T.R. van Woudenberg
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1
The Optimization and Parametric Design of Low-Carbon Concrete Structures
The development of a tool for reinforced concrete flat slab floor systems
The construction sector is responsible for a significant share of global greenhouse gas emissions, with floor systems often being responsible for a substantial amount of embodied carbon emissions for building projects. Reinforced concrete flat slabs are widely used due to their practicality, but they require large quantities of concrete and reinforcement. This thesis investigates how combinations of selected design variables influence the embodied carbon and material cost of reinforced concrete flat slab floors, and how cost- and carbon-efficient designs can be identified while satisfying selected structural design requirements.
To support this objective, a parametric design tool was developed for reinforced concrete flat slab floor systems. The tool combines FEM, cross-sectional capacity calculations, Eurocode verifications, embodied carbon calculations and cost calculations into one workflow. The design variables used in this tool are slab thickness, column spacing in x- and y-direction, concrete strength classes, reinforcement diameters and reinforcement center to center distances. All variables are defined in a discrete way and an exhaustive search approach is used to evaluate all possible combinations of those variables. The resulting data is then filtered, allowing infeasible designs to be removed and the data of feasible designs to be visualized.
A fictive design example was used to demonstrate the developed approach. In general, shorter spans, thinner slabs, lower concrete strength classes and smaller reinforcement diameters resulted in the lowest embodied carbon and material costs. However, the results also showed substantial overlap between the performance of individual variables. This demonstrates that no single variable fully determines the performance of a flat slab and that the variables should be evaluated in combination. Slab thickness and bottom reinforcement diameter showed the largest average influence within the investigated design example. A small trade-off between embodied carbon and material cost was also observed, although configurations with low values for both objectives could still be identified. This thesis also introduced a reduction of the top reinforcement, which resulted in a total steel quantity reduction of 18% and a subsequent decrease in the embodied carbon of a floor of 6-8%, compared to configurations which did not have this reduction. This illustrates that reinforcement detailing can significantly influence the embodied carbon of floors.
The developed workflow supports transparent decision-making during early design stages by providing a selection of well-performing alternatives. In addition, it clearly visualizes the relationships between the selected variables, embodied carbon and material cost. The main limitations are the computational time of the exhaustive search, its sensitivity to user-defined variable ranges and the simplifications used in the structural and reinforcement modelling. Future development should focus on improving computational efficiency, refining the reinforcement model and extending the approach to other floor systems. ...
To support this objective, a parametric design tool was developed for reinforced concrete flat slab floor systems. The tool combines FEM, cross-sectional capacity calculations, Eurocode verifications, embodied carbon calculations and cost calculations into one workflow. The design variables used in this tool are slab thickness, column spacing in x- and y-direction, concrete strength classes, reinforcement diameters and reinforcement center to center distances. All variables are defined in a discrete way and an exhaustive search approach is used to evaluate all possible combinations of those variables. The resulting data is then filtered, allowing infeasible designs to be removed and the data of feasible designs to be visualized.
A fictive design example was used to demonstrate the developed approach. In general, shorter spans, thinner slabs, lower concrete strength classes and smaller reinforcement diameters resulted in the lowest embodied carbon and material costs. However, the results also showed substantial overlap between the performance of individual variables. This demonstrates that no single variable fully determines the performance of a flat slab and that the variables should be evaluated in combination. Slab thickness and bottom reinforcement diameter showed the largest average influence within the investigated design example. A small trade-off between embodied carbon and material cost was also observed, although configurations with low values for both objectives could still be identified. This thesis also introduced a reduction of the top reinforcement, which resulted in a total steel quantity reduction of 18% and a subsequent decrease in the embodied carbon of a floor of 6-8%, compared to configurations which did not have this reduction. This illustrates that reinforcement detailing can significantly influence the embodied carbon of floors.
The developed workflow supports transparent decision-making during early design stages by providing a selection of well-performing alternatives. In addition, it clearly visualizes the relationships between the selected variables, embodied carbon and material cost. The main limitations are the computational time of the exhaustive search, its sensitivity to user-defined variable ranges and the simplifications used in the structural and reinforcement modelling. Future development should focus on improving computational efficiency, refining the reinforcement model and extending the approach to other floor systems. ...
The construction sector is responsible for a significant share of global greenhouse gas emissions, with floor systems often being responsible for a substantial amount of embodied carbon emissions for building projects. Reinforced concrete flat slabs are widely used due to their practicality, but they require large quantities of concrete and reinforcement. This thesis investigates how combinations of selected design variables influence the embodied carbon and material cost of reinforced concrete flat slab floors, and how cost- and carbon-efficient designs can be identified while satisfying selected structural design requirements.
To support this objective, a parametric design tool was developed for reinforced concrete flat slab floor systems. The tool combines FEM, cross-sectional capacity calculations, Eurocode verifications, embodied carbon calculations and cost calculations into one workflow. The design variables used in this tool are slab thickness, column spacing in x- and y-direction, concrete strength classes, reinforcement diameters and reinforcement center to center distances. All variables are defined in a discrete way and an exhaustive search approach is used to evaluate all possible combinations of those variables. The resulting data is then filtered, allowing infeasible designs to be removed and the data of feasible designs to be visualized.
A fictive design example was used to demonstrate the developed approach. In general, shorter spans, thinner slabs, lower concrete strength classes and smaller reinforcement diameters resulted in the lowest embodied carbon and material costs. However, the results also showed substantial overlap between the performance of individual variables. This demonstrates that no single variable fully determines the performance of a flat slab and that the variables should be evaluated in combination. Slab thickness and bottom reinforcement diameter showed the largest average influence within the investigated design example. A small trade-off between embodied carbon and material cost was also observed, although configurations with low values for both objectives could still be identified. This thesis also introduced a reduction of the top reinforcement, which resulted in a total steel quantity reduction of 18% and a subsequent decrease in the embodied carbon of a floor of 6-8%, compared to configurations which did not have this reduction. This illustrates that reinforcement detailing can significantly influence the embodied carbon of floors.
The developed workflow supports transparent decision-making during early design stages by providing a selection of well-performing alternatives. In addition, it clearly visualizes the relationships between the selected variables, embodied carbon and material cost. The main limitations are the computational time of the exhaustive search, its sensitivity to user-defined variable ranges and the simplifications used in the structural and reinforcement modelling. Future development should focus on improving computational efficiency, refining the reinforcement model and extending the approach to other floor systems.
To support this objective, a parametric design tool was developed for reinforced concrete flat slab floor systems. The tool combines FEM, cross-sectional capacity calculations, Eurocode verifications, embodied carbon calculations and cost calculations into one workflow. The design variables used in this tool are slab thickness, column spacing in x- and y-direction, concrete strength classes, reinforcement diameters and reinforcement center to center distances. All variables are defined in a discrete way and an exhaustive search approach is used to evaluate all possible combinations of those variables. The resulting data is then filtered, allowing infeasible designs to be removed and the data of feasible designs to be visualized.
A fictive design example was used to demonstrate the developed approach. In general, shorter spans, thinner slabs, lower concrete strength classes and smaller reinforcement diameters resulted in the lowest embodied carbon and material costs. However, the results also showed substantial overlap between the performance of individual variables. This demonstrates that no single variable fully determines the performance of a flat slab and that the variables should be evaluated in combination. Slab thickness and bottom reinforcement diameter showed the largest average influence within the investigated design example. A small trade-off between embodied carbon and material cost was also observed, although configurations with low values for both objectives could still be identified. This thesis also introduced a reduction of the top reinforcement, which resulted in a total steel quantity reduction of 18% and a subsequent decrease in the embodied carbon of a floor of 6-8%, compared to configurations which did not have this reduction. This illustrates that reinforcement detailing can significantly influence the embodied carbon of floors.
The developed workflow supports transparent decision-making during early design stages by providing a selection of well-performing alternatives. In addition, it clearly visualizes the relationships between the selected variables, embodied carbon and material cost. The main limitations are the computational time of the exhaustive search, its sensitivity to user-defined variable ranges and the simplifications used in the structural and reinforcement modelling. Future development should focus on improving computational efficiency, refining the reinforcement model and extending the approach to other floor systems.
Fleet Planning Modelling for Greenery Maintenance
A Case Study For Maaskracht
The construction sector is under increasing pressure to reduce emissions, and the adoption of zero-emission equipment is a key part of this transition. However, while the literature has focused mainly on the optimisation of equipment acquisition, the shift from diesel to battery-electric machinery also introduces operational challenges in the day-to-day scheduling and use of the equipment that have received less attention. Battery capacity is limited, charging takes time, and charging infrastructure is spatially fixed and dispersed. These constraints fundamentally change how work can be planned and executed, yet no dedicated planning tools exist to support contractors in managing them.
This thesis investigates how modelling can provide insights into the use of zero-emission equipment at Maaskracht, a consortium responsible for floodplain maintenance along the river Meuse in the Netherlands. Maaskracht operates with a growing fleet of battery-electric vehicles and faces the daily challenge of scheduling maintainence activities across dispersed locations while respecting energy constraints.
The research follows an engineering design approach. Semi-structured interviews with three Maaskracht planners and the SSEB coordinator documented current planning practices and established that the transition from diesel to zero-emission equipment changes the nature of the planning problem. For diesel machinery, fuel is widely available, so energy is not a binding constraint on the schedule. For battery-powered machinery the limited capacity, charging time, and fixed charging locations make energy the binding constraint. In current practice the basis of the schedule is drawn up without the battery constraint and then adjusted afterwards to fit the energy requirements. The interviews establish that energy needs to be considered in a holistic approach.
Based on these findings, a graph-based planning model was developed, producing a schedule that is guaranteed to respect battery constraints while completing all assigned activities.
The model was validated through two structured sessions with stakeholders. Both groups confirmed that the tool addresses a real operational need and produces actionable output. Route planning completes within 30 seconds for up to 10 activities, meeting the usability requirements established through the interviews. Also, from the validation sessions strategic applications surfaced beyond daily planning, including what-if analysis for equipment acquisition decisions by changing input parameters and the identification of operational bottlenecks through visualisation of the results that would not be apparent in manual planning processes.
The main finding is that modelling fleet utilisation provides insights by integrating battery constraints directly into the planning process, rather than treating energy as an afterthought. The model reveals feasible activity sequences that planners currently avoid under conservative assignment, makes operational bottlenecks visible by distinguishing energy-intensive activities from cumulative travel constraints, and produces output that connects daily planning to equipment procurement.
...
This thesis investigates how modelling can provide insights into the use of zero-emission equipment at Maaskracht, a consortium responsible for floodplain maintenance along the river Meuse in the Netherlands. Maaskracht operates with a growing fleet of battery-electric vehicles and faces the daily challenge of scheduling maintainence activities across dispersed locations while respecting energy constraints.
The research follows an engineering design approach. Semi-structured interviews with three Maaskracht planners and the SSEB coordinator documented current planning practices and established that the transition from diesel to zero-emission equipment changes the nature of the planning problem. For diesel machinery, fuel is widely available, so energy is not a binding constraint on the schedule. For battery-powered machinery the limited capacity, charging time, and fixed charging locations make energy the binding constraint. In current practice the basis of the schedule is drawn up without the battery constraint and then adjusted afterwards to fit the energy requirements. The interviews establish that energy needs to be considered in a holistic approach.
Based on these findings, a graph-based planning model was developed, producing a schedule that is guaranteed to respect battery constraints while completing all assigned activities.
The model was validated through two structured sessions with stakeholders. Both groups confirmed that the tool addresses a real operational need and produces actionable output. Route planning completes within 30 seconds for up to 10 activities, meeting the usability requirements established through the interviews. Also, from the validation sessions strategic applications surfaced beyond daily planning, including what-if analysis for equipment acquisition decisions by changing input parameters and the identification of operational bottlenecks through visualisation of the results that would not be apparent in manual planning processes.
The main finding is that modelling fleet utilisation provides insights by integrating battery constraints directly into the planning process, rather than treating energy as an afterthought. The model reveals feasible activity sequences that planners currently avoid under conservative assignment, makes operational bottlenecks visible by distinguishing energy-intensive activities from cumulative travel constraints, and produces output that connects daily planning to equipment procurement.
...
The construction sector is under increasing pressure to reduce emissions, and the adoption of zero-emission equipment is a key part of this transition. However, while the literature has focused mainly on the optimisation of equipment acquisition, the shift from diesel to battery-electric machinery also introduces operational challenges in the day-to-day scheduling and use of the equipment that have received less attention. Battery capacity is limited, charging takes time, and charging infrastructure is spatially fixed and dispersed. These constraints fundamentally change how work can be planned and executed, yet no dedicated planning tools exist to support contractors in managing them.
This thesis investigates how modelling can provide insights into the use of zero-emission equipment at Maaskracht, a consortium responsible for floodplain maintenance along the river Meuse in the Netherlands. Maaskracht operates with a growing fleet of battery-electric vehicles and faces the daily challenge of scheduling maintainence activities across dispersed locations while respecting energy constraints.
The research follows an engineering design approach. Semi-structured interviews with three Maaskracht planners and the SSEB coordinator documented current planning practices and established that the transition from diesel to zero-emission equipment changes the nature of the planning problem. For diesel machinery, fuel is widely available, so energy is not a binding constraint on the schedule. For battery-powered machinery the limited capacity, charging time, and fixed charging locations make energy the binding constraint. In current practice the basis of the schedule is drawn up without the battery constraint and then adjusted afterwards to fit the energy requirements. The interviews establish that energy needs to be considered in a holistic approach.
Based on these findings, a graph-based planning model was developed, producing a schedule that is guaranteed to respect battery constraints while completing all assigned activities.
The model was validated through two structured sessions with stakeholders. Both groups confirmed that the tool addresses a real operational need and produces actionable output. Route planning completes within 30 seconds for up to 10 activities, meeting the usability requirements established through the interviews. Also, from the validation sessions strategic applications surfaced beyond daily planning, including what-if analysis for equipment acquisition decisions by changing input parameters and the identification of operational bottlenecks through visualisation of the results that would not be apparent in manual planning processes.
The main finding is that modelling fleet utilisation provides insights by integrating battery constraints directly into the planning process, rather than treating energy as an afterthought. The model reveals feasible activity sequences that planners currently avoid under conservative assignment, makes operational bottlenecks visible by distinguishing energy-intensive activities from cumulative travel constraints, and produces output that connects daily planning to equipment procurement.
This thesis investigates how modelling can provide insights into the use of zero-emission equipment at Maaskracht, a consortium responsible for floodplain maintenance along the river Meuse in the Netherlands. Maaskracht operates with a growing fleet of battery-electric vehicles and faces the daily challenge of scheduling maintainence activities across dispersed locations while respecting energy constraints.
The research follows an engineering design approach. Semi-structured interviews with three Maaskracht planners and the SSEB coordinator documented current planning practices and established that the transition from diesel to zero-emission equipment changes the nature of the planning problem. For diesel machinery, fuel is widely available, so energy is not a binding constraint on the schedule. For battery-powered machinery the limited capacity, charging time, and fixed charging locations make energy the binding constraint. In current practice the basis of the schedule is drawn up without the battery constraint and then adjusted afterwards to fit the energy requirements. The interviews establish that energy needs to be considered in a holistic approach.
Based on these findings, a graph-based planning model was developed, producing a schedule that is guaranteed to respect battery constraints while completing all assigned activities.
The model was validated through two structured sessions with stakeholders. Both groups confirmed that the tool addresses a real operational need and produces actionable output. Route planning completes within 30 seconds for up to 10 activities, meeting the usability requirements established through the interviews. Also, from the validation sessions strategic applications surfaced beyond daily planning, including what-if analysis for equipment acquisition decisions by changing input parameters and the identification of operational bottlenecks through visualisation of the results that would not be apparent in manual planning processes.
The main finding is that modelling fleet utilisation provides insights by integrating battery constraints directly into the planning process, rather than treating energy as an afterthought. The model reveals feasible activity sequences that planners currently avoid under conservative assignment, makes operational bottlenecks visible by distinguishing energy-intensive activities from cumulative travel constraints, and produces output that connects daily planning to equipment procurement.
Steel Truss Optimisation and Segmentation Strategies for Large Spans
A case study on The Pier redevelopment in Scheveningen
This thesis presents a material-efficient steel roof truss design for the redevelopment of The Pier in Scheveningen. A parametric workflow integrating Rhino-Grasshopper, Karamba3D structural analysis, and cross-section optimisation is developed to minimise total structural steel weight. Four architecturally compatible truss alternatives are explored, resulting in the selection of a Pratt truss with a total steel mass of 31 tonnes. This configuration corresponds to approximately 48 tonnes of embodied carbon (CO2-eq, lifecycle stages A1–A3) and achieves a 27% mass reduction compared to the heaviest considered alternative. Insights from four Haskoning case studies informed a practical two-segment truss segmentation strategy, with splice plates strategically located in the shear-dominant zones. This segmentation approach limits individual segment weights to 11.3 tonnes, complying with Dutch transport restrictions (maximum dimensions of 23 m × 3.5 m × 2 m x 32 t). Validation of the optimisation and segmentation methodology was performed using RFEM analysis and Eurocode-based hand calculations, demonstrating deviations below 1% for mass and deflection criteria. Interviews conducted with Dutch steel fabricators underscored steel member self-weight as the primary driver of fabrication costs, reinforcing the validity of the weight-focused optimisation objective. The study demonstrates the effectiveness of integrating parametric geometry optimisation, cross-sectional sizing, and transport-driven segmentation strategies. The resulting design approach achieves substantial reductions in material use and embodied carbon emissions while ensuring practical constructability and compliance with structural codes.
...
This thesis presents a material-efficient steel roof truss design for the redevelopment of The Pier in Scheveningen. A parametric workflow integrating Rhino-Grasshopper, Karamba3D structural analysis, and cross-section optimisation is developed to minimise total structural steel weight. Four architecturally compatible truss alternatives are explored, resulting in the selection of a Pratt truss with a total steel mass of 31 tonnes. This configuration corresponds to approximately 48 tonnes of embodied carbon (CO2-eq, lifecycle stages A1–A3) and achieves a 27% mass reduction compared to the heaviest considered alternative. Insights from four Haskoning case studies informed a practical two-segment truss segmentation strategy, with splice plates strategically located in the shear-dominant zones. This segmentation approach limits individual segment weights to 11.3 tonnes, complying with Dutch transport restrictions (maximum dimensions of 23 m × 3.5 m × 2 m x 32 t). Validation of the optimisation and segmentation methodology was performed using RFEM analysis and Eurocode-based hand calculations, demonstrating deviations below 1% for mass and deflection criteria. Interviews conducted with Dutch steel fabricators underscored steel member self-weight as the primary driver of fabrication costs, reinforcing the validity of the weight-focused optimisation objective. The study demonstrates the effectiveness of integrating parametric geometry optimisation, cross-sectional sizing, and transport-driven segmentation strategies. The resulting design approach achieves substantial reductions in material use and embodied carbon emissions while ensuring practical constructability and compliance with structural codes.
S.P.O.T. Scenario Planning Optimisation Tool
A Tool for Comparing Individually Optimised Construction Scenarios Based on Cost Minimisation
Construction planning often involves comparing multiple execution scenarios to identify the most effective approach. In practice, this process relies heavily on the planner’s experience and intuition, making decisions difficult to substantiate. This study addresses the question: “How can construction planning scenarios of high-rise apartment buildings (<70m) be effectively compared to support data-driven decision-making in the Netherlands?” Using the Design Science Research method, the study investigates the challenges planners face and explores how construction scenarios can be evaluated more systematically. The aim is to develop a framework and design a tool that supports holistic, effective, and data-driven comparisons of construction scenarios.
Through a literature review and 13 practitioner interviews, six key factors were identified as essential for scenario comparison. These include cost, time, resources, risk, sustainability, and cash flow. These factors and their methods of incorporation were included in the framework. A decision-support tool was designed to operationalise the framework. It largely automates the comparison process, incorporates familiar visualisations, and allows planners to input project-specific scenarios. To improve comparability, the tool applies two optimisations: crane allocation (resource-based) and float-based task shifting (technique-based). Each scenario is evaluated under near-optimal conditions using a full, or rule-based, exhaustive search, reducing inefficiencies and focusing comparisons on strategic differences. Validation showed the tool aligned with planners’ working methods and mostly improved the speed and quality of scenario analysis.
The research demonstrates that the developed framework and tool improve scenario comparison by supporting quick, transparent, well-substantiated decisions. Optimisation ensures fair comparisons, and the use of familiar visuals improves usability and communication. Further development and real-world application are recommended to refine the tool and increase adoption. Future research is based on the limitations and could extend the approach to other project types, phases, or stakeholders.
...
Through a literature review and 13 practitioner interviews, six key factors were identified as essential for scenario comparison. These include cost, time, resources, risk, sustainability, and cash flow. These factors and their methods of incorporation were included in the framework. A decision-support tool was designed to operationalise the framework. It largely automates the comparison process, incorporates familiar visualisations, and allows planners to input project-specific scenarios. To improve comparability, the tool applies two optimisations: crane allocation (resource-based) and float-based task shifting (technique-based). Each scenario is evaluated under near-optimal conditions using a full, or rule-based, exhaustive search, reducing inefficiencies and focusing comparisons on strategic differences. Validation showed the tool aligned with planners’ working methods and mostly improved the speed and quality of scenario analysis.
The research demonstrates that the developed framework and tool improve scenario comparison by supporting quick, transparent, well-substantiated decisions. Optimisation ensures fair comparisons, and the use of familiar visuals improves usability and communication. Further development and real-world application are recommended to refine the tool and increase adoption. Future research is based on the limitations and could extend the approach to other project types, phases, or stakeholders.
...
Construction planning often involves comparing multiple execution scenarios to identify the most effective approach. In practice, this process relies heavily on the planner’s experience and intuition, making decisions difficult to substantiate. This study addresses the question: “How can construction planning scenarios of high-rise apartment buildings (<70m) be effectively compared to support data-driven decision-making in the Netherlands?” Using the Design Science Research method, the study investigates the challenges planners face and explores how construction scenarios can be evaluated more systematically. The aim is to develop a framework and design a tool that supports holistic, effective, and data-driven comparisons of construction scenarios.
Through a literature review and 13 practitioner interviews, six key factors were identified as essential for scenario comparison. These include cost, time, resources, risk, sustainability, and cash flow. These factors and their methods of incorporation were included in the framework. A decision-support tool was designed to operationalise the framework. It largely automates the comparison process, incorporates familiar visualisations, and allows planners to input project-specific scenarios. To improve comparability, the tool applies two optimisations: crane allocation (resource-based) and float-based task shifting (technique-based). Each scenario is evaluated under near-optimal conditions using a full, or rule-based, exhaustive search, reducing inefficiencies and focusing comparisons on strategic differences. Validation showed the tool aligned with planners’ working methods and mostly improved the speed and quality of scenario analysis.
The research demonstrates that the developed framework and tool improve scenario comparison by supporting quick, transparent, well-substantiated decisions. Optimisation ensures fair comparisons, and the use of familiar visuals improves usability and communication. Further development and real-world application are recommended to refine the tool and increase adoption. Future research is based on the limitations and could extend the approach to other project types, phases, or stakeholders.
Through a literature review and 13 practitioner interviews, six key factors were identified as essential for scenario comparison. These include cost, time, resources, risk, sustainability, and cash flow. These factors and their methods of incorporation were included in the framework. A decision-support tool was designed to operationalise the framework. It largely automates the comparison process, incorporates familiar visualisations, and allows planners to input project-specific scenarios. To improve comparability, the tool applies two optimisations: crane allocation (resource-based) and float-based task shifting (technique-based). Each scenario is evaluated under near-optimal conditions using a full, or rule-based, exhaustive search, reducing inefficiencies and focusing comparisons on strategic differences. Validation showed the tool aligned with planners’ working methods and mostly improved the speed and quality of scenario analysis.
The research demonstrates that the developed framework and tool improve scenario comparison by supporting quick, transparent, well-substantiated decisions. Optimisation ensures fair comparisons, and the use of familiar visuals improves usability and communication. Further development and real-world application are recommended to refine the tool and increase adoption. Future research is based on the limitations and could extend the approach to other project types, phases, or stakeholders.