Reducing hazard-induced disruptions to infrastructure functionality is cardinal to regional resilience. Specifically, effective strategies to enhance regional resilience require: (a) developing mathematical models for infrastructure recovery; (b) quantifying resilience associated with the developed recovery process; and (c) developing a computationally manageable approach for resilience optimization. This paper proposes a rigorous mathematical formulation to model recovery, quantify resilience, and optimize the resilience of large-scale infrastructure. Specifically, a multiscale model of the recovery process is proposed that significantly reduces the computational cost, while favoring practical and easily manageable recovery schedules. To quantify regional resilience, resilience metrics are proposed that capture the temporal and spatial variations of the recovery process. The paper then formulates a multiobjective optimization problem that aims to improve regional resilience in terms of the proposed metrics, while minimizing the recovery cost. Finally, the paper illustrates the proposed formulation by considering interdependent infrastructure in Shelby County, Tennessee, United States.

The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault detection accuracy and efficiency were compared. The comparative analysis showed that the random forest (RF) and support vector machine (SVM) could make full use of the CNN feature extraction ability.