Network structure,complexity, and adaptation in water resource systems

Infrastructure systems are often complex. Many have both natural and built components. For such systems, including water resource networks, resilience is a common policy goal. In the formalised study of complex systems, the structure and function of networks can contribute directly to system resilience. One branch of complex systems studies, network science, describes how connectivity between individual components can explain some system-wide properties of growth and reliability. Water resource systems analysis has only begun to apply techniques from network theory and complexity science to assess adaptability and resilience. We present an analysis of connectivity in a network model of California's water infrastructure system using several network science techniques. Results indicate that nodes in California's water system are clustered but without scale-free properties. The network originates from a mixture of top-down (centralised) and bottom-up (dispersed interactions of parties) planning. This structure provides managers greater flexibility to use local and distant water sources. We use the analysis to illustrate how several disciplinary notions of resilience apply to civil infrastructure planning. We also explore how adaptability, not just complexity, influences resilience in planning. Creating systems that can respond to future changes must be an important policy goal in planning civil infrastructure.     

Topological surrogates for computationally efficient seismic robustness optimization of water pipe networks

The criticality of seismic robustness of the water pipe networks cannot be overstated. Current methodologies for optimizing seismic robustness of city‐scale water pipe networks are scarce. A very few studies that can be found are also prone to long optimization runtimes due to the requirement of repeated hydraulic analysis. Hence, there is a critical need for the identification of computationally efficient surrogate optimization methods for maximizing seismic robustness of water pipe networks. To address this need, this research was conducted to identify, for the first time, computationally efficient topological surrogates for hydraulic simulation‐based optimization. The computational efficiency of surrogate optimization was measured in terms of solution quality (i.e., post‐earthquake serviceability) and computational runtime. Ten different topological connectivity metrics were evaluated out of which five were considered computationally infeasible due to their prohibitive optimization runtime. Five remaining metrics were then used to formulate five surrogate objective functions for seismic robustness of water pipe networks. Each of these functions was optimized using a simulated annealing‐based algorithm. Application of the proposed approach to city‐level benchmark networks helped to identify two metrics out of ten that offered a substantial reduction in optimization runtime with a minimal loss in solution quality. These findings will be highly valuable to water distribution network managers for identifying economical rehabilitation policies for enhancing the seismic robustness at a city‐scale within a reasonable amount of time.     

A random field based technique for the efficiency enhancement of bridge network life-cycle analysis under uncertainty

Studies associated with distributed civil infrastructure systems are usually very demanding from a computational point of view, especially when they involve life-cycle analysis, uncertainty, and optimization. For this reason, computational tools that enhance the efficiency of the analysis and make it feasible for complex practical applications are of utmost importance. In this paper, a computational technique for the efficiency enhancement of bridge network life-cycle analysis under uncertainty is presented and its impact in terms of CPU time reduction is investigated.The proposed technique consists in the joint use of random field theory and probabilistic reliability models for the simulation of the individual bridge service states over the life-cycle of the infrastructure. This random field based approach is extremely efficient and takes simultaneously into account the deterioration in time of the bridge reliability and the correlation in space of the service states of bridges belonging to the same transportation network. Compared to other techniques previously used to perform the same task, the proposed methodology is theoretically more solid and improves the computational efficiency by more than two orders of magnitude.A numerical example is provided to validate the proposed technique. Moreover, a second example involving the life-cycle performance analysis of a complex bridge network in Santa Barbara, CA, is presented.     

Bridge life-cycle performance and cost: analysis,prediction, optimisation and decision-making

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.     

Infrastructure-level traffic micro-simulation for probabilistic analysis of bridge loads

In state-of-the-art building codes, the traffic loads for the design or assessment of bridges should derive from a probabilistic characterization. However, because traffic depends on the vehicle flow peculiar to the transportation infrastructure of interest, the frequency of exceedance of code-assigned loads is factually unknown. This study presents a methodology to probabilistically characterize the traffic loads on bridges based on network-level traffic micro-simulation and its application to the A56, that is, the urban highway connecting Naples’ (Italy) districts. One year of traffic simulations, in conjunction with structural modeling of the bridges featured in the infrastructure, enabled the probabilistic characterization of the traffic-induced structural demand and the determination of the bridge-specific safety margins along the highway. The results of the study and of the application to A56 ultimately show that: (i) traffic micro-simulation appears to be a suitable approach to bridge-specific structural safety assessment; (ii) structural actions deriving from code-assigned loads tend to be conservative with respect to their traffic-simulation-derived counterparts; and (iii) structural demand induced by traffic loads can vary along the same transportation infrastructure.     

Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to the Biofuel Industry

Abstract: As the biofuel industry continues to expand, the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply points to the refineries and biofuel shipment to the consumption locations, which increases traffic demand in the transportation network and contributes to additional congestion (especially in the neighborhood of the refineries). Hence, it is beneficial to form public‐private partnerships to simultaneously consider transportation network expansion and biofuel supply chain design to mitigate congestion. This article presents an integrated mathematical model for biofuel supply chain design where the near‐optimum number and location of biorefinery facilities, the near‐optimal routing of biomass and biofuel shipments, and possible highway/railroad capacity expansion are determined. The objective is to minimize the total cost for biorefinery construction, transportation infrastructure expansion, and transportation delay (for both biomass/biofuel shipment and public travel) under congestion. A genetic algorithm framework (with embedded Lagrangian relaxation and traffic assignment algorithms) is developed to solve the optimization model, and an empirical case study for the state of Illinois is conducted with realistic biofuel production data. The computational results show that the proposed solution approach is able to solve the problem efficiently. Various managerial insights are also drawn. It shall be noted that although this article focuses on the booming biofuel industry, the model and solution techniques are suitable for a number of application contexts that simultaneously involve network traffic equilibrium, infrastructure expansion, and facility location choices (which determine the origin/destination of multi‐commodity flow).     

Rethinking bus-to-metro accessibility in new town development: Case studies in Shanghai

With the rapid urbanization in cities around the world, new towns that are close to the existing urban fringe have been developed to accommodate the increasing population. However, due to the long development time of the infrastructure systems in these new towns, the establishment of public transportation services usually lags behind the population expansion. Therefore, to ensure urban accessibility, governments utilize bus networks to bridge the connectivity gaps of metro systems. To assist the design and decision-making required for bus and metro interconnectivity and optimize public transportation networks, this study proposed a quantitative network-based framework. The proposed framework extended the existing social network analysis theory and identified five indicators to assess and optimize the network design. To validate the proposed method, nine typical cases in Shanghai were examined. The results based on the proposed analysis framework suggest that more edges between access points (bus stops within the walkable area of a metro station entrance) and other nodes can improve the accessibility of the study area and create a well-integrated system. Therefore, this study is able to provide an insightful understanding of intermodal transportation coordination and transport facility arrangement.     

Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Many bridge structures, one of the most critical components in transportation infrastructure systems, exhibit signs of deteriorations and are approaching or beyond the initial design service life. Therefore, structural health inspections of these bridges are becoming critically important, especially after extreme events. To enhance the efficiency of such an inspection, in recent years, autonomous damage detection based on computer vision has become a research hotspot. This article proposes a three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies. The convolutional neural network for image classification, object detection, and semantic segmentation are, respectively, proposed to conduct system‐level failure classification, component‐level bridge column detection, and local damage‐level damage localization. To enable efficient training and prediction using a small data set, the model robustness is a crucial aspect to be taken into account, generally through its hyperparameters’ selection. This article, based on Bayesian optimization, proposes a principled manner of such selection, with which very promising results (well over 90% accuracies) and robustness are observed on all three‐level deep learning models.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Surrogate‐Based Optimization of Expensive‐to‐Evaluate Objective for Optimal Highway Toll Charges in Transportation Network

This article adopts a family of surrogate‐based optimization approaches to approximate the response surface for the transportation simulation input–output mapping and search for the optimal toll charges in a transportation network. The computational effort can thus be significantly reduced for the expensive‐to‐evaluate optimization problem. Meanwhile, the random noise that always occurs through simulations can be addressed by this family of approaches. Both one‐stage and two‐stage surrogate models are tested and compared. A suboptimal exploration strategy and a global exploration strategy are incorporated and validated. A simulation‐based dynamic traffic assignment model DynusT (Dynamic Urban Systems in Transportation) is utilized to evaluate the system performance in response to different link‐additive toll schemes implemented on a highway in a real road transportation network. With the objective of minimizing the network‐wide average travel time, the simulation results show that implementing the optimal toll predicted by the surrogate model can benefit society in multiple ways. The travelers gain from the 2.5% reduction (0.45 minutes) of the average travel time. The total reduction in the time cost during the extended peak hours would be around US$65,000 for all the 570,000 network users assuming a US$15 per hour value of time. Meanwhile, the government benefits from the 20% increase of toll revenue compared to the current situation. Thus, applying the optimized pricing scheme in real world can be an encouraging policy option to enhance the performance of the transportation system in the study region.     

A systems theoretic approach to second moment modelling of probabilistic engineering systems∗

A new approach to modelling probabilistic and stochastic engineering systems using graph theory is outlined. This approach emphasizes the introduction of probabilistic and stochastic concepts at the very beginning of the model building process. This is in marked contrast to past tendencies to add probabilistic concepts to the end of an otherwise deterministic model building process. The development of topological models founded on oriented probabilistic measurements is given. which leads to the identification of some useful statistical properties of the’ random interconnection equations’. The development of probabilistic component models is given, where a component can be characterized independently of other components. Methods for automatically building the second moment representation of the Mixed Nodal Tableau are discussed. The application of the above theory to the probabilistic analysis of a pipe network problem is briefly described.     

Integration of Sustainability Issues in Strategic Transportation Planning: A Multi‐criteria Model for the Assessment of Transport Infrastructure Plans

Abstract: Last decades have witnessed significant advances in transportation planning methodologies, facilitated by the development of computational algorithms, technologies, spatial modeling tools—such as geographical information systems (GIS) and decision support systems (DSS). However, at strategic planning levels, a commonly accepted assessment model integrating the sustainability paradigm is still lacking. This work presents a novel contribution to this research line, with the proposal of a multi‐criteria assessment model embedded in a GIS. The criteria have been designed covering the three dimensions of sustainability: economic, social, and environmental. This assessment model constitutes an interdisciplinary approach tightly linking network analysis, spatial geography, regional economic, and environmental issues in a GIS‐based computer framework. The validity of the methodology is tested with its application in a case study: the extension of the high speed rail (HSR) network included in the Spanish Transport and Infrastructure Plan 2005‐2020 (PEIT).     

Multiagent Simulation for Complex Adaptive Modeling of Road Infrastructure Resilience to Sea‐Level Rise

Infrastructure systems in coastal areas are exposed to episodic flooding exacerbated by sea‐level rise stressors. To enable assessing the long‐term resilience of infrastructure to such chronic impacts of sea‐level rise, the present study created a novel complex system modeling framework that integrates: (i) stochastic simulation of sea‐level rise stressors, based on the data obtained from downscaled climate studies pertaining to future projections of sea level and precipitation; (ii) dynamic modeling of infrastructure conditions by considering regular decay of infrastructure, as well as structural damages caused by flooding; and (iii) a decision‐theoretic modeling of infrastructure management and adaptation processes based on bounded rationality and regret theories. Using the proposed framework and data collected from a road network in Miami, a multiagent computational simulation model was created to assess the long‐term cost and performance of the road network under various sea‐level rise scenarios, adaptation approaches, and network degradation effects. The results showed the capabilities of the proposed computational model for robust planning and scenario analysis to enhance the resilience of infrastructure systems to sea‐level rise impacts.     

Reliability Assessment of Lifeline Systems with Radial Topology

Abstract: The increased susceptibility of lifeline systems to failure due to aging and external hazards requires efficient methods to quantify their reliability and related uncertainty. Monte Carlo simulation techniques for network‐level reliability and uncertainty assessment usually require large computational experiments. Also, available analytical approaches apply mainly to simple network topologies, and are limited to providing average values, low order moments, or confidence bounds of reliability metrics. This study introduces a closed form technique to obtain the entire probability distribution of a reliability metric of customer service availability (CSA) for generic radial lifeline systems. A special case of this general formulation reduces to a simple sum of products equation, for which a recursive algorithm that exploits its structure is presented. This special‐case algorithm computes the probability mass function (PMF) of CSA for systems with M elements in operations, relative to conventional operations, and opens the possibility of finding recursive algorithms for the general radial case. Parametric models that approximate the CSA metric are also explored and their errors quantified. The proposed radial topology reliability assessment tools and resulting probability distributions provide infrastructure owners with critical insights for informed operation and maintenance decision making under uncertainty.     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

Deep convolutional neural networks for uncertainty propagation in random fields

The development of a reliable and robust surrogate model is often constrained by the dimensionality of the problem. For a system with high‐dimensional inputs/outputs (I/O), conventional approaches usually use a low‐dimensional manifold to describe the high‐dimensional system, where the I/O data are first reduced to more manageable dimensions and then the condensed representation is used for surrogate modeling. In this study, a new solution scheme for this type of problem based on a deep learning approach is presented. The proposed surrogate is based on a particular network architecture, that is, convolutional neural networks. The surrogate architecture is designed in a hierarchical style containing three different levels of model structures, advancing the efficiency and effectiveness of the model in the aspect of training. To assess the model performance, uncertainty quantification is carried out in a continuum mechanics benchmark problem. Numerical results suggest the proposed model is capable of directly inferring a wide variety of I/O mapping relationships. Uncertainty analysis results obtained via the proposed surrogate have successfully characterized the statistical properties of the output fields compared to the Monte Carlo estimates.     

基于语义信息的建筑尺度多模式应急路径诱导方法

该文分析了当前应急路径诱导方法,提出了多模式应急诱导的研究意义和内容,并利用语义信息自动构建虚拟节点、虚拟道路来实现三维环境下的集城市道路、小区、建筑单体一体化的建筑尺度多模式应急诱导,最后以实例验证该方法的有效性.     

Optimum structural design based on seismic reliability and response distribution

An analytical approach to the optimum structural design parameters is developed on the basis of seismic reliability and inter-story response distribution. An earthquake-like stationary random excitation being assumed, probabilistic earthquake response is estimated as the solution of a simple simultaneous algebraic equation. Based on this probabilistic earthquake response, the optimum parameters are determined by selecting seismic structural reliability and an inter-story response distribution index as objective functions. The validity of this approach is demonstrated by examining the perspective and contour lines of the objective functions regarded as a function of design parameters for elastic structural systems with six degrees of freedom. The possible application of this approach is also presented to the determination of elasto-plastic structural parameters with bi-linear hysteretic characteristics. Other aspects of the approach's validity are also examined from the response distribution viewpoint for the elastic structural system with recorded strong earthquake motions. The influence of the higher modal response and the earthquake's predominant angular frequency on the optimum parameters are also discussed.     

Estimation of life expectancy of wood poles in electrical distribution networks

Wood poles have been commonly used to support electrical lines throughout Canada. The electrical networks in Ontario alone use over 2 million wood poles to support distribution lines. The aging and degradation of wood pole infrastructure built in 1950s and 1960s have potential to increase the risk of pole failure resulting in power outage and costly unplanned maintenance work. Therefore, the development of a scientific and cost-effective asset management philosophy has become increasingly important to power utilities.The paper presents a probabilistic approach to minimize the life-cycle cost of inspection and refurbishment of wood poles in a large distribution network. A key input of probabilistic methodology is the life-time distribution function of wood pole, though its estimation is often hampered by the lack of data. The paper presents a comprehensive statistical analysis and interpretation of actual wood pole inspection and surveillance data collected by power utilities. The statistical analysis provides estimates of life expectancy and the survival curve of a typical distribution wood pole in-service in the Canadian climate. The optimization of refurbishment policy presented in the paper is generic and equally applicable to the asset management of other civil infrastructure systems.     

Maintenance optimization of infrastructure networks using genetic algorithms

This paper presents an approach to determining the optimal set of maintenance alternatives for a network of infrastructure facilities using genetic algorithms. Optimal maintenance alternatives are those solutions that minimize the life-cycle cost of an infrastructure network while fulfilling reliability and functionality requirements over a given planning horizon. Genetic algorithms are applied to maintenance optimization because of their robust search capabilities that resolve the computational complexity of large-size optimization problems. In the proposed approach, Markov-chain models are used for predicting the performance of infrastructure facilities because of their ability to capture the time-dependence and uncertainty of the deterioration process, maintenance operations, and initial condition, as well as their practicality for network level analysis. Data obtained from the Ministére des Transports du Québec database are used to demonstrate the feasibility and capability of the proposed approach in programming the maintenance of concrete bridge decks.