A.A. Roubos
Please Note
11 records found
1
Preference-based and optimization-based decision support for port infrastructure renewal
A two-part research combining preference function modelling and net present value optimization at the Port of Rotterdam
Part I improves the evaluation process through the application of the Preference Function Modelling (PFM) methodology (Barzilai, 2010) within the Tetra software. Three weighting scenarios were constructed for case study I, in which equal weights, standardized survey weights, and project-specific weights were applied to nine decision criteria. The project-specific scenario reproduced the real-world decision, whereas the equal-weight and standardized survey scenarios did not. These results suggest that the standardized survey weights may serve as an organizational reference, but that they cannot replace project-specific input without critical evaluation.
Part II addresses the generative design stage through a single-objective model that maximizes value quantitatively by means of the Net Present Value (NPV). The model was implemented in Python, which combined a differential evolution algorithm with a Monte Carlo simulation of 500 iterations to account for demand uncertainty. Application to case study II yielded a near-optimal configuration of three berths of 360 m and a modal NPV of approximately EUR 303 M. The model identified the Very Large Bulk Carrier (VLBC) as the design vessel class, whereas the real-world quay wall accommodates the Valemax class. This divergence appears to be driven by the locational quay length constraint and by the dominance of cumulative seaport dues over the operational period.
These two parts together suggest that the framework can improve both the transparency and the economic justification of quay wall design decisions. The PFM-based tool provides a traceable and mathematically consistent evaluation process for quay wall projects, while the optimization model reframes the design problem in terms of value generation rather than cost minimization. Both tools were validated through semi-structured interviews with practitioners, who confirmed their added value as decision support instruments rather than as prescriptive design tools. One limitation is that the optimization adopts a single financial objective from the perspective of the PoR Authority, whereas the conventional process also involves other stakeholders. Future research could therefore extend the model towards a multi-objective optimization, for example through the Preferendus methodology by Wolfert (2023), which would broaden its applicability within the port industry. ...
Part I improves the evaluation process through the application of the Preference Function Modelling (PFM) methodology (Barzilai, 2010) within the Tetra software. Three weighting scenarios were constructed for case study I, in which equal weights, standardized survey weights, and project-specific weights were applied to nine decision criteria. The project-specific scenario reproduced the real-world decision, whereas the equal-weight and standardized survey scenarios did not. These results suggest that the standardized survey weights may serve as an organizational reference, but that they cannot replace project-specific input without critical evaluation.
Part II addresses the generative design stage through a single-objective model that maximizes value quantitatively by means of the Net Present Value (NPV). The model was implemented in Python, which combined a differential evolution algorithm with a Monte Carlo simulation of 500 iterations to account for demand uncertainty. Application to case study II yielded a near-optimal configuration of three berths of 360 m and a modal NPV of approximately EUR 303 M. The model identified the Very Large Bulk Carrier (VLBC) as the design vessel class, whereas the real-world quay wall accommodates the Valemax class. This divergence appears to be driven by the locational quay length constraint and by the dominance of cumulative seaport dues over the operational period.
These two parts together suggest that the framework can improve both the transparency and the economic justification of quay wall design decisions. The PFM-based tool provides a traceable and mathematically consistent evaluation process for quay wall projects, while the optimization model reframes the design problem in terms of value generation rather than cost minimization. Both tools were validated through semi-structured interviews with practitioners, who confirmed their added value as decision support instruments rather than as prescriptive design tools. One limitation is that the optimization adopts a single financial objective from the perspective of the PoR Authority, whereas the conventional process also involves other stakeholders. Future research could therefore extend the model towards a multi-objective optimization, for example through the Preferendus methodology by Wolfert (2023), which would broaden its applicability within the port industry.
Predicting mooring line forces of large-scale vessels using machine learning
A case study at the Port of Rotterdam
Most existing port infrastructure and mooring equipment were designed for smaller ships, making accurate estimation of mooring line forces increasingly critical.
Metamodels, machine learning models trained on numerically simulated data, offer a promising alternative to traditional, computationally expensive simulation-based methods by enabling rapid predictions with a useful level of accuracy.
This study proposes a metamodeling approach for the numerical Dynamic Mooring Analysis (DMA) to predict mooring line forces from input parameters that describe environmental conditions, mooring systems, and ship characteristics.
The methodology is demonstrated in a case study involving a 333-meter container vessel moored at a berth in the Port of Rotterdam.
A total of 11,520 scenarios were simulated using the DMA model aNySIM and used to train and test two candidate metamodels: Linear Regression (LR) and Multilayer Perceptron (MLP).
After evaluating both models on predictive accuracy, efficiency in terms of prediction speed and development effort, and interpretability, the MLP was selected as the preferred DMA metamodel.
It achieved high predictive performance, with an RMSE of 10 kN and an R2 of 0.996, while offering prediction times measured in microseconds. This is more than seven orders of magnitude faster than the numerical DMA, thereby enabling large-batch predictions.
The metamodel revealed that pretension is clearly the most dominant feature for predicting the mean mooring line force, followed by MBL. For the maximum mooring line force, the most influential features were identified as pretension, windvelocity, and wind direction. ...
Most existing port infrastructure and mooring equipment were designed for smaller ships, making accurate estimation of mooring line forces increasingly critical.
Metamodels, machine learning models trained on numerically simulated data, offer a promising alternative to traditional, computationally expensive simulation-based methods by enabling rapid predictions with a useful level of accuracy.
This study proposes a metamodeling approach for the numerical Dynamic Mooring Analysis (DMA) to predict mooring line forces from input parameters that describe environmental conditions, mooring systems, and ship characteristics.
The methodology is demonstrated in a case study involving a 333-meter container vessel moored at a berth in the Port of Rotterdam.
A total of 11,520 scenarios were simulated using the DMA model aNySIM and used to train and test two candidate metamodels: Linear Regression (LR) and Multilayer Perceptron (MLP).
After evaluating both models on predictive accuracy, efficiency in terms of prediction speed and development effort, and interpretability, the MLP was selected as the preferred DMA metamodel.
It achieved high predictive performance, with an RMSE of 10 kN and an R2 of 0.996, while offering prediction times measured in microseconds. This is more than seven orders of magnitude faster than the numerical DMA, thereby enabling large-batch predictions.
The metamodel revealed that pretension is clearly the most dominant feature for predicting the mean mooring line force, followed by MBL. For the maximum mooring line force, the most influential features were identified as pretension, windvelocity, and wind direction.
Reliability assessment of flexible dolphins
Reducing uncertainty in the design approach of flexible dolphins
Berth location and pathway optimisation of port basins
Using generative design
Preference-Based Decision Support for Quay Wall Assessment
Determining the preferred configuration of structural adjustments using the optimisation method of Preferendus
The tool, developed using computational science, uses Python because of its capability to automate repetitive calculations. It integrates with the program D-Sheet Piling that is used for specific sheet pile calculations. The tool is designed to be used during the preliminary design phase, enabling quick assessment of potential reinforcement adjustments and facilitating insight into the preferred solution. It evaluates three main strategies for quay wall reinforcement: lowering active soil stress, increasing passive soil stress, and enhancing pile stability. The tool uniquely focuses on maximising the aggregated preference of involved stakeholders and is capable of evaluating failure mechanisms of sheet piles.
It is tested on three case studies, all located in the industrial harbour Loven in Tilburg. The results show that the tool effectively proposes which structural adjustments are applicable to create a sheet pile design that satisfies. The thesis concludes by drawing specific conclusions for each structural adjustment considered in the project. Moreover, it concludes that the development of a decision support tool has been successful. In particular, the tool enhances efficiency in sheet pile calculations, offers detailed insights into the environmental and financial impact of adjustments and enables the direct determination of the preferred configuration of structural adjustments. This eliminates the need to choose the preferred configuration from a number of designed variants, which is the current approach.
Additionally, the thesis recommends to conduct a follow-up research on the applicability of underwater anchors, which have shown significant structural potential. Furthermore, it recommends to conduct laboratory tests to potentially improve the cohesion and angle of internal friction of soil layers. If those soil parameters can be improved, no structural adjustment may be necessary to reinforce quay walls at all. ...
The tool, developed using computational science, uses Python because of its capability to automate repetitive calculations. It integrates with the program D-Sheet Piling that is used for specific sheet pile calculations. The tool is designed to be used during the preliminary design phase, enabling quick assessment of potential reinforcement adjustments and facilitating insight into the preferred solution. It evaluates three main strategies for quay wall reinforcement: lowering active soil stress, increasing passive soil stress, and enhancing pile stability. The tool uniquely focuses on maximising the aggregated preference of involved stakeholders and is capable of evaluating failure mechanisms of sheet piles.
It is tested on three case studies, all located in the industrial harbour Loven in Tilburg. The results show that the tool effectively proposes which structural adjustments are applicable to create a sheet pile design that satisfies. The thesis concludes by drawing specific conclusions for each structural adjustment considered in the project. Moreover, it concludes that the development of a decision support tool has been successful. In particular, the tool enhances efficiency in sheet pile calculations, offers detailed insights into the environmental and financial impact of adjustments and enables the direct determination of the preferred configuration of structural adjustments. This eliminates the need to choose the preferred configuration from a number of designed variants, which is the current approach.
Additionally, the thesis recommends to conduct a follow-up research on the applicability of underwater anchors, which have shown significant structural potential. Furthermore, it recommends to conduct laboratory tests to potentially improve the cohesion and angle of internal friction of soil layers. If those soil parameters can be improved, no structural adjustment may be necessary to reinforce quay walls at all.
This research was conducted using measurement data from the HHTT quay wall at the port of Rotterdam, applying a recently developed analytical anchor displacement model, and the Bayesian updating technique ’aBUS-SuS’. The combination of both of these methods in this context is a novelty approach to the subject. The first step was to derive the soil parameters necessary to reproduce the measurements to prove firstly, that the model is capable to capture anchor behavior, secondly to get a better understanding of how the model functions in this context, and thirdly to establish a baseline of the parameters necessary for the estimation of prior distributions.
The second step was the application of the Bayesian updating algorithm. There, the prior distributions, which are needed as an input, are generated using the results from the first step. The resulting posterior distributions are then related to failure limits of the critical soil parameters that were derived according to a common anchor failure criterion. This gives an indication of the anchor’s utilization and disposition to failure.
The results exhibited failure conditions for those anchors that were at failure, verifying that the novelty approach can be applied to the problem. Another finding was that depending on the soil parameter, conclusions can be drawn about the likeliness of the failure type. This means how brittle or elastic the failure might be.
The analyses showed that the studied procedure has potential and the anchor’s proneness to failure can be assessed. The underlying analytical model is suitable for the applied type of Bayesian updating because the results are in the same range as for the calibration, but on average slightly lower in their mean magnitude. Thus, additional information is obtained, and the overall uncertainty is reduced. Furthermore, the analyses of the suitability tests gave a quantifiable indication of the anchors’ reliability under failure load that was in line with measured failure indication under working load.
Even though the uncertainty that surrounds the anchor-design was reduced, limitations to the framework arose. The analytical model requires uncommon soil parameters as an input. Those parameters need to be specifically determined in laboratory investigations and cannot be tested for on site. The model in- and output needs to match the measurements to the failure criterion of the anchors, and in anchor design, advanced analytical models, like the one used in this research are scarce. The measurement always have certain errors inherent. These errors can lead to diffuse and noisy posterior distributions. Furthermore, a minimum amount of measurements must be available in the first place, otherwise the algorithm does not converge properly towards a solution.
This is why it is recommended to get detailed insight of the soil parameters at question also under higher stresses and strains than the anticipated working loads. This helps to give better estimations of the prior distributions and their bounds, and also gives larger confidence in the posterior distributions as a result. Furthermore, this also helps to establish precise parametric limits corresponding to failure, for which the reliability can be evaluated. ...
This research was conducted using measurement data from the HHTT quay wall at the port of Rotterdam, applying a recently developed analytical anchor displacement model, and the Bayesian updating technique ’aBUS-SuS’. The combination of both of these methods in this context is a novelty approach to the subject. The first step was to derive the soil parameters necessary to reproduce the measurements to prove firstly, that the model is capable to capture anchor behavior, secondly to get a better understanding of how the model functions in this context, and thirdly to establish a baseline of the parameters necessary for the estimation of prior distributions.
The second step was the application of the Bayesian updating algorithm. There, the prior distributions, which are needed as an input, are generated using the results from the first step. The resulting posterior distributions are then related to failure limits of the critical soil parameters that were derived according to a common anchor failure criterion. This gives an indication of the anchor’s utilization and disposition to failure.
The results exhibited failure conditions for those anchors that were at failure, verifying that the novelty approach can be applied to the problem. Another finding was that depending on the soil parameter, conclusions can be drawn about the likeliness of the failure type. This means how brittle or elastic the failure might be.
The analyses showed that the studied procedure has potential and the anchor’s proneness to failure can be assessed. The underlying analytical model is suitable for the applied type of Bayesian updating because the results are in the same range as for the calibration, but on average slightly lower in their mean magnitude. Thus, additional information is obtained, and the overall uncertainty is reduced. Furthermore, the analyses of the suitability tests gave a quantifiable indication of the anchors’ reliability under failure load that was in line with measured failure indication under working load.
Even though the uncertainty that surrounds the anchor-design was reduced, limitations to the framework arose. The analytical model requires uncommon soil parameters as an input. Those parameters need to be specifically determined in laboratory investigations and cannot be tested for on site. The model in- and output needs to match the measurements to the failure criterion of the anchors, and in anchor design, advanced analytical models, like the one used in this research are scarce. The measurement always have certain errors inherent. These errors can lead to diffuse and noisy posterior distributions. Furthermore, a minimum amount of measurements must be available in the first place, otherwise the algorithm does not converge properly towards a solution.
This is why it is recommended to get detailed insight of the soil parameters at question also under higher stresses and strains than the anticipated working loads. This helps to give better estimations of the prior distributions and their bounds, and also gives larger confidence in the posterior distributions as a result. Furthermore, this also helps to establish precise parametric limits corresponding to failure, for which the reliability can be evaluated.
In this research, parallel hull sections are used in numerical simulations to investigate the allowable load of fenders and to derive the influence of panel size and dimensions (tall or wide). Including detailed parallel hull sections for a representative group of vessels, makes it possible to look beyond simplified geometries, such as stiffened panels, and specific case studies. First, the structural response and corresponding governing failure modes were studied. In addition to existing failure modes described in fender-induced loads, tripping of stiffeners as a possible governing failure mode was included. A modification to available analytical formulations was made to describe the critical tripping pressure of stiffeners with a flange under patch loads more accurately. The proposed critical tripping pressure induced by a fender is underestimated by the analytical model in comparison to the numerical simulations of the parallel sections. When the rotational restraint of the web frame attached to the tripping stiffener is considered, a closer correlation between the analytical results and the numerical simulations of the parallel hull is foreseen. For the numerical simulations, a parametric approach was adopted, where different impact locations and contact areas were applied for several vessel types and sizes. The lowest steel grade of vessels currently applied in shipbuilding was implemented to obtain the lower limit of allowable fender-induced loads.
The key finding of this study is that allowable fender-induced loads are largely influenced by the vessel's structural dimensions, such as web frame spacing, and the size of the fender panel with respect to the ship's geometry. The constant hull pressure criterion currently used by PIANC can be maintained but should be limited to a total allowable reaction force, because, for large panels, it overestimates the capacity. Furthermore, it has been shown that for large ships, wide panels outperform tall panels because they activate web frame(s). Making panels much wider does not necessarily yield more capacity because the stress concentration remains in the web frames. For small vessels, the trend is less clear, as the web frame is activated at an earlier stage (less far apart) and the capacity does not increase exponentially with the width. In addition, high panels on small vessels sometimes lead to the activation of a deck and thus increase the allowable load. The overall conclusion of this research is that the PIANC criterion should be limited to a total reaction force. Furthermore, by correctly sizing fender panels, more efficient use of the vessel's capacity can be ensured, as web frames provide more capacity. The findings of this research can be used to allow small and large vessels to safely berth onto existing facilities. ...
In this research, parallel hull sections are used in numerical simulations to investigate the allowable load of fenders and to derive the influence of panel size and dimensions (tall or wide). Including detailed parallel hull sections for a representative group of vessels, makes it possible to look beyond simplified geometries, such as stiffened panels, and specific case studies. First, the structural response and corresponding governing failure modes were studied. In addition to existing failure modes described in fender-induced loads, tripping of stiffeners as a possible governing failure mode was included. A modification to available analytical formulations was made to describe the critical tripping pressure of stiffeners with a flange under patch loads more accurately. The proposed critical tripping pressure induced by a fender is underestimated by the analytical model in comparison to the numerical simulations of the parallel sections. When the rotational restraint of the web frame attached to the tripping stiffener is considered, a closer correlation between the analytical results and the numerical simulations of the parallel hull is foreseen. For the numerical simulations, a parametric approach was adopted, where different impact locations and contact areas were applied for several vessel types and sizes. The lowest steel grade of vessels currently applied in shipbuilding was implemented to obtain the lower limit of allowable fender-induced loads.
The key finding of this study is that allowable fender-induced loads are largely influenced by the vessel's structural dimensions, such as web frame spacing, and the size of the fender panel with respect to the ship's geometry. The constant hull pressure criterion currently used by PIANC can be maintained but should be limited to a total allowable reaction force, because, for large panels, it overestimates the capacity. Furthermore, it has been shown that for large ships, wide panels outperform tall panels because they activate web frame(s). Making panels much wider does not necessarily yield more capacity because the stress concentration remains in the web frames. For small vessels, the trend is less clear, as the web frame is activated at an earlier stage (less far apart) and the capacity does not increase exponentially with the width. In addition, high panels on small vessels sometimes lead to the activation of a deck and thus increase the allowable load. The overall conclusion of this research is that the PIANC criterion should be limited to a total reaction force. Furthermore, by correctly sizing fender panels, more efficient use of the vessel's capacity can be ensured, as web frames provide more capacity. The findings of this research can be used to allow small and large vessels to safely berth onto existing facilities.
Bowthruster-induced flow on the bottom of a vertical quay wall
A field measurement
The results of this field measurement showed mean flow velocities near the quay wall generally in the order of magnitude of 1 m/s, with the exception of one test, where mean flow velocities in the order of magnitude of 2 m/s were recorded. This relatively low mean flow velocities were often correlated with large turbulent fluctuations, leading to values of relative turbulence intensities higher than the ones found in literature, and sometimes even equal to 1. Comparison with the theoretical calculations of velocities according to Dutch and German methods suggested by PIANC, showed both methods to be conservative if compared with data from most tests. Furthermore, it appeared that both formulae’s sensitivity to wall and keel clearance was not reflected by the data. Similarly, results from this measurement showed that the flow generated by simultaneous use of two bowthrusters was characterized by velocities on the bed lower than expected according to the guidelines. Recommendation would be to use either linear superposition or to multiply by square root of n (where n is the number of used propellers) when considering the use of multiple propellers, but this was not reflected by most of the data. However, two of the tests taken into exam represented an exception to these general observations: ADV1, the instrument nearer to the quay wall, recorded velocities higher than the theoretical values for tests 12 (use of bowthruster 2 at high water) and 22 (use of both bowthrusters simultaneously at low water). Results from this study showed how the use of a 4-channel bowthruster system induced a flow on the bottom of a vertical quay wall which is mainly divided in two zones. Near the quay wall is where the highest velocities have been measured, and where the flow is strictly influenced by use of the bowthrusters. There is a return flow beneath the ship, which is dissipated in the space of few meters. Underneath the suction points of the bowthrusters, it is the inflow to determine the flow characteristics on the bed. In this research, the extent of the bowthruster-induced flow was found to be less than 14 m from the quay wall. The instrument hereby located, in fact, didn't record velocities which were affected by the use of bowthrusters. This research represents a step towards filling the knowledge gaps about use of bowthrusters at a vertical quay wall. The unique dataset collected can be used in the future for validating numerical or on-scale models, working for a better understanding of the phenomenon and a more accurate and optimized design of bed protections. ...
The results of this field measurement showed mean flow velocities near the quay wall generally in the order of magnitude of 1 m/s, with the exception of one test, where mean flow velocities in the order of magnitude of 2 m/s were recorded. This relatively low mean flow velocities were often correlated with large turbulent fluctuations, leading to values of relative turbulence intensities higher than the ones found in literature, and sometimes even equal to 1. Comparison with the theoretical calculations of velocities according to Dutch and German methods suggested by PIANC, showed both methods to be conservative if compared with data from most tests. Furthermore, it appeared that both formulae’s sensitivity to wall and keel clearance was not reflected by the data. Similarly, results from this measurement showed that the flow generated by simultaneous use of two bowthrusters was characterized by velocities on the bed lower than expected according to the guidelines. Recommendation would be to use either linear superposition or to multiply by square root of n (where n is the number of used propellers) when considering the use of multiple propellers, but this was not reflected by most of the data. However, two of the tests taken into exam represented an exception to these general observations: ADV1, the instrument nearer to the quay wall, recorded velocities higher than the theoretical values for tests 12 (use of bowthruster 2 at high water) and 22 (use of both bowthrusters simultaneously at low water). Results from this study showed how the use of a 4-channel bowthruster system induced a flow on the bottom of a vertical quay wall which is mainly divided in two zones. Near the quay wall is where the highest velocities have been measured, and where the flow is strictly influenced by use of the bowthrusters. There is a return flow beneath the ship, which is dissipated in the space of few meters. Underneath the suction points of the bowthrusters, it is the inflow to determine the flow characteristics on the bed. In this research, the extent of the bowthruster-induced flow was found to be less than 14 m from the quay wall. The instrument hereby located, in fact, didn't record velocities which were affected by the use of bowthrusters. This research represents a step towards filling the knowledge gaps about use of bowthrusters at a vertical quay wall. The unique dataset collected can be used in the future for validating numerical or on-scale models, working for a better understanding of the phenomenon and a more accurate and optimized design of bed protections.
In this study, more insight is acquired into the relationship between the construction costs and the reliability index of quay walls. Firstly, the two quay walls are designed semi-probabilistic in RC1, RC2 and RC3, using D-Sheet Piling for the double anchored combi-wall and using Plaxis 2D for the combi-wall with a relieving platform. Thereafter, the construction costs of these designs are calculated and compared. Besides that, the influence of the partial safety factors, which are defined in the Eurocodes and distinguish the reliability classes, on the construction costs is quantified. The same was done for the influence of three of the critical failure mechanisms; ‘passive resistance inadequate’, ‘sheet pile profile fails’ and ‘tension member anchorage fails’. For these failure mechanisms the reliability indices are estimated using the reliability analyses module of D-Sheet Piling, which is based on a probabilistic level II analysis, the First Order Reliability Method (FORM).
It appeared that the marginal costs of safety investments for both quay walls is relatively low, even significantly lower than suggested by Roubos et al. (2018). It followed that the differentiation in construction costs between the reliability classes is considerably less than the differentiation in construction costs between quay walls in practice. Therefore, it seems that the current reliability classes and the corresponding set of partial safety factors, as defined in the Eurocodes and CUR 211, are non-functional for quay walls. Besides that, it can be concluded that when designing a quay wall, the determination of the angle of internal friction of the soil strongly influences the construction costs, followed by the surface- and crane loads. The influence of the cohesion of the soil and the bollard load on the construction costs is very small. Furthermore, the influence of the failure mechanisms ‘passive resistance inadequate’ and ‘tension member anchorage fails’ on the construction costs of the double anchored combi-wall is relatively low. Therefore, it is suggested that the reliability index of the quay wall can be increased in a economically attractive manner by increasing the length of the tubular piles of the combi-wall or the steel sectional area of the anchor rod. Due to these influences, it can be economically beneficial to increase the target reliability index of the failure mechanism ‘passive resistance inadequate’ and decrease the target reliability index of ‘sheet pile profile fails’. ...
In this study, more insight is acquired into the relationship between the construction costs and the reliability index of quay walls. Firstly, the two quay walls are designed semi-probabilistic in RC1, RC2 and RC3, using D-Sheet Piling for the double anchored combi-wall and using Plaxis 2D for the combi-wall with a relieving platform. Thereafter, the construction costs of these designs are calculated and compared. Besides that, the influence of the partial safety factors, which are defined in the Eurocodes and distinguish the reliability classes, on the construction costs is quantified. The same was done for the influence of three of the critical failure mechanisms; ‘passive resistance inadequate’, ‘sheet pile profile fails’ and ‘tension member anchorage fails’. For these failure mechanisms the reliability indices are estimated using the reliability analyses module of D-Sheet Piling, which is based on a probabilistic level II analysis, the First Order Reliability Method (FORM).
It appeared that the marginal costs of safety investments for both quay walls is relatively low, even significantly lower than suggested by Roubos et al. (2018). It followed that the differentiation in construction costs between the reliability classes is considerably less than the differentiation in construction costs between quay walls in practice. Therefore, it seems that the current reliability classes and the corresponding set of partial safety factors, as defined in the Eurocodes and CUR 211, are non-functional for quay walls. Besides that, it can be concluded that when designing a quay wall, the determination of the angle of internal friction of the soil strongly influences the construction costs, followed by the surface- and crane loads. The influence of the cohesion of the soil and the bollard load on the construction costs is very small. Furthermore, the influence of the failure mechanisms ‘passive resistance inadequate’ and ‘tension member anchorage fails’ on the construction costs of the double anchored combi-wall is relatively low. Therefore, it is suggested that the reliability index of the quay wall can be increased in a economically attractive manner by increasing the length of the tubular piles of the combi-wall or the steel sectional area of the anchor rod. Due to these influences, it can be economically beneficial to increase the target reliability index of the failure mechanism ‘passive resistance inadequate’ and decrease the target reliability index of ‘sheet pile profile fails’.
Only the most relevant failure mechanisms were considered, which are yielding of the combi-wall, yielding of the anchor bar, shear failure of the grout body and soil mechanical failure. These failure mechanisms are complex soil-structure interaction problems. Therefore, both the soil and the quay structure have been modelled with the finite element program Plaxis 2D, using the Hardening soil model. For performing the probabilistic calculations on this model, the probabilistic module ProbAna has been used. This is a package developed by Plaxis which couples several types of reliability methods to the finite element software of Plaxis 2D. As the computational effort is relatively large when using FEM, the First Order Reliability Method (FORM) was used over sampling methods like Directional Sampling and Crude Monte Carlo simulation.
The results for the simple quay wall showed that the reliability level was sufficient for all considered limit states. Hence, almost all partial factors derived on this quay wall were lower than currently prescribed by the Eurocode. In the second case study, a quay wall with relieving platform, monitoring data of multiple years was used for calibration of the Plaxis-model. Thereafter, the reliability for the limit states yielding of the wall and soil mechanical failure was evaluated. It turned out that for both limit states, the reliability index was too low compared to the target reliability.
Although each case study concerned a different type of quay wall, the results reveal that choices made in the design, either optimistic or pessimistic, can have large influence on the reliability. Perhaps just as important, are the assumptions made regarding the stochastic description of the soil. It is still under discussion up to what distance soil parameters are correlated in space and how spatial averaging should be applied. Reference calculations showed that choices regarding the amount of independent soil layers and the degree of spatial averaging have a large influence on the reliability. More fundamental research to these topics is therefore recommended.
...
Only the most relevant failure mechanisms were considered, which are yielding of the combi-wall, yielding of the anchor bar, shear failure of the grout body and soil mechanical failure. These failure mechanisms are complex soil-structure interaction problems. Therefore, both the soil and the quay structure have been modelled with the finite element program Plaxis 2D, using the Hardening soil model. For performing the probabilistic calculations on this model, the probabilistic module ProbAna has been used. This is a package developed by Plaxis which couples several types of reliability methods to the finite element software of Plaxis 2D. As the computational effort is relatively large when using FEM, the First Order Reliability Method (FORM) was used over sampling methods like Directional Sampling and Crude Monte Carlo simulation.
The results for the simple quay wall showed that the reliability level was sufficient for all considered limit states. Hence, almost all partial factors derived on this quay wall were lower than currently prescribed by the Eurocode. In the second case study, a quay wall with relieving platform, monitoring data of multiple years was used for calibration of the Plaxis-model. Thereafter, the reliability for the limit states yielding of the wall and soil mechanical failure was evaluated. It turned out that for both limit states, the reliability index was too low compared to the target reliability.
Although each case study concerned a different type of quay wall, the results reveal that choices made in the design, either optimistic or pessimistic, can have large influence on the reliability. Perhaps just as important, are the assumptions made regarding the stochastic description of the soil. It is still under discussion up to what distance soil parameters are correlated in space and how spatial averaging should be applied. Reference calculations showed that choices regarding the amount of independent soil layers and the degree of spatial averaging have a large influence on the reliability. More fundamental research to these topics is therefore recommended.