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

Long-term deterioration effects on the buckling strength of metallic bridge girders

Bridges are an essential part of the transport infrastructure. A considerable number of these bridges are metallic, in many cases exceeding 100 years of age having suffered deterioration from environmental attack such as atmospheric corrosion. In order for infrastructural managers to make informed decision in terms of life-cycle cost perspective, reliable prediction of the remaining strength and service life of deteriorating bridges is essential. Deterioration models have been developed over the years to predict long-term material loss under different atmospheric conditions and environments. The aim of this paper is to quantify the effects of long-term deterioration, based on these models, on the remaining strength of metallic bridge girders, comprising of a number of plates. To obtain a useful insight into this problem, the finite element method is employed. In this paper, different plate elements, of varying slenderness and boundary conditions and representative of real bridge configurations, are analysed under different deterioration scenarios, brought about through material loss at different locations of the element. The effects of various parameters such as the degree/severity of material loss and the corrosion pattern (uniform versus non-uniform) on the buckling strength of the plates are quantified through both linear eigenvalue and non-linear analyses. The results of this study show that critical buckling strength of web panels may significantly drop at higher percentages of corrosion degradation and patterns, with the failure mode likely to change with increased deterioration. Differences between the critical buckling stresses obtained from the linear and non-linear analyses are presented

A vector-valued ground motion intensity measure incorporating normalized spectral area

A vector-valued intensity measure is presented, which incorporates a relative measure represented by the normalized spectral area. The proposed intensity measure is intended to have high correlation with specific relative engineering demand parameters, which collectively can provide information regarding the damage state and collapse potential of the structure. Extensive dynamic analyses are carried out on a single-degree-of-freedom system with a modified Clough–Johnston hysteresis model, using a dataset of 40 ground motions, in order to investigate the proposed intensity measure characteristics. Response is expressed using the displacement ductility, and the normalized hysteretic energy, both of which are relative engineering demand parameters. Through regression analysis the correlation between the proposed intensity measure and the engineering demand parameters is evaluated. Its domain of applicability is investigated through parametric analysis, by varying the period and the strain-hardening stiffness. Desirable characteristics such as efficiency, sufficiency, and statistical independence are examined. The proposed intensity measure is contrasted to another one, with respect to its correlation to the engineering demand parameters. An approximate procedure for estimating the optimum normalized spectral area is also presented. It is demonstrated that the proposed intensity measure can be used in intensity-based assessments, and, with proper selection of ground motions, in scenario-based assessments

Assessment of debonding in gfrp joints using damage identification techniques

Pultruded sections are used in many different civil engineering applications involving FRP composites, including a number of footbridges. These sections are typically joined through adhesive bonding and/or mechanical interlock. The joint is clearly critical to load transfer and the avoidance of unintended failure modes. As a contribution towards studying damage identification and assessment in FRP joints, this paper examines the dynamic performance of bonded GFRP pultruded sections. Experimental testing and FE modelling were employed to model damage in the joints and to assess whether debonding can be detected through differences in the dynamic characteristics - namely frequencies and mode shapes - of the components. Debonding in the joints was simulated by progressively reducing their bonded area. Four damage identification techniques (based on modal curvature, flexibility, damage index and the curvature of the flexibility-based uniform load surface) were used to assess damage. The results show that significant damage has to be present before debonding can be identified through changes in resonant frequencies and mode shapes. Once such levels are present, damage identification techniques can be applied effectively to locate damage. It is concluded that vibration-based damage assessment methods should be used in conjunction with other non-destructive evaluation techniques

Performance updating of concrete bridges using proactive health monitoring methods

Uncertainties associated with modelling of deteriorating bridges strongly affect management decisions, such as inspection, maintenance and repair actions. These uncertainties can be reduced by the effective use of health monitoring systems, through which information regarding in situ performance can be incorporated in the management of bridges. The objectives of this paper are twofold; first, an improved chloride induced deterioration model for concrete bridges is proposed that can quantify degradation in performance soon after chlorides are deposited on the bridge, rather than when initiation of corrosion at the reinforcement level takes place. As a result, the implications of introducing proactive health monitoring can be assessed using probabilistic durability criteria. Thus, the second objective of the paper is to present a methodology for performance updating of deteriorating concrete bridges fitted with a proactive health monitoring system. This methodology is illustrated via a simple example of a typical bridge element, such as a beam or a part of a slab. The results highlight the benefits from introducing 'smart' technology in managing bridges subject to deterioration, and quantify the reduction in uncertainties and their subsequent effect on predictions of future bridge performance

Sensitivity of uncertainties in performance prediction of deteriorating concrete structures

Deterioration models for the condition and reliability prediction of civil infrastructure facilities involve numerous assumptions and simplifications. Furthermore, input parameters of these models are fraught with uncertainties. A Bayesian methodology has been developed by the authors, which uses information obtained through health monitoring to improve the quality of prediction. The sensitivity of prior and posterior predicted performance to different input parameters of the deterioration models, and the effect of instrument and measurement uncertainty, is investigated in this paper. The results quantify the influence of these uncertainties and highlight the efficacy of the updating methodology based on integrating monitoring data. It has been found that the probabilistic posterior performance predictions are significantly less sensitive to most of the input uncertainties. Furthermore, updating the performance distribution