Prediction Of Durability Research Articles

Prediction of durability of the concrete using electro-mechanical impendence technique: an experimental and machine learning approaches

Prediction of durability of the concrete using electro-mechanical impendence technique: an experimental and machine learning approaches

Hygrothermal aging and durability prediction of 3D-printed hybrid fiber composites with continuous carbon/Kevlar-fiber and short carbon-fiber

Hybrid Fiber Reinforced Polymers (HFRP) have excellent mechanical properties. However, preparation with 3D-printing technology is prone to hydrothermal aging compared to conventional method. Currently, complex hygrothermal aging and degradation mechanisms of 3D-printed HFRP are still unclear. Thus, hygrothermal aging and durability prediction of 3D-printed HFRP with continuous carbon/Kevlar-fiber and short carbon-fiber are studied, which considers four typical stacking sequences. All experimental samples are manufactured by Fused Deposition Modeling (FDM) 3D-printing. The artificial accelerate aging testing is conducted on samples at different times, and then flexural testing is adopted to evaluate the residual mechanical properties and failure modes. The results show that the stacking sequence significantly influences the moisture absorption behaviors, and [OC4O2K4]S absorbed more moisture than [OK4O2C4]S. During the aging process, the flexural properties of the 3D-printed HFRP first decrease rapidly and then keep stable. Among them, [OK4O2C4]S have the fastest degradation but possessed the highest residual strength. OC8O4K8O possess the slowest degradation but have a lower residual strength. The morphologies and micro failure modes/mechanisms are analyzed to explain the significant mechanical degradation. Finally, the Arrhenius relationship is adopted to accurately predict and analyze the degradation evolution process of specimens. This work offers a novel perspective on the hygrothermal aging and durability of 3D-printed HFRP.

Influence of hygrothermal aging on the lifetime and mechanical properties of GFRP/Al adhesive joints

The study aimed to investigate the impact of hygrothermal aging on the degradation of mechanical properties and predict the lifetime of adhesive joints between aluminum alloy (AA6061) and glass fiber-reinforced epoxy (GFRP) composites. The adhesive joints were prepared in a single lap joint (SLJ) configuration and subjected to hygrothermal conditions to induce aging. Immersion tests were conducted at a water temperature of 70°C with varying aging periods (10, 18, 40, 54, 60, and 75 days) to assess the long-term performance and predict the lifetime of the joints. The tensile and shear strength of the adhesive joints were evaluated using a tensile test. The test was performed at room temperature (RT) with a speed rate of 1.3 mm/min. Among the different methods available for lifetime prediction, a semi-empirical approach was selected to analyze the changes in the mechanical properties of the adhesive joints. A comprehensive comparison was made between aged and unaged specimens. The experimental results revealed a reduction in the ultimate tensile and shear strength of the adhesive bonds over the 75-day aging period. The semi-empirical relationship used for long-term durability prediction of adhesive joint structures indicated a 78% decrease in ultimate tensile strength for the adhesive joints over a one-year aging period. This highlights the significant influence of hygrothermal aging on the mechanical properties of the investigated adhesive joints. During the aged periods, various failure modes were observed, including adhesive failure, cohesive failure, and structural failure.

Review of aging mechanisms, mechanical properties, and prediction models of fiber‐reinforced composites in natural environments

AbstractFiber‐reinforced composites are widely used in civil engineering, aerospace, automotive, and medical sectors. However, these composites undergo aging due to factors such as temperature, humidity, load, and so on, during their usage. As a result, their performance deteriorates, and predicting their durability under real service conditions is a significant challenge. In order to better understand the durability of fiber‐reinforced composites, this article summarizes their microscopic aging mechanism under natural aging conditions and analyzes the changing rules of tensile, bending, and shear properties of composites under seven typical climate types, including tropical desert climate, temperate continental climate, temperate oceanic climate, Mediterranean climate, seasonal climate, tropical rainforest climate, and seawater immersion. This article reviews various durability prediction models, such as the Arrhenius model, prediction models based on residual modulus of elasticity and residual strength, and median strength regression analysis. To enhance the accuracy of aging life prediction during the natural aging process of composites, it is important to consider the influence of loads under real service conditions, incorporate different climatic types, utilize comprehensive mechanical property indices, establish an equivalent conversion relationship between natural aging and accelerated aging, and create a database with unified test standards.Highlights The aging mechanisms of FRP after exposure in natural aging environments are discussed. The tensile, bending, and shear properties of FRP after exposure under seven climate types are discussed. The predictive models are proposed for the FRP used in natural aging. The future research needs are proposed for the FRP used in natural aging.

Characteristics and Models of Moisture Uptake in Fiber-Reinforced Composites: A Topical Review.

Fiber-reinforced composites are commonly exposed to environments associated with moisture and solution, resulting in uptake, which causes changes in the bulk resin, the fiber-matrix interface, and even the fiber itself. Knowledge about uptake behavior and diffusion mechanisms and characteristics are critical to better understanding the response of these materials to environmental exposure faced through service to developing better materials through selection of constituents and to the prediction of long-term durability. This paper reviews aspects of uptake mechanisms and subsequent response, as well as models that describe the sorption process, with the aim of providing a comprehensive understanding of moisture-uptake-related phenomena and characteristics such as uptake rate, diffusion and relaxation/deterioration constants, transitions in regimes, and overall response.

Prediction of Strain Hardening and Durability Based on the Calculated Non-Proportional Cyclic Hardening Coefficient

Prediction of Strain Hardening and Durability Based on the Calculated Non-Proportional Cyclic Hardening Coefficient

Simulation of fuel cell membrane durability under vehicle operation

In this work, a statistical fuel cell chemo-mechanical membrane degradation model is developed based on the ionomer fibrillar morphology as a framework for use-level membrane durability prediction for fuel cell electric vehicles. The mechanical and chemical degradation modes are separately calibrated with pressure differential-accelerated mechanical stress tests and accelerated membrane durability tests, respectively. Finite element simulations are used to estimate the initial stress distribution across the membrane, while the genetic algorithm with the least squares method is employed to calibrate the model parameters with experimental results, thus reaching a good agreement. Next, the validated model is utilized for a case study of fuel cell electric transit bus operation in the city of Victoria, B.C., Canada. The initial cell voltage profile is obtained using a dynamic fuel cell power system model applied to the transit bus drive cycle recorded during real-world operation. According to the model predictions, reducing the stack nominal power from 396 to 132 kW results in a 148% membrane lifetime enhancement, whereas decreasing the cell temperature from 90 to 70°C results in an 11-fold increase in the membrane lifetime under simulated transit bus operation, thereby exceeding the 25,000-h lifetime target for this application.

Optimizing the Carbon Footprint of Performance-Engineered Concrete Mixtures

This paper describes a process to optimize concrete mixture proportions to meet specific performance criteria through the minimization of a user-defined objective function that considers material cost and CO2 cost. The performance criteria impose constraints in the form of required mechanical properties (e.g., compressive strength), microstructural parameters (e.g., porosity or calcium hydroxide content), transport properties (i.e., the formation factor), and durability predictions (e.g., time to corrosion initiation or time to critical saturation). A thermodynamics-based model is used to predict reaction products and these results are used to predict performance. A feasible region of mixture proportions satisfying these constraints is identified after varying the water-to-binder ratios, cement replacement levels using limestone or supplementary cementitious materials, and air contents. The objective function is minimized to identify the optimal mixture proportions that meet all constraints while minimizing the material cost and CO2 footprint.

Prediction of durability of reinforced concrete based on hybrid-Bp neural network

Mix proportion design has a significant influence on the durability of reinforced concrete (RC). Conventional simple equations encounter challenges in effectively guiding the enhancement of durability in the design process. To address this issue, a backpropagation neural network (BPNN) model with a topological structure of 6–16–2 is devised to build a prediction model for RC durability, offering both forward design and reverse guidance for determining optimal mix proportion. Moreover, the model is optimized by the Bat Algorithm (BA), Ant Colony Optimization Algorithm (ACO), and Particle Swarm Optimization (PSO) Algorithm. The input layer parameters of the model consist the water-binder ratio and the quantity of cement, coarse aggregate, river sand (RS), fly ash (FA), slag, while the output layer parameters include the failure time for corrosion current density of reinforcement (T1) and the failure time for concrete damage degree (T2). The dataset for the model comprises 100*2 sets, which are divided into 70*2 sets for training data, 15*2 sets for validation data, and 15*2 sets for testing data. The relationship between the experimental raw materials and the durability of RC was determined through correlation analysis, and comparative analysis was conducted on the durability evaluation indices for RC. The results indicate a positive correlation between cement content and the durability of RC, whereas the content of RS, coarse aggregate, FA and slag, and water-binder ratio exhibit negative correlation with concrete durability. Among the RC groups, the A3 group demonstrated the highest performance, and the D group stood out as the optimal mineral admixture group. Utilizing the PSO-BPNN model, the following performance indices were predicted for T1: R2=0.850, MAE=38.14, MAPE=0.094, RMSE=47.218, and for T2: R2=0.872, MAE=34.541, MAPE=0.071, RMSE=42.355. Consequently, the PSO-BPNN model demonstrates the highest accuracy in predicting the correlation between concrete mix proportions and the durability of RC, thereby offering valuable guidance for the mix proportion design of RC.

Valorization of waste wood fly ash in environmentally friendly lime-based plasters with enhanced strengths for renovation purposes

Waste wood fly ash (WWFA) generated during wood chips combustion in a power plant does not have any application nowadays and is commonly disposed of or landfilled. Nevertheless, considering its composition and reactivity in the presence of calcium hydroxide, it is one of the promising materials that could be used as an active admixture for modification and partial replacement of lime in lime plasters. This solution seems to be a suitable step to both improve the properties of the traditional lime plaster and reduce carbon dioxide emissions. Therefore, this comprehensive study describes a wide range of properties of lime plaster with WWFA (up to 50 wt% lime replacement) as an active admixture. Compositions of studied plasters were firstly analyzed with the help of XRD, DSC, and SEM, and then the effect of WWFA on the physical, mechanical, hygric, and thermal properties was investigated. The obtained results showed that the application of WWFA led to lower total porosity, highly improved mechanical properties, higher thermal conductivity, and lower water transport capability, which could be attributed to its pozzolanic activity. The performed experiments fundamentally broadened the existing insufficient knowledge about the utilization of WWFA for lime-based plasters improvement. All tested lime-based plasters (with 10, 20, 30 and 50% lime replacement by WWFA) have a high potential for common use in practice, especially in the historical buildings renovation. The originality of this paper lies, among others, in using a specific type of fly ash (originating from waste wood, not coal) and in testing a wider range of characteristics and properties of designed lime-based plasters with WWFA with respect to their changes over time, which enables prediction of plasters' durability.

Durability prediction of geopolymer mortar reinforced with nanoparticles and PVA fiber using particle swarm optimized BP neural network

Abstract In this study, polyvinyl alcohol (PVA) fibers and nanoparticles were incorporated to enhance the durability of geopolymer mortar (GM) with metakaolin (MK) and fly ash (FA). The dosage of nano-SiO2 (NS) was 0–2.5% and that of PVA fiber was 0–1.2%. The durability of GM includes resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion. Compared with the single BP neural network (BPNN) model, a particle swarm optimized BPNN (PSO-BPNN) model was utilized to predict the resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GMs with different dosages of nanoparticles and PVA fibers. In the model, the dosage of NS, PVA fiber, FA, and MK were used as input layers, and the durability parameters of electric flux, mass loss, and compressive strength loss of GMs were used as output layers. The result exhibits that the root mean square errors (RMSEs) of BPNN for resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GM mixed with nanoparticles and PVA fibers are 145.39, 6.43, and 2.19, whereas RMSEs obtained from PSO-BPNN are 76.33, 2.87, and 1.03, respectively. The NN optimized by particle swarm algorithm has better prediction accuracy. The PSO-BPNN can be utilized for estimating durability of GM reinforced by NS and PVA fiber, which can provide a guide for the proportion design of GM with PVA fiber and NS as well as for the engineering practice in the future.

Durability of Lead-Free Solder Interconnections for Printed Circuit Board Applications: Comparing Energy-Based Thermo-Mechanical Fatigue Models

Fatigue models for predicting the cycles to failure of solder interconnections under temperature cycling situations have been discussed and developed for decades. However, most models were developed for different solder materials, components, and printed circuit boards. No previous work has systematically compared these models. Therefore, the variability of their durability predictions is unknown. This study compared nine existing low-cycle energy-based fatigue models for different solder materials and components and then analyzed the differences among them. Each fatigue model had a specific combination of the factors that affect the strain energy density accumulation. Therefore, we adjusted the strain energy density (input) and the predicted cycles to failure (output) in a consistent way to compare the selected fatigue models on the same basis. The differences among the predictions on cycles to failure from the fatigue models was significantly reduced after applying the adjustments, and they exhibited excellent consistency around 1 mJ/mm 3 strain energy density. In the end, factors that can affect the prediction consistency of fatigue mode were provided, including the number of data points while building the fatigue model, the range of strain energy density while selecting the electronic components, and the application of volume-weighted averaging technique on the critical solder joint.

Investigation of Low-Frequency Data Significance in Electric Vehicle Drivetrain Durability Development

The digitalization of the automotive industry presents significant potential for technical advantages, such as the online collection of customer driving data. These data can be used for customer-oriented development to improve the durability of components or systems. However, due to current limitations in data transfer, the sampling frequency is typically lower than that of classic dataloggers. This paper examines the importance of low-frequency data in the development of drivetrain durability and investigates the extent to which these data can be utilized for a drivetrain durability analysis. Real driving data were utilized as a database to demonstrate the impact of downsampling on data significance, with the deviation in damage serving as the criteria. The findings suggest that low-frequency data, when available in sufficient quantities, can provide valuable information for predicting durability in rollover and time at level classification. The deviation in the damage prediction is less than 2% for distances exceeding 5000 km. However, low-frequency data are not suitable for rainflow analysis. Finally, the database size was adjusted to assess the statistical stability of the durability prediction. A larger dataset typically reduces variance. The paper presents evidence for the quality and usability of cloud data in drivetrain durability design. Cloud data from a significant number of customer vehicles can be used for certain analyses of representative customer load collectives, which can reduce development time and costs.

Durability Prediction of Cyclically Loaded CP-W800 Fillet Welds

Two methods of durability prediction of fillet welds were researched in this study. Namely, the structural Hot-Spot method and the structural stress method fe-safe Verity were applied to fatigue life estimation of a double plate lap fillet weld made of high-strength complex phase CP-W800 steel. Durability predictions were compared against available high-cycle fatigue experimental data obtained for the same weld detail and material. Both 2D and 3D finite element meshes were considered in the simulations. It was shown that comparable predictions were obtained using either the Hot-Spot method or the fe-safe Verity module in the case of the 3D FE mesh. On the contrary, a less conservative durability prediction was observed using the Hot-Spot method and a more conservative durability prediction was gained using the fe-safe Verity module in the case of the 2D FE mesh due to a different consideration of stress concentration around the weld.

Compression behavior of CFST columns under combined influence of gap and freeze-thaw

Owing to factors related to construction quality and environment, concrete-filled steel tube (CFST) structures that have been in use for many years in regions experiencing seasonal freeze–thaw cycles are susceptible to the combined effects of a spherical-cap gap and freeze–thaw cycling. Therefore, in this study, a combination of experiments and the finite element method is used to investigate the effects of these two factors on the axial compression behavior of CFST stub columns. The experimental results show that the main failure mode of the specimen is local buckling at the end or the middle and inward depression of the steel tube on the existing gap side. The degradation of mechanical parameters, such as the ultimate bearing capacity and ductility of the specimens, gradually increased with increasing gap ratio and number of freeze–thaw cycles. In the numerical simulation section, the change in the stress response to freeze–thaw cycling on the gap side is analysed. The impact of the constraint effect coefficient on the ultimate bearing capacity degradation of the specimens is also investigated. Finally, a formula for calculating the ultimate capacity of CFST stub columns affected by the two factors is proposed, and the formula for calculating the ultimate bearing capacity in the code is improved. The results of this study can provide a reference for durability prediction and structural safety evaluation of CFST structures with a spherical-cap gap in cold regions.

Experimental and Numerical Study of CFRP Laminate Strengthened Concrete Beams Under Hygrothermal Environment

Carbon fiber reinforced polymer (CFRP) laminates are widely used to strengthen and retrofit deteriorated concrete structures, yet their long‐term performance and durability under varied environmental conditions remain critical areas of investigation. This study comprehensively examined the behavior of small concrete beams externally bonded with CFRP laminates under three distinct hygrothermal regimes: immersion in water at 23, 45, and 60°C for up to 112 days. The experimental program encompassed direct tension pull‐off testing of the CFRP–concrete assembly and four‐point bending tests on the beams. Pull‐off tests revealed complex failure modes, with bonding epoxy failure (43%) and concrete substrate cohesion failure (39%) being dominant. Notably, pull‐off strength exhibited nonmonotonic trends across all environments, suggesting that environmental degradation is not solely time‐dependent. Flexural capacity increased by 20%, 49%, and 11% for room temperature (RT), moderate temperature (MT), and high temperature (HT) conditions, respectively, after 112 days, highlighting the synergistic effect of CFRP strengthening and ongoing concrete curing. A calibrated three‐dimensional finite element model, developed using Abaqus, accurately replicated the experimental results and facilitated parametric studies on factors influencing CFRP‐strengthened beam performance. This included concrete compressive strength, number of CFRP layers, laminate thickness, and FRP type. The study elucidates the complex interplay between environmental exposure, material properties, and structural performance, contributing valuable insights for enhancing durability predictions and design guidelines for CFRP‐strengthened concrete structures in diverse climatic conditions.

Long-Term Durability Prediction of Slag–Fly Ash-Blended Engineered Cementitious Composite Subjected to Chloride and Sulfate Salt

Long-Term Durability Prediction of Slag–Fly Ash-Blended Engineered Cementitious Composite Subjected to Chloride and Sulfate Salt

Experimental Study on the Effect of Carbon Graphitization Degree and Pore Structure on the Electrochemical Durability of Gas Diffusion Layers

Gas diffusion layers (GDLs) in high-temperature, high-humidity, and high-electric-potential environments can be affected by the carbon corrosion and degradation of Polytetrafluoroethylene (PTFE) network structures, resulting in reduced reliability and hydrophobicity. By using cyclic voltammetry and offline characterization, a high-potential scanning of 1–1.5 V is applied to the GDL in the three-electrode system, considering the role of gradient graphitization degree and pore size structure in corrosion. Accelerating the electrochemical corrosion process of carbon and PTFE allows the identification of corrosion location, extent, and determinants. The results indicate that after 800 cycles of high-potential triangulation scanning, the graphitization of gas diffusion base has the most significant impact on the GDL’s durability. On the other hand, the durability of the GDL’s microporous layer is influenced by its small pore size structure rather than its graphitization degree. Furthermore, the corrosion process of GDLs with a small pore size structure tends to be relatively slow, providing a basis for GDL selection and durability prediction.

Vold–Kalman filter order tracking of axle box accelerations for track stiffness assessment

Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of railway infrastructure in the face of increasing demands on cost and operations efficiency. Numerous studies have highlighted track stiffness as a main parameter influencing the evolution of degradation that drives maintenance processes. As such, the measurement of track stiffness is fundamental for characterizing the performance of the track in terms of deterioration rate and noise emission. In this work, we propose a rail stiffness assessment scheme relying on low-cost, on board monitoring sensors, namely axle-box accelerometers, that are mounted on in-service trains and enable frequent, real-time monitoring of the railway infrastructure network. A Vold–Kalman filter is proposed for decomposing the signal into periodic wheel and track related excitation–response pair functions. We demonstrate that these components are in turn correlated to operational conditions, such as wheel out-of-roundness and the rail type. We further illustrate the relationship between the track stiffness, the measured wheel-rail forces and the sleeper passage amplitude, which can ultimately serve as an indicator for predictive track maintenance and prediction of track durability.

Prediction of durability of a drum pelletizer based on the strength criterion of non-driven support rollers at the design and operation stage

Prediction of durability of a drum pelletizer based on the strength criterion of non-driven support rollers at the design and operation stage