Design Of Infrastructure Systems Research Articles

Integrating systems thinking and flexibility in infrastructure management

Infrastructure systems are subjected to demands imposed by extreme natural events and increasing socioeconomic pressures. The latter are derived, for example, from a rise in the export of goods and services, and population growth, which is also associated with problems such as access to housing and basic sanitation. According to the World Risk Report 2016, inadequate infrastructure increases the frequency of losses to life and property, as well as the risk that an extreme natural event might become a disaster. Thus, frequently, traditional infrastructure systems design and management have fail to meet the stakeholders’ expectations. The main problems emerge from decisions based on the assumption that systems are static, instead of changing continuously, and on uncertainty management. This paper presents, first, a conceptual proposal that combines systems thinking and flexibility as the main pillars to facilitate systems to respond and change over time. Systems thinking recognizes the dynamic evolution of systems and helps understand how processes and subprocesses interact at different levels (i.e., hierarchical arrangement of processes). Flexibility, which is the ability of a system to respond or change, is crucial to managing better risks and varying demands, thus fostering resilience to disasters and climate change. Incorporating flexibility in the development of infrastructure contributes to enhancing the management of planned or unplanned events, taking advantage of investment/business opportunities, reducing possible cost overruns, and handling the perceptions and interests of stakeholders. Based on the conceptual framework, the second part of the paper presents a proposal to integrate these concepts into a physical and financial infrastructure model. The outcome of this model is a set of management policies that render the actions that best reflect the interest of stakeholders. The conceptual ideas and the proposed model are illustrated with two examples showing the value of integrating systems thinking and flexibility in infrastructure management over time.

Assisting decision-makers select multi-dimensionally efficient infrastructure designs – Application to urban drainage systems

Multi-objective design approaches can help identify future infrastructure system designs that appropriately balance different engineering, environmental, and other societal goals. Planners benefit from assessing the trade-offs implied by the best-performing infrastructure system solutions. However, a large number of possible efficient system designs, obtained when using multi-objective optimization, can be overwhelming to interpret. This study attempts to aid decision-making in multi-criteria infrastructure system design by reducing the complexity of the identified set of efficient infrastructure designs, i.e., the Pareto-front. A soft clustering algorithm is applied, which identifies similarities between solutions, partitions the front accordingly, and selects a set of representative solutions while preserving the multi-dimensional structure of the solutions on the efficiency frontier. Three post-optimization decision-making metrics are introduced to help quantify the overall performance of the Pareto-optimal designs to further summarize design process outputs for decision-makers. We apply the method to an illustrious urban drainage network case study. Results show how the approach can simplify Pareto-fronts with thousands of solutions into sets of highlighted designs that aid interpreting the trade-offs implied by the best-performing simulated systems.

Predicting geological interfaces using stacking ensemble learning with multi-scale features

Understanding the variation of geological interfaces plays a crucial role in the analysis and design of infrastructure systems. Generally, there are two classes of techniques for predicting geological interfaces, for example, interpolation/regression-based techniques and machine-learning-based techniques. In this paper, a Multi-scale Meta-learning Model (M3) methodology is proposed. The new methodology improves the current state-of-the-art techniques by fusing two levels of information: (i) generic characteristics of the sampling locations, for example, coordinates, and (ii) location-specific characteristics, for example, local-scale predictions. The implementation starts from using an array of classic interpolation/regression-based techniques as base learners to provide first-level predictions at a local scale. These predictions are then combined with generic characteristics to train a meta-learner following the stacking ensemble learning framework. In this manner, the location-specific information from the base learners can be simultaneously considered with the generic information in the training process. The variation of rockhead elevation is predicted using the M3 methodology and a comprehensive borehole dataset in Singapore. A detailed comparative study involving several existing methods is also carried out to rigorously validate the M3 methodology. The results show that the M3 methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization.

Network Connectivity: Concepts, Computation, and Optimization

Networks naturally appear in many high-impact domains, ranging from social network analysis to disease dissemination studies to infrastructure system design. Within network studies, network connectivi

Zoning based cascade infrastructure system design for urban waterlogging prevention

With the development of global climate change and urbanization in China, urban waterlogging caused by rainstorm has a serious impact on the safety of people’s life and property and the development of social and economic. Based on the hydraulic model, taking Xining City as an example, a set of multi-layer flood prevention infrastructure construction technology system is proposed, in which the microscale-drainage-system mainly includes the sponge transformation of building blocks, the construction of sponge park; the mesoscale-drainage-system mainly includes the transformation of pipe system for different return periods, the transformation of combined flow and separate flow system; the macroscale-drainage-system mainly includes utility tunnel system, natural and artificial storage facilities. Through quantitative evaluation of rainstorm with 50 year return period, under the construction of microscale-drainage-system and mesoscale-drainage-system with different return period index, the total drainage capacity increased from 1.407 million m3 to 1.573 million m3 and 1.830 million m3. The total accumulated water is 1.073 million m3 and 0.816 million m3 respectively, which are less than 1.173 million m3 of macroscale-drainage-system new storage capacity.

Stormwater harvesting infrastructure systems design for urban park irrigation: Brimbank Park, Melbourne case study

Abstract Stormwater harvesting for residential and non-residential reuse is an important and crucial aspect to reduce freshwater demand to address climate change, population growth and urbanisation challenges. It is important that freshwater be conserved as much as possible through capturing rainwater and stormwater and using these resources for fit for purpose end uses such as irrigation of public open parks and residential non-potable end uses. The paper describes a methodology for the planning and design of a stormwater harvesting system for park irrigation. The application of suitable models for storage tank capacity and pipe sizing considering peak flows are described. The application of the approach is demonstrated with a local case study for the benefit of wider water professionals engaged in water-sensitive urban design.

Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming

A novel approach to EMS infrastructure systems design, planning, and operations under long-term uncertainty is introduced based on multi-stage stochastic programming and decision rules, accounting for strategic flexibility (also known as real options – RO). Different from standard RO analysis, the approach mimics real-world decision-making by exercising flexibility based on conditional-go decision rules. The objective is to minimize the expected total costs over the system’s life cycle, and the outputs are the optimal initial configuration and decision rules. A flexible solution provides lower expected cost than stochastically optimal rigid solutions, especially valuable when required incident coverage rate is >95%.

Long-term adaption decisions via fully and partially observable Markov decision processes

Long-term decisions, such as the design of infrastructure systems and buildings or the planning of risk mitigation measures, should be made in consideration of the uncertain future. The initial design of a system determines its flexibility, i.e. its ability to cope with potential future changes. Increasing flexibility is generally considered to be a good approach to dealing with future uncertainty, such as climate change uncertainty, but its effects have not been systematically investigated. We propose the use of Markov Decision Processes combined with Influence Diagrams to solve adaptation planning problems. This framework can identify the optimal system type and capacity and determine the value of flexibility. It is here applied to two numerical examples: Planning of a waste water treatment plant under uncertainty in future population growth and planning of a flood protection system under uncertain climate change scenarios. Based on these idealized examples, it is shown that for flexible systems a lower initial capacity of the system is recommendable, while for inflexible systems a conservative design (with high safety factors) should be applied. The value of flexibility is shown to be high when significant learning is expected in the future, i.e. if information gathered in the future significantly reduces uncertainty.

Life-cycle considerations in risk-informed decisions for design of civil infrastructures

Civil structures and infrastructures, such as buildings, bridges, and other facilities, are constructed and built for long service lives; many for over 50 or 100 years. In this light, the performance of an engineered system over its useful life span is naturally of major concern. The concern must include the assurance of a minimum level of reliability of performance, which will necessarily require inspections, repair, and maybe even retrofitting or replacement. The whole-life cost associated with this life-cycle performance, therefore, is the pertinent cost that ought to be the basis for determining the cost during the design stage (in terms of present value); this must include the maintenance and potential damage costs, besides the initial cost, over the life of a system. In this regard, significant technical and economic uncertainties can be expected and are unavoidable; therefore, decisions required in the design of such systems must consider risk associated with the probability of non-performance and serious damage or failure, as well as of the financial risk. The importance of these factors in the decision process at the design stage is emphasised and a practical approach for life-cycle consideration in formulating risk-informed decisions in the planning and design of infrastructure systems is described. The approach is illustrated with specific applications in the optimal design of civil infrastructure systems.

Multiattribute Selection from Alternative Designs of Infrastructure Components for Accidental Situations

Abstract: The design of infrastructure systems for “accidental” situations, i.e., abnormal service conditions, may include a comparison of alternative design solutions of system components. These solutions are compared by means of applying a universal methodology known as a multiattribute selection (MAS). The alternative designs are described by a number of attributes that characterize each of them. Failure probability and quantitative measure of the risk are used as safety‐related attributes of the alternative designs. This article shows how to carry out the MAS in the presence of epistemic uncertainties in safety‐related attributes. Distributions of epistemic uncertainties are specified by means of methods used for quantitative risk assessment. The MAS is carried out as a propagation of these uncertainties. The Monte Carlo simulation is used to propagate uncertainties through the algorithm of MAS. The approach proposed in this article is illustrated by two examples, which consider alternative designs of a protective barrier and alternative sprinkler systems.

Charging Stations for Urban Settings the design of a product platform for electric vehicle infrastructure in Dutch cities

This paper reflects the essential role of supportive infrastructure in the mass implementation of electric drive vehicle technology. A focus is placed on the development of comprehensive systems that provide efficient and diverse recharging solutions for vehicle drivers. Mass adoption of electric mobility will reflect advances in the vehicles themselves, their infrastructural networks, and their position in the automotive market. Progress in these areas is highly related, and mandates coordinated design efforts. This paper discusses current problem areas, ongoing developments, and future trends in the design and development of charging systems for battery electric vehicles. The application of infrastructures that are simple, familiar, and context-sensitive is essential to promote consumer confidence and thereby enable successful market penetration. Specific requirements and conditions for the design of infrastructure systems for electric mobility are presented.

Development of Next-Generation U.S. Infrastructure Systems

Although most of the problems of aging infrastructure systems are well understood in technical terms, the planning and organizational framework for addressing them is fractured among many well-insulated institutions and political jurisdictions with little inclination and few incentives to collaborate or coordinate services. This paper proposes that the government needs to begin its work addressing infrastructure management with a whole perspective, a clean slate of assumptions about next best steps, a diverse and inclusive set of participants, and the readiness to embrace disruptive ideas and confront tough tradeoffs in order to protect the long-term integrity of public services. A dedicated infrastructure campaign should successfully communicate a broad and compelling vision, sound investment principles and strategies, and clear evidence of shared commitment by all levels of government, the private sector and the general public. The concept of sustainability could serve as an effective organizing principle to guide large-scale infrastructure system design and development decisions.