Journal of Infrastructure Systems

The detection of bridge cracks is an important task in bridge maintenance. It can also reflect the health of the bridge. However, cracks are usually in the form of strips, which are different from the concrete surface. Most crack detection algorithms cannot adapt to this situation well. In this paper, the original image of bridge cracks is collected and the data set is obtained through image processing. A bridge crack detection method based on improving encoder-decoder and mixed pooling module is proposed in this article. The basic features of the crack images are extracted by an encoder with dilated convolution. In this way, the resolution of the feature image can be guaranteed, and large receptive field can be obtained. Then the feature picture through the mix pooling module, which helps to capture remote context information and establish a remote dependency. Finally, the decoder restores the picture to its original size and integrates the original features. In the comparison experiment with the same experimental conditions, we compared with the classic image segmentation methods such as PSPNet, U-Net, FCN, and DeepLabv3+. The results show that our method achieves 98.3%, 97.3%, 97.6%, and 84.5% in precision, recall, F1-score, and MIoU. The results show that our method does have certain advantages in the field of crack detection and segmentation.

Accurate crack detection and characterization on concrete are essential for the maintenance, safety, and serviceability of various infrastructures. In this paper, an innovative approach was developed to automatically measure the cracks from 3D point clouds collected by a phase-shift terrestrial laser scanner (TLS) (FARO Focus3D S120). The approach integrates several techniques to characterize the cracks, which include the deviation on point normal determined using k-nearest neighbor (kNN) and principal components analysis (PCA) algorithms to identify the cracks, and principal axes and curve skeletons of cracks to determine the projected and real dimensions of cracks, respectively. The coordinate transformation was then performed to estimate the projected dimensions of cracks. Curve skeletons and cross sections of cracks were extracted to represent the real dimensions. Two cases of surface cracks were used to validate the developed approach. Because of the differences in definitions of the crack dimension in the three methods and due to the curve shape of the crack, the width and depth of cracks obtained from the cross-section method and manual measurement were close but slightly smaller than those measured by the projection algorithm; whereas the length of cracks determined by the curve-skeletons method was slightly larger than those obtained by the manual measurement and projection method. The real dimension of a crack has good agreements with real situations when compared with the results of the manual measurement and projection method.

Project bundling is the process of awarding a single contract to several infrastructure projects to address construction, rehabilitation, replacement, or maintenance needs. This novel approach has presented several benefits to the infrastructure industry in terms of time and cost savings as well as enhanced efficiencies in project deliveries. However, due to the relative recency of, and interest in, the project bundling approach, the associated factors impacting the decision-making process are still not well understood. Moreover, little-to-no previous research efforts have particularly focused on the decision-making factors that need to be considered when implementing bundling strategies. To that extent, this paper addresses this knowledge gap through studying the decision-making factors affecting project bundling based on actual case studies that have used bundling strategies. In relation to that, this paper followed an analytical approach that is based on the implementation of network analysis and clustering analysis to explore data collected from bundled projects in the US. First, data was gathered from 30 case studies that relied on bundling strategies to deliver their projects. Based on the collected data, 23 decision-making factors related to project bundling have been identified. Second, network analysis was conducted to quantify the co-occurrences or dependencies among the identified factors. Finally, cluster analysis was used to group or prioritize the factors into highly connected clusters. The findings of this paper showed that the central and most critical decision-making factors that need to be considered when determining project bundling strategies are: geographic proximity; similarity in project types; homogeneity of work types; and condition rating of projects. In addition, the outcomes of this paper highlighted the importance of different decision-making factors in ensuring effective bundling practices. Ultimately, this research adds to the body of knowledge by providing a better understanding of the decision-making factors related to project bundling and determining or prioritizing the factors that agencies need to take into consideration when bundling their projects. To this end, this paper equips project stakeholders with the needed guidance to help them make optimal bundling decisions and capitalize on the benefits of their project bundling strategies.

There has been a significant amount of research related to the valuation of real options that are increasingly complex in terms of payoff structure. In addition, infrastructure procurement is becoming more complex as individual assets are increasingly viewed as being part of a larger network of interdependent systems. In response, this investigation utilizes a System Dynamics (SD) model to capture the relationships between competing assets where simulation results elucidate the causal effects between multiple investments that traverse infrastructure systems. An illustrative case study includes an SD model that simulates historical real-world relationships between water treatment alternatives within the shale gas value chain. This research contributes to the existing body of knowledge by showing how SD models can be used to value multiple types of real options within a portfolio of competing infrastructure assets. The valuation of each investment must consider the value of an underlying asset as well as the compounding effects of competing alternatives. Although this case study is based on water treatment options in the shale gas industry, the same methodology can be used to value real options found in other infrastructure systems.

Abstract

Large-scale water distribution networks (WDNs) are vulnerable to internal faults and external attacks. When exposed to various types of natural or artificial disasters, a glitch may trigger cascading failures and even paralyze the entire WDN. An improved load-based cascading failure model is proposed to analyze both structural and functional properties in a unified framework. The load–capacity relationship, load redistribution principle, and nodal breakdown probability are key factors needed to determine the evolvement of cascading failures. Different attack strategies that represent the scenarios of random failures, intermediate-level attacks, and catastrophic disasters are performed separately. The single-node attacks are repeated on each node to locate stricken components and identify weak points. Several novel metrics, such as node vulnerability, service level, and damage size, are adopted to characterize the spatiotemporal vulnerability of WDN. The simulation results illustrate that the traditional degree-based method inevitably results in the underestimation of potential losses in disaster scenarios. The improved load-based model that stresses network topology and physical significance can better reflect the reality. spatiotemporal vulnerability analysis is proven to be a powerful tool for optimizing system design and balancing the time–cost–reliability triad.

Practical Applications

Cascading failure is a ubiquitous phenomenon in network-based critical infrastructure, in which a glitch or an external disturbance can trigger large-scale disruptions and even paralyze the entire system. This study proposes a load-based model that characterize the interplay between hydraulic components in interconnected and interdependent water distribution networks. For different disaster scenarios with corresponding attack intensities, such as internal faults (e.g., leaking pipes) or intentional attacks (e.g., pump station explosions), the cascading failure model investigates the probability of the nodal breakdown and reveals the intrinsic mechanism that explains why some components may fail whereas some others may not. Specific paths for failure diffusion can be identified by probing the inherent load–capacity relationships, and eventually the damaged area can be predicted before a disaster occurs. Such simulations of potential disasters can be performed in advance to locate the weak points and risky parts, and maintain the operability and reliability. The analysis of spatiotemporal vulnerability is instructive for comprehensively improving the robustness and resilience with effective protective measures. The modeling approach can be extended into other typical critical infrastructure by substituting the definition of nodal load and relevant operation regimes.

Abstract

Knowing the extent of displacement experienced by railroad bridges when loaded is useful when evaluating the safety and serviceability of the bridge. However, measuring displacements under train crossing events is difficult. If simplified reference-free methods would be accurate and validated, owners would conduct objective performance assessment of their bridge inventories under trains. Researchers have developed new sensing technologies (reference-free) to overcome the limitations of reference point-based displacement sensors. Reference-free methods use accelerometers to estimate displacements, by decomposing the total displacement in two parts: a high-frequency dynamic displacement component, and a low-frequency pseudostatic displacement component. In the past, researchers have used the Euler-Bernoulli beam theory formula to estimate the pseudostatic displacement assuming railroad bridge piles and columns can be simplified as cantilever beams. However, according to railroad bridge managers, railroad bridges have a different degree of fixity for each pile of each bent. Displacements can be estimated assuming a similar degree of fixity for deep foundations, but inherent errors will affect the accuracy of displacement estimation. This paper solves this problem expanding the 1 degree of freedom (1DOF) solution to a new 2 degrees of freedom (2DOF), to collect displacements under trains and enable cost-effective condition-based information related to bridge safety. Researchers developed a simplified beam to demonstrate the total displacement estimation using 2DOF and further conducted experimental results in the laboratory. The authors assumed linearity on properties and connections and used real train crossing event data as input to evaluate the accuracy of the proposed 2DOF method. The estimated displacement of the 2DOF model is more accurate than that of the 1DOF model for ten train crossing events. With only one sensor added to the ground of the pile, this method provides owners with approximately 40% more accurate displacements. Future work includes the study of nonlinearities and parametric study of the effect of resonance in displacement estimation accuracy for practical field implementation.

Practical Applications

The main application of this research is the optimization of the number of sensors and the simplification of sensor location, for lateral displacement estimation. Infrastructure managers can install a minimum number of sensors in tall piers or structures and determine the transverse displacement. The main practical aspect of this work is that engineers can collect the rotation at the base of the pier by locating sensors in the base. Using both the rotation both at the bottom and the top of the pier, the total displacement at the top can be obtained. In the practical domain, timber railroad piers could demonstrate additional lateral displacement under train crossing events not associated with the rotation above ground. Engineers could observe or collect that lateral displacement and add it to the total estimated displacement to obtain the total transverse displacement. Based on the field observations of the main author of this paper the lateral separation between the timber pile at the base of the pier is small, however it can be a practical next step in further development of the proposed method for practical applications.

Nowadays, build-operate-transfer (BOT) contracts have become particularly important. Since the private-sector’s return in this type of contract depends on the duration of the concession period (CP), its determination is a challenging issue. The present study provides a framework for determining the CP of BOT contracts using agent-based modeling. The proposed framework includes six steps: project recognition, factor recognition, exploring the parameters affecting agents’ behavior, agents’ behavioral modeling, designing the communication pattern, and finally, calculating the CP. To better understanding, the proposed methodology was employed in a real freeway project. After six steps, the effect of toll rates and construction costs was investigated over the project’s CP in five scenarios. The results showed that by using this method in different scenarios, the optimal CP in BOT projects can be varied. Also, based on the sensitivity analysis performed in the case study, it was found that the simulated model output is very sensitive to the parameter of the economic status of large businesses and, in contrast, has the least effect on the air pollution variable.

Megaprojects are facing great challenges and traditional project governance theories are gradually showing inadaptability. The purpose of this study is to investigate the dynamic impact of megaproject governance and project performance using a dynamic approach based on a fuzzy cognitive map (FCM). From the existing governance review, six governance factors for megaprojects were identified: government coordination, government supervision, risk sharing, revenue distribution, relationship maintenance, and cultural development. Then the causal relationships and weights between megaproject governance and performance were established through a questionnaire survey of experts, thus the FCM model was constructed. Finally, the dynamic interactions between megaproject governance factors and performance were investigated through predictive analysis, diagnostic analysis, and sensitivity analysis. The findings indicate that (1) each of the six governance factors are positively correlated with project performance; (2) revenue distribution has the greatest positive impact on the project performance of megaprojects, which is also the fundamental factor for changes in project performance; and (3) the impact of governmental governance on project performance cannot be ignored during the implementation of megaproject management. This study contributes to (1) analyzing the key factors influencing project performance by exploring dynamic interactions between governance factors and performance, and ultimately promoting project success; and (2) providing new perspectives to improve performance and guide governance practices for megaprojects.

Ensuring the continued functionality of hydropower infrastructure is of the greatest importance, considering the devastating socioeconomic and environmental impacts of dam operation failures. Among the different approaches currently adopted in hydropower dam operational safety, those that are resilience-based are at the leading edge because they focus on assessing the dynamic system performance pre-, during-, and post-hazard exposures. However, the main challenge for such assessment pertains to the complexity associated with the dynamic operation simulation of hydropower dam systems that consist of several components with nonlinear interdependencies. Moreover, the infrastructure’s exposure to a multihazard environment, which may impact one or more hydropower critical system components, poses further challenges to understanding possible subsequent dam operation failure scenarios. This study develops a resilience-centric system dynamics simulation model that provides a holistic representation of hydropower dam system components to estimate the system’s dynamic resilience in multihazard environments. The study also discusses a combinatorial procedure to generate multihazard scenarios, where a primary hazard can trigger one or more subsequent hazards. Finally, an actual hydropower dam is employed to demonstrate the developed model utility in assessing the resilience of complex infrastructure under a wide range of multihazard scenarios. The proposed model provides valuable decision support tools for infrastructure systemic risk mitigation in multihazard environments—facilitating the development of effective resilience planning strategies.

The fundamental objective of structural damage detection is to ascertain the presence of damage in the structure under monitoring. Environmental variations (especially temperature) may also cause the aberration of dynamic properties. This could result in false alarms and pose a threat to the reliability of vibration-based damage detection. In this paper, a new approach toward damage detection under varying environmental conditions is investigated based on the deviation patterns of natural frequencies. The idea is derived from the fact that most damage occurs in a local area of a structure while environmental variations affect the whole structure. Natural frequencies deviate from their previous state in different patterns based on the causation of the deviation. The method uses simple mathematics to distinguish the two patterns using the first 10 natural frequencies of a structure. The proposed method was applied in two case studies including a numerical model with severe temperature variations simulated in ABAQUS and 1-year health monitoring data of a cable-stayed bridge in China as a real case study. By implementing the proposed method, the effects of environmental variations were eliminated in both case studies and damage was accurately detected before it was evident to the visual inspections of the bridge.

A resilient transportation hub complex affected by a disturbance can maintain minimum performance and rapidly resume operations, while dealing with growing service demands imposed by the unfolding disturbance management. To strengthen the disturbance response ability and achieve postdisturbance rapid recovery, this paper proposes a hybrid approach that integrates a theoretical framework, failure model, and restoration model to assess the resilience of the streamline network of the transportation hub complex. In the resilience assessment framework, system performance is chosen as the key proxy for resilience, which is measured based on a service performance index. Subsequently, a failure model is designed to determine the service performance loss of a streamline network following a disturbance. To obtain maximum service performance, we propose a restoration model that integrates an optimal recovery sequencing strategy and a load redistribution strategy. A test example is used to demonstrate the applicability of the proposed approach. This approach can be used as a decision tool to estimate streamline network resilience and provide useful references for developing future policies.

In China, public–private partnership (PPP) projects consist of six phases, from inception to completion. In the final phase, known as the transfer phase (TP), the project assets are transferred back to the government, and the project is effectively closed out. Although success factors (SF) covering the earlier phases of PPPs have been well-documented, little is known about the effective execution of the TP in China. The main reason is that PPPs are a relatively new phenomenon, with few having reached the end of their life cycle, although this is happening now. Consequently, the determination of critical success factors (CSFs) in the TP is of great interest. This study aims to identify those CSFs that are most impactful to the success of the TP. A total of 23 process factors, arranged in four factor groups, are taken from an earlier study. These factors were presented to China’s water PPP project experts in an online questionnaire, with 52 valid responses returned. Of the 23 process factors, 22 were revealed to be statistically critical to the success of the TP, which were identified as critical transfer success factors (CTSFs). The top five CTSFs are (1) contract system, (2) infrastructure, (3) transferred assets, (4) transfer acceptance criteria, and (5) overhaul information. The identification of the relative criticality of CTSFs and, in particular, the identification of the leading factors, can be expected to support the government and contractor entities in effecting the smooth transfer of many Chinese PPP water projects approaching maturity.

The rising demand for electric vehicles (EVs), motivated by their environmental benefits, is generating an increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. Such investment decisions, which include EV charging station locations and capacities, and the timing of such investments require robust estimates of future travel demand and EV battery range constraints. This paper develops and implements a framework to establish an optimal schedule and locations for new charging stations and decommissioning gasoline refueling stations over a long-term planning horizon, considering the uncertainty in future travel demand forecasts and the driving range heterogeneity of EVs. A robust mathematical model is proposed to solve the problem by minimizing not only the worst-case total system travel cost but also the total penalty for unused capacities of charging stations. This study uses an adaptation of the cutting-plane method to solve the proposed model. Based on two key decision criteria (travelers’ cost and charging supply sufficiency), the results indicate that the robust scheme outperforms its deterministic counterpart.

Scour is an important cause of bridge failures. This article investigates the impact of climate change on bridge-pier scour in all 246 Swedish catchment areas. Although a few previous studies assessed the impact of climate change on bridge-pier scour in other countries, none of those studies identified the locations within a certain country where climate change is projected to have the highest impacts on bridge scour. A novel national-level method, based on possibility theory, is proposed herein for addressing this gap. The proposed method provides answers to the following questions: For which catchment areas is the projected increase in pier-scour depth highest or lowest? What is the percentage of catchment areas where climate change is projected to have either a positive or negative impact on scour risk in all scenarios? Which climate change scenarios cause the highest or lowest increase in equilibrium scour depth? Although these questions are addressed particularly for Sweden, the proposed method is generally applicable for any other location. The catchment area Riebnesströmmen (located in the northernmost county in Sweden) was identified to have the highest increase in the equilibrium scour depth in all considered reference periods (up to $\sim 25\%$ increase), whereas the catchment area with the lowest increase varied depending on the considered reference period. The answers to the second question depended on the reference period and ranged from $\sim 5\%$ to $\sim 10\%$ and $\sim 8\%$ to $\sim 14\%$ for positive and negative impacts on scour risk, respectively. Interestingly, for the third question, it was found that higher-emission scenarios are not always more critical than lower-emission ones. These findings demonstrate the importance of performing national-level analyses of climate change impacts on infrastructure considering the different scenarios. The proposed method enables an efficient allocation of resources for adapting bridges to the increased scour risk due to climate change. Additionally, it can serve as a useful tool for quickly estimating the impact of climate change on bridge-pier scour for a certain location.

Financing demands for the increasing number of large-scale build–own–operate–transfer (BOOT) hydropower projects in Nepal have necessitated greater mobilization of foreign investment. International investors and lenders perceive significant foreign exchange (FoREX) risk when investing in such projects. In Nepal and various developing countries, FoREX risk-allocation mechanisms offered by the public sector allocate significant FoREX risks to project sponsors and have failed to attract substantial foreign investment. The industry is thus in need of alternative FoREX risk-allocation mechanisms that ensure reasonable returns to project sponsors, minimize downside risk to the public sector, and maintain reasonable electricity tariffs for consumers. To address this need, this study proposes a feasible good-for-all risk-allocation mechanism and presents a financial analysis of the proposed mechanism via a case study of an ongoing Nepalese BOOT hydropower project. The comparative merits of the proposed risk-allocation mechanism over the existing mechanism are illustrated via a case study of an ongoing BOOT hydropower project in Nepal. The case study is also extended to represent different hydropower projects using various cost and revenue scenarios. The results of extended analysis showed that the proposed risk-allocation mechanism should be more effective for economic projects, and that implementation is feasible based on the current institutional arrangements observed in Nepal.

Bridge management systems are primarily employed to predict the future condition state of deteriorating bridges and plan for the maintenance and repair actions required for them. The deterioration models utilized in the current bridge management systems have been developed using Markov processes, the accuracy of which depends on the transition probabilities generated based on a combination of inspection data and expert elicitation. However, there are notable limitations in both sources of information, including the absence of long-term inspection recordings and the uncertainty in expert opinions. To address such limitations, the main motivation of this research investigation was to incorporate the physics-based models developed for the study of deterioration into the current bridge management systems. Thus, a set of physics-based models were used to generate the transition probabilities required for the Markov processes. The transition probabilities were then trained by the bridge inspection data obtained from eight counties of the state of Florida. The training was performed with (1) bridges in a specific county, (2) bridges in a specific county with a specific environmental condition, and (3) bridges in the entire eight counties with a specific environmental condition and age range. The results revealed that the physics-based models trained with a fraction of inspection data (from a subset of bridges) can predict the condition state of other similar bridges, for which no data was available, with adequate accuracy. The outcome of this study is expected to enhance the capabilities of bridge management systems without requiring a significant investment to overhaul the current bridge inspection process.

Tropical cyclones have intensified and are expected to strengthen throughout the 21st century due to climate change impacts. Storm intensification poses a serious threat to coastal municipalities and infrastructure that are not designed to withstand future wind and storm surge potential. Coupled modeling software has been widely used to replicate extreme weather events and simulate climate change effects to understand the magnitude of vulnerability coastal regions face. However, these studies are often conducted at too coarse resolutions to provide decision makers with actionable information for facility-level adaptation. This study addresses this limitation using a coupled modeling approach by simulating climate-intensified hurricane events at Eglin Air Force Base (AFB) along the Florida Panhandle. The study simulates hurricane impacts throughout the 21st century using multithreat projections of storm surge, sea-level rise, and wind intensification. Hazus Multi-Hazard software was used to simulate hurricanes at four threat horizons for five possible climate change scenarios. Storm hazard intensities were extracted at the facility level using geographic information system software to determine the spatiotemporal impact on Eglin AFB’s infrastructure. This analysis provides asset managers with likely intensities that buildings will experience and determine when and to what extent vulnerable facilities need to be made resilient. Results showed Eglin AFB could sustain between $773 million and $1.8 billion (2020 USD) in facility damage by the end of the century. This methodology can be applied to other coastal municipalities, provided adjustments are made to fit regional conditions and extremes, and could be used to inform infrastructure adaptation and development plans.