Surface transportation systems are an essential part of urban transportation infrastructure and are susceptible to damage from earthquakes. This damage, along with the lack of prior warning of earthquake events, may lead to severe and unexpected disruption of normal traffic patterns, which may seriously impair post-disaster response. Accordingly, it is important to understand the performance of urban transportation systems immediately following an earthquake, to evaluate their capability to support emergency response, e.g., the movement of firefighters, search and rescue teams and medical personnel, and the transportation of injured people to emergency treatment facilities. For this purpose, a scenario-based methodology is developed to model the performance of a transportation network immediately following an earthquake using an agent-based model. The model accounts for the abrupt changes in destination, irrational behavior of drivers in the chaotic aftermath of a severe earthquake, unavailability of traffic information and impairment in traffic capacity due to bridge damage and building debris. An illustration using the road network of Tangshan City, China shows that the method can capture the traffic flow characteristics immediately after an earthquake and can determine the capability of the transportation network to transfer injured people to hospitals. Thus, it can provide rational support for evaluating the performance of the surface transportation system under immediate post-disaster emergency conditions.

Electric power systems play a critical role in assuring modern society’s functionalities. Earthquakes are one of the most destructive natural hazards that affect the serviceability of electricity transmission systems. The earthquake excitations applied to each component of a power system (e.g. plants, substations and transmission lines) are spatially correlated by nature due to common causes. Yet, limited attention has been paid to the impact of this spatial correlation on the power grid seismic performance. This article presents an approach for estimating the seismic performance of large-scale electric power systems subjected to spatially correlated earthquake ground motions. The network flow theory is used to model the power flow allocation over the grid components, and a stochastic ground motion model is employed to represent the spatial characteristics of earthquake excitations. The proposed method is illustrated through the seismic performance assessment of the national power grid of Italy. The overall vulnerability is measured and evaluated through a proposed metric, and the critical components of the grid system are identified. The impact of spatial correlation of earthquake ground motion on the grid system’s post-hazard performance is also investigated.

Bridge resistance and reliability may deteriorate with time due to aggressive environmental conditions and increasing road freight volumes, resulting in an

The successful past performance of an existing bridge is evidence of its resistance, which reduces the uncertainty associated with the resistance and so increases the bridge reliability. However, the resistance deterioration and increases in traffic load may negate this effect. In this paper, statistical techniques are used to combine the prior estimate of time-dependent resistance with new information from the service load history, thereby obtaining an updated estimate of bridge resistance, as well as an updated reliability for subsequent years. The application of the method is illustrated using an existing bridge. The influences of the intensity and nonstationarity of service load history and the shape and variance of deterioration function on updated estimates of bridge resistance and reliability are examined using parametric examples. It is found that the updated resistance of an existing bridge with ever-increasing traffic load is close to that of the same bridge with a stationary traffic loa...

Civil infrastructure systems provide physical supports to a community’s functionalities and are expected to achieve acceptable safety levels subjected to extreme load effects. However, these systems may deteriorate with time as a result of aggressive environmental or operating conditions in service, implying that the system reliability may decline beyond the baseline as assumed for design. Moreover, the increasing trend of the external loads may also contribute to the reduction of the system reliability. In this paper, a semi-analytical method is proposed for assessing the reliability of aging systems subjected to non-stationary loads. The series system is considered, where the system failure is defined as the failure of any single component (structure) among the system. The application of the proposed method is illustrated using a representative series bridge network with several individual bridges. The role of parameters such as the variations in the load intensity, resistance correlation and number of components under attack in the system reliability are investigated.

Physical models for carbonation-induced reinforcement corrosion and concrete cracking have been proposed mainly based on accelerated carbonation tests. However, it has been criticised for not being representable of the natural corrosion process, and may not be applicable to real structures, so it is meaningful to calibrate and modify these models with in-situ inspection data of reinforced concrete (RC) structures in actual carbonation environment. The available in-situ data with the current inspection technologies (non-destructive) include concrete cover thickness, concrete strength, carbonation depth, reinforcement corrosion proportion, cracking proportion, etc. Based on Bayesian updating theorem, the paper proposes an integrated procedure for the updating of carbonation-induced deterioration models with inspection data, and then the durability assessment of existing RC structures in remaining service lives. In the procedure, the carbonation depth model and the probability of reinforcement corrosion are firstly updated with the inspected data, and then the un-carbonated depth model, the cracking probability and the critical corrosion depth model for cracking are updated subsequently. The methodology is illustrated using a case study of a RC industrial building with an age of 33 years. The influences of prior distribution and inspection data amount on the updating effect are discussed. Especially, the uncertainty associated with the number of corroded samples due to the error in corrosion inspection is discussed, and its effect on updated corrosion probability is investigated.