A framework for predicting the residual load carrying capacity of concrete structures exposed to aggressive environments

Reinforced concrete structures are susceptible to a variety of deterioration mechanisms due to creep and shrinkage, alkali-silica reaction (ASR), carbonation, and corrosion of the reinforcement. The deterioration problems can affect the integrity and load carrying capacity of the structure. Substantial research has been dedicated to these various mechanisms aiming to identify the causes, reactions, accelerants, retardants and consequences. This has improved our understanding of the long-term behaviour of reinforced concrete structures. However, the strengthening of reinforced concrete structures for durability has to date been mainly undertaken after expert assessment of field data followed by the development of a scheme to both terminate continuing degradation, by separating the structure from the environment, and strengthening the structure. The process does not include any significant consideration of the residual load-bearing capacity of the structure and the highly variable nature of estimates of such remaining capacity. Development of performance curves for deteriorating bridge structures has not been attempted due to the difficulty in developing a model when the input parameters have an extremely large variability. This paper presents a framework developed for an asset management system which assesses residual capacity and identifies the most appropriate rehabilitation method for a given reinforced concrete structure exposed to aggressive environments. In developing the framework, several industry consultation sessions have been conducted to identify input data required, research methodology and output knowledge base. Capturing expert opinion in a useable knowledge base requires development of a rule based formulation, which can subsequently be used to model the reliability of the performance curve of a reinforced concrete structure exposed to a given environment.

Challenges in residual service life assessment for refurbishment

A need for an efficient life care management of building portfolio is becoming increasingly due to increase in aging building infrastructure globally. Appropriate structural engineering practices along with facility management can assist in optimising the remaining life cycle costs for existing public building portfolio. A more precise decision to either demolish, refurbish, do nothing or rebuilt option for any typical building under investigation is needed. In order to achieve this, the status of health of the building needs to be assessed considering several aspects including economic and supply-demand considerations. An investment decision for a refurbishment project competing with other capital works and/or refurbishment projects can be supported by emerging methodology residual service life assessment. This paper discusses challenges in refurbishment projects of public buildings and with a view towards development of residual service life assessment methodology

Structural health monitoring of bridges using acoustic emission technology and signal processing techniques

Structural health monitoring (SHM) is the term applied to the procedure of monitoring a structure’s performance, assessing its condition and carrying out appropriate retrofitting so that it performs reliably, safely and efficiently. Bridges form an important part of a nation’s infrastructure. They deteriorate due to age and changing load patterns and hence early detection of damage helps in prolonging the lives and preventing catastrophic failures. Monitoring of bridges has been traditionally done by means of visual inspection. With recent developments in sensor technology and availability of advanced computing resources, newer techniques have emerged for SHM. Acoustic emission (AE) is one such technology that is attracting attention of engineers and researchers all around the world. This paper discusses the use of AE technology in health monitoring of bridge structures, with a special focus on analysis of recorded data. AE waves are stress waves generated by mechanical deformation of material and can be recorded by means of sensors attached to the surface of the structure. Analysis of the AE signals provides vital information regarding the nature of the source of emission. Signal processing of the AE waveform data can be carried out in several ways and is predominantly based on time and frequency domains. Short time Fourier transform and wavelet analysis have proved to be superior alternatives to traditional frequency based analysis in extracting information from recorded waveform. Some of the preliminary results of the application of these analysis tools in signal processing of recorded AE data will be presented in this paper.

Urban network travel time estimation from stop-line loop detector data and signal controller data

This paper presents a model to estimate travel time using cumulative plots. Three different cases considered are i) case-Det, for only detector data; ii) case-DetSig, for detector data and signal controller data and iii) case-DetSigSFR: for detector data, signal controller data and saturation flow rate. The performance of the model for different detection intervals is evaluated. It is observed that detection interval is not critical if signal timings are available. Comparable accuracy can be obtained from larger detection interval with signal timings or from shorter detection interval without signal timings. The performance for case-DetSig and for case-DetSigSFR is consistent with accuracy generally more than 95% whereas, case-Det is highly sensitive to the signal phases in the detection interval and its performance is uncertain if detection interval is integral multiple of signal cycles.

Online estimation of distorted power system signal parameters

An algorithm based on the concept of Kalman filtering is proposed in this paper for the estimation of power system signal attributes, like amplitude, frequency and phase angle. This technique can be used in protection relays, digital AVRs, DSTATCOMs, FACTS and other power electronics applications. Furthermore this algorithm is particularly suitable for the integration of distributed generation sources to power grids when fast and accurate detection of small variations of signal attributes are needed. Practical considerations such as the effect of noise, higher order harmonics, and computational issues of the algorithm are considered and tested in the paper. Several computer simulations are presented to highlight the usefulness of the proposed approach. Simulation results show that the proposed technique can simultaneously estimate the signal attributes, even if it is highly distorted due to the presence of non-linear loads and noise.