R. Schoenmaker
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9 records found
1
Application of business intelligence as decision support systems in asset management of water connections
Case study in the Netherlands, in collaboration with water company “Evides”
Due to the lack of availability of collapse records, this study considered a proxy of collapse, which is loss of watertightness on the pipes. Furthermore, defects that potentially cause loss of watertightness are used as a proxy of the loss of watertightness on a pipe. Defects are obtained from the CCTV reports.
The first sub-question was answered based on the proxy failure under study. It was found in the literature review ten defects that potentially cause loss of watertightness, which were classified according to the coding of the standard NEN 3399. These defects are: cracks (BAB), break (BAC), defective connection (BAH), intruding sealing material (BAI), displaced joint (BAJ), porous pipe (BAN), soil visible through defect (BAO), void visible through defect (BAP), infiltration (BBF) and exfiltration (BBG).
The second sub-question was answered based on some explanatory variables that were available in the dataset of the inspections, which included: sewer system type, materials, shape, diameter, length, and pipe’s above ground material.
To answer this question, a descriptive statistical analysis and two survival methods were implemented: a non-parametric and a semi-parametric model.
The non-parametric model consists of an extended version of the Nelson-Aalen estimator of the cumulative hazard (ENE) and its derivative the Extended survival estimator (ESE). Implementing ESE, each characteristic was analysed using the aggregate information of the defects that potentially cause loss of watertightness, and each defect individually. This was done to identify the influence in the failure probabilities (the probabilities of occurrence of defects that potentially cause loss of watertightness).
The semi-parametric model that was used is the Cox proportional hazard function, used to estimate the risk ratio associated with one unit increase in one of the characteristics under study.
Results of the ESE model showed that:
The defects displaced joint (BAJ), infiltration (BBF) and defective connection (BAH), are the ones that have more incidence on the loss of watertightness for the two municipalities.
Based on the information of aggregate defects it was observed for both municipalities that the median survival probability is past 14 years.
Also, the analysis showed that stormwater sewers have a lower survival probability than foulwater sewers, that PVC pipes have a higher survival probability than concrete pipes, and that shorter pipes have a higher survival probability than longer pipes. In the case of the diameters, for the aggregate defects and defects displaced joint (BAJ) and defective connection (BAH), smaller diameters have a higher survival probability than larger ones. But this tendency is the other way around for defects like cracks (BAB), break (BAC) and porous pipes (BAN), where the diameter has a higher survival probability when are larger than when they are smaller.
Characteristics shape and above ground material were only analysed for Breda. It was observed that egg-shaped pipes have a lower survival probability than circular shapes, and that pipes with green fields and floor tiles above them have a lower survival probability than pipes that have above asphalt and pavement.
For both municipalities, the results of ESE showed that material is that characteristic that influences the most the probabilities of failure.
Results of the Cox proportional model showed that: Almere’s results met the proportionality assumption and showed that the characteristics sewer system type and length are the one that influences the most the failure probabilities. Breda’s data is no appropriate to be used with this model, as it does not meet the proportionality assumption. Recommendations of analysing Breda with an extended Cox are given, as this version of the model allows to use time-dependent variables.
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Due to the lack of availability of collapse records, this study considered a proxy of collapse, which is loss of watertightness on the pipes. Furthermore, defects that potentially cause loss of watertightness are used as a proxy of the loss of watertightness on a pipe. Defects are obtained from the CCTV reports.
The first sub-question was answered based on the proxy failure under study. It was found in the literature review ten defects that potentially cause loss of watertightness, which were classified according to the coding of the standard NEN 3399. These defects are: cracks (BAB), break (BAC), defective connection (BAH), intruding sealing material (BAI), displaced joint (BAJ), porous pipe (BAN), soil visible through defect (BAO), void visible through defect (BAP), infiltration (BBF) and exfiltration (BBG).
The second sub-question was answered based on some explanatory variables that were available in the dataset of the inspections, which included: sewer system type, materials, shape, diameter, length, and pipe’s above ground material.
To answer this question, a descriptive statistical analysis and two survival methods were implemented: a non-parametric and a semi-parametric model.
The non-parametric model consists of an extended version of the Nelson-Aalen estimator of the cumulative hazard (ENE) and its derivative the Extended survival estimator (ESE). Implementing ESE, each characteristic was analysed using the aggregate information of the defects that potentially cause loss of watertightness, and each defect individually. This was done to identify the influence in the failure probabilities (the probabilities of occurrence of defects that potentially cause loss of watertightness).
The semi-parametric model that was used is the Cox proportional hazard function, used to estimate the risk ratio associated with one unit increase in one of the characteristics under study.
Results of the ESE model showed that:
The defects displaced joint (BAJ), infiltration (BBF) and defective connection (BAH), are the ones that have more incidence on the loss of watertightness for the two municipalities.
Based on the information of aggregate defects it was observed for both municipalities that the median survival probability is past 14 years.
Also, the analysis showed that stormwater sewers have a lower survival probability than foulwater sewers, that PVC pipes have a higher survival probability than concrete pipes, and that shorter pipes have a higher survival probability than longer pipes. In the case of the diameters, for the aggregate defects and defects displaced joint (BAJ) and defective connection (BAH), smaller diameters have a higher survival probability than larger ones. But this tendency is the other way around for defects like cracks (BAB), break (BAC) and porous pipes (BAN), where the diameter has a higher survival probability when are larger than when they are smaller.
Characteristics shape and above ground material were only analysed for Breda. It was observed that egg-shaped pipes have a lower survival probability than circular shapes, and that pipes with green fields and floor tiles above them have a lower survival probability than pipes that have above asphalt and pavement.
For both municipalities, the results of ESE showed that material is that characteristic that influences the most the probabilities of failure.
Results of the Cox proportional model showed that: Almere’s results met the proportionality assumption and showed that the characteristics sewer system type and length are the one that influences the most the failure probabilities. Breda’s data is no appropriate to be used with this model, as it does not meet the proportionality assumption. Recommendations of analysing Breda with an extended Cox are given, as this version of the model allows to use time-dependent variables.
Introducing a Load Trend to the Reliability Analysis of Hydraulic Structures
Application of Bayesian Network-supported Reliability Analysis to predict future failure of Pumping Station IJmuiden
Creating line-of-sight in performance management
A search for and application of a practical method for brownfield asset management organisations
Understanding unintended responses to performance-based maintenance contracts
A qualitative research into managing highway maintenance in the Netherlands
Performance-based maintenance contracts for offshore wind farms
A decision-making flowchart to structure the sourcing process for the post-warranty O&M phase of offshore wind farms
In practice, however, risk assessments of assets don’t always yield sufficient information to support a prioritization process and help decision makers to decide which investment in maintenance for which asset is necessary first. It appears that to be able to successfully conduct the risk assessment and prioritize maintenance, the asset manager must connect with two actor groups whose perspectives and interests have a big influence on the process which seeks to identify critical assets. One actor is the asset owner, whose interests towards assets are expressed at a strategic level and who speaks a non-technical, political language, has a long-term point of view and cares about social accountability. The service provider on the other hand speaks a technical, non-political language, has a short-term point of view and cares primarily about the functioning of the assets. So, these actor groups do not speak the same language, use different time perspectives and have diverging interests. Thus the asset manager has a challenging position and needs to translate content between these strategic and operational levels. This friction strongly affects the extent to which the asset manager can claim any degree of control over the process of identifying risks of all assets.
A literature review yielded many methods to identify the critical components of an object, which is necessary for determining the necessary maintenance measures to prevent risks. Nevertheless, no method in literature explains how asset managers can identify critical objects using risk assessments. However, such a method is required when an asset manager wants to prioritize objects of an infrastructure system in order to determine which object needs additional maintenance measures first to treat the current risks.
The objective of this research is therefore to develop a systematic process model for the asset manager to identify the critical public infrastructure objects that simultaneously complies with the interests of the three management levels involved in the asset management process: the asset owner, the asset manager and the service provider. This model is presented in an IDEF0 model, a type of process model, which shows the necessary activities, inputs, outputs, mechanisms and controls underlying the risk assessment process.
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In practice, however, risk assessments of assets don’t always yield sufficient information to support a prioritization process and help decision makers to decide which investment in maintenance for which asset is necessary first. It appears that to be able to successfully conduct the risk assessment and prioritize maintenance, the asset manager must connect with two actor groups whose perspectives and interests have a big influence on the process which seeks to identify critical assets. One actor is the asset owner, whose interests towards assets are expressed at a strategic level and who speaks a non-technical, political language, has a long-term point of view and cares about social accountability. The service provider on the other hand speaks a technical, non-political language, has a short-term point of view and cares primarily about the functioning of the assets. So, these actor groups do not speak the same language, use different time perspectives and have diverging interests. Thus the asset manager has a challenging position and needs to translate content between these strategic and operational levels. This friction strongly affects the extent to which the asset manager can claim any degree of control over the process of identifying risks of all assets.
A literature review yielded many methods to identify the critical components of an object, which is necessary for determining the necessary maintenance measures to prevent risks. Nevertheless, no method in literature explains how asset managers can identify critical objects using risk assessments. However, such a method is required when an asset manager wants to prioritize objects of an infrastructure system in order to determine which object needs additional maintenance measures first to treat the current risks.
The objective of this research is therefore to develop a systematic process model for the asset manager to identify the critical public infrastructure objects that simultaneously complies with the interests of the three management levels involved in the asset management process: the asset owner, the asset manager and the service provider. This model is presented in an IDEF0 model, a type of process model, which shows the necessary activities, inputs, outputs, mechanisms and controls underlying the risk assessment process.