Wenjuan Sun

This chapter provides a comprehensive review of available methods of modeling infrastructure interdependencies for quantification, prediction, and enhancement of disaster resilience. A specific characteristic of intrasystem dependency is the dependency of the functionality of an infrastructure system on the functionality of all its components, and the dependency of the functionality of a component on the functionality of all its subcomponents. Some findings of the PRAISys project are summarized to provide a comprehensive review of existing methods of modeling and assessing infrastructure interdependencies. Models and classifications are presented in a general context to highlight their essence, while specific dependencies and interdependencies between two infrastructure types are noted.

Crack is one of the important pavement damages. The number and types of cracks are usually collected and inspected by human, but lately computer vision has shown great potential for automatically identifying pavement cracks. However, training a convolutional neural network (CNN) requires large amount of data. The limited number of crack images and the variety of crack image background put challenges for developing CNN model with high accuracy. This study proposes an intelligent method for crack pattern classification based on limited field images. The proposed method has a two-step data preprocessing. It first applied data augmentation method to enlarge the dataset, solving the data imbalance problem and providing more training data. Then a crack extraction method was applied to convert the original image into a binary back-and-white image. This step significantly reduced the input feature and simplified the model. Both of data preprocessing steps were designed to decrease the bias of the model. Then we performed a model selection and hyper-parameter tunning for CNN. We explored the application of AlexNet, SE-Net, and ResNet with a variety of configurations. The results show that the proposed data augmentation can enlarge the dataset significantly. The crack extraction also significantly increased the test accuracy. The ResNet with 50 layers has the highest test accuracy. With data augmentation, crack extraction and model selection combining together, the final approach can improve the test accuracy from 52.68% to 87.50% compared to the original limited images. The result suggests our proposed method works well to classify crack images of asphalt pavement with limited sample size.

Critical infrastructure systems are interdependent to ensure normal operations for supporting a national economy and social well-being. In the wake of a disaster, such interdependencies may introduce additional vulnerability and cause cascading failures. Therefore, understanding interdependencies and assessing their impact are essential to mitigate such adverse consequences and to enhance disaster resilience in the long term. There have been various models developed to capture dependencies and interdependencies across infrastructure systems. However, problems of inconsistent usage and a lack of technical guidance hinder practical applications of interdependency models. Therefore, this study presents a new classification of interdependency models based on the following implementation methods: dependency tables, interaction rules, and data-driven approaches. For every class of interdependency model, fundamental assumptions and detailed implementation methods are described, with discussion of appropriate application areas, advantages, and limitations. This study also compares different types of models to facilitate analysts in choosing models based on their needs. Due to the intrinsic complexity of dependencies and interdependencies, there are many challenging modeling issues; this study discusses future research directions to address such challenges.

Due to continuous population expansion and the threat of climate change, the past century has witnessed increasing occurrences of natural hazards, leading to significant global losses and requiring substantial restoration efforts. This issue challenges decision makers to act in a timely and effective manner to protect infrastructure systems from future natural hazards. This study presents a policy-based decision model for restoration planning, as part of the PRAISys platform, to support informed disaster mitigation of interdependent infrastructure systems under uncertainty. Following the concept of disaster recovery priority used in practice, this model determines the priority rank of each recovery task from pre-defined policies and simulates the restoration accordingly. This model captures different types of interdependencies with rigorous models at the component and system levels and predicts possible system recoveries under a given damage scenario in a probabilistic manner. This model can quantitatively evaluate the effectiveness of decision strategies on system recovery and resilience under different disaster recovery policies. As a demonstration example, this study applies the proposed model to the post-earthquake recovery simulation of three interdependent infrastructure systems (i.e., power, communication, and transportation) in the Lehigh Valley, Pennsylvania, USA. A total of sixteen cases were considered to represent different restoration strategies. For every case, the uncertainties in the recovery steps are captured by probabilistic simulation, and system resilience is calculated for every recovery sample. Simulation results from different strategies are compared to evaluate the effectiveness of non-intuitive strategies on system recovery and resilience. The proposed model uses a simple and straightforward concept to mimic practical disaster recovery plans. It is easy to understand and implement for modelers, and it is also useful to compare outcomes from different recovery criteria and decision strategies for practitioners.

Infrastructure interdependencies have been widely recognized, especially in the postdisaster restoration process. It is essential to develop models to simulate interdependencies and quantify their impact on the functionality recovery of infrastructures. This study presents a generalized simulator to investigate the impact of different types of interdependency on functionality recovery. The proposed simulator considers that there are multiple possible modes to execute a restoration task by framing the restoration process of interconnected systems as a multimode resource-constrained project scheduling problem (MRCPSP). In addition, it considers three sets of uncertainties: restoration duration and resource demand to execute a task, as well as intersystem functionality dependency. By solving the MRCPSP with the objective of minimal restoration completion time, the optimal restoration schedules for different systems are calculated to predict functionality recovery. This simulator implements three types of interdependencies at both the component level and the system level: resource-sharing interdependency, restoration precedence dependency, and functionality dependency. Through a simple example, it is demonstrated how the proposed approach can quantitatively evaluate the impact on system recovery due to different types of interdependency. Research findings from this study can help to identify the interdependencies with the strongest impact and then develop preventive mitigation actions and effective plans of emergency response and disaster recovery for interconnected systems.

A fuzzy analytical hierarchy process (FAHP) is an effective risk assessment method in which a questionnaire is used to collect experts’ responses. However, determining fuzzy numbers and establishing a consistent judgment matrix are difficult in the FAHP. This study proposes a new consulting process for solving the previously mentioned problems in the triangular FAHP. The proposed consulting process consists of a newly designed questionnaire and a new approach for determining fuzzy numbers. Experts’ responses collected from the questionnaire are employed to determine fuzzy numbers and establish a consistent judgment matrix. The proposed method has been applied to the Jinan metro tunnel construction to demonstrate its validity. Both the traditional questionnaire and the new questionnaire are used to establish the judgment matrix in the case study. The results show that the judgment matrix determined from the new questionnaire can determine triangular fuzzy numbers and establish a consistent judgment matrix. Compared with the traditional questionnaire, the proposed questionnaire can be used to collect experts’ opinions with greater convenience and in less time.

Understanding the EPB shield tunneling performance is vital to operating and controlling the shield machine during the tunnel excavation. However, predicting the earth pressure balance (EPB) shield tunneling performance with a good accuracy is considered as a nonlinear and multivariable complex problem. This paper proposes a computational model to predict the EPB shield performance during tunneling with a hybrid multi-objective optimization technique. The proposed technique integrates an improved particle swarm optimization (PSO) with adaptive neuro-fuzzy inference system (ANFIS), based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through the principle component analysis (PCA). The hybrid model is applied to the prediction of the tunnelling performance in the tunnel section of Guangzhou metro line 9 in China. Prediction results show that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of multi-objective fitness function. The good agreement between actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance in other projects with a good accuracy.

This case report presents two pedestrian passages underneath an urban expressway with a spacing of 0.5 m, which were constructed using rectangular pipe jacking technology in Nanjing, China. To better understand the field performance and the geoenvironmental impacts of rectangular pipe jacking technology, real-time monitoring was conducted with the installation of measurement devices at 43 measurement points. These points were set up to measure lateral earth pressure, excess pore water pressure, soil lateral displacement, and ground surface settlement. By analyzing field measurement data and operational parameters, this study documents operational parameters throughout the jacking process of two successively constructed tunnels and presents geoenvironmental impacts of the rectangular pipe jacking technology. In this process, the study finds that large ground settlements around the jacking shaft were due to the strong soil-carrying effect of the rectangular pipe jacking technology.