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Journal of Infrastructure Systems

Journal of Infrastructure Systems

Archives Papers: 175
The American Society of Civil Engineers
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Dry Parallel Seismic: A Novel Method for Determining Thickness of Buried Concrete Pad Foundations
Joshua I. Scott; Akash Nikam, P.E.; David Milligan; and Armita Mohammadian, Ph.D., P.E.
Abstracts:The structural capacity of concrete foundations supporting, for example, telecommunications and electrical transmission towers depends, among other things, upon the thickness of the concrete foundation. Current methodology for investigating the thickness of concrete foundations nondestructively has largely been focused upon deep foundations, such as caissons, and in situations where the top of the foundation is exposed. For example, the parallel seismic (PS) method is a borehole method that uses the arrival times of waves imparted in a foundation at a sensor next to a buried foundation to detect the thickness of the foundation but is typically used for deep caisson type foundations. However, in many instances, these foundations are pad and pier type footings that can be buried deep below ground and can be relatively thin, for example, less than 45 cm in thickness. The objective of the present work is to present a nondestructive method for determining the thickness of concrete pads with no direct access. The dry PS method was developed to determine the thickness of thin, buried concrete foundations. The method was first used to verify the as-built thickness, as mentioned in the engineering drawings of a guyed tower dead-man block. Second, it was used to determine the thickness of the pad foundations of five telecommunication towers with pad and pier concrete foundations in South Asia. These towers had foundation thicknesses ranging from 16 to 36 cm, as verified through excavation. Analysis of the data collected during dry PS testing resulted in a mean absolute error (MAE) of 18 mm, which equates to approximately 6% error. Thus, this paper demonstrates that the dry PS method overcomes the limitations of the current methodology and is capable of accurately determining the thickness of buried concrete foundations.
Optimizing Locations of Energy Storage Devices and Speed Profiles for Sustainable Urban Rail Transit
Leon Allen, Ph.D.; and Steven Chien, Ph.D., M.ASCE
Abstracts:Urban growth and the resulting highway congestion is driving up demand for rail transit. Rail, a significant component of transportation infrastructure, is critical to economic efficiency and is one of the least energy-intensive modes. However, the scale of operations results in high energy consumption, atmospheric pollution, and operating costs. Fortunately, some of the braking energy can be harvested and either used to power a simultaneously accelerating train or stored to power subsequent accelerations. The objective of this research was to optimize the number of locations of the energy storage devices and speed profiles. First, kinematic equations were applied to simulate energy consumption. Then, a genetic algorithm (GA) was developed to optimize the speed profiles that minimize the energy consumption with and without a wayside energy storage unit (WESS) for a rail transit line. Finally, a model was developed to optimize the WESS locations that maximized the net benefit. A case study was conducted to examine the model in a real-world setting and to demonstrate its effectiveness. The results indicate that about 980 MWh of electrical energy, or an additional 5%, could be saved by optimizing the WESS locations over only applying speed profile optimization. In addition to significant energy savings, environmental emissions could be mitigated using these methods.
Policy Gradient–Based Focal Loss to Reduce False Negative Errors of Convolutional Neural Networks for Pavement Crack Segmentation
Enhui Yang; Youzhi Tang; Allen A. Zhang; Kelvin C. P. Wang, M.ASCE; and Yanjun Qiu
Abstracts:Convolutional neural networks (CNNs) have achieved tremendous success in pavement crack segmentation. However, it is difficult for CNN-based crack segmentation methods to minimize false-negative and false-positive errors. Compared with false-positive errors, false-negative errors are more difficult to observe and reduce manually. This paper proposes a fine-tuning method for trained CNNs, called policy gradient-based focal loss (focal-PG loss). The trained CNNs will be further trained by focal-PG loss for only one epoch. The proposed focal-PG loss can be applied to reduce the false-negative errors of the trained CNNs by sacrificing their precision. The experimental results show that focal-PG loss greatly improves the crack recognition rate of the trained encoder–decoder network (EDNet). EDNet (focal-PG loss) achieves an overall precision of 96.05%, recall of 99.68%, and F1-score of 97.83% on 100 validation images. In addition, overall precision of 95.53%, recall of 99.58%, and F1-score of 97.51% are observed for the 150 testing images. U-net, LinkNet, and the feature pyramid network are also tested in the paper to validate the effectiveness of focal-PG loss. The results demonstrate that the focal-PG loss can also improve the performance of the aforementioned networks.
Resource Allocation Framework for Optimizing Long-Term Infrastructure Network Resilience
Jingran Sun; Zhe Han, A.M.ASCE; and Zhanmin Zhang, A.M.ASCE
Abstracts:With the increase in frequency and severity of extreme weather events, it is essential to incorporate the resilience of infrastructure networks into the decision-making process of resource allocation for maintenance planning. Considering that infrastructure systems are interdependent in nature, the impact of both extreme events and maintenance treatments on infrastructure resilience could be further amplified. Consequently, infrastructure interdependencies should be considered when analyzing the impact of extreme events and maintenance treatments. Additionally, uncertainties (such as uncertainties associated with the occurrence of extreme events and maintenance treatment effects) should also be into consideration. This paper proposes a resource allocation framework that incorporates these factors to optimize long-term resilience of infrastructure networks. The proposed framework capitalizes on integrating agent-based modeling with a double deep Q-network model to support decision-making in resource allocations; it allows infrastructure management agencies to maximize the long-term resilience of infrastructure networks while keeping their physical condition at an acceptable level. The results obtained from the case study show that the proposed framework is effective and can be customized to various local conditions.
Multiobjective Optimization Method for Pavement Segment Grouping in Multiyear Network-Level Planning of Maintenance and Rehabilitation
Siyuan Meng; Qiang Bai, Ph.D.; Lin Chen, Ph.D.; and Aihui Hu
Abstracts:Pavements often are divided into short segments for data collection, analysis, and management in practice. However, in pavement maintenance and rehabilitation (M&R) planning, it is impractical and uneconomical to create M&R projects based on such short segments. Road agencies generally group consecutive pavement segments to facilitate larger projects. However, the grouping is not easy, especially for large networks, and inappropriate grouping might result in resource wastage and ineffective M&R plans. Thus, an advanced and effective grouping method is required for decision makers when making M&R plans. This study focused on grouping consecutive pavement segments in the context of network-level multiyear M&R planning and developed a multiobjective optimization (MOO)-based grouping method. The MOO model was established with three conflicting objectives: minimizing the total agency costs, minimizing the total road user costs, and maximizing the network pavement performance, subject to the constraints of annual budgets, individual and network pavement performance, and the minimal and maximal length required for a M&R project. The proposed MOO-based grouping method was tested and compared with the clustering-based grouping method and no grouping method using a real road network–based case study in the context of a 5-year pavement M&R plan. The results showed that the proposed method can help decision-makers effectively conduct segment grouping and generate cost-effective solutions when conducting pavement M&R planning at the multiyear network level.
Deterioration of Flexible Pavements Induced by Flooding: Case Study Using Stochastic Monte Carlo Simulations in Discrete-Time Markov Chains
David Vallès-Vallès and Cristina Torres-Machi, Ph.D., M.ASCE
Abstracts:Flooding is and has historically been the most frequent natural disaster in the globe. There is strong evidence that the frequency of flooding is increasing. Pavements, however, are currently designed based on historic climatic conditions assuming a stationary climate, which no longer seems to be a good proxy for future conditions. The goal of this study is to characterize the performance of flooded pavement and quantify the impact of flooding on pavement deterioration and service life. To achieve this goal, the study analyzes the floods occurring in Colorado, United States, in 2013, and uses empirical data on pavement conditions to (1) quantify whether flooding impacts pavement deterioration, (2) define a conceptual model to characterize the deterioration of flooded pavements, and (3) quantify the loss of service life derived from flooding. To address these inquiries, the study used statistical analysis, stochastic Markov deterioration modeling, and Monte Carlo simulations. The study found that flooding accelerates pavement deterioration. Specifically, flooding induces a sudden drop in condition, followed by an accelerated long-term (i.e., multiyear) deterioration that reduces the pavement service life. The better the condition before flooding, the higher the loss of pavement service life. This new understanding of the impact of flooding on pavement conditions will help transportation agencies in the design of resilience programs and postflood strategies. Further research is recommended to analyze other flood events using similar methodologies to understand the influence of factors such as flood characteristics, location, and climate conditions in this phenomenon.
Statistical Data Analysis for Trackway Asset Management Using Low-Level Nonconformance Rates
Stephen M. Stark, Ph.D.; and Ilan Juran
Abstracts:Well-documented conditions of the country’s subway systems suggest that the development of a management strategy for aging infrastructure is an important asset management challenge. The purpose of this work is to address this challenge by developing a statistical data analysis approach for forecasting the aging effect on the time-dependent deterioration rate of an identified trackway cluster. The infrastructure database is the New York City subway system and the trackway that supports the delivery of service. While much focus has been given to the steel rail subcomponent of the trackway, little has been done in the way of statistical analysis on the low-level nonconformances in the support structure of those rails—namely, the invert, ties, fasteners, and plates. Though detectable, these conditions pose no risk to either safety or the serviceability of that asset, yet they do provide a certain degree of awareness that an entirely intuitive lifecycle deterioration process is underway. This paper illustrates the development of a statistical data analysis methodology using available trackway data sets for forecasting the aging effect on the system performance.
Bridge Infrastructure Management System: Autoencoder Approach for Predicting Bridge Condition Ratings
Monica Rajkumar; Sudhagar Nagarajan, Ph.D.; and Madasamy Arockiasamy, Ph.D., P.E., P.Eng., F.ASCE
Abstracts:Being an essential component of our economy, bridges play a vital role in facilitating transportation of people and goods all over the world. Bridge infrastructure systems help in developing an effective way to monitor and protect structures in all aspects. However, the bridges are exposed to various kinds of damages due to aging, heavy load of traffic, quality of construction, and so on. Hence, determining the condition of such bridges through a proper infrastructure management system is important for understanding the potential loss in the longevity of the structures. This research paper presents the development and evaluation of an autoencoder-random forest (AE-RF) model to predict the condition rating of bridges using the National Bridge Inventory (NBI) database. To demonstrate the proposed model, a case study using bridges present in the US state of Florida has been performed using historical NBI data (2011–2020). Through this research, it was identified that when one of the deep learning models, named autoencoder, is combined with random forest (RF), it results in an efficient model for determining the condition ratings of the bridge components with fewer input parameters. The developed model was about 90% accurate in predicting the bridge’s deck condition by using other rating values as input variables and about 79% accurate without the use of any other rating factors, thereby addressing the existing research gap in determining the condition rating without the use of historic condition rating. On the other hand, the model was 78% and 77% accurate in determining the condition ratings for superstructure and substructure without using historic ratings and evaluation parameters. Hence, the proposed model would be more reliable in the evaluation of condition of existing bridges across the nation.
Modeling the Vulnerability and Resilience of Interdependent Transportation Networks under Multiple Disruptions
Chen Chen; Shuliang Wang; Jianhua Zhang; and Xifeng Gu
Abstracts:Because infrastructure systems are highly interconnected, it is crucial to analyze their vulnerability and resilience with the consideration of interdependencies. This paper constructed a bus–metro interdependent network model based on the passenger transfer relationship and used deep learning to identify the network topology attributes. The vulnerability process of the interdependent network to different disruptions under structural and functional perspective was studied. On this basis, this paper adopted a resilience assessment framework and mainly focused on modeling and resilience analysis of interdependent networks’ recovery processes. The optimal and instructive recovery strategy was determined, and it is shown that the increase of the coupling distance cannot alleviate the vulnerability of the interdependent network effectively; after the tolerance coefficient reaches the threshold, the effect on the vulnerability of the dependent network is weakened; and a betweenness-based strategy (BBS) works best in the preferential recovery of key nodes.
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