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Bounded Rationality in Construction-Phase Change Impact Assesment
Mechanisms shaping scope and depth
Construction-phase Change Impact Assessment (CIA) is routinely formally complete but remains se lectively narrow. Impacts not surfaced before commitment often resurface later as corrective work. This thesis asks how bounded rationality affects the quality of construction-phase CIA and explains how that selective narrowing arises in complex engineering projects. The framework is empirically tested on six CIA episodes in a single industrial construction complex and triangulated with six semi-structured interviews: two with practitioners involved in the case, two with cross-project procedural experts, and two with practitioners working on adjacent projects. The findings are reported throughout at calibrated evidence weights (Strong, Moderate, Preliminary). Time pressure is confirmed as the precipitating condition that determines whether an assessment enters a bounded rational regime at all. Information uncertainty and cognitive load determine which mechanism becomes salient within a bounded rational regime. This is the baseline condition, not a variable to be reduced. Four refinements follow. A meta-BR layer at the contracting stage results in an inherited assessment environment through a design-maturity pathway (Strong) and a procurement fragmentation pathway (Moderate). Early closure operates on two tiers, per change and at portfolio level via bundled settlement, expanding the scope from individual to decision portfolios. Four protect ive conditions bounded the mechanisms. Three were derived from the framework and one, relational backing, was inductively derived as the meta-condition that supports the others under sustained time pressure. Finally, the three constraints work as a self-reinforcing system rather than as parallel inputs, with each decision reducing the resources available for the next. Read through an ecological account of heuristics, the mechanisms turn out to be ambivalent. The samestrategies that narrowed assessment and corrective work in four episodes also closed two others with none. Bounded rationality thus both supports and undermines assessment quality, depending on whether a protective condition makes the residue of a simplified assessment visible and recoverable. Three of the six tools are used within a single assessment cycle, and three are used across cycles. A workflow specification carrying the tools into the documentation environment of the case organization is also presented. The results shift CIA’s practical goal from completeness to managed incompleteness: knowing what was not assessed and having a plan for what to watch for during implementation.
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Construction-phase Change Impact Assessment (CIA) is routinely formally complete but remains se lectively narrow. Impacts not surfaced before commitment often resurface later as corrective work. This thesis asks how bounded rationality affects the quality of construction-phase CIA and explains how that selective narrowing arises in complex engineering projects. The framework is empirically tested on six CIA episodes in a single industrial construction complex and triangulated with six semi-structured interviews: two with practitioners involved in the case, two with cross-project procedural experts, and two with practitioners working on adjacent projects. The findings are reported throughout at calibrated evidence weights (Strong, Moderate, Preliminary). Time pressure is confirmed as the precipitating condition that determines whether an assessment enters a bounded rational regime at all. Information uncertainty and cognitive load determine which mechanism becomes salient within a bounded rational regime. This is the baseline condition, not a variable to be reduced. Four refinements follow. A meta-BR layer at the contracting stage results in an inherited assessment environment through a design-maturity pathway (Strong) and a procurement fragmentation pathway (Moderate). Early closure operates on two tiers, per change and at portfolio level via bundled settlement, expanding the scope from individual to decision portfolios. Four protect ive conditions bounded the mechanisms. Three were derived from the framework and one, relational backing, was inductively derived as the meta-condition that supports the others under sustained time pressure. Finally, the three constraints work as a self-reinforcing system rather than as parallel inputs, with each decision reducing the resources available for the next. Read through an ecological account of heuristics, the mechanisms turn out to be ambivalent. The samestrategies that narrowed assessment and corrective work in four episodes also closed two others with none. Bounded rationality thus both supports and undermines assessment quality, depending on whether a protective condition makes the residue of a simplified assessment visible and recoverable. Three of the six tools are used within a single assessment cycle, and three are used across cycles. A workflow specification carrying the tools into the documentation environment of the case organization is also presented. The results shift CIA’s practical goal from completeness to managed incompleteness: knowing what was not assessed and having a plan for what to watch for during implementation.
Beyond Kelly
Distributional Bet Sizing and Portfolio Insurance in Regime-Switching Markets
Master thesis
(2026)
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F.F.W. Endtz, R.J. Fokkink, L.E. Meester, Michael van Enkhuizen, Luuk Hendrikx, M.B. van Gijzen
The Kelly criterion maximizes expected log-growth and is asymptotically optimal, yet over a finite horizon the resulting wealth distribution carries a severe lower tail. This thesis places a downside objective, a quantile of annual log-growth, alongside the Kelly criterion and compares the allocations the two select, first in controlled betting games and then in a portfolio of an S&P 500 index, a trend-following program, and a rolling protective put on the index.
Index returns switch between bull and bear regimes under a two-state Gaussian Hidden Markov Model, and because the regime is hidden, the put is priced on the observable filtered market state with the Extended Girsanov Principle. Allocations under both objectives are selected by Monte Carlo simulation.
Under the main calibration the two objectives select nearly the same split between index and trend-following but different insurance. Kelly spends 1.5% of wealth on puts struck 14% out of the money, whereas the fifth-percentile objective doubles this budget and moves the strike to the money, the boundary of the tested strike range. Moving from the Kelly allocation to the fifth-percentile allocation improves the fifth percentile by 6.95 percentage points at a cost of 2.12 points of mean log-growth. Removing the early-2000s decline from the estimation window leads Kelly to abandon trend-following entirely, while the fifth-percentile solution keeps its structure. ...
Index returns switch between bull and bear regimes under a two-state Gaussian Hidden Markov Model, and because the regime is hidden, the put is priced on the observable filtered market state with the Extended Girsanov Principle. Allocations under both objectives are selected by Monte Carlo simulation.
Under the main calibration the two objectives select nearly the same split between index and trend-following but different insurance. Kelly spends 1.5% of wealth on puts struck 14% out of the money, whereas the fifth-percentile objective doubles this budget and moves the strike to the money, the boundary of the tested strike range. Moving from the Kelly allocation to the fifth-percentile allocation improves the fifth percentile by 6.95 percentage points at a cost of 2.12 points of mean log-growth. Removing the early-2000s decline from the estimation window leads Kelly to abandon trend-following entirely, while the fifth-percentile solution keeps its structure. ...
The Kelly criterion maximizes expected log-growth and is asymptotically optimal, yet over a finite horizon the resulting wealth distribution carries a severe lower tail. This thesis places a downside objective, a quantile of annual log-growth, alongside the Kelly criterion and compares the allocations the two select, first in controlled betting games and then in a portfolio of an S&P 500 index, a trend-following program, and a rolling protective put on the index.
Index returns switch between bull and bear regimes under a two-state Gaussian Hidden Markov Model, and because the regime is hidden, the put is priced on the observable filtered market state with the Extended Girsanov Principle. Allocations under both objectives are selected by Monte Carlo simulation.
Under the main calibration the two objectives select nearly the same split between index and trend-following but different insurance. Kelly spends 1.5% of wealth on puts struck 14% out of the money, whereas the fifth-percentile objective doubles this budget and moves the strike to the money, the boundary of the tested strike range. Moving from the Kelly allocation to the fifth-percentile allocation improves the fifth percentile by 6.95 percentage points at a cost of 2.12 points of mean log-growth. Removing the early-2000s decline from the estimation window leads Kelly to abandon trend-following entirely, while the fifth-percentile solution keeps its structure.
Index returns switch between bull and bear regimes under a two-state Gaussian Hidden Markov Model, and because the regime is hidden, the put is priced on the observable filtered market state with the Extended Girsanov Principle. Allocations under both objectives are selected by Monte Carlo simulation.
Under the main calibration the two objectives select nearly the same split between index and trend-following but different insurance. Kelly spends 1.5% of wealth on puts struck 14% out of the money, whereas the fifth-percentile objective doubles this budget and moves the strike to the money, the boundary of the tested strike range. Moving from the Kelly allocation to the fifth-percentile allocation improves the fifth percentile by 6.95 percentage points at a cost of 2.12 points of mean log-growth. Removing the early-2000s decline from the estimation window leads Kelly to abandon trend-following entirely, while the fifth-percentile solution keeps its structure.
The aviation sector is responsible for approximately 2.5% of global CO2 emissions. While the CO2 emissions reduced at a compound annual growth rate (CAGR) of 2.8% between 2000 and 2019, the passenger demand is projected to more than double by 2044 as compared to 2024 at a CAGR of about 4.2%, outpacing the reduction in emissions. Unconventional aircraft (for example blended wing body aircraft) are projected to reduce emissions by more than 20% as compared to widebody aircraft of comparable mission and size, making them instrumental for reducing aviation emissions. However, these novel configurations also introduce new crashworthiness challenges that must be addressed early in the design process to prevent cost overruns and delays at later stages of the design process.
Historically, preliminary crashworthiness analysis has relied on linkage-kinematic joint representation of the aircraft, with characteristic curves for the kinematic joints derived using experimental or numerical campaigns. Even the most recent multi-body and FEM-kinematic joint hybrid models still depend on characteristic curves derived through simulations or experiments. Consequently, while these models are fast and ideal for preliminary crashworthiness analysis, design exploration is limited by the amount of component level test/simulation data, highlighting a clear research gap for analytical/semi-analytical models that can inform such multi-body models on the go with material and geometry data as inputs.
This thesis addresses that gap by focusing on the two dominant energy absorption mechanisms in metallic fuselage structures: axial crushing and plastic bending. For axial crushing of closed-section metallic tubular structures, existing models are evaluated and a generalized expression for various cross-section shapes is proposed. For bending collapse, a semi-analytical framework combining spring-linkage based elasto-plastic stage prediction, transition algorithm, and Kecman model for collapse stage prediction is proposed to obtain complete force–displacement curves. Although aircraft structures commonly use open sections, the scope of the present work is limited to closed sections in order to establish a robust and consistent methodological foundation within the available time frame.
Finally, the bending framework is extended to hybrid materials, specifically GLARE (Glass Laminate Aluminium Reinforced Epoxy). A maximum strain-based damage model based on the spring-linkage method is proposed, the approach predicts stiffness degradation and progressive central cracking failure for GLARE laminates. The results demonstrate that analytical and semi-analytical models can be further extended to hybrid systems, enabling fast, early-stage crashworthiness evaluation of unconventional aircraft designs utilizing hybrid materials. ...
Historically, preliminary crashworthiness analysis has relied on linkage-kinematic joint representation of the aircraft, with characteristic curves for the kinematic joints derived using experimental or numerical campaigns. Even the most recent multi-body and FEM-kinematic joint hybrid models still depend on characteristic curves derived through simulations or experiments. Consequently, while these models are fast and ideal for preliminary crashworthiness analysis, design exploration is limited by the amount of component level test/simulation data, highlighting a clear research gap for analytical/semi-analytical models that can inform such multi-body models on the go with material and geometry data as inputs.
This thesis addresses that gap by focusing on the two dominant energy absorption mechanisms in metallic fuselage structures: axial crushing and plastic bending. For axial crushing of closed-section metallic tubular structures, existing models are evaluated and a generalized expression for various cross-section shapes is proposed. For bending collapse, a semi-analytical framework combining spring-linkage based elasto-plastic stage prediction, transition algorithm, and Kecman model for collapse stage prediction is proposed to obtain complete force–displacement curves. Although aircraft structures commonly use open sections, the scope of the present work is limited to closed sections in order to establish a robust and consistent methodological foundation within the available time frame.
Finally, the bending framework is extended to hybrid materials, specifically GLARE (Glass Laminate Aluminium Reinforced Epoxy). A maximum strain-based damage model based on the spring-linkage method is proposed, the approach predicts stiffness degradation and progressive central cracking failure for GLARE laminates. The results demonstrate that analytical and semi-analytical models can be further extended to hybrid systems, enabling fast, early-stage crashworthiness evaluation of unconventional aircraft designs utilizing hybrid materials. ...
The aviation sector is responsible for approximately 2.5% of global CO2 emissions. While the CO2 emissions reduced at a compound annual growth rate (CAGR) of 2.8% between 2000 and 2019, the passenger demand is projected to more than double by 2044 as compared to 2024 at a CAGR of about 4.2%, outpacing the reduction in emissions. Unconventional aircraft (for example blended wing body aircraft) are projected to reduce emissions by more than 20% as compared to widebody aircraft of comparable mission and size, making them instrumental for reducing aviation emissions. However, these novel configurations also introduce new crashworthiness challenges that must be addressed early in the design process to prevent cost overruns and delays at later stages of the design process.
Historically, preliminary crashworthiness analysis has relied on linkage-kinematic joint representation of the aircraft, with characteristic curves for the kinematic joints derived using experimental or numerical campaigns. Even the most recent multi-body and FEM-kinematic joint hybrid models still depend on characteristic curves derived through simulations or experiments. Consequently, while these models are fast and ideal for preliminary crashworthiness analysis, design exploration is limited by the amount of component level test/simulation data, highlighting a clear research gap for analytical/semi-analytical models that can inform such multi-body models on the go with material and geometry data as inputs.
This thesis addresses that gap by focusing on the two dominant energy absorption mechanisms in metallic fuselage structures: axial crushing and plastic bending. For axial crushing of closed-section metallic tubular structures, existing models are evaluated and a generalized expression for various cross-section shapes is proposed. For bending collapse, a semi-analytical framework combining spring-linkage based elasto-plastic stage prediction, transition algorithm, and Kecman model for collapse stage prediction is proposed to obtain complete force–displacement curves. Although aircraft structures commonly use open sections, the scope of the present work is limited to closed sections in order to establish a robust and consistent methodological foundation within the available time frame.
Finally, the bending framework is extended to hybrid materials, specifically GLARE (Glass Laminate Aluminium Reinforced Epoxy). A maximum strain-based damage model based on the spring-linkage method is proposed, the approach predicts stiffness degradation and progressive central cracking failure for GLARE laminates. The results demonstrate that analytical and semi-analytical models can be further extended to hybrid systems, enabling fast, early-stage crashworthiness evaluation of unconventional aircraft designs utilizing hybrid materials.
Historically, preliminary crashworthiness analysis has relied on linkage-kinematic joint representation of the aircraft, with characteristic curves for the kinematic joints derived using experimental or numerical campaigns. Even the most recent multi-body and FEM-kinematic joint hybrid models still depend on characteristic curves derived through simulations or experiments. Consequently, while these models are fast and ideal for preliminary crashworthiness analysis, design exploration is limited by the amount of component level test/simulation data, highlighting a clear research gap for analytical/semi-analytical models that can inform such multi-body models on the go with material and geometry data as inputs.
This thesis addresses that gap by focusing on the two dominant energy absorption mechanisms in metallic fuselage structures: axial crushing and plastic bending. For axial crushing of closed-section metallic tubular structures, existing models are evaluated and a generalized expression for various cross-section shapes is proposed. For bending collapse, a semi-analytical framework combining spring-linkage based elasto-plastic stage prediction, transition algorithm, and Kecman model for collapse stage prediction is proposed to obtain complete force–displacement curves. Although aircraft structures commonly use open sections, the scope of the present work is limited to closed sections in order to establish a robust and consistent methodological foundation within the available time frame.
Finally, the bending framework is extended to hybrid materials, specifically GLARE (Glass Laminate Aluminium Reinforced Epoxy). A maximum strain-based damage model based on the spring-linkage method is proposed, the approach predicts stiffness degradation and progressive central cracking failure for GLARE laminates. The results demonstrate that analytical and semi-analytical models can be further extended to hybrid systems, enabling fast, early-stage crashworthiness evaluation of unconventional aircraft designs utilizing hybrid materials.
Optimal sizing of renewable generation and storage for grid-connected CO2 electrolysis
A Dutch case study on syngas production for e-SAF
Co-electrolysis of CO2 (CO2E) and water enables the production of syngas for sustainable aviation fuels (SAF) that are compliant with European RFNBO (renewable fuels of non-biological origin) regulations. However, a mismatch exists between the intermittent renewable electricity supply and the continuous operation of the downstream Fischer–Tropsch plants. To address this, we developed a two-stage linear optimization model to optimize the operation of a 540 MW electrolysis plant, alongside the sizing and operation of the connected renewable generation, battery storage, and syngas storage. Applying this model to a Dutch case study, we explored grid integration with an electrolyzer across future scenarios with global warming potentials (GWPs) ranging between 35 and 370 g CO2-eq per kWh. For generation, the preferred renewable mix is onshore wind combined with PV. When grid mix electricity consumption is restricted, the electrolyzer has an optimal capacity factor of 78% but requires a battery of comparable capacity to the electrolyzer and multi-kilotonne syngas storage to ensure continuous output. Crucially, we found that producing RFNBO-compliant syngas for SAF is impossible with the 2025 Dutch grid mix. Even at a reduced grid intensity of 205 g CO2-eq per kWh, RFNBO compliance limits grid consumption to just 1% of grid mix electricity per hour. This results in a levelized cost of 2350 EUR2019 per tonne syngas. Unrestricted grid electricity consumption becomes feasible when emissions drop below 36 g CO2-eq per kWh, reducing production costs by 43% (1344 EUR2019 per tonne syngas). Consequently, we demonstrate that grid composition intensity is a bottleneck for the short-term economic viability and regulatory compliance of CO2E-based SAF in the Netherlands.
...
Co-electrolysis of CO2 (CO2E) and water enables the production of syngas for sustainable aviation fuels (SAF) that are compliant with European RFNBO (renewable fuels of non-biological origin) regulations. However, a mismatch exists between the intermittent renewable electricity supply and the continuous operation of the downstream Fischer–Tropsch plants. To address this, we developed a two-stage linear optimization model to optimize the operation of a 540 MW electrolysis plant, alongside the sizing and operation of the connected renewable generation, battery storage, and syngas storage. Applying this model to a Dutch case study, we explored grid integration with an electrolyzer across future scenarios with global warming potentials (GWPs) ranging between 35 and 370 g CO2-eq per kWh. For generation, the preferred renewable mix is onshore wind combined with PV. When grid mix electricity consumption is restricted, the electrolyzer has an optimal capacity factor of 78% but requires a battery of comparable capacity to the electrolyzer and multi-kilotonne syngas storage to ensure continuous output. Crucially, we found that producing RFNBO-compliant syngas for SAF is impossible with the 2025 Dutch grid mix. Even at a reduced grid intensity of 205 g CO2-eq per kWh, RFNBO compliance limits grid consumption to just 1% of grid mix electricity per hour. This results in a levelized cost of 2350 EUR2019 per tonne syngas. Unrestricted grid electricity consumption becomes feasible when emissions drop below 36 g CO2-eq per kWh, reducing production costs by 43% (1344 EUR2019 per tonne syngas). Consequently, we demonstrate that grid composition intensity is a bottleneck for the short-term economic viability and regulatory compliance of CO2E-based SAF in the Netherlands.
Global climate change is accelerating environmental shifts and intensifying extreme weather events, creating significant uncertainty for the reliability, safety, and continuity of underground pipeline systems. As climatic conditions evolve, traditional reliability-informed pipeline safety management strategies are limited to pre-failure prevention. This highlights the need for a lifecycle-based evolution of resilience in underground pipeline systems. To mitigate climate-related impacts, reliability assessments should therefore extend to encompass post-failure functionality and recovery. This work proposes a quantitative, lifecycle-based resilience assessment framework for underground pipelines subjected to climate change. The framework is fundamentally governed by climate-coupled probabilistic models that integrate a Kusuda-Achenbach heat-transfer model and an Arrhenius-based degradation mechanism to simulate the dynamic evolution of corrosion. Climate-driven vulnerability and recovery processes are incorporated to capture the evolution of system functionality. A case study involving climate change-induced extreme weather events (Flood) combined with typical failure mechanisms (Corrosion) illustrates the applicability and practical value of the proposed approach. The results show that the proposed approach can quantitatively characterize the degradation–recovery trajectory of pipeline systems under different climate scenarios, providing actionable insights for reliability-informed operation, maintenance prioritization, and resilience enhancement of critical infrastructure systems. This work contributes to the fields of reliability engineering and system safety by extending traditional failure-based assessments toward climate-adaptive, resilience-oriented decision support.
...
Global climate change is accelerating environmental shifts and intensifying extreme weather events, creating significant uncertainty for the reliability, safety, and continuity of underground pipeline systems. As climatic conditions evolve, traditional reliability-informed pipeline safety management strategies are limited to pre-failure prevention. This highlights the need for a lifecycle-based evolution of resilience in underground pipeline systems. To mitigate climate-related impacts, reliability assessments should therefore extend to encompass post-failure functionality and recovery. This work proposes a quantitative, lifecycle-based resilience assessment framework for underground pipelines subjected to climate change. The framework is fundamentally governed by climate-coupled probabilistic models that integrate a Kusuda-Achenbach heat-transfer model and an Arrhenius-based degradation mechanism to simulate the dynamic evolution of corrosion. Climate-driven vulnerability and recovery processes are incorporated to capture the evolution of system functionality. A case study involving climate change-induced extreme weather events (Flood) combined with typical failure mechanisms (Corrosion) illustrates the applicability and practical value of the proposed approach. The results show that the proposed approach can quantitatively characterize the degradation–recovery trajectory of pipeline systems under different climate scenarios, providing actionable insights for reliability-informed operation, maintenance prioritization, and resilience enhancement of critical infrastructure systems. This work contributes to the fields of reliability engineering and system safety by extending traditional failure-based assessments toward climate-adaptive, resilience-oriented decision support.