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M.G. van der Krogt
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11 records found
1
Journal article
(2023)
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Johannes C. Pol, Paulina Kindermann, Mark G. van der Krogt, Vera M. van Bergeijk, Guido Remmerswaal, Willem Kanning, Sebastiaan N. Jonkman, Matthijs Kok
Structural reliability analysis often considers failure mechanisms as correlated but non-interacting processes. Interacting failure mechanisms affect each others performance, and thereby the system reliability. We describe such interactions in the context of flood defenses, and analyze under which conditions such interactions have a large impact on reliability using a Monte Carlo-based quantification method. We provide simple examples and an application to levee failure due to landward slope instability and backward erosion piping (BEP). The examples show that the largest interaction effects are expected when the trigger mechanism is relatively likely to occur and the affected mechanism has a relatively large contribution to the system reliability. For the studied levee example, interactions between slope instability and BEP increased the failure probability up to a factor 4. Implications for the assessment and design of flood defenses are discussed.
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Structural reliability analysis often considers failure mechanisms as correlated but non-interacting processes. Interacting failure mechanisms affect each others performance, and thereby the system reliability. We describe such interactions in the context of flood defenses, and analyze under which conditions such interactions have a large impact on reliability using a Monte Carlo-based quantification method. We provide simple examples and an application to levee failure due to landward slope instability and backward erosion piping (BEP). The examples show that the largest interaction effects are expected when the trigger mechanism is relatively likely to occur and the affected mechanism has a relatively large contribution to the system reliability. For the studied levee example, interactions between slope instability and BEP increased the failure probability up to a factor 4. Implications for the assessment and design of flood defenses are discussed.
Project Summary D4 - Incorporating past performance
Improved methods for estimating dike failure probability
Reliability updating for slope stability
Improving dike safety assessments using performance information
Dikes are crucial for the protection against floods. One of the ways in which dikes can fail is by the instability of the inner slope. Credible probabilities of failure for slope stability are essential for the safety assessment of existing dikes and the design of dike reinforcements. This dissertation focuses on improving failure probability estimates for slope stability by using observed behaviour and performance of dikes. Examples of performance information are survived loads such as flood water levels or proof loads, and measurements during such loading conditions.
The research uses Bayesian analysis to account for one or more performance observations or measurements in estimating failure probabilities. Using multiple case studies, this research identifies the observations and success factors leading to significantly lower failure probabilities. Furthermore, Bayesian decision analysis was used to consider the cost-effectiveness (Value of Information) of performance information, to determine which strategy of dike reinforcement and/or uncertainty reduction leads to the lowest overall cost to comply with a given safety level.
It is found that incorporating multiple data of the behaviour and performance of dikes improves estimates of the failure probability for slope stability, leading to better safety
assessments and more efficient design of dike reinforcements. The cases considered in this dissertation suggest that savings of several million euros per kilometre dike reinforcement are possible (10-35% compared to the current dike reinforcement costs), for the Dutch situation with typically relatively high cost of dike reinforcements compared to the costs and risks of obtaining performance information. The use of performance information therefore contributes to improving the efficiency of managing flood risk in the Netherlands, and in particular the dike reinforcements in the Dutch Flood Protection Programme.
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Dikes are crucial for the protection against floods. One of the ways in which dikes can fail is by the instability of the inner slope. Credible probabilities of failure for slope stability are essential for the safety assessment of existing dikes and the design of dike reinforcements. This dissertation focuses on improving failure probability estimates for slope stability by using observed behaviour and performance of dikes. Examples of performance information are survived loads such as flood water levels or proof loads, and measurements during such loading conditions.
The research uses Bayesian analysis to account for one or more performance observations or measurements in estimating failure probabilities. Using multiple case studies, this research identifies the observations and success factors leading to significantly lower failure probabilities. Furthermore, Bayesian decision analysis was used to consider the cost-effectiveness (Value of Information) of performance information, to determine which strategy of dike reinforcement and/or uncertainty reduction leads to the lowest overall cost to comply with a given safety level.
It is found that incorporating multiple data of the behaviour and performance of dikes improves estimates of the failure probability for slope stability, leading to better safety
assessments and more efficient design of dike reinforcements. The cases considered in this dissertation suggest that savings of several million euros per kilometre dike reinforcement are possible (10-35% compared to the current dike reinforcement costs), for the Dutch situation with typically relatively high cost of dike reinforcements compared to the costs and risks of obtaining performance information. The use of performance information therefore contributes to improving the efficiency of managing flood risk in the Netherlands, and in particular the dike reinforcements in the Dutch Flood Protection Programme.
During construction of a dike, slope stability often reaches critical levels, due to the excess pore water pressures in the foundation. The loading condition during construction has similarities with the design conditions during flood loading. Not only in terms of the pore water pressures as the main driving force, but also in terms of criticality of the stability. This paper examines how the information of survival of the construction stage can be used to improve the reliability estimate for a dike in flood conditions, using Bayesian updating. The approach is exemplified for a range of typical dikes and for a case study of a full-scale test embankment. The main result is that the reliability can increase significantly by including the information of construction survival and the uncertainty reduction involved, especially for dikes on soft soil blankets. For the investigated cases, the posterior failure probability was up to several orders of magnitude lower than the prior failure probability. The main factors influencing the degree of reliability update, were the ground conditions and the degree of criticality of the slope stability during construction. In conclusion, using the information of the survived construction leads to improved reliability-based safety assessments of dikes, and consequently to more targeted and cost-effective flood protection.
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During construction of a dike, slope stability often reaches critical levels, due to the excess pore water pressures in the foundation. The loading condition during construction has similarities with the design conditions during flood loading. Not only in terms of the pore water pressures as the main driving force, but also in terms of criticality of the stability. This paper examines how the information of survival of the construction stage can be used to improve the reliability estimate for a dike in flood conditions, using Bayesian updating. The approach is exemplified for a range of typical dikes and for a case study of a full-scale test embankment. The main result is that the reliability can increase significantly by including the information of construction survival and the uncertainty reduction involved, especially for dikes on soft soil blankets. For the investigated cases, the posterior failure probability was up to several orders of magnitude lower than the prior failure probability. The main factors influencing the degree of reliability update, were the ground conditions and the degree of criticality of the slope stability during construction. In conclusion, using the information of the survived construction leads to improved reliability-based safety assessments of dikes, and consequently to more targeted and cost-effective flood protection.
Journal article
(2020)
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Mark G. van der Krogt, Wouter J. Klerk, Wim Kanning, Timo Schweckendiek, Matthijs Kok
Spatial variability and limited measurements often result in low reliability estimates of geotechnical failure modes of dikes (i.e., earthen flood defences). Required dike reinforcements are usually not executed within a few years after inception, which enables efforts to improve reliability estimates by reducing uncertainty. Often decision makers are unclear on whether uncertainty reduction is worth investing, and which (combination of) methods yields the highest Value of Information (VoI). This paper presents a framework to assess the VoI of two uncertainty reduction methods (proof loading and pore pressure monitoring) for a case study of a typical river dike with an insufficiently stable inner slope, using a decision tree. In all cases, a positive VoI was found for at least one strategy consisting of a proof load test, monitoring or both. The optimal strategy of proof loading and monitoring has a VoI of 4.0 M€, being a reduction in total cost of 25% compared to a conventional dike reinforcement. It was also found that sometimes proof loading enhances the VoI of pore pressure monitoring, which demonstrates the benefits of jointly considering different methods in a single decision tree. The decision framework yields insight in total cost and VoI of risk reduction strategies, which enables decision makers to determine where proof loading and/or pore pressure monitoring are efficient, leading to more efficient flood defence asset management.
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Spatial variability and limited measurements often result in low reliability estimates of geotechnical failure modes of dikes (i.e., earthen flood defences). Required dike reinforcements are usually not executed within a few years after inception, which enables efforts to improve reliability estimates by reducing uncertainty. Often decision makers are unclear on whether uncertainty reduction is worth investing, and which (combination of) methods yields the highest Value of Information (VoI). This paper presents a framework to assess the VoI of two uncertainty reduction methods (proof loading and pore pressure monitoring) for a case study of a typical river dike with an insufficiently stable inner slope, using a decision tree. In all cases, a positive VoI was found for at least one strategy consisting of a proof load test, monitoring or both. The optimal strategy of proof loading and monitoring has a VoI of 4.0 M€, being a reduction in total cost of 25% compared to a conventional dike reinforcement. It was also found that sometimes proof loading enhances the VoI of pore pressure monitoring, which demonstrates the benefits of jointly considering different methods in a single decision tree. The decision framework yields insight in total cost and VoI of risk reduction strategies, which enables decision makers to determine where proof loading and/or pore pressure monitoring are efficient, leading to more efficient flood defence asset management.
In this paper we show how to calibrate a site-specific transformation model to measure the undrained shear strength from CPTs. This practically unbiased transformation model can then be used to make a better estimation of the spatial average parameter, such as the depth-average of a soil layer in a homogenous deposit, using less costly indirect measurements. We show that there is a considerable difference in terms of characteristic value and probability density, between the assumptions of entirely random or systematic transformation uncertainty. Ultimately we show what the effect is of making more realistic choices for the random and systematic part in the transformation uncertainty for a case study with actual data.
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In this paper we show how to calibrate a site-specific transformation model to measure the undrained shear strength from CPTs. This practically unbiased transformation model can then be used to make a better estimation of the spatial average parameter, such as the depth-average of a soil layer in a homogenous deposit, using less costly indirect measurements. We show that there is a considerable difference in terms of characteristic value and probability density, between the assumptions of entirely random or systematic transformation uncertainty. Ultimately we show what the effect is of making more realistic choices for the random and systematic part in the transformation uncertainty for a case study with actual data.
One of the failure mechanisms of dikes is slope instability at the landward side. Often, one instability does not lead to flooding, and several successive instabilities are needed before the dike overtops, and erosion and breaching can occur, especially at lower water levels. In this paper we propose a method to estimate the probability of flooding, taking into account the residual resistance against flooding after the first instability. We base ourselves on basic probabilistic techniques and common slope stability analyses and estimate the probability of flooding by calculating the probability of several successive (conditional) instabilities. Because the geotechnical failure and dike failure is not the same for each water level, we evaluate the probability for different water levels. The case example shows that there is a considerable margin between the probability of geotechnical failure and the probability of flooding, especially at relatively low water levels. It also shows that the current practice of assuming that the probability of flooding is equal to the probability of instability is very conservative.
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One of the failure mechanisms of dikes is slope instability at the landward side. Often, one instability does not lead to flooding, and several successive instabilities are needed before the dike overtops, and erosion and breaching can occur, especially at lower water levels. In this paper we propose a method to estimate the probability of flooding, taking into account the residual resistance against flooding after the first instability. We base ourselves on basic probabilistic techniques and common slope stability analyses and estimate the probability of flooding by calculating the probability of several successive (conditional) instabilities. Because the geotechnical failure and dike failure is not the same for each water level, we evaluate the probability for different water levels. The case example shows that there is a considerable margin between the probability of geotechnical failure and the probability of flooding, especially at relatively low water levels. It also shows that the current practice of assuming that the probability of flooding is equal to the probability of instability is very conservative.
Transformation models are used to infer geotechnical properties from indirect measurements. A site-specific transformation model can be calibrated with direct and indirect measurements from a site. When such a model is used, then spatial variability, measurement errors and statistical uncertainty propagate into the uncertainty of the spatial average, which is the variable of interest in most geotechnical analyses. This research shows how all components enter the total uncertainty of a transformation model for undrained shear strength from cone resistance. A method is proposed to estimate the uncertainty in the spatial average undrained shear strength, particularly focusing on the role of averaging of all spatially variable error components. The main finding is that if a considerable share of the measurement and transformation errors is random or spatially variable, the uncertainty estimates can be considerably lower compared to methods proposed earlier, and hence, characteristic values can be considerably higher.
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Transformation models are used to infer geotechnical properties from indirect measurements. A site-specific transformation model can be calibrated with direct and indirect measurements from a site. When such a model is used, then spatial variability, measurement errors and statistical uncertainty propagate into the uncertainty of the spatial average, which is the variable of interest in most geotechnical analyses. This research shows how all components enter the total uncertainty of a transformation model for undrained shear strength from cone resistance. A method is proposed to estimate the uncertainty in the spatial average undrained shear strength, particularly focusing on the role of averaging of all spatially variable error components. The main finding is that if a considerable share of the measurement and transformation errors is random or spatially variable, the uncertainty estimates can be considerably lower compared to methods proposed earlier, and hence, characteristic values can be considerably higher.
Conference paper
(2017)
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Wim Kanning, Ana Teixeira, Mark van der Krogt, Katerina Rippi, Timo Schweckendiek, B Hardeman
Dikes in the Netherlands are by law required to comply with predefined safety standards. The goal of this paper is to derive the safety format and the required factor of safety for the inner slope (landside slope) instability failure mode. The first step is to derive the required target reliability for a cross-section. The second step is to derive a safety format in which all uncertain variables in the slope stability computation are identified and accounted for by sufficiently safe parameters estimates. The third step is to derive the relation between target reliability and required factor of safety. This is done by evaluating 48 test cases. For each test case, the factor of safety is computed based on the safety format, as well the corresponding reliability index. This paper shows how to derive a required factor of safety based on a predefined target reliability. However, the spread in this relationship is significant. Hence, a full probabilistic analysis would lead to more accurate slope stability assessments.
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Dikes in the Netherlands are by law required to comply with predefined safety standards. The goal of this paper is to derive the safety format and the required factor of safety for the inner slope (landside slope) instability failure mode. The first step is to derive the required target reliability for a cross-section. The second step is to derive a safety format in which all uncertain variables in the slope stability computation are identified and accounted for by sufficiently safe parameters estimates. The third step is to derive the relation between target reliability and required factor of safety. This is done by evaluating 48 test cases. For each test case, the factor of safety is computed based on the safety format, as well the corresponding reliability index. This paper shows how to derive a required factor of safety based on a predefined target reliability. However, the spread in this relationship is significant. Hence, a full probabilistic analysis would lead to more accurate slope stability assessments.
Conference paper
(2017)
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Timo Schweckendiek, Mark G. Van Der Krogt, Ana Teixeira, Wim Kanning, Rob Brinkman, Katerina Rippi
Dikes and levees play a crucial role in flood protection. The main causes of levee failures are of geotechnical nature. Geotechnical failure modes are also the main contributors to the probability of failure of flood defenses such as levees due to the large uncertainties in ground conditions. Hence, information on ground conditions and soil properties is crucial in safety assessments and retrofitting designs of levees. The experience in practice with designs and risk assessments is that even with a substantial amount of site investigation and laboratory testing the uncertainties in soil properties often remain large. One way to further reduce uncertainties is to include past performance information. The present papers shows how information of survived loads in the past can be incorporated in reliability assessments by means of Bayesian posterior analysis using the same (physics-based) performance models as the prior reliability assessments. The incorporation of survival information leads to reduced uncertainties and probabilities of failure. Furthermore, an approximate approach using fragility curves is proposed, mainly to make problems with computationally expensive performance functions tractable. After briefly recapping Bayesian reliability updating, we provide examples for slope stability of (flood protection) dikes. The examples illustrate that posterior analysis enables us to reduce the large prior uncertainties in soil strength parameters by "eliminating" implausible samples or regions of the joint PDF of the strength properties using the evidence of survived load conditions (i.e. water levels). The results suggest that the effect of updating the probability of failure with survival evidence can be considerable, especially when the uncertainty in the strength properties dominates the reliability. Dominant strength uncertainties are rather typical in geotechnical engineering, as opposed to, for example, structural engineering, implying that using the method is promising also for other geotechnical applications.
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Dikes and levees play a crucial role in flood protection. The main causes of levee failures are of geotechnical nature. Geotechnical failure modes are also the main contributors to the probability of failure of flood defenses such as levees due to the large uncertainties in ground conditions. Hence, information on ground conditions and soil properties is crucial in safety assessments and retrofitting designs of levees. The experience in practice with designs and risk assessments is that even with a substantial amount of site investigation and laboratory testing the uncertainties in soil properties often remain large. One way to further reduce uncertainties is to include past performance information. The present papers shows how information of survived loads in the past can be incorporated in reliability assessments by means of Bayesian posterior analysis using the same (physics-based) performance models as the prior reliability assessments. The incorporation of survival information leads to reduced uncertainties and probabilities of failure. Furthermore, an approximate approach using fragility curves is proposed, mainly to make problems with computationally expensive performance functions tractable. After briefly recapping Bayesian reliability updating, we provide examples for slope stability of (flood protection) dikes. The examples illustrate that posterior analysis enables us to reduce the large prior uncertainties in soil strength parameters by "eliminating" implausible samples or regions of the joint PDF of the strength properties using the evidence of survived load conditions (i.e. water levels). The results suggest that the effect of updating the probability of failure with survival evidence can be considerable, especially when the uncertainty in the strength properties dominates the reliability. Dominant strength uncertainties are rather typical in geotechnical engineering, as opposed to, for example, structural engineering, implying that using the method is promising also for other geotechnical applications.