with limited resources.

Food Control System in Libya

The food control system in the country should be effective and able to ensure the safety and quality of food. The study aimed to assess the current situation of the food control system in Libya. The mandates of food control are fragmented among different bodies, which sometimes perform the same functions and thus cause an unnecessary burden on the country's budget. Although there appear to be activities related to national standards that align with international standards, the 1973 health law has yet to be updated. The food inspection has not been based on a risk analysis. Also, there are inadequate laboratory support services, and laboratories are unaccredited. There is also an absence of action plans in both the short and long term for information, communication, and education. The inspectors need training, especially in risk-based inspections. Importantly, analysts' lack of expertise makes determining the compliance of food products with Libyan legislation difficult. Poor-quality and unsafe foods enter Libya due to the control system's weakness and the political crisis; therefore, the government should implement an integrated food control system to improve the situation. As well, the United Nations should politically support Libya's stability.

Development of a data-driven method for assessing health and welfare in the most common livestock species in Switzerland: The Smart Animal Health project.

Improving animal health and welfare in livestock systems depends on reliable proxies for assessment and monitoring. The aim of this project was to develop a novel method that relies on animal-based indicators and data-driven metrics for assessing health and welfare at farm level for the most common livestock species in Switzerland. Method development followed a uniform multi-stage process for each species. Scientific literature was systematically reviewed to identify potential health and welfare indicators for cattle, sheep, goats, pigs and poultry. Suitable indicators were applied in the field and compared with outcomes of the Welfare Quality® scores of a given farm. To identify farms at risk for violations of animal welfare regulations, several agricultural and animal health databases were interconnected and various supervised machine-learning techniques were applied to model the status of farms. Literature reviews identified a variety of indicators, some of which are well established, while others lack reliability or practicability, or still need further validation. Data quality and availability strongly varied among animal species, with most data available for dairy cows and pigs. Data-based indicators were almost exclusively limited to the categories "Animal health" and "Husbandry and feeding". The assessment of "Appropriate behavior" and "Freedom from pain, suffering, harm and anxiety" depended largely on indicators that had to be assessed and monitored on-farm. The different machine-learning techniques used to identify farms for risk-based animal welfare inspections reached similar classification performances with sensitivities above 80%. Features with the highest predictive weights were: Participation in federal ecological and animal welfare programs, farm demographics and farmers' notification discipline for animal movements. A common method with individual sets of indicators for each species was developed. The results show that, depending on data availability for the individual animal categories, models based on proxy data can achieve high correlations with animal health and welfare assessed on-farm. Nevertheless, for sufficient validity, a combination of data-based indicators and on-farm assessments is currently required. For a broad implementation of the methods, alternatives to extensive manual on-farm assessments are needed, whereby smart farming technologies have great potential to support the assessment if the specific monitoring goals are defined.

Explaining of the experts' points of view about the types and importance of man-made hazards in Iran’s process industries: A qualitative study

IntroductionProcess industries may have different hazards that can cause serious injury to humans, the environment, and the economy. Considering the importance of man-made risks in process industries, Experts' Points of View should be considered to apply risk reduction strategies Hence, the present study was performed to explain the views of experts regarding the types and importance of man-made hazards in process industries. Materials and methodsThis study was conducted using a qualitative method of directed content analysis with a deductive approach. The participants included 22 experts in the field of process industries. The selection of samples started purposeful and continued until data saturation. Data collection was done through semi-structured interviews. ResultsBased on experts' points of view, 5 man-made hazards in process industries were classified into 14 subcategories. The man category was divided into three subcategories: human error, technical knowledge error, and management error, the Material category was divided into 3 sub-categories of leakage and rupture, chemical properties, physical properties, Medium category was divided into two sub-categories of incorrect location selection and placement and harmful environmental factors, Machines category was divided into three sub-categories of failure in design, failure in Preventive Maintenance (PM), failure in Safety Instrumented System (SIS), and the Methods category was classified into three sub-categories: defects in inspection, defects in information, defects in executive instructions. ConclusionTechnical training to reduce personnel errors, conducting risk-based inspections to control leaks and possible ruptures, careful design and site selection in the initial phase of the project, is recommended. The use of engineering methods and artificial intelligence to obtain the risk number and control methods to reduce the harmful effects of risks can be helpful.

Moving from routine to risk-based official controls in slaughterhouses: Development of a scoring tool for the risk of non-compliance with animal welfare regulations

Slaughter of animals is a necessary step of meat production process that takes place out of public view, but that is a subject of consumers' and civil society organisations’ concern. Public policies are progressing constantly to meet social expectations and optimize official controls in slaughterhouses. Regarding animal welfare, the main objective of official controls of food business operators (FBOs) is to ensure that animals are spared any avoidable pain, distress, or suffering during their killing and related operations. In this context of improving the efficiency of public action, the aim of this study was to develop a risk-based approach for the control of compliance with animal welfare regulations in red meat slaughterhouses. After a preliminary phase of data mapping, data qualitative assessment and expert knowledge elicitation, the statistical study was divided into four steps: (i) identification of risk factors associated with FBOs not being compliant with animal welfare requirements; (ii) development of a risk score formula to predict the risk of non-compliance with animal welfare regulations and assignment of a risk score to each process operating in a slaughterhouse; (iii) ranking of processes and slaughterhouses into different risk groups according to the score; (iv) validation of the model using data from the year 2020.Among the 16 variables included in the statistical model, five were found statistically associated with a higher risk of non-compliance with animal welfare regulations: (i) total annual slaughter volume (intermediate tonnage slaughterhouses being more at risk), (ii) predominance of the category of animals slaughtered (categories representing less than 15% of the total volume of the slaughterhouse being more at risk), (iii) stunning method (head only electrical stunning and slaughter without prior stunning being more risky techniques), (iv) economic situation of the slaughterhouse (a worrying financial situation being more at risk), and (v) the overall evaluation of the previous year's animal welfare control (D-ratings being particularly indicative of high risk). A risk score formula was proposed based on the regression coefficients of each modality of these five variables to predict the risk of non-compliance with animal welfare requirements. Risk scores (RS) calculated for each process ranged from 1 to 8.4. Processes and slaughterhouses were classified into three risk groups based on that score: low risk (RS ≤ 2.5), medium risk (2.5 < RS < 4.5), high risk (RS ≥ 4.5). The validation phase finally allowed to validate the risk score developed. This innovative and comprehensive study will support risk management decisions to improve animal welfare in slaughterhouses, by moving from routine to risk-based inspections and adjusting control efforts frequency and nature of official controls to the level of risk of the establishments. Similar approaches could be developed for poultry slaughterhouses and for food hygiene requirements.

Unsupervised Neural Network for Data-Driven Corrosion Detection of a Mining Pipeline

Pipelines failure often caused by corrosion may result in safety, environmental and economic issues. In this study, an unsupervised neural network, Self-Organizing Maps (SOM), is applied to create clusters representing the corrosion impact assessed with ultrasound periodic inspections. Based on this work, it is expected that the new insight into thickness data representation using unsupervised neural network will facilitate planning of corrosion mitigation activities through risk-based inspections of mining slurry pipelines. As a result, SOM led to the reduction of the variables in two-dimensional space nodes. Hierarchical ascending classification (HAC) was then used to classify these nodes regrouping thickness loss measurements. The proposed method by combining both SOM and HAC succeeded in detecting the extent of corrosion in a mining pipeline.

Methods to perform risk-based inspections of food companies.

Risk‐based monitoring programs are increasingly applied for cost‐effective monitoring of food safety. Such programs ideally consist of three steps: risk‐ranking, risk‐based inspections, and cost‐effective monitoring. Various methods have been described to perform the first step of risk‐based monitoring. However, once the risk‐ranking has been completed, identifying the hazard‐food combinations to monitor, the frequency of inspection needs to be established based on a prioritization of food business operators (FBOs). The aim of this paper is to provide an overview of methods available for risk‐based inspections. Literature shows that FBO's food safety compliance can be assessed based on company size, historical monitoring data, and socio‐economic factors influencing compliance behavior. Non‐compliance can either be intentional or unintentional. The latter can be assessed by evaluating the food safety culture of a company. Various models—ranging from qualitative (e.g., focus groups) to quantitative (e.g., scoring)—can be used for this purpose. These models usually include an evaluation of the organizational structure (e.g., management control, communication, commitment), the technical food safety environment (e.g., hygienic design, zoning), and employee characteristics (e.g., knowledge, risk awareness). Intentional non‐compliance can be assessed using food fraud vulnerability tools. These tools incorporate factors influencing the likelihood of food fraud at the company, that is, opportunity, motivation, and (lack of) control measures. The literature indicates that either self‐assessment tools or risk matrices are applied. There is no global consensus on the methods to apply for risk‐based inspections. Depending on time and budget available as well as preferred output, one of the presented methods may be applied for prioritizing FBOs.

Unauthorized Food Manipulation as a Criminal Offense: Food Authenticity, Legal Frameworks, Analytical Tools and Cases.

Food fraud is a criminal intent motivated by economic gain to adulterate or misrepresent food ingredients and packaging. The development of a reliable food supply system is at great risk under globalization, but Food Business Operators (FBOs) have a legal obligation to implement and maintain food traceability and quality at all stages of food production, processing, and distribution. Incidents of food fraud have a strong negative impact on consumer confidence in the food industry. Therefore, local and international regulatory mechanisms are established to prevent or mitigate food fraud. This review brings new perspectives linking EU and US legislation, as well as new definitions and descriptions of the criminal aspect of food fraud incidents. It also describes certain new insights into the application of state-of-the-art methods and techniques that provide valuable tools for geographic, botanical, or other chemical markers of food authenticity. The review also provides an overview of the most common cases of food fraud worldwide from 2010 to 2020. Further research is needed to support the development of predictive models for innovative approaches to adulteration, especially when some valuable nutrients are replaced by toxic ingredients. A possible solution to minimize food fraud incidents is to increase the level of risk-based inspections, establish more productive monitoring and implementation of food protection systems in the supply chain, and implement better ingredient control and certification. National and international (e.g., regional) police offices for food fraud should be introduced, possessing knowledge and skills in food, food safety, food processing, and food products, as initial positive results have emerged in some countries.

Incorporation of deficiency data into the analysis of the dependency and interdependency among the risk factors influencing port state control inspection

Port State Control (PSC) inspection aids to control substandard ships and ensure safety at sea. Current risk-based PSC research and practice fail to incorporate ship deficiency records into detention probability analysis, because of the difficulty introduced by the involved big deficiency data. In this paper, a new Bayesian Network (BN) based PSC risk probabilistic model is developed to analyze the dependency and interdependency among the risk factors influencing PSC inspections based on big data derived from the inspection database of Tokyo MoU for the period between 2014 and 2017. The results reveal that ship's safety condition related deficiencies as well as technical features of the inspected vessel itself are among the most influential factors concerning PSC inspections and ship detention. New Bayesian learning methods are used to improve the model efficiency in ship detention prediction. As a result, the newly developed model has shown a reliable performance on dynamic prediction and cause-effect diagnosis of ship detention probabilities by pioneering the incorporation of ship deficiency records in the analysis. The findings provide important insights on how to facilitate risk-based PSC inspections for both ship owners and port states. They provide support for port state authorities to implement rational inspection policies.

Flexible pipe tensile armor monitoring using eddy current technique

ABSTRACT Flexible risers are composite cylindrical structures, which are widely used in offshore oil and gas production facilities. Failure of a flexible riser during operation causes major economic and environmental losses. The improvement of structural integrity programs based on nondestructive techniques and risk-based inspections is therefore utterly important. In this article, the feasibility of using the eddy current technique to identify wire rupture of a riser tensile armor is studied. As an important part of the work, an innovative coil prototype (probe) is developed based on the eddy current technique. To assist the construction of the prototype, numerical simulations using the Finite Element Method are applied to reproduce the physical nondestructive test (NDT) and the results are used as support for determining the parameters of the inspection circuit. The resulting signal in all analyses proved the efficacy and viability of Eddy Current Test to inspect the tensile armors of flexible pipes with eventual ruptures.

A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.

Ranking of process safety cultures for risk-based inspections using indicative safety culture assessments

Although major hazard industries have no legal obligations to demonstrate a good safety culture, it is an increasingly important factor for risk-based inspections. This paper describes the development and evaluation of a relatively simple method for external indicative assessment of the process safety culture in major hazard industries. As a first step, a tool was developed. Second, the tool was successfully tested in a workshop with major hazard inspectors. After this ‘proof of concept’ and a few adaptations to improve the tool, quantitative evaluation and validation were carried out with a representative sample of major hazard inspectors in the Netherlands. In this stage, the tool comprised 30 questions, of which 19 sufficiently frequent valid responses were given for the validity test. The evaluations demonstrate that without specific preparations, inspection teams can use the tool to discriminate between plants with poor or good safety cultures. That is sufficient for ranking as additional input for risk-based inspection strategies. The resulting indicative ‘scores’ for safety culture correlate meaningfully with the quality of process safety management systems and the number of (legal) non-compliances, which were identified by the inspection team at the same occasion. The use of the tool can be further improved by training inspectors, making minor adaptations of the inspection process, and broadening the scope of a few questions. It is expected that external assessments of safety culture will become increasingly important not only for inspectorates, but also for a range of other external stakeholders.

How do Risk-Based Inspections Impact Auditor Behavior? Experimental Evidence on the PCAOB's Process

ABSTRACT I provide theory-based causal evidence on the effects of risk-based regulatory inspections, modeled after the PCAOB's, on auditor behavior in a multi-client setting where clients with relatively higher misstatement risk (“higher-risk” clients) have a higher risk of being inspected than clients with relatively lower misstatement risk (“lower-risk” clients). I predict and find that inspections increase auditor effort, but only for higher-risk clients. Inspections also impair auditors' decision performance for lower-risk clients relative to a regime without inspections and relative to higher-risk clients within an inspections regime, ceteris paribus. Theory-based process model results show that inspections increase auditors' perceived inspection risks, which increase auditor effort for higher-risk clients, but also increase auditors' task-related anxiety, resulting in decreased decision performance for lower-risk clients. Notwithstanding the previously identified benefits, this study identifies potential unintended consequences of risk-based regulatory inspections.

Technology Focus: Production and Facilities (December 2019)

Technology Focus Innovation is alive and well in our industry, as evidenced by this year’s feature selections. The major thrust of papers relating to production and facilities continue to be big data analytics, process and field optimization, piping and pipelining, and the performance of inflow-control devices (ICDs and autonomous ICDs, or AICDs). For ICDs and AICDs, publications have evolved from descriptions of the devices and how they work to field reports of their successful implementation. As with most new technologies, their degree and frequency of success are rapidly improving. Progress is exemplified by efforts to couple ICD/AICD installations with reservoir simulation (paper SPE 193364), thus giving technologists the ability to develop and digitally test placement models for ICD/AICD devices.• One paper (SPE 195450) reports on the completion of three wells in one formation. The well with ICDs outperformed the wells with a slotted liner or•perforations. • For big data analytics, novel development work continues to focus on improving algorithms for directing preventative maintenance and risk-based inspections with fewer false-positive alarms. This technology has even begun migrating from the research and development stage by operators to use by service companies. One step-out application of big data analytics is the use of autonomous ground vehicles for the monitoring of buried onshore pipelines (paper SPE 192773). This allows for much more sophisticated pipeline inspections with far less health, safety, and environmental exposure.• A project-management paper deserving attention describes a successful multiteam project with emphasis on interdisciplinary communication. The objective was to construct and start up a large liquefied natural gas train. The project involved 370 deliverables with over 7,000 unique line items (paper SPE•191984). Other papers describing creative new technology include the testing of nano-paint-aided electromagnetic pigging of pipelines and production tubing (paper SPE 196112), a tetherless autonomous device capable of traveling with or against production flow to accomplish pigging and inspection (paper SPE• 195709), and the use of a solid propellant accumulator for activating blowout preventers (paper OTC 29151). These and a number of other publications are great examples of creative work on nascent technologies to improve all aspects of oil and gas production. Recommended additional reading at OnePetro: www.onepetro.org. SPE 191984 Facility Operations Readiness and Project Startup Support and Integration by Michael Maher, ConocoPhillips SPE 196112 Nanopaint-Aided Electromagnetic Pigging in Pipelines and Production Tubing by Ningyu Wang, The University of Texas at Austin, et al. OTC 29151 Solid Propellant Accumulators—Next-Generation Hydraulic•Power Source for Cold-Weather BOPs by Nazareth Bedrossian, Bastion Technologies

Some observations on the troublesome implementation of risk-based inspections in the Netherlands

For more almost 15 years now the official inspection creed in the Netherlands has been that inspection should be risk based. In this article we have a first look into what has been implemented. We observe (a) that the national policy formulated in 2005 that inspections should be risk-based, step by step has been altered into a focus on indepence of inspections and stronger sanctions, (b) that only a few inspectorates claim to have implemented risk-based inspection and (c) that those inspectorates do not operate in line with their own claims. Organising risk-based inspection is clearly a challenge for Dutch inspectorates. Some barriers hindering the implementation of risk-based inspection in the Netherlands are identified. The first being that there is no consensus as to what risk actually is. A second that inspectorates actually have no incentive to implement risk-based inspection. We conclude that this line of research is necessary to better understand the implementation process of risk-based inspections in order to better use public resources.

Assessment of Welfare Problems in Finnish Cattle and Pig Farms Based on Official Inspection Reports.

Simple SummaryOfficial on-farm inspections are carried out throughout the European Union every year to ensure farm compliance with animal welfare standards. The aim of this study was to analyze Finnish inspection data in order to find out how well cattle and pig farms comply with animal welfare standards, to reveal the most common non-compliances and to identify possible farm risk factors. About every fourth inspected Finnish cattle and pig farm did not comply with the animal welfare standards. Examples of factors that increased the risk of non-compliance were small herd size, tie-stall housing and outdoor rearing. Inadequate lying area in cattle farms and a lack of enrichment material in pig farms were the most common non-compliances. The regional differences found may indicate differences in inspectors’ interpretations or ways in conducting inspections. As the official inspection reports contain valuable information about the welfare problems on farms, the reports should be better utilized in risk analysis, in targeting farmer education, and in making the inspections more uniform.The competent authorities of the Member States of the European Union are required to perform animal welfare inspections on livestock farms. The data obtained from these official inspections performed in Finnish cattle and pig farms in 2010–2015 were used with the aim of estimating the prevalence of the most common non-compliances and identifying underlying risk factors. The prevalence of non-compliant cattle and pig farms was 24.2% and 27.9%, respectively. In cattle, the most common problem was an inadequate lying area followed by deficient housing conditions for calves; in pigs, it was a lack of enrichment material. The non-compliances concerning cattle were most frequently detected in autumn and in farms with small herd size, with tie-stall housing and outdoor rearing year-round. The pig farms with a farrow-to-finish unit had a higher prevalence of non-compliances than other production types. The prevalence of the non-compliant farms differed notably between the regions. It can be concluded that the cattle welfare inspections should be performed with a focus on the cold and rainy seasons and at small farms, whereas the pig welfare inspections should mainly focus on farrow-to-finish units. The data received from official inspections should be efficiently utilized in the development of animal welfare inspection system, with the aim of risk-based, consistent and uniform inspections. In addition, the data should be utilized in targeting information for farmers.

The implementation of EU environmental policy: Why the scope conditions have improved?

Implementation gaps of European Union (EU) environmental policy are explained, inter alia, by shortcomings in knowledge, a lack of administrative capacities and weak enforcement practices. However, this article presents evidence that the scope conditions have improved, based on document analysis and semi-structured interviews with actors involved in the implementation process. Four main factors are considered. First, the Commission’s monitoring capacities benefit from an improved access to data sources other than those provided by the member states, and from a reduced legislative agenda, allowing for a more intense focus on implementation. Second, subnational actors have improved their implementation capacity through knowledge transfers and strategic planning, facilitated by improved statistical data. Third, inspection networks benefitted from technical advances, such as the access to satellite images, presenting new opportunities for the detection of environmental crimes, and new software programs facilitating risk-based inspections. Finally, citizens’ and non-governmental organizations’ improved access to justice in environmental matters contributes to a stricter enforcement of EU law.&#x0D; &#x0D; Keywords: networks; European Union; software programs

Investigations of Failure Mechanism and Structural Integrity of a Hydro-Energetic Component

Hydro-energetic equipment and installations are part of a national energy system. Age and their wear, lack of implementation of the risk-based inspections on the structural integrity of components and a proper risk management system can lead to unexpected in-service failure, which causes temporary disturbances in electricity distribution networks and financial losses. Paper presents the structural integrity analysis of the cause of failure of a spindle from a hydro-aggregate. For the material characterization, tensile tests, notch impact tests, hardness tests and non-destructive visual and magnetic particle tests (NDT) were performed. The experimental program also includes complementary investigation for chemical and structural analysis using Energy-Dispersive X-ray spectroscopy (EDX) and Scanning Electron Microscopy (SEM) techniques to assess the quantity and dispersion of elements in different areas of the fractured surface to highlight the particularities of material degradation. Connecting the inspections results leads to an in-depth understanding of the components failure mechanisms, which allows increasing the safety level in operation of the installations by adopting specific measures for avoidance of similar failure. From analysis and experimental results of this study, overload by shock in operation, particularly low steel toughness and non-metallic aligned or clustered inclusions are shown to be the main reason of spindle failure.

Risk-based scheduling methodology for audit inspections of curves on high-speed mainline tracks

Agencies with safety oversight responsibilities of railroad tracks often perform walking audit inspections of tracks (also referred to as quality audits) to complement and oversee the regular inspections performed by the railway operator or maintenance manager. Traditionally, these audit inspections are scheduled based on the qualitative evaluation of the rail line by the inspectors, together with the available schedule of the inspector(s). This paper presents an approach to replace the current qualitative decision-making process for determining when and where to conduct audit inspections with a quantitative decision-making process. This quantitative process first establishes an acceptable level of risk in a given territory, and then taking into account the defect history and real-time track conditions, it schedules audit inspections based on those conditions. This risk-based scheduling methodology of audit inspections can be used by the safety oversight agencies and inspectors to monitor and “spot” check track conditions and provide oversight over the normal inspection process. The audit inspection’s frequency algorithm, presented in this paper, establishes the acceptable level of risk based on six years of Federal Railway Administration safety audit inspections data of the Amtrak North East Corridor. This methodology takes into account the track conditions in terms of the curve defect rate and optimizes the scheduling of audit inspections of mainline curves based on this defect condition. The risk-based curve audit inspections interval methodology outputs the required maximum curve audit inspections interval (time until next audit inspection or reinspection) while maintaining an accepted level of risk in the presence of real-time curve defect rates.