The condition of flood defence revetments is influenced by many different degradation processes such as animal burrowing, rutting and growth of weeds. Many of these processes are shock-based rather than progressive continuous. As shocks can cause a drop in performance, this means that the condition of a revetment can suddenly decrease, meaning that revetments can have significant initial damage at the beginning of a storm. Combined with the limited detection probability of common visual inspections of flood defences, this can have a significant influence on the reliability of flood defence systems, something typically not considered in reliability analysis. In this paper we study the reliability of a flood defence system subject to shock-based degradation. Various maintenance concepts are compared for a case study of a riverine flood defence of 20 kilometres length. This demonstrates that the current maintenance concept is insufficient to satisfy the reliability requirements for failure of the revetment. Overall, the joint influence of degradation and the existing maintenance concept leads to a 20 times higher failure probability estimate compared to a typical assessment without these aspects. Next, we demonstrate that both additional inspections, and targeted interventions to reduce the impact of for instance animal burrowing, can significantly reduce total cost and improve robustness of the considered flood defence system.

Improvement in rail asset safety, comfortability and serviceability is gaining importance. This requires effective service life management by incorporating multi-stakeholder desires in the early design stage. Typical stakeholders are asset owners, train users and maintenance service providers. To allow for this change, the traditional single-sided engineering mechanics track design approach requires a shift towards an integrative design approach that best fits for common purpose while assuring continuous rail asset performance. Within the current paper, a multi-objective simulation-based optimization methodology that combines finite element modeling with preference function modeling is proposed to integrate multi-stakeholder preferences into the service life design. The applicability of the methodology is demonstrated in a design case for level crossings. It is shown that integrating specific stakeholders’ preferences will substantially influence the optimal track design configuration, allowing the level crossing design to be managed focusing on best fit for common purpose rather than on mechanical behavior only.

Floating wind farms are a promising solution for offshore wind energy production in deep waters. However, the design optimisation process of these farms is difficult due to their complex and multidisciplinary nature. Furthermore, current optimisation methods: 1) ignore and/or provide no insight into the dynamic interplay between the preference-dominated management domain and the object-performance-dominated engineering domain; 2) are limited to evaluating potentially sub-optimal design alternatives; 3) contain fundamental aggregation modelling errors; 4) do not return a single optimal design point. This paper presents an optimisation framework that overcomes these shortcomings and enables truly integrative multi-objective design optimisation. It includes a surrogate model that interacts with the wind turbine simulation tool OpenFAST to enable preliminary design of the structure’s mooring system. Applied to a demonstration project and validated against real projects in a maritime contractor environment, the workflow shows improvements in tender performance and added value over single-sided cost optimisations.

Transition zones such as level crossing and bridge approaches are critical links in railway networks due to higher degradation rates and maintenance needs. In this context, parametric optimization has been applied to improve the design in transition zones; however, it requires a more computationally efficient tool to support repetitive function evaluations, since the involved vehicle–track dynamic simulations are becoming more expensive to evaluate. For this purpose, a surrogate-based simulation methodology is proposed to search for an optimal combination of parameters relevant to the geometry and elasticity of track structures. Specifically, the presented methodology integrates finite element (FE)-based modeling with surrogate-assisted optimization: (1) the FE model is developed to characterize the dynamic behavior of a level crossing under a moving vehicle; (2) the optimization problem is formulated upon this mechanical model by extending the expensive FE simulations to an adaptive surrogate modeling scheme. This integration facilitates efficient exploration of the track design space (thereby reducing the computational cost), and a reasonable balance can be achieved between solution quality and computational effort. The methodology is applied to a Dutch railway case. Results show that compared to a reference design, the optimized design significantly improves performance indicators relevant to wheel–rail contact forces and energy dissipation in the ballast layer. The solution brings great potential in achieving a more desirable vehicle–track interaction and improving the connecting performance between level crossings and transitions. The methodology is applicable to other railway structures and may also contribute to improvements in current track design practices.

Current systems design optimisation methodologies are one-sided, as these ignore the socio-technical integration between stakeholder preferences (‘what a human wants’) and the capability of technical assets (‘what a system can deliver’). Moreover, classical multi-objective optimisation methods contain fundamental mathematical flaws. Also, the often-used classical Pareto front does not provide a single best-fit design configuration, but rather a set of design alternatives. This leaves designers without a unique solution to their problems. Finally, current multi-objective optimisation processes are not well aligned with design practices, because they do not sufficiently involve decision makers and do not translate their interests into a single common preference domain to find an overall group optimum. This paper introduces a new Open Design Systems (Odesys) methodology and a new Integrative Maximisation of Aggregated Preferences (IMAP) method, implemented in the Preferendus tool. Its added value and use are exemplified in two infrastructure design applications, which show how to achieve the pure best-fit for common-purpose design results.

Current systems design and decision management methodologies can be single-sided, ignoring or failing to capture the dynamic interplay between multi-stakeholder preferences (‘what they want’) and system performances (‘what they can’). In addition, these methodologies often contain fundamental modelling errors and do not provide single best-fit solutions. This leaves designers or decision-makers without unique answers to their problems. Above all, mainstream higher education primarily applies instructivist and research-based learning methods, and therefore does not adequately prepare students for designing solutions to future complex problems.

This book introduces both a state-of-the-art participatory design methodology (Odesys), and a design-based learning concept (ODL), which together overcome the aforementioned issues. Odesys is a pure act of open design integration to confront conflicting socio-technical interests and is the key to unlocking these complexities to deliver socially responsible systems. Odesys’ design engine, the Preferendus, enables stakeholders to cooperatively identify their best-fit design synthesis. It employs a novel optimisation method that maximises the aggregated preferences, integrating sound mathematical and extended U-modelling via open technical-, social-, and purpose cycles. The art of ODL is a constructivist design-based and well-proven learning concept fostering students’ design capabilities to become open and persistent problem solvers. It is a reflective, creative, and engaged learning approach that opens human development and unlocks new knowledge and solutions.

The author also introduces new management features such as the corporate social identifier (CSI), the ‘socio-eco’ threefold organization model and U-model based open loop management. Finally, the author places Odesys & ODL within the integrative context of empiricism, rationalism, spiritualism, and constructivism to unite the open design impulse.

This book will be of interest to both academics and practitioners working in the field of complex systems design and managerial decision-making, and functions as a textbook on systems design and management for master students from diverse backgrounds.

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit states, which combines an extreme-oriented sensitivity method with polynomial chaos expansion. This enables engineers to perform sensitivity analysis near given limit states and visualize the relevance of input factors to different design criteria and corresponding thresholds. The polynomial chaos expansion is used to approximate the model output and alleviate the computational cost in sensitivity analysis, which features sparsity and adaptivity to enhance efficiency. The accuracy and efficiency of the method are verified in a truss structure, which is then illustrated on a dynamic train–track–bridge system. The role of the input factors in response variability is clarified, which differs in terms of the design criteria chosen for sensitivity analysis. The method incorporates multi-scenarios and can thus be useful to support decision-making in design and management of engineering structures.

A co-simulation solution based on direct equilibrium of contact forces is proposed to simulate vehicle-track interaction (VTI) dynamics. It is developed in two platforms, which presents an iterative feedback loop that exchanges contact force files and structure response field in real-time. The load vector acting on the structure is described by a moving Gaussian pulse, which approximates the Dirac-delta function. Through this approach, system matrices are not exported and the additional identification of the correspondence between structure nodes and vehicle positions is avoided, which is contrary to the typical scheme in existing co-simulation methods for VTI problems. The direct information exchange in the current solution simplifies the VTI model development, making it easier for the application in track design and maintenance phase. The solution is demonstrated by a general beam model subject to a quarter car and has been calibrated and verified by benchmark cases coded in MATLAB. The example presented is a baseline model for demonstration purposes. And the proposed scheme allows for flexibility in incorporating more complex structure configurations and vehicle motions for further study.

Within traditional design processes, decisions are often made based on individualistic values and late-stage assessments conducted on a predefined set of design alternatives potentially leading to suboptimal design decisions and conflicts. The issues are further amplified by the growing complexity of construction project management where an increasing number of stakeholders are involved. To address those issues, a shift is needed towards a collaborative early stage optimization-based design process. The current optimization-based design approaches are not appropriately integrating stakeholders and their preferences in the optimization process and thereby not reflecting the real-life design and construction process. In this study, we present a pioneering multi-stakeholder design methodology combining preference function modeling theory and a priori optimization enabling stakeholders to find the group-optimal design fairly representing their preferences. The application of the developed methodology is demonstrated on a real-life multi-storey building design case. The study provides a novel approach for managing design and construction projects for academic and industry stakeholders. It also sets the foundation for the further development of stakeholder-oriented optimization-based design.

Changes in a construction project schedule can impact the project's planned duration, resulting in penalties. A manual trial-and-error probabilistic approach is usually conducted to find an appropriate set of corrective measures to mitigate delays of the overall project. However, this approach does not capture the actual goal-oriented behavior of project managers who react to the actual scenarios causing delays, leading to a fundamental modeling error. Moreover, it does not employ control and automation concepts when finding the optimal mitigation strategy. To remove this modeling error and to automate the mitigation process, the Mitigation Controller (MitC) software is developed. The MitC searches for the most cost-effective set of mitigation measures considering risk events and durations uncertainties of activities. Moreover, the MitC captures activity correlations and enables contractual penalty/reward schemes in the simulation. As a result, it returns the most effective mitigation strategy that minimizes the mitigation cost and penalty and maximizes the reward potential. The Mitigation Controller introduced here constitutes an open-source code written in Matlab

Construction project management requires dynamic mitigation control to ensure a project's timely completion. Current mitigation approaches are usually performed by an iterative Monte Carlo (MC) analysis which does not reflect (1) the project manager's goal-oriented behavior, (2) contractual project completion performance schemes, and (3) stochastic dependence between construction activities. Therefore, the development statement within this paper is to design a method and implementation tool that properly dissolves all of the aforementioned shortcomings ensuring the project's completion date by finding the most effective and efficient mitigation strategy. For this purpose, the Mitigation Controller (MitC) has been developed using an integrative approach of nonlinear stochastic optimization techniques and probabilistic Monte Carlo analysis. MitC's applicability is demonstrated using a recent Dutch large infrastructure construction project showing its added value for dynamic control on-the-run. It is shown that the MitC is a state-of-the-art decision support tool that a-priori automates and optimizes the search for the best set of mitigation strategies on-the-run rather than a-posteriori evaluating the potentially sub-optimal and over-designed mitigation strategies (as commonly done with modern software such as Primavera P6).

Building Information Models (BIM) should reflect all aspects and phases of the construction projects life cycle. However, in current contractor practices, a proliferation of different information systems has arisen, each of which in turn illuminates a different information model dimension (nD) for usually only one construction project management purpose. To solve this problem it seems obvious to build a unique overarching system based on a single prescribed data modelling structure. We argue that this attempt is unrealistic, will not serve industrial practitioners and has failed already several times. We propose to link BIM to the concept of Systems Integration (SI), to develop tailormade and integrative information systems for its intended multidimensional (nD) modelling and construction projects management purpose: i.e. a targeted BIMSI fit for nD purpose enhancing better construction projects management. In this paper this nD BIMSI concept is introduced and demonstrated by a number of grassroot projects, which have been developed and validated in close cooperation with the AEC industrial practice and construction management and (civil) engineering master student projects at TU Delft. An overview of these grass root development projects is provided. These projects show how the BIMSI concept improves construction project management in areas such as generative design, safety during construction, and AI applications for effective budgeting. The focus of these projects is not on extending or evaluating BIM knowledge and theory, but rather on transforming BIM concepts into integrative information systems to solve real life problems. Finally, a state of the art education concept developed at the TU Delft is presented to demonstrate its unique position in master education on information systems for the construction industry. This so-called Open Design Learning (ODL) education integrates the nD BIMSI concept to better prepare students for both industrial and R&D construction project management practices.

Performance evaluation and maintenance planning are gaining importance with ageing rail infrastructure and increasing demand on track safety and continuous availability. The discrete/point railway assets (e.g. bridges, level crossings) together with extended track sections constitute the main railway network infrastructure. The former has important implications in train safety, riding comfort and operating expenditures due to local intensified degradation and plays a role in effective network capacity due to their large quantity. The heterogeneity in asset features and operating environment also adds difficulties to efficient maintenance planning of multiple discrete assets. The current review screens the issue to level crossings, as little concern has been engaged to this asset type, and draws together different perspectives related to their maintenance management. The systems thinking approach is integrated and two levels of asset management (i.e. micro- and macro-level) are used to structure the synthesis, which are interdependent and synergistic. Two major approaches, namely, the mechanistic and data-driven modelling are synthesised. Both contribute to the maintenance knowledge and their comparisons are elaborated. Limitations in existing studies are identified and directions for future research are provided, aiming to contribute to a more refined ‘inspection and diagnosis’ process to properly capture the local track issues and move towards system-level maintenance approach for multiple level crossings.

Climate change and deterioration require a continuous effort to reinforce flood defences and meet reliability requirements. To efficiently upgrade flood defence systems, insight in costs and benefits of measures at a system level is required throughout the process of planning and design. Due to the size of flood defence systems the number of possible decisions is large, which hampers system optimization. We describe a greedy search algorithm that can find (near-)optimal combinations of reinforcement measures for dike segments. The algorithm has been validated by comparing results for 2800 different dike segments to an integer programming implementation. The difference in objective value (Total Cost) is only 0.04% on average, which is small compared to other uncertainties in assessment and design of dike segments. The algorithm is applied to a reinforcement project for a dike segment of 41 independent sections, and compared to the common design practice which uses reliability-based requirements on a section level. It is found that the resulting reinforced dike segment is 42% cheaper to construct than the one obtained from the common approach, based on the same input information. This illustrates the practical and societal value of the design approach using a greedy search algorithm in this context.

The wellbeing of modern societies is dependent upon the functioning of their infrastructure networks. This paper introduces the 3C concept, an integrative multi-system and multi-stakeholder optimization approach for managing infrastructure interventions (e.g., maintenance, renovation, etc.). The proposed approach takes advantage of the benefits achieved by grouping (i.e., optimizing) intervention activities. Intervention optimization leads to substantial savings on both direct intervention costs (operator) and indirect unavailability costs (society) by reducing the number of system interruptions. The proposed optimization approach is formalized into a structured mathematical model that can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators), and it can accommodate different types of intervention, such as maintenance, removal, and upgrading. The different types of interdependencies, within and across infrastructures, are modeled using a proposed interaction matrix (IM). The IM allows integrating the interventions of different infrastructure networks whose interventions are normally planned independently. Moreover, the introduced 3C concept accounts for central interventions, which are those that must occur at a pre-established time moment, where neither delay nor advance is permitted. To demonstrate the applicability of the proposed approach, an illustrative example of a multi-system and multi-actor intervention planning is introduced. Results show a substantial reduction in the operator and societal costs. In addition, the optimal intervention program obtained in the analysis shows no predictable patterns, which indicates it is a useful managerial decision support tool.

Prioritisation of flood defence maintenance is typically based on visual inspection. However, literature shows that the Probability of Detection (PoD) of visual inspection can vary significantly. Here we investigate the PoD for visual inspections of flood defence structures, the consistency of damage classification, and the influence of different variables on the PoD, such as past experience. Four flood defence sections were inspected by 22 different inspectors for a variety of damage types, such as animal burrowing and damage to block revetments. It is found that the PoD varies significantly both per damage type and inspector. Additionally, the estimated severity of damages varies significantly in comparison to the reference situation: over half of the registered damages is assigned a different severity compared to the reference, which potentially leads to incorrect maintenance measures. A likely explanation for the variation in results is the complexity of inspection guidelines and task definitions. Therefore it is advised to simplify inspection guidelines and use more focussed inspections for the most important types of damage. This likely leads to both a reduction of the number of false negatives associated with an increase in flood risk, and better risk-based asset management and maintenance prioritisation in general.

Probabilistic Monte Carlo simulations are often used to determine a project's completion time given a required probability level. During project execution, schedule changes negatively affect the probability of meeting the project's completion time. A manual trial and error approach is then conducted to find a set of mitigation measures to again arrive at the required probability level. These are then implemented as scheduled activities. The mitigation controller (MitC) proposed in this paper automates the search for finding the most cost-effective set of mitigation measures using multiobjective linear optimization so that the probability of timely completion remains at the required level. It considers different types of uncertainties and risk events in the probabilistic simulation. Moreover, it removes the fundamental modeling error that exists in the traditional probabilistic approach by incorporating human control and adaptive behavior in the simulation. Its usefulness is demonstrated using an illustrative example derived from a recent Dutch construction project in which delay is not permitted. It is shown that the MitC is capable of identifying the most effective mitigation strategies allowing for substantial cost savings.

Utility operators must rely on predictive analyses regarding the availability of their subsurface assets, which highly depend on damage by increasing amounts of excavation work. However, straightforward use of standard statistical techniques, such as logistic regression or Bayesian logistic regression, does not allow for accurate predictions of these rare events. Therefore, in this paper, alternative approaches are investigated. These approaches involve weighting the likelihood as well as over-and undersampling the data. It was found that these data methods could substantially improve the accuracy of predicting rare failure events. More specifically, an application based on the real data of a Dutch water utility operator showed that undersampling and weighting improved the balanced accuracy, varying between 0.61 and 0.66, whereas the proposed methods resulted in failure predictions on between 38% and 58% of the validation data set. Hence, the proposed methods will enable utility operators to arrive at more accurate forecasts, enhancing their asset operation decision-making.

Despite widespread attention for the governance of infrastructures and improvements of the way infrastructure assets are maintained, the interdependency of infrastructures is not often considered from a systemic maintenance and renovation perspective. In this context, the issue of unforeseen intervention delays is of particular relevance because its impact on cost and hindrance might be too costly for infrastructure operators. Building upon the 3C concept (Centralize, Cluster, and Calculate) for intervention scheduling and optimization, this paper investigates the effect of unforeseen intervention delays on the intervention costs and the corresponding closure cost of the affected interdependent systems. A stochastic approach has been proposed to deal with the uncertainty of such delays. Uncertain intervention times are combined by Monte Carlo simulations to obtain the probabilistic distribution of the total cost at the end of the timeline of an intervention program affecting multiple interdependent infrastructures. Also, the change in the statistical deviation over time of the total cost with respect to the planned (optimal) cost during the entire program is obtained. This analysis yields interesting conclusions, such as the need for specific mitigation plans conducted during the course of the program to reduce the cost generated by the cumulative delays, and the need for enhanced communication between infrastructure managers, which could bring significant (economic) benefits to all stakeholders by jointly planning their intervention activities.

The capacity of a community to react to and resist during an emergency situation is highly related to the proper functioning of its infrastructure systems. Improving the infrastructure response and recovery capacity through management and adaptation strategies can help increase community resilience. Infrastructural assets are considered outdated in almost all countries in the world. This poses a clear vulnerability to infrastructure systems when subjected to disaster events such as earthquakes. This paper proposes a statistical probabilistic approach to quantify the resilience of large-scale Water Distribution Networks (WDN). The resilience of the network is evaluated using two indices: (1) the number of users without water, and (2) the drop in the total water supply, assuming that the local failure of the system occurs when the water flow and the water pressure go below a certain threshold. A series of earthquake scenarios are applied to the studied water network whose damage is determined using fragility functions that integrate the WDN characteristics with the seismic intensity. As an illustration, the proposed approach is used to quantify the resilience of a WDN in a virtual community testbed with 900,000 inhabitants. Results obtained show interesting correlation between the earthquake occurrence time, the water demand pattern, and the pipes material. The presented approach is the first step towards a systemic planning of the maintenance activities and budget allocation of pipeline networks, where both vulnerability and criticality of pipes are combined to obtain a more comprehensive resilience index of the network.