Improvement in performance of corroding concrete structures using health monitoring systems

Predicting future condition and reliability of the deteriorating structures is vital for their effective management. Probabilistic models have been developed to estimate and predict the extent of deterioration in concrete structures but their input parameters are fraught with uncertainties, hence limiting the effective use of the models for long term predictions. On the other hand, continuous innovations in the sensing and measurement technology have lead to the development of monitoring instruments that can provide continuous (or almost continuous) real time information regarding structural performance. Thus, powerful decisionsupport tools may be developed by combining information obtained through structural health monitoring with probabilistic performance prediction models. The potential benefits of improving performance prediction using health monitoring systems and their implications on the management of deterioration prone structures are presented in this paper. A typical structural element of a bridge (eg slab, beam or a cross beam etc) subjected to chloride induced deterioration is considered. It is shown that the confidence in predicted performance can be improved considerably through the use of health monitoring methods and hence, the management activities such as inspections, repair and maintenance etc can be adjusted whilst keeping consistent target performance levels. A comparison of various probabilistic models for the input parameters (eg exposure conditions, threshold chloride concentration etc) indicates that the effects of uncertainty can be minimised through the inservice health monitoring systems

A risk ranking strategy for network level bridge management

Life-Cycle Civil Engineering - Proceedings of the 1st International Symposium on Life-Cycle Civil Engineering, IALCCE '08 2008, Pages 643-648At present almost all bridge owners and managers, i.e. Network Rail, Highways Agency and local authorities in the UK, carryout bridge inspections at regular intervals to collect information on condition and performance of bridges. Introduction of risk based approaches in the selection of inspection regimes can provide consistent safety levels within the network in a cost effective manner. This paper presents the development of a qualitative risk ranking strategy to characterize a network of bridges into groups with similar risk levels, which can form the basis for developing a risk based inspection regime over the network. There are a multitude of factors that affect risk. These factors are identified and rationally combined to present various attributes of bridges. A qualitative scoring system is then introduced which utilizes the attributes to rank bridges in terms of their relative risk. Sensitivity analysis is performed to quantify the effect of relative weights of the attributes on the risk scores. The methodology is demonstrated through its application on UK's Network Rail bridge stock comprising of about 40,000 bridges. The criteria to classify the severity of the attributes are established for the network. A random sample of bridges is ranked to illustrate the proposed methodology. © 2008 Taylor & Francis Group, London

Predictive SHM-supported deterioration modelling of reinforced concrete bridges

Deterioration, increase in loading demand and change in utilization have induced an unknown level of risk in the use of transport infrastructure systems. Bridges being a vital element of such systems, due to their very nature as well as their exposure to harsh environmental conditions, should be effectively managed for the benefit of the overall transport network. Predicting the future condition and reliability of the bridges is vitally important in this process. Probabilistic models have been developed to estimate and predict the extent of deterioration in, for example, concrete bridges. However, the input parameters of these models are fraught with uncertainties, thus severely limiting their accuracy, particularly over longer time frames. On the other hand, continuous innovations in the sensing and measurement technology have lead to the development of monitoring instruments that can provide continuous (or almost continuous) data regarding the actual structural performance in the time frame. This information cannot be used directly for the prediction of future performance, first because it typically pertains to a small number of specific locations, and secondly because it needs to be combined with a whole host of other knowledge components. Furthermore, uncertainties in the instruments/measurements and in the future behaviour of the structure and its interaction with the environment (e.g. including the effects of deterioration) also hinder the predictive capability of current modelling tools. The potential benefits of improving performance prediction through the integration of health monitoring systems with probabilistic predictive models, and their implications on the management of deterioration prone structures are presented in this paper through the development of an integrated methodology. It is shown, through application case studies, that the confidence in predicted performance can be significantly increased through the use of SHM-supported modelling of deterioration and the major inspection and maintenance activities can be delayed on the account of increased confidence in the predicted performance. An example of such integration is illustrated in Figure 1 for various cases of sensor outputs including attainment of limiting value as well as (Graph Presented) confirmation of safety at various points in time during the service life. It is clear that the uncertainty is reduced with the availability of additional information and the level of this reduction depends on the quality and timing of information obtained through sensing equipment. A sensitivity study of various input parameters has concluded that the range of predicted performance is considerably reduced through the updating methodology presented in this paper. A typical result is shown in Figure 2, which quantifies the influence of the number of sensors on the coefficient of variation for the time to corrosion initiation at rebar revel for various hypothesized exposure conditions. The case with '0' sensor indicates the prior corrosion initiation times. It can be seen that the influence of various models that could be assumed for exposure conditions is minimized by the integration of data obtained through SHM into the predictive models. Finally a life-cycle cost analysis for various management strategies (with and without the use of SHM) highlighted the safety and cost benefits that can be obtained through the use of SHM-supported predictive models (Figure 3). It is clear from the figure that the LCC is minimized for the case where decisions are aided with predictive models updated through SHM. It is recognized that the above conclusions are obtained from a limited number of application case studies. Clearly more work is needed in this area including physical tests and field data collection to improve our understanding of the underlying phenomena and to reduce prior uncertainties, especially those related with modelling and measurement (epistemic components). © 2006 Taylor & Francis Group

Reliability based inspection planning applications for offshore structures

Proceedings of the International Offshore and Polar Engineering Conference Volume 4, 1996, Pages 565-572Experience from the development and application of reliability based inspection planning and optimisation methodologies on a range of offshore structures is discussed in this paper. A series of studies are discussed which demonstrate the progressive application from fixed platforms to floaters and jack-ups highlighting the particular methods that need to be adopted for each platform type. While the basic methodology is common in all cases there are a number of considerations specific to each type relating to their structural form, operational conditions and loading history which need to be taken into account when applying these techniques. The benefits obtained from the application of these methods are discussed with relevant example applications. Important trends and key parameters in this type of analysis are highlighted through the results of relevant parametric sensitivity studies. Areas where further work is needed to advance these applications are also highlighted and discussed in this paper

Reliability based inspection planning of offshore structures

This paper presents a series of studies on the development and application of reliability based inspection planning techniques for offshore structures including fixed and floating platforms and jack-up drilling rigs. The general methodology is presented which broadly applies to all three types of offshore structure and differences between the characteristics of the various structures which need to be reflected in the detailed methodology are highlighted in the paper. The need for using these techniques is discussed together with the benefits achieved from their application. Case studies are presented to demonstrate the application and benefits of these techniques and important trends are highlighted through the results of parametric sensitivity studies. Furthermore, areas where more work is required to advance these applications further are also identified and discussed

Overview of advanced structural and reliability techniques for optimum design of fixed offshore platforms

The paper addresses the increasing use of advanced structural and reliability techniques in design optimisation affixed offshore platforms. Recent changes in regulatory and contract practices within the offshore industry coupled with developments in structural and reliability methods and increased hardware capacity create a suitable framework for increased design optimisation. The paper examines the current impact of advanced structural analysis techniques at component and system level and examines how recent developments in structural system reliability techniques are likely to affect the design process in future. Areas that need to be addressed before we are able to maximise the benefits from the use of these techniques are discussed together with recommendations for a more effective integration in the design process

Optimum preventative maintenance strategies using genetic algorithms and Bayesian updating

Preventative maintenance (PM) includes proactive maintenance actions that aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds because it can extend the life of bridges and avoid the need for unplanned essential maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed an optimisation methodology based on genetic algorithm (GA) principles, which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. To further improve the reliability of estimating the degree of deterioration of an element, which is a key element in predicting optimum PM strategies using the GA methodology, Bayesian updating is utilised. The use of Bayesian updating enables the updating of the probability of failure based on data from site inspection or laboratory experiments and the adjustment, if necessary, of the timing of subsequent PM interventions. For the case study presented in this paper, the probability of failure is expressed as the probability of corrosion initiation of a reinforced concrete element due to de-icing salt