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Reliable estimations of the extent of corrosion and time required to reach specific safety limits are crucial for assessing the reliability of aging reinforced concrete (RC) bridges. Engineers and decision-makers can use these figures to plan suitable inspection and maintenance operations.

Analytical, empirical and numerical approaches for estimating the service life of corroded RC structures were presented and compared. The concrete cover cracking times, which were predicted by the previously proposed analytical models, were compared with the experimentally obtained cracking times to identify the model/s for RC bridges. The shortcomings and limitations of the existing models are discussed.

The empirical models typically depend on the rate of corrosion, diameter of steel reinforcement and concrete cover depth and based on basic mathematical formula. In contrast, the analytical and numerical models contain the strength and stiffness properties of concrete as well as type of corrosion products and incorporate more complex mechanical factors. Four existing analytical models were analyzed and their performance was evaluated against existing experimental data in literature. All the considered analytical models were assumed thick-walled cylinder models. The maximum difference between observed cracking time from different test data and calculated cracking time using the developed models is 36.5%. The cracking times extend with increase in concrete cover and decrease with corrosion current density. The development of service life prediction models that considers factors such as heterogeneity of concrete, non-uniform corrosion along rebar, rust production rate and a more accurate representation of the corrosion accommodating region are some of the areas for further research.

Outcome of this paper partially bridge the gap between theory and practice, as it is the basis to estimate the serviceability of corrosion-affected RC structures and to propose maintenance and repair strategies for the structures. For structural design and evaluation, the crack-width criterion is the greatest practical importance, and structural engineers, operators and asset managers should pay close attention to it. Additionally, repair costs for corrosion-induced serviceability failures, particularly concrete cracking and spalling, are significantly higher than those for strength failures. Therefore, to optimize the maintenance cost of RC structures, it is essential to precisely forecast the serviceability of corrosion-affected concrete structures. The lifespan of RC structures may be extended by timely repairs. This helps stake holders to manage the resources.

In order to improve modeling of corrosion-induced cracking, important areas for future research were identified. Heterogeneity properties of concrete, concept of porous zone (accommodation effect of pores should be quantified), actual corrosion morphology (non-uniform corrosion along the length of rebar), interaction between sustain load and corrosions were not considered in existing models. Therefore, this work suggested for further researches should consider them as input and develop models which have best prediction capacity.

This work has positive impact on society and will not affect the quality of life. Predicting service life of structures is necessary for maintenance and repair strategy plans. Optimizing maintenance strategy is used to extend asset life, reduce asset failures, minimize repair cost, and improve health and safety for society.

The degree of accuracy and applicability of the existing service life prediction models used for RC were assessed by comparing the predicted cracking times with the experimentally obtained times reported in the literature. The shortcomings of the models were identified and areas where further research is required are recommended.

In armored vehicles integrated transmissions, residual life prediction based on oil spectrum data is crucial for condition monitoring and reliability assessment. This paper aims to use the advantages of real-time and accurate prediction of binary Wiener process, the residual life prediction of clutch is studied.

First, combined with the wet clutch life test, the indicator elements Cu and Pb and the failure threshold of the residual life prediction of the clutch are extracted through the oil replacement correction of the spectral data of the whole life cycle; second, the correlation characteristics of indicating elements are analyzed by MATLAB Copula function, then the correlation function of residual life will be derived; third, according to the inverse Gaussian principle, the performance degradation mathematical models of the unary and binary Wiener processes of the above two indicator elements are established; finally, the maximum likelihood estimation method is used to estimate the parameters, and the monadic and binary performance degradation mathematical models are used to predict the residual life of the tested clutch.

By comparing the prediction results with the test results, with the passage of time, 81.25% of the predicted value error of the residual life prediction method based on the binary Wiener process is controlled within 20%, while 56.25% of the predicted value error of the residual life prediction method based on the unitary Wiener process is controlled within 20%. At the same time, the prediction accuracy of the binary prediction model is 2%–16.7% higher than that of the unitary prediction model.

This paper studies the residual life prediction theory of wet clutch, which can develop the theory and method of comprehensive transmission health monitoring, and provide theoretical and technical support for the construction of a reliable health management system for high-speed tracked vehicles.

Aero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty.

To study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model.

The results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation.

The proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft.

The novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.

The purpose of this paper is to compare the effect of rust inhibitors and surface strengthening materials on the service life of RC structures in tropical marine environments and ultimately to provide basis and recommendations for the durability design of reinforced concrete (RC) structures.

Slag concrete specimens mixed with four kinds of rust inhibitors and coated with four kinds of surface strengthening materials were corroded by seawater exposure for 365 days, and the key parameters of chloride ion diffusion were obtained by testing. Then a new service life prediction model, based on the modified model for chloride ion diffusion and reliability theory, was applied to analyze the effect of rust inhibitors and surface strengthening materials on the service life of RC structures in tropical marine environments.

Rust inhibitors and surface strengthening materials can effectively extend the service life of RC structures through different effects on chloride ion diffusion behavior. The effects of rust inhibitors and surface strengthening materials on the service life extension of RC structures adhered to the following trend: silane material > cement-based permeable crystalline waterproof material > hydrophobic plug compound > spray polyurea elastomer > water-based permeable crystalline waterproof material > calcium nitrite > preservative > amino-alcohol composite.

Using a new method for predicting the service life of RC structures, the attenuation law of the service life of RC structures under the action of rust inhibitors and surface strengthening materials in tropical marine environments is obtained.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

This study aims to provide a model to predict the service life of a thick organic coating.

A series of thin coating films are rapidly tested under the same exposure condition as the thick coating in its service condition by means of electrochemical impedance spectroscopy, scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction.

The validity of the model is successfully verified. The long-term protectiveness or service life of a thick organic coating can be rapidly predicted.

The prediction model does not require long-term experiments or any test that may alter the degradation mechanism of the thick coating.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.