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A novel analytical model is proposed to improve the accuracy of predicting the mechanical response of a strand with asymmetrically broken wires. This model incorporates a new approach to compute the contact force between the broken wire and the duct. When there is more than one asymmetrically broken wire, the previous method cannot handle this situation due to the large deflection of the center wire near the breaking end. This deflection results in a significant contact force between the center and broken wires. The omission of this force in the previous method leads to unrealistic solutions for two or three asymmetrically broken wires. This paper addresses the issue appropriately. The calculated results have been compared with those from a previous experiment, demonstrating satisfactory agreement. Furthermore, the proposed analytical model has been adopted to predict the mechanical behavior of the broken wire in multi-strand scenarios relevant to a prestressed concrete bridge. The contact force between strands is accurately calculated. The findings indicate that the axial force of the asymmetrically broken wire in multi-strands can recover after a short distance from the broken end. This recovery is attributed to the confinement effect of the other strands. [ABSTRACT FROM AUTHOR]

Progressive and sudden deteriorations are the main reasons affecting the functionality of a transportation network. This paper presents a general probabilistic approach in which the ensembles of regression trees (ERT) are innovatively adopted to predict the life-cycle system reliability of asphalt pavements using Monte Carlo simulations, and pavement segments are considered with bridges in the analysis, prediction, and management of the functionality of transportation networks under both progressive and sudden deterioration due to multiple hazards. Four performance indicators of asphalt pavement subjected to multiple hazards were modeled using ERT trained with the Long-Term Pavement Performance database. The specific hazard types corresponding to each pavement performance indicator for the associated ERT model training were identified. The structural performance associated with bridge superstructures and substructures was analyzed by considering corrosion, traffic loading, and seismic hazards. The proposed approach is illustrated on an existing transportation network in Pennsylvania. The essential retrofitting timing, importance measure, and retrofitting priority associated with the individual component were investigated utilizing the calculated time-variant connectivity-based functionality and resilience associated with the network. The results demonstrate that asphalt pavements have a significant impact on the network functionality and should be considered in the postevent decision-making process of retrofitting strategies. [ABSTRACT FROM AUTHOR]

To estimate the connectivity of a road network, it is crucial to evaluate the correlation of hazard intensities among individual bridge locations since the probability of multiple bridges being damaged simultaneously depends on the degree of this correlation. However, research on connectivity assessment of bridge networks considering spatial correlations associated with flood intensities is scarce in the literature. When quantifying the spatial correlation of flood intensities, modeling based on the stream distance rather than the Euclidean distance is required, taking into account that river flow is restricted only within the stream network. To achieve this purpose, a novel methodology is proposed to evaluate the spatial correlation of a stream network based on a geostatistical linear model and stream network covariance models. In addition, this study considers the spatial correlation of seismic hazard intensity. With the proposed method, it is possible to identify which bridges play an important role in ensuring the connectivity of the road network under multiple hazards, i.e. flood and seismic. As an illustrative example, the proposed method is applied to a hypothetical bridge network in Kumamoto Prefecture, Japan. The results demonstrate that improved network connectivity can be achieved by implementing a relevant retrofitting strategy for important bridges. [ABSTRACT FROM AUTHOR]

Infrastructure systems, such as bridges, are a driver for the economic growth and sustainable development of countries. Similarly, the development of operation and maintenance strategies for infrastructure systems may aim at optimal management using Key Performance Indicators (KPIs) such as reliability, redundancy, availability, safety, economy, environmental performance and resilience. Recent research and development projects, such as COST TU1406, highlight that infrastructure managers make decisions based on a mix of qualitative and quantitative data from various sources paired with models of various levels of complexity as well as expert judgement. Similarly, recent state-of-the-art academia reports on a variety of different decision-making models applicable to the optimal management of infrastructure systems may be used. Within IABSE Commission 5 on Existing Structures, Task Group 5.4 has performed a survey on implemented decision-making models among 23 infrastructure managers from 20 countries. It highlights some similarities in relation to KPIs, condition rating and limit state checks. This has stimulated the standardisation of decision making. The application of risk-based methods, performance prediction and intervention modelling are somewhat more scattered and may call for further research and development as well as training. The need to bridge the gap between implemented decision-making models and research is of paramount importance. [ABSTRACT FROM AUTHOR]

Bridges and pavements are two major infrastructure components of a transportation network providing mobility of freight and commodities for economic vitality and access to a range of users as social benefits. However, the lack of a comprehensive infrastructure management system incorporating bridges and pavements inhibits accurate performance prediction, optimal maintenance actions, and the associated use of shrinking budgets. This paper presents an integrated probabilistic life-cycle connectivity framework for the performance analysis of transportation networks containing bridges and asphalt pavements by considering flexural and shear failure modes for prestressed concrete and steel bridges and four failure modes, including international roughness index, rut depth, alligator cracking, and transverse cracking, for asphalt pavements. In this framework, neural network–based deep learning models are used to assess the probabilistic performance of transportation networks and to provide guidance for the associated maintenance strategies. An existing transportation network consisting of bridges and asphalt pavement segments is selected to investigate its life-cycle connectivity reliability and component importance using the matrix-based system reliability method. Results show that the consideration of asphalt pavement failure probability has a significant effect on the probability of transportation network connectivity. [ABSTRACT FROM AUTHOR]