Journal of Infrastructure Systems

Transmission mains (TMs) and water distribution systems (WDS) are, among the others, fundamental networks on which society relies on. Age, pipeline materials, and internal and external stresses are just few of the numerous factors that undermine the efficiency of these infrastructures and potentially reduce performance and functionality, leading to leakages or causing dangerous conditions in the surrounding environment, such as flooding or disruption. Accurate condition assessment, with suitable monitoring techniques, allows for proper management and maintenance throughout the infrastructures’ lifetime. Starting from the hypothesis that leakages could be related to soil deformation, this study evaluated the opportunities offered by the differential interferometric synthetic aperture radar (DInSAR) method in identifying soil deformations in nonurban areas potentially related to leak positions along TMs. Satellite images were analyzed to derive millimetric evolution of slow deformation processes over large surfaces and a long time frame. To test the capability of the proposed method and its operability as a monitoring tool, data acquired between 2014 and 2019 by Sentinel-1 satellites, were processed to investigate two transmission pipelines located in two areas of Italy (Veneto and Toscana regions). An analysis of historical damages in the hydraulic network and soil deformations obtained by synthetic aperture radar (SAR) data examination along the pipeline path suggests how and in which conditions potential relationships between leakages along pipeline and deformations of the ground surface can be defined.

Foreign infrastructure investment (FII) is essential to the economic growth of developing countries. However, developing countries are prone to higher levels of political risk, which act as detractors to such investment. The nature of political risk has been well documented and includes such factors as corruption, inadequate legal protections, opaque regulatory processes, and unstable governments prone to volatile shifts in project support. Even so, the impact of political risk on FII does not arise from environmental factors alone, but also emanates from the internal attributes of the infrastructure delivery system; which is to say not all projects are impacted to the same extent by a given political climate. Although the external political risk environment has been extensively explored, the internal vulnerabilities are not well understood. This study identifies the impacts of foreign infrastructure vulnerability on FII and its mediating capacity in offsetting the adverse effects of political risk on FII in developing countries. It draws on a database of China and Brazil from 2006 to 2019, as exemplars of developing nations. A partial least-square structural equation model (PLS-SEM) method is employed in analyzing the database against existing system vulnerability factors as identified: political risk exposure of the system, system’s capability of dealing with political risk, and the relationship between host and home countries. The findings confirm that, ceteris paribus, FII flows are hindered when infrastructure projects carry higher risk exposure. Moreover, where systems possess the capacity to mediate political risk, FII participation is facilitated, while also diluting the negative impacts of that risk on the delivery system. These findings add to the body of knowledge in the domain of vulnerability theory, while offering a practical political risk management pathway for multination investors.

Rail corrugation, a common defect of rails, causes a high frequency self-oscillation effect in track slabs when there is an external load. In order to analyze the influence of multi-scales of rail corrugation on the dynamic characteristics of track slab under an external moving load, the relationship between the nature vibration modes of a single track slab and the resonance of the wheel-rail at different wavelengths, wave depths, and velocities is investigated. The results clearly show that the vertical acceleration of the track slab is the largest when the wavelength is 60 mm and the wave depth is 0.5 mm. At this point, the peak frequency of the track slab approaches its natural frequency. The maximum vertical acceleration decreases as velocity increases. The resonance effect between the track slab and the vehicle body will trigger a side-frequency phenomenon in the track slab frequency domain diagram. The indicator margin factor (MF) is more sensitive when the wavelength and wave depth are different, and the MF and peak-to-peak value (PPV) are more sensitive when the vehicle velocity is different. They can be used to judge the degree of influence of different scales of rail corrugation on the vertical acceleration of track slab.

Abstract

The normal operation of critical infrastructure is related to the stability of society and a country’s development. Based on game theory and network science, this paper analyzes the attack–defense issues of critical infrastructure, proposing a network attack–defense game model based on virtual flow. Five coupling methods are established according to the complex interdependent characteristics and important evaluation indicators (the degree value and betweenness value) in the network. Considering the network’s information transmission, redistribution, and cascading failures, a cascading failure model based on betweenness virtual flow is established. A two-player game is adopted, in which the attacker and the defender act simultaneously. The scale-free, small-world, and random networks are related using these five coupling methods. Simulation and theoretical analyses are carried out on the interdependent network game scenario under different coupling modes. The game equilibrium under different network coupling conditions and coupling methods is simulated, and the preferences of the game participants are determined. In addition, a sensitivity analysis of the game parameters is conducted, facilitating the identification of certain effective strategies that can support decision-makers.

Practical Applications

Critical infrastructure networks play an important role in maintaining social stability. Based on game theory and network science, this paper analyzed the attack–defense problems of critical infrastructures and proposed an attacker–defender game model based on the betweenness virtual flow. The attack–defense game in the interdependent network is analyzed in terms of coupling modes, network types, and tolerance coefficient. A sensitivity analysis of the game parameters is also conducted. The results show that the interdependent networks under random coupling and reverse coupling strategies are more vulnerable to attack. In addition, the cost-sensitive parameter is a key factor affecting the game equilibrium. Even if the two subnetworks in the interdependent network are the same, the payoffs and the robustness will be different when the order of removing nodes is different. This paper provides decision support for actual infrastructure protection.

One of the goals of infrastructure asset management research is to find appropriate repair policies for infrastructure systems. The annual repair cost of the system may vary when a repair policy meant only to minimize life cycle cost is applied to each facility in the system. Such variance in the annual repair cost leads to difficulty in securing the budget for system managers; thus, a desirable, practicable repair policy should reduce not only life cycle cost but also the dispersion of annual repair cost. In this study, we first formulate a network-level repair policy optimization problem for an infrastructure system comprising a finite number of facilities to minimize the total cost over the planning period, which is defined as the weighted sum of the repair cost and its variance. Then we propose two methods for solving it: (1) the exact one based on the Markov decision process; and (2) an approximate one using preventive repair rules. The former can be used in small-scale systems, whereas the latter can be used to simplify the repair policy regardless of the size of the system and to determine an approximate repair policy for large-scale systems. The proposed methodology is applied to two numerical investigations of (1) a small-scale infrastructure system; and (2) a large-scale infrastructure system. In the first case, we find the Pareto frontier of the repair cost and the variance in annual repair costs by the exact solution method and show that the preventive repair rule–based method provides a near-optimal solution. In the other case, the preventive repair rule–based method leads to a superior policy on aggregation of the optimal solutions independently found for each decomposed subsystem.

With the increase in the operation risks of water distribution networks (WDNs), the prediction of pipe failures is of great significance in developing efficient maintenance strategies. This study used a residual network (ResNet), a newly proposed deep learning (DL) algorithm, to predict pipe failure, and its effectiveness was compared with that of a classic convolution neural network (CNN) algorithm. Network structure of ResNet used in the classification of one-dimensional pipe vectors was built. The synthetic minority oversampling technique (SMOTE) was used to improve the prediction accuracy because of the imbalanced pipe database provided by the local water sector. The analysis of a real WDN in China showed that ResNet performed better than CNN in terms of recall rate and area under the receiver operating characteristic curve with reasonable time costs. The maintenance rate was defined and discussed to measure the efficiency of maintenance activities. More than half of the failures can be prevented by maintaining less than 10% of the pipes based on the proposed ResNet algorithm. In addition, the Shapley Additive exPlanations (SHAP) method was used to interpret the DL model. The SHAP method evaluated the impact of different features on pipe failure, and the pipe length and diameter were proved to be two influential features.

Abstract

This article studies the distribution characteristics and differences of China’s provincial rural infrastructure construction levels from the temporal and spatial evolution perspective. Based on the Statistical Yearbook, an evaluation index system for the development level of rural infrastructure construction was established. The level of rural infrastructure development in different provinces is calculated. The following conclusions can be drawn through the analysis of the calculation results and the spatiotemporal evolution images. First, China’s rural infrastructure construction level has been continuously improving. However, the contribution ratio of various infrastructures in the overall construction level has changed from similar to unbalanced, and there is a trend toward further expansion. Second, the spatial distribution of China’s rural infrastructure development level presents a ladderlike pattern of high in the east and low in the west, and the gap is gradually narrowing. Third, economic development level, policy promotion, and regional resource advantages are the main driving factors for the level of rural infrastructure construction. It is beneficial to provide a path to analyze the level of rural infrastructure construction and guide many developing countries to control critical driving factors and promote high-quality rural infrastructure development.

Practical Applications

Rural infrastructure is an integral part of promoting rural development. However, many developing countries still have problems, such as inadequate construction and unbalanced regional development. Clarifying the construction level and distribution characteristics of rural infrastructure is the basis for solving the above problems. Therefore, this work selected China as its case study and constructed a framework for evaluating the level of rural infrastructure construction from four aspects: production infrastructure, living infrastructure, ecological infrastructure, and social infrastructure. Using the data from the Statistical Yearbook, the construction level of China’s provincial rural infrastructure from 2009 to 2019 was calculated. The spatiotemporal evolution characteristics and main driving factors of the construction level were finally obtained through the analysis of the two dimensions of time and space. This method is not only conducive to improving the level of rural infrastructure construction and narrowing regional disparities, but also can guide many developing countries to control critical driving factors and provide reform experience to solve rural infrastructure development problems.

The Chinese government continues to contribute to tackling the global climate challenge and plans to continue lowering carbon emissions in the transportation infrastructure field. The comprehensive assessment of the impact of carbon emissions is mainly focused on high-speed railways (HSRs). This paper proposes a life-cycle cost considering carbon emissions (LCCCCE) model for HSR projects, which consists of a general life-cycle cost (GLCC) model and a quantified carbon emissions cost (CEC) model. The Beijing-Shanghai HSR is employed as a case study, in which the LCCCCE and sensitivity of carbon emission factors are analyzed using the proposed methodology. The results show that the LCCCCE of the Beijing-Shanghai HSR is 64.56 billion USD, and that its CEC is 1.04 billion USD, which contributes 1.61% to the LCCCCE. Thus, the LCCCCE analysis of HSRs or transportation infrastructure projects is an effective methodology to quantify the impacts of carbon emissions in China and around the world.

When exposed to hot and dry weather, concrete slabs and pavements frequently develop plastic shrinkage cracks. Such cracks have an influence on the quality and durability of concrete infrastructure, which is a cause of concern for the concrete construction sector. Existing methods for monitoring plastic shrinkage on the job site are inconvenient for the construction industry and are rarely used because they are time-consuming, labor-intensive, and costly. This research proposes two unique, cost-effective, and easily deployable IoT-enabled smart prototype systems for real-time monitoring of plastic shrinkage. Existing codes and standards advocate monitoring plastic shrinkage merely based on evaporation rate, ignoring the critical aspect of “bleed water status.” Accordingly, the first system is developed considering the evaporation rate, in accordance with ACI 305R standards. The novelty of the present research is the design of the second system, which considers the bleed water status to monitor plastic shrinkage. The combination of the first and second systems, as well as cloud communication, allows for precise monitoring in real-time without the need for human intervention from any remote location. Laboratory tests on three sets of concrete specimens demonstrate the efficiency of the proposed systems.