Tunnel lining defects are an important indicator reflecting the safety status of shield tunnels. Inspired by the state‐of‐the‐art deep learning, a method for automatic intelligent classification and detection methodology of tunnel lining defects is presented. A fully convolutional network (FCN) model for classification is proposed. Information about defects, collected using charge‐coupled device cameras, was used to train the model. The model's performance was compared to those of GoogLeNet and VGG. The best‐set accuracy of the proposed model was over 95% at a test‐time speed of 48 ms per image. For defects detection, image features were computed from large‐scale images by the FCN and then detected using a region proposal network and position‐sensitive region of interest pooling. Some indices (detection rate, detection accuracy, and detection efficiency, locating accuracy) were used to evaluate the model. The comparisons with faster R‐CNN and a traditional method were conducted. The results show that the model is very fast and efficient, allowing automatic intelligent classification and detection of tunnel lining defects.

The quest to answer the longstanding question of how safe is safe enough has led to an evolution of design philosophies. The traditional philosophy has been based on ensuring that system capacity exceeds demand. This philosophy has been manifested in such design methods as allowable stress design (ASD) and load and resistance factor design (LRFD). The level of safety in these methods is commonly calibrated to the acceptable practice using structural reliability methods. Such an approach to design aims to balance life safety and construction costs and works relatively well for service loads under which strength is nearly always a sufficient representative of system capacity. Under extreme loads, however, not only do factors such as ductility or energy-dissipation capacity come into play, but also the consequences of failure are vastly larger and may go beyond the failure site. For this reason, the more modern design philosophy of performance-based design emerged. According to this philosophy, a system is designed to meet a target performance, defined by limits on its performance metrics. These metrics were initially defined on structural responses, such as deformations and accelerations, and later evolved into risk measures based on monetary loss, downtime, and casualties. This philosophy is gradually making its way into modern design codes. Typical performance-based criteria focus primarily on what transpires during extreme loads. The need to incorporate the recovery of the system after such events has prompted the design philosophy to make another leap into resilience-based design (RBD). Resilience is defined herein as the ability to recover, within a predetermined period of time, in the aftermath of extreme events. Resilience transcends risk, i.e., a resilience analysis incorporates not only a risk analysis but also a recovery analysis. This paper provides a review of RBD in continuation of PBD and further introduces various applications of RBD into the design and assessment of civil infrastructure. The review covers the state of the art in resilience quantification approaches and associated computational platforms.