Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas.     

Adaptation Planning and Hazard Mitigation for Interdependent Infrastructure Systems to Enhance Urban Resilience Under Climate Change

Climate change and natural hazards have created multiple impacts on human settlements. Urban planning and design are effective tools in dealing with climate adaptation and mitigation issues. However, climate risk and its impacts are multiscale and complex due to interdependence between urban infrastructure systems. Identifying adaptation strategies to cope with these impacts requires planners to understand potential interdependent and interrelated consequences of infrastructure failure under natural hazards, and evaluate cascading and cumulative effects of climate change. This article discussed opportunities and challenges to incorporate interdependent social and physical infrastructure systems in the adaptation planning and hazard mitigation process, including climate hazard assessment, adaptation goal identification, adaptation strategy development, and implementation. The availability of urban big data and high computational resources will enable urban planners and decision-makers to better deal with those complex impacts from climate change and natural hazards. Successful adaptation planning and hazard mitigation for interdependent infrastructure systems also needs to solve issues in uncertainties of climate projection, institutional barriers of adaptation, and challenges of urban big data. Potential solutions to these challenges would include cooperation among multi-disciplinary experts, coordination between different levels of governments, and developing the ethical framework for data protection and robust methodologies to detect and reduce data bias.     

Urban climate resilience through socio-ecological planning: a case study in Charlottetown,Prince Edward Island

ABSTRACT

Climate change impacts, especially on coastal cities, can no longer be ignored and in order to avoid significant losses in the built environment, the economy, and, by consequence, human health and life, it is imperative to address these impacts. We extrapolate the three pathways to urban resilience (persistence, adaptation, and transformation), as a function of the interrelations among the design of built forms (urban and landscape design), blue and green infrastructure (ecosystems), and knowledge-to-action (inclusion of local people and their knowledge). Accordingly, four urban and landscape design theories that integrate urban ecosystems are identified and linked to urban resilience and to the local ecological knowledge (LEK) through an inclusive design process (the charrette). The model is then applied to Charlottetown, Prince Edward Island, a city that is increasingly subjected to intense storm surges and to sea level rise in Atlantic Canada, where a series of design charrettes integrated the LEK into urban climate resilience proposals that serve as policy recommendations for future action.     

重建沿海韧性——旧金山湾盐沼修复及其启示

潮洪灾害、海平面上升已促使沿海城市由"抵御"向"适应"转变。通过修复盐沼提高海岸防护的韧性,已被认可是一种有效的适应对策。选取旧金山湾为案例,基于文献和实地调研,探究城市盐沼修复的实践路径和设计方法。研究表明,适应性管理是旧金山湾三代修复实践得以不断优化、发展的关键,帮助制定、调整目标和方法,弹性应对不确定性;旧金山湾通过发展修复设计导则,加速盐沼修复,促进形成更自然的盐沼;并修复过渡区,提供盐沼向内陆迁移的空间,缓解海岸挤迫。最后,提出中国河口海岸城市实施盐沼修复的可借鉴之处,推进海岸防护措施向基于自然的途径转变。     

A Spatial Network Model for Civil Infrastructure System Development

Infrastructure networks play an important role in improving economic prosperity, enabling movement of resources, and protecting communities from hazards. As these networks serve population, they evolve in response to social, economic, environmental, and technological changes. Consideration of these interactions has thus far been limited by use of simplified data sets and idealized network structures, and is unable to explain the complexity and suboptimal structures displayed by real infrastructure networks. This article presents a new computational model that simulates the growth and evolution of infrastructure systems. Empirical evidence obtained from analysis of nontrivial real‐world data sets is used to identify the mechanisms that guide and govern system‐scale evolution of infrastructure networks. The model investigates the interplay of three key drivers, namely network demand, network efficiency, and network cost in shaping infrastructure network architectures. The validity of the model is verified by comparing key topological and spatial properties of simulated networks with real‐world networks from six infrastructure sectors. The model is used to develop and explore different scenarios of infrastructure network futures, and their resilience is shown to change as a result of different infrastructure management policies. The model can therefore be used to identify system‐wide infrastructure engineering strategies to reduce network costs, increase network efficiency, and improve the resilience of infrastructure networks to disruptive events.     

Probabilistic analysis of interdependent infrastructures subjected to weather-related hazards

Simulation of the infrastructure performance using numerical models may significantly assist in developing strategies for improving its resilience to harmful effects of climate change. This paper presents a model for simulating the performance of interdependent infrastructure systems based on an extended network flow approach, i.e. infrastructure systems are considered as a network of nodes connected by directed edges. The model has been specifically developed to simulate the infrastructure performance at a community scale and has higher node resolution compared to typical models of infrastructure systems at the national level. The model is time-dependent so that the infrastructure performance can be assessed at discrete points over a period of time. Parameters describing the performance of infrastructure assets (e.g. production and flow capacities, demands) can be treated as random variables or probabilities can be assigned to failures of the assets. The application of the model is illustrated by the probabilistic assessment of the performance of two interdependent infrastructure systems – electrical power and water, damaged by flooding.     

A graph deep learning method for short‐term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.     

Resilience-vulnerability balance to urban flooding: A case study in a densely populated coastal city in China

City areas experiencing disproportionate vulnerability levels to urban flooding events have attracted attention. Resilience is widely accepted as a strategy for reducing the risks of vulnerability and maintaining sustainable development. This research conceptualized vulnerability to hazard and exposure, and resilience to adaptation to urban flooding and explores their associations from a spatial balance perspective. The hazard of urban flooding was evaluated by hydrographic models, whereas exposure and adaptation were examined by indexes. Shenzhen, a densely populated socialist Chinese city, was selected as the case city. Results revealed that districts in the marginalized areas of Shenzhen experience high vulnerability to urban flooding because of poor geographic factors, immature drainage systems of urban villages, and the influx of rural migrants with sensible populations driven by high housing prices in urban center. The situation is becoming increasingly serious because of strong spatial mismatch between vulnerability and resilience with urban overutilization and the rural underutilization of adaptation resource allocation. Social segregation on adaptation resource occurs for public service provision in a marketization situation instead of socialism. Therefore, exploring the mechanisms of the spatial imbalance between vulnerability and resilience in socialist city, such as Shenzhen is necessary for reducing such impacts in future research.     

地震灾害下城市生命线体系恢复力双维度综合评估

针对现有生命线恢复力研究中单一维度评估的缺陷,引入基础设施网络均衡理论,设计综合考虑生命线网络物理状态和输送能力的体系性能时程响应函数PRF,同时构建包含灾害概率、灾害后果、恢复速率三个主要因素在内的,贯穿技术和组织双维度的生命线体系恢复力评估框架,实现地震灾害下城市生命线体系恢复力双维度综合评估。在以江苏省连云港市区供水网络为例的案例研究中,依据评估框架构建体系恢复力仿真流程,采用场景地震,对环状和网状两种供水体系的震害恢复力进行对比分析,结果显示,尽管网状供水网络的性能水平高于环状网络,但对体系恢复力的提升作用并不明显。而且城市供水体系恢复力随着恢复资源投入量的变化曲线显示,恢复资源的投入量存在最优值,能够以高效费比实现体系恢复力最大化。     

绿色基础设施城市主义和气候变化

气候变化产生的环境影响：雨水污染加剧、城市洪水、海平面上升和城市热岛效应，已成为全球性问题。绿色基础设施（GI）越来越多地被推广为解决气候变化导致的环境恶化的“灵丹妙药”，尤其是在城市地区。城市本质上为高密度且不透水的空间，仅有少量的绿地来吸收预计增加的降雨量。位于沿海地区的城市容易受到海平面上升的影响，同时，大量的硬质表面会使空气温度的上升加剧。研究认为：可以利用绿色基础设施来改变当前的城市形态以构建应对环境影响的生态韧性。为了实现这一概念，必须将城市视为区域景观的一部分。采用一种基于流域的方法来探索如何用绿色基础设施改善气候变化对环境的影响，并使用推测性案例研究来证明这种方法，表明重新设计的城市形态可以优先安排绿色基础设施，而不会影响建筑项目和房地产投资回报。虽然该研究位于新西兰，但基于流域的方法也适用于中国的城市。     

A Computational Methodology for Assessing the Time‐Dependent Structural Performance of Electric Road Infrastructures

An infrastructure adapted to dynamic wireless recharging of electric vehicles is often referred to generically as Electric Road (“e‐road”). E‐roads are deemed to become essential components of future grid environments and smart city strategies. Several technologies already exist that propose different ways to integrate dynamic inductive charging systems within the infrastructure. One e‐road solution uses a very thin rail with box‐section made of fibre‐reinforced polymer, inside which an electric current flows producing a magnetic field. In spite of the great interest and research generated by recharging technologies, the structural problems of e‐roads, including vibrations and structural integrity in the short and/or long period, have received relatively little attention to date. This article presents a novel computational methodology for assessing the time‐dependent structural performance of e‐roads, including a recursive strategy for the estimation of the lifetime of surface layers. The article also reports some numerical findings about e‐roads that will drive further numerical analyses and experimental studies on this novel type of infrastructure. Finally, numerical simulations have been conducted to compare an e‐road with a traditional road (“t‐road”), in terms of static, dynamic and fatigue behavior.     

FINAL DESIGNS OF THE RESILIENT BY DESIGN | BAY AREA CHALLENGE

Launched in May 2017, the Resilient by Design | Bay Area Challenge was a year-long collaborative design challenge bringing together local residents, public officials and local, national and international experts to develop innovative solutions that will strengthen our region’s resilience to sea level rise, severe storms, flooding, and earthquakes. The Resilient by Design | Bay Area Challenge has not only created tangible solutions for threatened communities across the region — it is now a blueprint for how communities can collaborate in the future to tackle the challenges related to climate change we are facing in the future. Each team’s proposal illuminates both immediate and longer-term ways to safeguard the Bay Area and make it more environmentally, socially, and economically resilient.     

Integrative modeling of performance deterioration and maintenance effectiveness for infrastructure assets with missing condition data

Deterioration modeling is an important analytical component in infrastructure asset management. It concerns the prediction of performance and remaining service life of assets of different designs under omnifarious working environments. For long‐term prediction, it also requires to characterize maintenance effectiveness because maintenance activities do not necessarily bring an asset to a completely renewed status. Deterioration modeling research has for decades been largely focusing on the modeling of the natural deterioration process per se, whereas the modeling of maintenance effectiveness is only a recent focus of investigation, mainly in pavement research. In practice, the asset conditions immediately before and after a given maintenance treatment both are not often known. This has made the modeling of maintenance effectiveness and long‐term deterioration prediction a challenging task. To bridge the gap, this paper presents a novel approach that integrates the modeling of deterioration and maintenance effectiveness into one process. The natural deterioration of asset performance is modeled as a continuous‐time Markov chain, whereas the effectiveness of a maintenance measure is modeled as a discrete‐time Markov chain. To account for missing condition data before and after the maintenance event, the paper also develops a robust statistical method based on Markov chain Monte Carlo simulation. A real‐life case study on a municipal sewer pipe system is carried out for demonstration of the proposed integrated modeling approach. The functional deterioration of sewer pipes and the effectiveness of flushing operations that target to bring flow capacity to intact state are modeled. Influences of pipe length, diameter, slope, and sewershed area are examined. The present work is a valuable step toward development of evidence‐based risk‐informed asset management framework.     

Designing,planning, and managing resilient cities: A conceptual framework

To what must cities be resilient? How can cities, as complex systems, be resilient? Building a capacity for resilience might be a daunting task when one considers the multitude of components, processes, and interactions that take place within and beyond a city’s physical, logical (i.e. legal), and virtual (cyberspace) boundaries. Planning for resilience to the impacts of stressors within cities requires an evaluation of the vulnerable components of cities, an understanding of the key processes, procedures, and interactions that organize these components and develop the capacity to address various structuring of components and their interactions with the ultimate goal of achieving resilience. This paper provides a deeper look at resilience in cities, proposes a conceptual resilience framework, and includes a discussion and analysis of the framework. The proposed framework is meant to serve as a more holistic approach to designing, planning, and managing for resilience by including an evaluation of cultural and process dynamics within cities as well as their physical elements.     

广州市南沙区农业区域 海平面上升应对策略

It envinces that sea level rise aggravates low-lying terrain inundation, storm surges, beach erosion, and other ecological damages. The developed agricultural system in the Pearl River Delta is at a high risk to floods; and, in light of the tactical significance of the Guangdong-Hong Kong-Macao Greater Bay Area and its high vulnerability to sea level rise and storm surges, it urgently requires to study their impacts on the agricultural areas in this region. Taking Nansha District, Guangzhou City, Guangdong Province, China as the study area, this study builds a vulnerability evaluation model of agricultural areas with the Source-Pathway-Receptor-Consequence framework using an indicator system upon exposure, sensibility, and adaptation, and quantitatively predicts the inundation risk level, financial loss, and vulnerability patterns of varied scenarios of sea level rise superimposed with storm surges with the ArcGIS. The main findings include 1) the stimulated proportion of inundated areas in minimum-risk and maximum-risk scenarios is 73.38% and 87.96% respectively, and the estimated financial loss in both scenarios is RMB 3,897.3855 million and 7,140.4979 million, respectively; 2) the central Nansha will suffer from a higher inundation risk, and the northern and southern agriculatural parts within the study area have a higher vulnerability to flood disasters. Resilience strategies—through defense, adaptation, or relocation—for each vulnerable zone are then proposed accordingly.     

Evaluation of sustainable development alternatives: relevant concepts,resource assessment approaches and comparative spatial indicators

Environmental, social and cultural factors are increasingly recognized as affecting the long‐term viability of development initiatives. The application of selected ecological concepts and a “systems approach” to resource assessment is suggested to improve understanding of environmental constraints and impacts, while providing a more realistic framework for comprehensive land evaluation. This approach includes the analysis of socio‐economic, technological, and cultural indicators in a spatial and temporal framework supported by geographic information systems and relevant performance or risk/impact assessment models. To provide a spatial comparative perspective, the use of a single, composite indicator is suggested, reflecting economic development opportunities, realistic constraints and impacts. The derived comparative index may be used to provide a long‐term, spatial and comparative perspective of economic development opportunities by improving project appraisals and the selection of viable and sustainable development strategies.     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

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.     

Vulnerability of coastal Connecticut to sea level rise: land inundation and impacts to residential property

Vulnerability indices at the global or national scales require considerable data aggregation where local economic and social impacts remain unnoticed. The goal of this study was to analyze the extent of inundated land from sea level rise and its economic impacts to residential property in coastal communities. Geographic data were integrated with economic and social data at parcel level resolution through GIS. Cumulative land inundation in seven coastal municipalities was calculated as 15 and 25?km², while direct economic costs to residential property was estimated to be $1.3 billion and $2.2 billion for 1 and 2?m sea level rise, respectively. Normalised results were $14?million/km coastline and $4?million/km combined coastline and rivers for 1?m sea level rise. Results indicate that while impacts will mainly occur along the coastline, inland parcels as far as 3?km from the coastline situated along rivers are equally at risk of flooding. While results of the study can be used to estimate economic impacts for other locations that share similar geographic characteristics and development patterns, land use types, proximity to water bodies, and property values are some factors that may lead to differences when these numbers are extrapolated elsewhere.     

Systems-of-systems methodology for strategic infrastructure decision making: Ireland as a case study

Infrastructure development is a long term process, which cannot easily adapt to sudden change; and infrastructure assets can have long lifetimes. Poor investment choices risk locking in poor policy choices for substantial periods of time. The ‘need’ for a new infrastructure asset arises due to demographic, economic or policy changes. But historically Ireland’s infrastructure investment has also been driven, in part, by the pursuit of political / economic policies which have themselves ‘created’ infrastructure needs; and often decided on an isolated project-by-project basis. In contrast, a systems-of-systems (SoS) approach is a fusion of network modelling, consideration of various policy options, and appraisal of the impact of alternative demographic and economic scenarios on multiple systems. This paper assesses Ireland’s readiness to adopt a SoS approach to infrastructure decision-making, proposes a methodology for its development and implementation. This would enable the demand for new infrastructure to be tested under various policy scenarios, providing evidence for investment decisions.