Service Life Prediction Research Articles

A residual service life prediction of lithium-ion batteries based on decomposition algorithm and fully connected neural network

A residual service life prediction of lithium-ion batteries based on decomposition algorithm and fully connected neural network

Service life prediction model for biogenic acid corrosion: Advancement of life factor method for concrete sewer design

Current service life prediction models for concrete sewer design, including the widely used Life Factor Method (LFM), do not efficiently incorporate certain key corrosion contributing factors, such as material selection, concrete mix design, and sewer environment. This paper describes an advancement of the LFM model to cover these factors, broaden its usage, and improve concrete sewer design and applications. By integrating the first principles of the original LFM model, advanced knowledge from the literature, and results from field performance studies of various sewer concretes, a practical and relatively simple LFM model was advanced. With concrete mix design, oxide composition of all concrete ingredients, and sewer environmental and hydraulic conditions, the so-called ‘advanced LFM model’ could predict the corrosion rate of various concrete mixes. The model emphasised the influence of various modern binders, including calcium aluminate and calcium sulpho-aluminate cements, and various aggregate types including siliceous, calcareous, and alumina-based aggregates, when subjected to different sewer environments.

Exploring machine learning to study and predict the chloride threshold level for carbon steel reinforcement

Chloride-induced corrosion of steel reinforcing bar (rebar) is the primary cause of deterioration in reinforced concrete structures, posing a significant infrastructure challenge. The chloride threshold level (CTL) of rebar, which represents the critical amount of chloride needed to initiate active corrosion, is crucial in corrosion and service life prediction models. However, substantial uncertainties and a multitude of influencing factors, along with the absence of a universally accepted testing framework, hinder the achievement of a consistent CTL range for service life models and complicate comparisons of published values. This study addresses these challenges by developing multiple machine learning models to predict CTL, considering 21 carefully selected features. A comprehensive database of 423 data points was compiled from an exhaustive literature review. Seven machine learning models—linear regression, decision tree, random forest, K-nearest neighbors, support vector machine, artificial neural network, and an ensemble model—were developed and optimized. The ensemble model achieved superior prediction performance, with a mean absolute error of 0.218 % by weight of binder, root mean square error of 0.321 %, and a coefficient of determination of 0.751 on unseen CTL data. Partial dependence plots generated using the support vector machine model quantified the effect of each feature on CTL. The random forest model identified SiO₂ binder content and exposed rebar area to chlorides as the most influential factors. The study also examined the impact of supplementary cementitious materials (SCMs), finding that only blast furnace slag positively affected CTL.

Wear of Wave Energy Converters Mooring Lines Belts

Abstract Using a belt as a replacement for a rope on a rotary power take-offs (PTOs) system has become more common for wave energy converters, improving cyclic bend over sheave performance with a smaller bending thickness for belts. However, the service life predictions of PTOs are a major concern in design, because belt performance under harsh underwater environments is largely less studied. In this work, the effect of fleet and twist angles on wear life is being investigated both experimentally and numerically. Two three-dimensional equivalent static finite element models are constructed to evaluate the complex stress state of polyurethane-steel belts around steel drums. The first is to capture the response of the experimental investigation performed on the wear life, and the second to predict the wear life of an existing functional PTO. The results show a significant effect for fleet and twist angles on stress concentrations and estimated service life.

Laboratory Investigation of the Strength Degradation against Ultraviolet Radiation of Geonets for Slope Protection.

Sufficient light and high UV intensity pose significant challenges to the long-term performance of polymeric geonet materials for slope-protection structures. This study investigates strength degradation under the effect of UV radiation; five different types of geonets were selected, which can be categorized as polyamide (PA), polypropylene (PP), and polyethylene (PE) materials. A comprehensive experimental investigation was performed, including tension strength, peer strength, artificially accelerated aging, and SEM tests, to further establish a service life prediction method used for slope-protection design. The results showed that the tension strength, percentage of breaking elongation, and peer strength all depict a descending trend with aging-elapsed time, especially in the early 600 h. The decreasing tendency of these mechanical properties' magnitude differed in the diversity of direction and material type. Significant changes have been generated on the geonet surface after aging; materials with smooth surfaces exhibit a strong ability against strength degradation. Fitting results affirmed the predictive technique as a useful engineering tool for tension strength assessments, offering guidelines for using and designing of geonets for slope-protection structures.

Comparative study of corrosion-based service life prediction of reinforced concrete structures using traditional and machine learning approach

Comparative study of corrosion-based service life prediction of reinforced concrete structures using traditional and machine learning approach

An optimal goose lithium-ion batteries accurate and rapid RUL prediction method with automatic initial hyperparameters settings

Abstract Machine learning has emerged as a highly effective tool for addressing complex data problems, garnering significant attention in the field of equipment degradation and remaining service life prediction. Existing prediction models typically exhibit two primary shortcomings: on the one hand, the accuracy of life prediction reaches the desired level of precision while failing to achieve a sufficiently fast prediction speed, and on the other hand, generalization is not guaranteed while requiring the model to be robust. These two aspects present a significant challenge to the field of machine learning. In light of the aforementioned issues, we propose a prediction model based on the goose algorithm. Initially, we set the goose algorithm using adaptive initialization of the goose population to guarantee that the goose population is set at the appropriate interval, and we incorporate it into the extreme learning machine model through the improved goose algorithm. goose algorithm is used to predict the service life. Finally, we utilize different types of lithium batteries with varying operational conditions to conduct pertinent case studies to validate the proposed prediction model. The results demonstrated that the average accuracy was above 98% in all validated datasets. The shortest computation time was 0.19 s.

Freeze-thaw resistance and service life prediction of fly ash incorporated ultra-high ductility magnesium phosphate cement-based composites

To develop ultra-high ductility magnesium phosphate cement-based composites (UHDMC) suitable for cold areas and fully utilize solid waste, the effects of fly ash (FA) substitution and freeze-thaw cycles on the mechanical properties and freeze-thaw resistance of UHDMC specimens were explored. The mechanism of freeze-thaw damage was revealed by mercury intrusion porosimetry (MIP) and scanning electron microscope (SEM). Freeze-thaw damage model for UHDMC was established to predict its service life. The results showed that, after 300 freeze-thaw cycles, the compressive strength retention rate of the UHDMC specimens with FA substitution from 10 % to 30 % was above 88.7 %, and the tensile strain maintained above 3.48 %, still presenting the characteristics of multi-cracking and strain hardening. Meanwhile, after 600 freeze-thaw cycles, the mass loss rate and the dynamic elastic modulus of UHMPC specimens with FA substitution from 10 % to 30 % were below 1.38 % and above 81.1 %, respectively. The mechanical properties were mainly related to the total porosity and macropores. The effect of the water solubility of prismatic struvite-K crystals and the secondary hydration production of filamentous struvite potassium crystals on the matrix presented well explanation for the excellent freeze-thaw resistance of UHDMC specimens with FA substitution from 10 % to 30 %. At last, a freeze-thaw damage model for UHDMC specimens with FA substitution below 30 % based on the Weibull probability distribution function was established, predicting that their service life was up to 232 years in the northeastern of China.

Durability fragility curves for service life prediction of concrete with various supplementary cementitious materials subjected to natural carbonation

This study estimates the service life of concretes containing supplementary cementitious materials (SCM) subjected to carbonation from a probabilistic and reliability-based perspective. The aim is to enhance understanding of the risks associated with degradation and to propose an alternative approach for estimating the durability of reinforced concrete (RC) structures. The inclusion of supplementary cementitious materials (SCMs) is crucial for making concrete more sustainable, as these materials can reduce the carbon footprint of concrete production and improve its durability. Using available natural carbonation data from the literature on samples containing SCM, computational codes were developed for reliability analyses via the Monte Carlo Simulation method. The results indicated that all concrete samples exhibited a 100% probability of failure for coverages of 20 mm and 25 mm at an age of 100 years. For a coverage of 30 mm, all samples maintained a failure probability of over 98% within the same period. Notably, concretes incorporating metakaolin showed a substantial reduction in failure probability across all evaluated time periods, indicating superior durability compared to other samples. These findings significantly contribute to a deeper understanding of concrete degradation processes and provide valuable insights for the design and maintenance of RC structures, particularly in the context of using SCM. By adopting a probabilistic approach, the study underscores the importance of considering variability and uncertainties in predicting concrete durability, ultimately aiding in the development of more resilient and long-lasting infrastructure.

Distributed Embedded System for Multiparametric Assessment of Infrastructure Durability Using Electrochemical Techniques.

We present an autonomous system that remotely monitors the state of reinforced concrete structures. This system performs real-time follow-up of the corrosion rate of rebars (iCORR), along with other relevant parameters such as temperature, corrosion potential (ECORR), and electrical resistance of concrete (RE), at many of a structure's control points by using embedded sensors. iCORR is obtained by applying a novel low-stress electrochemical polarization technique to corrosion sensors. The custom electronic system manages the sensor network, consisting of a measurement board per control point connected to a central single-board computer in charge of processing measurement data and uploading results to a server via 4G connection. In this work, we report the results obtained after implementing the sensor system into a reinforced concrete wall, where two well-differentiated representative areas were monitored. The obtained corrosion parameters showed consistent values. Similar conclusions are obtained with ECORR recorded in rebars. With the iCORR follow-up, the corrosion penetration damage diagram is built. This diagram is particularly useful for identifying critical events during the corrosion propagation period and to be able to estimate structures' service life. Hence, the system is presented as a useful tool for the structural maintenance and service life predictions of new structures.

Report of RILEM TC 281-CCC: A critical review of the standardised testing methods to determine carbonation resistance of concrete

The chemical reaction between CO2 and a blended Portland cement concrete, referred to as carbonation, can lead to reduced performance, particularly when concrete is exposed to elevated levels of CO2 (i.e., accelerated carbonation conditions). When slight changes in concrete mix designs or testing conditions are adopted, conflicting carbonation results are often reported. The RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ has conducted a critical analysis of the standardised testing methodologies that are currently applied to determine carbonation resistance of concrete in different regions. There are at least 17 different standards or recommendations being actively used for this purpose, with significant differences in sample curing, pre-conditioning, carbonation exposure conditions, and methods used for determination of carbonation depth after exposure. These differences strongly influence the carbonation depths recorded and the carbonation coefficient values calculated. Considering the importance of accurately determining carbonation potential of concrete, not just for predicting their durability performance, but also for determining the amount of CO2 that concrete can re-absorb during or after its service life, it is imperative to recognise the applicability and limitations of the results obtained from different tests. This will enable researchers and practitioners to adopt the most appropriate testing methodologies to evaluate carbonation resistance, depending on the purpose of the conclusions derived from such testing (e. g. materials selection, service life prediction, CO2 capture potential).

Remaining useful life prediction based on multi-stage Wiener process and Bayesian information criterion

Equipment will go through multiple degradation stages under complex operating conditions, and the single-stage degradation model cannot accurately describe the degradation process of the equipment at different stages, resulting in inaccurate remaining service life prediction results and reliability analysis. Therefore, this paper establishes a multi-stage Wiener degradation process model that considers measurement errors and includes three different forms of drift functions. First, by calculating the Bayesian information criterion (BIC) values of these three degradation models separately and analysing the variation trends of the BIC values, a method for detecting change-points is proposed to achieve stage division. Next, by comparing the BIC values of the three models, a method for adaptively selecting the optimal model for each stage is proposed. Then, based on the results of stage division and optimal model selection, approximate analytical expressions for the RUL of each stage are derived, and parameter estimation is performed using maximum likelihood estimation (MLE). Finally, the RUL prediction study using the proposed method is carried out through simulation cases and practical cases. The results show that the accuracy of the proposed method is higher than the existing research methods, verifying the effectiveness of the proposed method.

Study on the effect of multi-factor compound action on long-term tensile performance of GFRP composite pipe and life prediction analysis

In this work, the glass fiber reinforced plastic (GFRP) composite pipes used as sewerage pipes in oil and gas field environments were studied. The accelerated aging experiments were carried out under the simulated environments of three typical oil field media which including circulating air, simulated produced water and standard simulated oil. Scanning electron microscopy (SEM) was adopted to observe the microscopic morphology of the aged GFRP tubes under the three conditions. The microstructural characteristics of the aged tubes and the evolution process of internal defects were also analyzed and evaluated in combination with micro-CT technology. The attenuation pattern of the hoop tensile strength of the aged pipes was measured via the separation disk method, and the service life prediction was carried out based on the Arrhenius theory with the hoop strength as the life index. The results show that the accumulation of aging time and continuous heat input will aggravate the expansion of internal pores and cause the aggregation of small-volume defects into large-volume defects. Especially in the simulated oil environment, which has the highest sensitivity to defects. The maximum porosity reaching 1.5 % and the maximum volume of individual defects exceeding 106 μm3 after aging for 4000 h at 95 °C. The thermal aging process under the three environmental conditions had similar aging characteristics, and interfacial damage of the glass fibers was observed in all of them. The variability of hydrothermal aging is related to the hydrolytic behavior of the glass fibers, which is associated with the progressive dissolution damage of the SiO2 network in the glass fibers, as well as the breaking of silane bonds. Thus, the hydrothermal environment had the most significant effect on the hoop tensile strength decay. Ultimately, 75 % strength retention was taken as the end-of-life indicator. The predicted service life based on the Arrhenius model for the three environments of thermo-oxygen, hydrothermal and simulated oil were 23.5, 23.1 and 28.5 years at 20 °C, and 12.2, 4.9 and 10.3 years at 40 °C, respectively.

Three-dimensional quantitative characterization of defects in inconel 625 superalloy based on deep learning image identification

Three-dimensional (3D) quantitative characterization of defects in superalloys is an important way to promote the ability of material design and service life prediction. In this work, 3D spatial distribution of defects for Inconel 625 superalloy manufactured by laser additive manufacturing (LAM) is carried out deep learning (DL) image identification technology and 3D image reconstruction. Firstly, computer tomography (CT) technology was used to obtain continuous slice images of sample. The U-net DL algorithm was applied to intelligently identify material defects in the continuous slices. On this basis, quantitative identification and analysis of spatial defect positions and typical sizes is achieved by using 3D reconstruction software. Compared with traditional threshold segmentation (TS) techniques, the defect recognition rate has significantly improved from 61.90 ​% to 95.00 ​%. This work provides a promising characterization method for efficient characterizing alloy defects and damage especially during material performance evaluation.

An Integrated Solution to FIB-Induced Hydride Artifacts in Pure Zirconium.

The preparation method of transmission electron microscopy (TEM) samples for pure zirconium was successfully executed using a focused ion beam (FIB) system. These samples unveiled artifact hydrides induced during the FIB sample preparation process, which resulted from stress damage, ion implantation, and ion irradiation. An innovative solution was proposed to effectively reduce the effect of artifact hydrides for FIB-prepared samples of hydrogen-sensitive materials, such as zirconium alloys. This development lays the groundwork for further research on the micro/nanostructures of zirconium alloys after ion irradiation, thereby facilitating the study of corrosion mechanisms and the prediction of service life for nuclear fuel cladding materials. Furthermore, the solution proposed in this study is also applicable to TEM sample preparation using FIB for other hydrogen-sensitive materials such as titanium, magnesium, and palladium.

Pavement Service Life Prediction with PLAXIS 3D in Bangladesh

This research aims to forecast the service life of the flexible pavement in Bangladesh. The service life of flexible pavement is greatly affected by the climate and traffic load. Pavements are severely deteriorated by the overweight trucks, shortening their lifespan. The study aims to predict the service life of a flexible road using the PLAXIS 3D software. The PYTHON algorithm was utilized for the pre- and post-processing of new mechanistic empirical approach based on the AASHTO 2008 design standard. PLAXIS 3D was used for realistic soil & damage modelling and to obtain a reliable service life. Rajshahi City Bypass Nowhata Road was chosen as the study area for this study because the road condition of Nowhata is defected day by day and the number of moving vehicles with high axle load also increasing. This research has established to give the valid data to the authority according to the construction materials of flexible pavement to get better performance and proper use of materials. This road is constructed under the Road & Highway Department (RHD), Rajshahi Zone. This study shows realistic results based on temperature, soil data of subgrade and the materials property used in the PLAXIS 3D software and PYTHON programming language.

Increased accuracy of service life prediction for fiber metal laminates by consideration of the manufacturing-induced residual stress state

Fiber metal laminates (FML) combine the high specific properties of fiber-reinforced polymers with the ductility and damage tolerance of metals. However, during the manufacturing process of FMLs, in-plane residual stresses arise in the single layers of the laminate. The stresses mainly originate from the different coefficients of thermal expansion of the two materials and lead to an inhomogeneous stress state in the thickness direction of the laminate. This work investigates the influence of such thermal residual stress (TRS) on the fatigue life in an FML by experimentally varying the TRS in identical layups using modified cure cycles. The experimental investigations show that the amount of TRS directly influences the fatigue performance of such laminates. Reducing the TRS, particularly the tensile residual stresses in the metal layers, slows the fatigue crack growth during cyclic loading. This leads to a significantly delayed crack initiation and slower crack propagation. Knowledge of material behavior is essential for utilizing the full potential of structural health monitoring (SHM) in component lifetime predictions. Predictive models require a set of input parameters to perform this task. Since the TRS in an FML significantly contributes to the component's fatigue life, its consideration in predictive approaches is inevitable. This finding is discussed based on a phenomenological damage accumulation model initially developed for composite materials.

Investigation of the mechanical and durability properties of concrete containing modified fly ash and modified zeolite powder: An effective transport model for sulfate ions in concrete

The application of conventional fly ash and zeolite powder in the field of concrete durability is limited. In this study, durability tests coupling dry-wet cycling and sulfate erosion were conducted on concrete with varying dosages of modified fly ash (MFA) and modified zeolite powder (MZP). The compressive strength, failure mode, water absorption rate, and ion concentration changes were systematically investigated. The reaction products and structure were analyzed through SEM, EDS, and XRD tests, correlating with macroscopic parameters. An effective transport model for sulfate ions in concrete was established, considering the material influence coefficient km and the effective diffusion coefficient De. The results indicate that a higher content of Ca phases in MFA leads to a reduction in concrete early strength, while later stages exhibit rapid strength growth due to pozzolanic reactions. MZP contains a significant amount of Al and Si phases, which promote both early and later-stage concrete strength. MFA and MZP enhance the resistance of concrete to sulfate erosion, with MZP exhibiting a significantly superior improvement effect compared to MFA. This is attributed to the following factors: on the one hand, the addition of MFA and MZP reduces the porosity and improves the pore structure of concrete, thus delaying the diffusion of sulfate ions into the concrete; on the other hand, the pozzolanic reactions of MFA and MZP consume available calcium hydroxide, leading to the formation of C–S–H and C-A-S-H gels, which densify the microstructure and reduce the probability of leaching erosion of hydration products, thereby limiting the formation of ettringite. The applicability and reliability of the sulfate-ion effective transport model were verified based on experimental data. The model can be employed to predict the distribution of sulfate ions in different types of concrete under various erosion durations, reducing workload and providing a theoretical basis for the durability design and service life prediction of concrete structures in sulfate environments.

Corrosion resistance behavior of enhanced passivation Cr-modified rebars and their service life prediction based on Monte Carlo simulation

This work investigates enhanced passivation and corrosion resistance of various Cr-modified rebars compared to carbon rebar. It also evaluates their service life through time-varying probability analysis based on Monte Carlo simulation, combined with the parameters obtained from sampling and testing reinforced concrete mortar samples exposed to various marine conditions over 5 years. The results show that the free energy required for oxide thickening of rebar decreases from 20.12 kJ/mol to 9.25 kJ/mol as the Cr content increases from 5 % to 11 % (wt.), which facilitates the developed Cr-modified rebars rapidly form a passive film, exhibiting a chloride threshold level 6–14 times higher than that of carbon rebar. In the harshest marine splash zone, sea sand concrete with Cr11 rebar ensures a theoretical 50-year service life, contrasting sharply with the much shorter lifespan estimated at 5–6 years for carbon rebar. The corrosion rate of Cr-modified rebars was notably lower than that of the carbon rebar, and the Cr oxides/hydroxides facilitate the transformation of FeO(OH) with larger volume expansion to Fe3O4 with smaller volume expansion, reducing from 130 μm in HRB to 70 μm in the case of the thinnest Cr11 rebar. The thin and dense rust layer of the developed rebars is advantageous in blocking Cl- and guarantees a smaller volume expansion of structure even if the rebars are corroded. This study underscores the suitability of Cr-modified rebars for sea sand concrete structures and sheds new light on rebar-reinforced concrete structure developments toward eco-efficient and sustainable infrastructures.

Property evolution and service life prediction of novel metallic materials for future lunar bases

Property evolution and service life prediction of novel metallic materials for future lunar bases