The Design of Resilient Engineering Infrastructure Systems

The concept of resilience has emerged from a number of domains to address how systems, people as well as organisations can handle uncertainty and thereby not only survive hardship, but also thrive and prosper. This is of particular importance for engineering infrastructure systems which, due to the inherently long lifecycles giving rise to many unknowns, need to be designed for resilience such that it not only maintains operations in the face of day-to-day demands, but also continue to be able to evolve for the future. While there has been substantial interest in resilience from both academia and industry, exactly how such systems may be endowed with resilience to address these concerns from an engineering design perspective is less clear. To this end, a literature review was first conducted to compile the definitions and characteristics of resilience across the domains of engineering, organisational management and ecology. The characteristics were found to comprise: absorbing disturbances, adapting for change and thriving for the future. These were then mapped to the engineering design ilities of robustness, adaptability and flexibility before being brought together in a conceptual model to form a strategic view for resilience. Further methods from resilience and engineering design literature were then consulted to understand how this particular view could be modelled and evaluated. This led to the development of a preliminary model using the Least Squares Monte Carlo method adapted for a telecommunications case study. The insights gained from these explorations were then used to drive the synthesis of a novel support method whereby the design for flexibility framework was adapted to include decision modelling with Bayesian Networks and for resilience analysis. Here, resilience is taken to be the maximisation of the system economic lifecycle value under uncertainty, as measured by Expected Net Present Value, through robust and flexible strategies. This was applied to two case studies involving infrastructure systems: the first built upon existing work based on a Waste-to-Energy system in Singapore to verify the new method while the second applied the support method with BT, a multinational telecommunications company based in the UK, to gauge reception of this approach in industry. In both cases, the initial capacity and maximum number of upgrades served as proxies for robustness and flexibility respectively. Results demonstrate that Bayesian Networks are able to model decision rules for flexibility by selecting technology options over time given observations on the system and are also useful for extracting expert domain knowledge. While the construction of Bayesian Networks are subjective, they present an intuitive visualisation of the dependencies in a system and as such, engaged stakeholder interest. Resilience analysis examined the effect of volatility and drift of demand on the design strategies and indeed, there existed a trade-off between robust and flexible strategies. Furthermore, the greater utility of the support method lies in aiding decision makers in exploring the solution space and prompting discussions for what-if scenarios for the organisation.BT Grou

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org