Corrosion cracking prediction updating of deteriorating RC structures using inspection information

It is well known that the corrosion-induced cracking of reinforced concrete (RC) structures varies in time and space due to the inherent spatial variability of concrete cover, concrete strength, surface chloride concentration and other material, environmental and dimensional properties. A model for predicting the likelihood and extent of RC corrosion-induced cracking considering spatial variability is reviewed. The uncertainties of predictions can be reduced by the effective use of information obtained from visual inspections regarding structural performance, such as cracking proportion and crack width. The paper uses a spatial time-dependent reliability analysis combined with visual inspection data to predict the likelihood and extent of RC corrosion-induced cracking. In this study, RC slabs and beams are used to illustrate the influence of inspection information updating on the future likelihood and extent of corrosion-induced cracking. Concrete strength, concrete cover and the surface chloride concentrations are modelled as spatial variables. Monte-Carlo simulation is employed to calculate the updated cracking proportions. The analysis considers various inspection scenarios which include different inspection intervals, inspection times, cracking proportion and crack width. It was found that the occurrence or observance of cracking changes the future cracking prediction significantly.     

Quantitative assessment of the oxidation potential of sulfide-bearing aggregates in concrete using an oxygen consumption test

In the presence of oxygen and humidity, the iron sulfide minerals present in some concrete aggregates can oxidize creating damage to concrete infrastructure. An oxygen consumption test was developed to assess the oxidation potential of concrete aggregate. A compacted layer of aggregate material is exposed to oxygen (O₂) in a hermetic cell, and the O₂ consumption is monitored. Optimized parameters included a 10 cm compacted layer of aggregate material with particle size <150 μm kept at 40% saturation degree with a 10-cm headspace left at the top of the cell. The consumption of the O₂ present in the headspace is monitored over a 3-h testing period at 22 °C. The test was able to discriminate the eight sulfide-bearing and control aggregates selected when using a threshold limit of 5% O₂ consumed. This draft limit will, however, require to be confirmed through the testing of a larger number of aggregates.     

跨海大桥健康监测的关键技术分析

以舟山大陆连岛工程为背景,阐述和讨论了在跨海大桥中开展健康监测的重要性和研究现状,并分析了跨海大桥健康监测和损伤识别所面临的关键技术问题和研究趋势,提出了考虑非线性效应和荷载激励特征来开展复杂结构损伤识别的研究思路,以提升通过大型健康监测系统来实现桥梁损伤评估的能力。     

Analysis of structural responses of bridges based on long-term structural health monitoring

Structural health monitoring system (SHMS) has been increasingly used for the maintenance of vehicle bridges nowadays. In this paper, a centralized SHMS is developed to monitor multiple bridges. The structural responses of two vehicle bridges in China, including the deformation of main components, creep and shrinkage of concrete, and strain caused by the temperature are estimated and analyzed based on the long-term monitored data. A numerical simulation has been carried out to investigate the deformation of the bridges, as well as the strain and stress on the specific sections. The results show a good agreement with the measured data. Wavelet analysis method has been introduced to investigate the characteristics in time and frequency domain, where the strain components caused by the season temperature variation, daily thermal effects, and random traffic loads can be clearly distinguished as the signals at four levels.     

A Bayesian approach to sequential monitoring of nonlinear profiles using wavelets

We consider change‐point detection and estimation in sequences of functional observations. This setting often arises when the quality of a process is characterized by such observations, called profiles, and monitoring profiles for changes in structure can be used to ensure the stability of the process over time. While interest in phase II profile monitoring has grown, few methods approach the problem from a Bayesian perspective. We propose a wavelet‐based Bayesian methodology that bases inference on the posterior distribution of the change point without placing restrictive assumptions on the form of profiles. By obtaining an analytic form of this posterior distribution, we allow the proposed method to run online without using Markov chain Monte Carlo (MCMC) approximation. Wavelets, an effective tool for estimating nonlinear signals from noise‐contaminated observations, enable us to flexibly distinguish between sustained changes in profiles and the inherent variability of the process. We analyze observed profiles in the wavelet domain and consider two possible prior distributions for coefficients corresponding to the unknown change in the sequence. These priors, previously applied in the nonparametric regression setting, yield tuning‐free choices of hyperparameters. We present additional considerations for controlling computational complexity over time and their effects on performance. The proposed method significantly outperforms a relevant frequentist competitor on simulated data.     

Performance evaluation of two methods for online monitoring of linear calibration profiles

We compare the performance of two phase II monitoring schemes for linear profiles, one based on the classical calibration method monitoring the deviations from the regression line (referred to as the NIST method) and the second based on individually monitoring the parameters of the linear profile (referred to as the KMW method). The comparison criterion is average run length performance under different sustained shifts in the intercept, slope and error standard deviation of the linear calibration line. A simulation study shows that the NIST method performs poorly compared to the combined control charting scheme of the KMW method.     

Development of life extension strategies for Australian military aircraft, using structural health monitoring of composite repairs and joints

The DSTO Centre of Expertise for Structural Mechanics (COE-SM) has recently developed methodologies for simulating structural health monitoring (SHM) systems for adhesively bonded composite repairs to Australian military aircraft. System design, interrogation strategy, and sensor placement are discussed, with particular emphasis on the development of techniques for embedding optical fibre sensors for optimal SHM system response.