Virtual structural health monitoring and remaining life prediction of steel bridges

In this study a Structural Health Monitoring (SHM) system is combined with Bridge Weigh-in-Motion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the 'Virtual Monitoring' concept, where all parts of the bridge that are not monitored directly using sensors, are 'virtually' monitored using the load information and a calibrated Finite Element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed Virtual Monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. In order to test the accuracy of the Virtual Monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Resilience Quantification of Interdependent Infrastructure System

Infrastructure networks do not exist in isolation. Rather they are interconnected to other infrastructures and, as technological development increases, so too does the linkage between networks. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. For example, failure of the water drain and sewer system due to 2002 Glasgow flooding affected many homes and closed many main roads and stations such as the A82 and A8 roads, Buchanan Street subway station and Dalmarnock through to Exhibition Centre stations on the Argyle Line. As infrastructures are becoming more interdependent at some sectors, there is an increasing demand for more effective management of these interactions and interdependencies. This paper provides details of a quantitative metric for the robustness, recoverability, rapidity and resourcefulness of the interdependent infrastructure network in response to hazardous event. By generating a quantitative measure of network resilience, considering infrastructure interdependencies, the most severe failure scenarios and their spatial impacts can be identified and mapped. This can lead to prioritise future business planning strategies for CI asset owners and managers. To illustrate the application of the proposed approach, a case study in North Argyll, Scotland is analysed and presented in this paper

Resilience Quantification of Interdependent Infrastructure System

Infrastructure networks do not exist in isolation. Rather they are interconnected to other infrastructures and, as technological development increases, so too does the linkage between networks. Interdependencies among Critical Infrastructure (CI) can cause cascading failures and hence amplify negative consequences due to these failures. This can also affect CI’s service restoration rate and consequently reducing their resilience in coping with these hazardous environmental events. For example, failure of the water drain and sewer system due to 2002 Glasgow flooding affected many homes and closed many main roads and stations such as the A82 and A8 roads, Buchanan Street subway station and Dalmarnock through to Exhibition Centre stations on the Argyle Line. As infrastructures are becoming more interdependent at some sectors, there is an increasing demand for more effective management of these interactions and interdependencies. This paper provides details of a quantitative metric for the robustness, recoverability, rapidity and resourcefulness of the interdependent infrastructure network in response to hazardous event. By generating a quantitative measure of network resilience, considering infrastructure interdependencies, the most severe failure scenarios and their spatial impacts can be identified and mapped. This can lead to prioritise future business planning strategies for CI asset owners and managers. To illustrate the application of the proposed approach, a case study in North Argyll, Scotland is analysed and presented in this paper

The sensitivity of bridge safety to spatial correlation of load and resistance

Random Field theory has emerged in recent years to model the statistical correlation of resistance in concrete structures and to determine its influence on the probability of structural failure. A major shortcoming in the work carried out to date is the spatial variability and corresponding correlation associated with applied traffic loads. In this paper the influence of spatial correlation of both traffic load and resistance is considered in the context of bridge safety assessment. The current study, explores, the nature of the problem by three theoretical examples. As a general trend, examples show that while traffic loads are weakly correlated, load effects are strongly correlated as the same heavy vehicle often causes extremes of load effect in different parts of the bridge which is due to the transverse sharing of load (measured here using a load sharing factor). It is found that the strength of correlation of load effect depends greatly on the load sharing factor which is treated in a simple way in many studies. In a more sophisticated beam-and-slab bridge example, load sharing factors are derived from a finite element analysis to assess transverse load sharing, and are shown to vary by girder number, girder segment and by load location. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. © 2015 Elsevier B.V

A Comparison of Critical Infrastructure Resilience Quantification Techniques

Promoting the resilience of critical infrastructure, when subjected to different hazardous events, is vital. However, applying inappropriate and/or imprecise resilience metrics or quantification techniques could increase the costs of resilience enhancement and reduce its effectiveness in critical infrastructure. This paper develops a method to evaluate and compare different resilience quantification techniques, in relation to different system failure states, in order to measure their effectivenes

Spatial time-dependent reliability analysis of reinforced concrete slab bridges subject to realistic traffic loading

Resistance and loads are often correlated in time and space. The paper assesses the influence of these correlations on structural reliability/probability of failure for a typical two-lane reinforced concrete (RC) slab bridge under realistic traffic loading. Spatial variables for structural resistance are cover and concrete compressive strength, which in turn affect the strength and chloride-induced corrosion of RC elements. Random variables include pit depth and model error. Correlation of weights between trucks in adjacent lanes and inter-vehicle gaps are also included and are calibrated against weigh-in-motion data. Reliability analysis of deteriorating bridges needs to incorporate uncertainties associated with parameters governing the deterioration process and loading. One of the major unanswered questions in the work carried out to date is the influence of spatial variability of load and resistance on failure probability. Spatial variability research carried out to date has been mainly focused on predicting the remaining lifetime of a corroding structure and spatial variability of material, dimensional and environmental properties. A major shortcoming in the work carried out to date is the lack of an allowance for the spatial variability of applied traffic loads. In this article, a two-dimensional (2D) random field is developed where load effects and time-dependent structural resistance are calculated for each segment in the field. The 2D spatial time-dependent reliability analysis of an RC slab bridge found that a spatially correlated resistance results in only a small increase in probability of failure. Despite the fact that load effect at points along the length of a bridge is strongly correlated, the combined influence of correlation in load and resistance on probability of failure is small. © 2015 Informa UK Limited, trading as Taylor & Francis Group

A Two-Dimensional Approach to the Probabilistic Assessment of Bridge Safety

A framework is presented for the assessment of the safety of a bridge deck, taking account of its 2-dimensional nature. Random field analysis is proposed to determine the spatial distribution of resistance probability but is not implemented in this paper. Monte Carlo simulation, calibrated using Weigh-in-Motion traffic data, is used to determine lifetime maximum distributions of bending moment throughout the bridge deck. The 2-dimensional approach is shown, for the example considered, to give much lower probabilities of failure than the alternative approach of considering points one by one