Influence of micro and macro cracks due to sustained loading on chloride-induced corrosion of reinforced concrete beams

<p>Chloride-induced corrosion of steel is one of the most commonly found problems affecting the durability of reinforced concrete structures in both marine environment and where de-icing salt is used in winter. As the significance of micro-cracks on chloride induced corrosion is not well documented, 24 reinforced concrete beams (4 different mixes - one containing Portland cement and another containing 35% ground granulated blastfurnace slag at 0.45 and 0.65 water-binder ratios) were subjected to three levels of sustained lateral loading (0%, 50% and 100% of the load that can induce 0.1 mm wide cracks on the tension surface of beam - F_0.1) in this work. The beams were then subjected to weekly cycles of wetting with 10% NaCl solution for 1 day followed by 6 days of drying at 20 (±1) °C up to an exposure period of 60 weeks. The progress of corrosion of steel was monitored using half-cell potential apparatus and linear polarisation resistance (LPR) test. These results have shown that macro-cracks (at load F_0.1) and micro-cracks (at 50% of F_0.1) greatly accelerated both the initiation and propagation stages of the corrosion of steel in the concrete beams. Lager crack widths for the F_0.1 load cases caused higher corrosion rates initially, but after about 38 weeks of exposure, there was a decrease in the rate of corrosion. However, such trends could not be found in 50% F _0.1 group of beams. The extent of chloride ingress also was influenced by the load level. These findings suggest that the effect of micro-cracking at lower loads are very important for deciding the service life of reinforced concrete structures in chloride exposure environments. © 2014 4th International Conference on the Durability of Concrete Structures.</p>

Chloride induced corrosion of steel in alkali activated slag concretes

<p>Alkali activated slag (AAS) is an alternative cementitious material. Sodium silicate solution is usually used to activate ground granulated blast furnace slag to produce AAS. As a consequence, the pore solution chemistry of AAS differs from that of Portland cement (PC). Although AAS offers many advantages over PC, such as higher strength, superior resistance to acid and sulphate environments and lower embodied carbon due to 100% PC replacement, there is a need to assess its performance against chloride induced corrosion duo to its different pore solution chemistry. For PC systems, resistivity measurement, as a type of nondestructive test, is usually used to evaluate its chloride diffusivity and the corrosion rate of the embedded steel. However, due to the different pore solution chemistry present in the different AAS systems, the application of this test in AAS concretes would be questionable as the resistivity of concrete is highly dependent on its conductivity of the pore solution. Therefore, a study was carried out using twelve AAS concretes mixes, the results of which are reported in this paper. The AAS mixes were designed with alkali concentration of 4%, 6% and 8% (Na₂O% of the mass of slag) and modulus (Ms) of sodium silicate solution of 0.75, 1.00, 1.50 and 2.00. A PC concrete with the same binder content as the AAS concretes was also studied as a reference. The chloride diffusion coefficient was determined using a non-steady state chloride diffusion test (NT BUILD 443). The resistivity of the concretes before the diffusion test was also measured. Macrocell corrosion current (corrosion rate) for steel rods embedded in the concretes was measured whilst subjecting the concretes to a cyclic chloride ponding regime (1 day ponded with salt solution and 6 days drying). The results showed that the AAS concretes had lower chloride diffusivity with associated higher resistivity than the PC concrete. The measured corrosion rate was also lower for the AAS concretes. However, unlike the PC, in which a higher resistivity yields a lower diffusivity and corrosion rate, there was no relationship apparent between the resistivity and either the diffusivity or the corrosion rate of steel for the AAS concretes. This is assigned to the variation of the pore solution composition of the AAS concretes. This also means that resistivity measurements cannot be depended on for assessing the chloride induced corrosion resistance of AAS concretes.</p>

Conductivity sensor technology application in steady state migration test

<p>In this paper the current development of the steady state migration test was reviewed. Experiments were carried out for a series of concrete mixes with the steady state migration test in which conductivity sensor technology is applied. With the developed steady state migration test, conductivity in anolyte, loop current and temperature can be monitored in real time. The experimental results are conductive to understand the mechanism of chloride migration during both unsteady state and steady state. The conductivity of anolyte could be used to calculate the chloride concentration in anolyte and the theoretical correlation between them was explained. Over all, the developed steady state migration is an effective, convenient, well-defined in theory and plentiful with information method which could be used to determine the chloride diffusion coefficient of cementitious materials.</p>

Integrated care pathways for airway diseases (AIRWAYS-ICPs)

<p>The objective of Integrated Care Pathways for Airway Diseases (AIRWAYS-ICPs) is to launch a collaboration to develop multi-sectoral care pathways for chronic respiratory diseases in European countries and regions. AIRWAYS-ICPs has strategic relevance to the European Union Health Strategy and will add value to existing public health knowledge by: 1) proposing a common framework of care pathways for chronic respiratory diseases, which will facilitate comparability and trans-national initiatives; 2) informing cost-effective policy development, strengthening in particular those on smoking and environmental exposure; 3) aiding risk stratification in chronic disease patients, using a common strategy; 4) having a significant impact on the health of citizens in the short term (reduction of morbidity, improvement of education in children and of work in adults) and in the long-term (healthy ageing); 5) proposing a common simulation tool to assist physicians; and 6) ultimately reducing the healthcare burden (emergency visits, avoidable hospitalisations, disability and costs) while improving quality of life. In the longer term, the incidence of disease may be reduced by innovative prevention strategies. AIRWAYSICPs was initiated by Area 5 of the Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing. All stakeholders are involved (health and social care, patients, and policy makers).</p><p><br/></p><p><br/></p><p><br/></p>

Chloride ingress through alkali activated slag concretes

Alkali activated slag (AAS) is a credible alternative to Portland cement (PC) based binder systems. The superior strength gain and low embodied carbon make it a potential binder for next generation concretes. However there is little known about the long term durability of AAS systems, especially the chloride transport and subsequent corrosion of reinforcing steel.<br/>In this study, chloride transport through 12 AAS concretes with different alkali concentrations (Na2O% of mass of slag) and different modulus (Ms) of sodium silicate solution activator was investigated. A non-steady state chloride diffusion test was used for this study due to its similarity to the real exposure environment in terms of chloride transport through concrete. The results showed that the chloride concentration at the surface (Cs) of AAS concretes was higher than that for PC concrete.<br/>However, lower non-steady state chloride diffusion coefficient (Dnssd) was obtained for the AAS concretes. The Dnssd of the AAS concretes decreased with the increase of Na2O% and Ms of 1.50 gave the lowest Dnssd. The results are encouraging and it can be concluded that AAS concrete offers a superior performance in terms of chloride transport.

Influence of compressive loading on chloride ingress through concrete

Chloride-induced corrosion of steel in concrete is one of most important durability and safety concern for reinforced concrete structures. To study chloride ingress into concrete is thus very important. However, most of the researchers focus on the studying chloride ingress through concrete samples without any loading. In reality concrete structures are subjected to different kinds of loads and therefore studying the effect of such loads on chloride transport is critical. In this work, 28 different concrete mixes were subjected to three levels of compressive load (0%, 50% and 75% of compressive failure load – f) for 24 hours. Further to unloading, these samples were subjected to non-steady state chloride diffusion test as per NT Build 443. The results were compared against the diffusion coefficient obtained for concrete samples that had no previous loading. D value for concretes subjected to 75% f showed a significant increase compared to 0% loading condition, but the increase was insignificant for 50% f. The results indicate that the influence of concrete mixes variables on D is more significant than that of loading level. Surface chloride concentration also increased with the loading level, which might be due to the increased concrete surface area caused by micro cracking.

Kernel-based local order estimation of nonlinear nonparametric systems

We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NARX), which may have different local dimensions at different points. By minimizing the kernel-based local information criterion introduced in this paper, the strongly consistent estimates for the local orders of the NARX system at points of interest are obtained. The modification of the criterion and a simple procedure of searching the minimum of the criterion, are also discussed. The theoretical results derived here are tested by simulation examples.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

<p>A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.</p>

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.

Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.