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Abstract This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities’ management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes.

This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities' management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes. [ABSTRACT FROM AUTHOR]

High-modulus asphalt mixtures (HMAMs) are potential materials for achieving long-life pavement performance, and understanding their long-term performance degradation mechanism is crucial for analyzing pavement performance degradation under long-term exposure to traffic and environmental coupling conditions. This paper aims to examine the long-term evolution of high- and low-temperature performance and water stability in HMAMs derived from modified nature asphalt, prepared using microparticle suspension technology, under heavy load and humid heat conditions. Four asphalt mixtures (AC-13, BBME-13, SMA-13, SBSAC-13) were prepared, and then their dynamic moduli were tested. A test plan for the long-term performance of the mixtures was designed, and the performances of HMAMs with different gradations and time were analyzed. Furthermore, corresponding long-term performance degradation models of HMAMs were established. The results show that the HMAMs of modified natural asphalt have better high-temperature performance than HMAMs of high-modulus agent/SBS-modified asphalt. However, following prolonged thermal aging and freeze-thaw cycles, the low-temperature performance shows the opposite trend. In addition, thermal aging had the same effect on the long-term low-temperature performance of both types of HMAM, with the HMAMs of modified natural asphalt being less affected by long-term freeze-thaw cycling. The long-term bending tensile strain attenuation of HMAMs can be simulated using an exponential function. Both types of HMAMs display similar water stability under dry-wet cycles and dynamic water erosion condition. The variations in residual stability during dry-wet cycles can be accurately represented using a power function. [ABSTRACT FROM AUTHOR]

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study. [ABSTRACT FROM AUTHOR]

Bridges deteriorate due to different causes such as ageing and climatic conditions. As a consequence, repair actions are considered more expensive than maintenance. Therefore, the prediction of maintenance at the bridge network level is particularly complex due to the considerable level of heterogeneity spanning various bridge types and functions. Consequently, in this article an analytical deterioration model called 'two-step cluster analysis' is presented and applied to predict the results of a homogeneous data set. This model had been used in different fields including civil engineering but not for superstructure deterioration forecasting. Variables that interfere with the performance of the structure were considered, such as geometry, volume of traffic, among others. The results show that deterioration drop rapidly if repair treatments were not performed. Variations of up to 20.9% at 50 years between the degradation of the database and the analysis of groups, demonstrates the importance of grouping within the analysis of the database, since the late maintenance can affect the life of the bridge. Preventive actions could significantly mitigate the deterioration process. The applied methodology benefits from current bridge inspection practice and produces a probabilistic rating that is consistent with the Markov approach for model deterioration and grouping variables. [ABSTRACT FROM AUTHOR]

Given that the non‐aqueous electrolyte in Li‐ion battery plays a specific role as an ion‐transport medium and interfacial modifier for both cathode and anode, understanding and evaluating the evolution and degradation of electrolytes throughout the life cycle is a fundamental concern within the lithium‐ion battery (LIB) community. This article provides a comprehensive overview of the electrolyte decomposition processes, mechanisms, effects of electrolyte degradation on the battery performance, characterization techniques, and modeling analysis. First, it thoroughly discusses the processes and mechanisms involved in electrolyte degradation from two primary perspectives: 1) the formation of the electrode‐electrolyte interphase and 2) the decomposition of the bulk electrolyte. Subsequently, it systematically outlines the effects of electrolyte degradation on the battery performance. The article further introduces cutting‐edge characterization and detection techniques used to assess electrolyte degradation, with a specific emphasis on quantitative methods for analyzing residual electrolytes in practical cells. Moreover, it summarizes advanced physical models of electrolyte decomposition. Finally, the paper concludes by offering insights into future trends and potential challenges in battery degradation research, it offers valuable references and guidance for further exploration of electrolyte degradation in LIBs. [ABSTRACT FROM AUTHOR]

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of a country as they handle a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. We start with reinforcement corrosion, which is considered as the main cause of distress, particularly in coastal zones, for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to a reduction in capacity. To guarantee the lifespan of infrastructures, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Artificial Neural Networks (ANNs) have gained recent prominence in this field due to their ability to learn intricate patterns from historical data, making them valuable instruments for predicting structural deterioration. Additionally, quantifying the condition of the structure from monitoring data plays a crucial part in providing information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change. [ABSTRACT FROM AUTHOR]

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material’s remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study.

In LEDs reliability prediction, not considering the color shift could generate undesired results; however, in the literature it is not frequent to find methods that consider this failure mode; and a great part of the existing methods do not integrate in the analysis the factors of temperature and current jointly. In this work, a new method was developed to predict the LEDs reliability based on the color shift, which takes into account the temperature and current. With the developed method, more reliability indicators, such as reliability function, risk function and mean time to failure (MTTF), can be predicted in comparison with the IES TM-21- 11 and IES TM-35-19 methods. In addition, the models of several reliability indicators of a sample of NF2L757DR LEDs from NICHIA CORPORATION are presented. The obtained graphs show that with increasing temperature and/or current, reliability decreases. [ABSTRACT FROM AUTHOR]

The anaerobic batch digestion of freeze‐dried and ground (< 1.5 mm) maize silage, wheat straw, cattle manure, pig manure, and cellulose filter paper was investigated with substrates placed in polyester filter bags. Gas production dynamics of bagged and non‐bagged substrates were compared. By using batch bottles running in parallel with those used for gas measurement, substrates could be sampled over time and various parameters measured for both substrate and bulk liquid. The bags allowed an immediate mass loss into the bulk liquid with some substrates and a generally lower rate of gas production. This method requires refinement but has potential for the study of the dynamics of substrate degradation during anaerobic digestion. [ABSTRACT FROM AUTHOR]

Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of a country as they handle a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. We start with reinforcement corrosion, which is considered as the main cause of distress, particularly in coastal zones, for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to a reduction in capacity. To guarantee the lifespan of infrastructures, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Artificial Neural Networks (ANNs) have gained recent prominence in this field due to their ability to learn intricate patterns from historical data, making them valuable instruments for predicting structural deterioration. Additionally, quantifying the condition of the structure from monitoring data plays a crucial part in providing information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

The key performance parameters of spinning frame are difficult to be indentified and the performance degradation model is difficult to be established. To solve the problem, a reliability evaluation method for multiple performance degradation process of spinning frame is proposed. The relationship between the performance parameters of the spinning frame was analyzed, and the subjective and objective comprehensive weight identification method was used to screen the monitored performance parameters. The parameters with comprehensive weight greater than 0.5 were selected as the characterization parameters of the performance degradation of the spinning frame. The Wiener process was used to fit the multiple parameter degradation failure track. The Copula function was used to analyze the correlation of multiple parameter degradation failure process, and the Gibbs algorithm was used to estimate the parameters. The application results show that the proposed reliability evaluation method is in line with the actual production, and has high accuracy, which is helpful to improve the reliability level of spinning frame performance.

Minimizing the divergence between two probability distributions offers an alternative parameter estimation method. The current literature mainly focuses on minimizing the Kullback-Leibler (K-L) divergence between the true and the proposed models in which the true model is assumed to be known or fixed. In this paper, we propose a parameter estimation method that minimizes the $f$ -divergence between two probability distributions. The method is suitable for different situations, no matter the true distribution is known or not. The statistical properties of the estimator, including consistency and asymptotic normality, are established. As an illustration, our method is employed to estimate the degradation model, which is a model frequently used to assess the lifetime of highly reliable products. A simulation study and a real degradation data analysis are presented to illustrate the effectiveness of the proposed estimation method. [ABSTRACT FROM AUTHOR]

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage. [Display omitted] • BEXP and gamma models have been considered to investigate the trifluralin dissipation. • A simulation study illustrated the importance of the standard error of half-life. • Provided mathematical computation of the standard error of the half-life. • The soil moisture levels influenced the half-life estimates of trifluralin. [ABSTRACT FROM AUTHOR]

The key performance parameters of spinning frame are difficult to be indentified and the performance degradation model is difficult to be established. To solve the problem, a reliability evaluation method for multiple performance degradation process of spinning frame is proposed. The relationship between the performance parameters of the spinning frame was analyzed, and the subjective and objective comprehensive weight identification method was used to screen the monitored performance parameters. The parameters with comprehensive weight greater than 0.5 were selected as the characterization parameters of the performance degradation of the spinning frame. The Wiener process was used to fit the multiple parameter degradation failure track. The Copula function was used to analyze the correlation of multiple parameter degradation failure process, and the Gibbs algorithm was used to estimate the parameters. The application results show that the proposed reliability evaluation method is in line with the actual production, and has high accuracy, which is helpful to improve the reliability level of spinning frame performance. [ABSTRACT FROM AUTHOR]

Stable perovskites light sources with long‐term stability are of great significance for future commercial applications. In this review, recent advances of the degradation models for perovskites and strategies for enhancing the stability of the corresponding light sources are summarized. Improving the stability and quality of perovskite materials is a common way for developing stable perovskite light sources. For strengthening the stability of perovskite light‐emitting diodes, approaches such as surface and interface engineering and employing more stable hole injection layers are effective. To enhance the stability of perovskite amplified spontaneous emission and lasers, methods such as protecting perovskites by materials with high intrinsic thermal conductivity and high stability, better thermal managements, and reducing the threshold carrier densities are useful. This work will be helpful for further prompting the performances, especially the stability of perovskite light sources in this fantastic field. [ABSTRACT FROM AUTHOR]

The maintenance strategy in railway transportation is crucial in ensuring safety, availability, and reducing operating costs. However, finding the optimal maintenance plan that takes into account the complex relationships between railway assets can be a challenging task. To address this challenge, this study introduces an Intelligent Petri Net (i PN) model to effectively consider the maintenance and operation of railway sections with a focus on optimizing ballast maintenance. The i PN model merges Petri net (PN) with Reinforcement Learning (RL) to create a model that is able to simulate and learn at the same time. The model is able to use diverse information, including usage, degradation rates, maintenance effectiveness, fault probabilities, and maintenance time, to simulate and learn at the same time. By considering the interconnections between these factors, the model found that reducing unnecessary maintenance actions increases the age of railway sections and leads to higher net profits. The study also introduced a method to reduce computational effort by dividing the PN into subnets and another method to make learning faster by using multiple RL environments. In conclusion, the developed i PN model presents a promising solution for optimizing ballast maintenance within railway operation. • An Intelligent Petri net model was created to optimize railway maintenance and operation. • Proposed a Petri net decomposition method that cuts computational costs. • Presented a novel approach: Multiple Reinforcement learning environments for efficient optimization. • The maintenance policy was optimized to lower risks, reduce costs, and boost revenues. [ABSTRACT FROM AUTHOR]

Highlights • Two approaches are presented for the RUL prediction of products with two-phrase degradation. • The RUL under gamma process without degradation rate change is considerably underestimated. • The RUL prediction for a specific product can be obtained, although the rate change has not occurred. • The SEM yields relatively less bias and more reliable interval estimates. • The Bayesian approach requires less computational time. Abstract Remaining useful life prediction has been one of the important research topics in reliability engineering. For modern products, due to physical and chemical changes that take place with usage and with age, a significant degradation rate change usually exists. Degradation models that do not incorporate a change point may not accurately predict the remaining useful life of products with two-phase degradation. For this reason, we consider the degradation analysis for products with two-phase degradation under gamma processes. Incorporating a probability distribution of the time at which the degradation rate changes into the degradation model, the remaining useful life prediction for a single product can be obtained, even though the rate change has not occurred during the inspection. A Bayesian approach and a likelihood approach via stochastic expectation-maximization algorithm are proposed for the statistical inference of the remaining useful life. A simulation study is carried out to evaluate the performance of the developed methodologies to the remaining useful life prediction. Our results show that the likelihood approach yields relatively less bias and more reliable interval estimates, while the Bayesian approach requires less computational time. Finally, a real dataset on LEDs is presented to demonstrate an application of the proposed methodologies. [ABSTRACT FROM AUTHOR]

Reliability assessment of complex systems is an important yet difficult task. The difficulty arises largely because of heterogeneous component-level data, for example, lifetime data, degradation data, component-level assessment results, and prior information. A method for reliability assessment is developed in this article for systems plagued by degradation and lifetime data. Our framework divides component-level information into two types. These two types are then combined using a systematic approach. This article describes the method along with some application examples that demonstrate the approach capable of overcoming several difficulties associated with conventional reliability assessment approaches. [ABSTRACT FROM AUTHOR]

The main goal of this study is to predict the service life of external paint finishes and contribute to the subject of construction elements´ durability. The service life is estimated using the factorial method proposed by ISO 15686, since, despite some criticism, this method excels as a simple and efficient tool with great potential for use in the planning of maintenance operations. The model proposed is based on a field survey of 323 coatings. The factor method estimates the service life of building components by multiplying their reference service life by weighting values applied to several factors affecting the performance of the elements under study. In this study the durability factors are obtained through iterative simulations. The results are analysed based on a set of criteria and statistical indicators. The results are suitable and reliable for most of the deterioration variables considered and the factor method proves to be an efficient tool to evaluate the service life of external paint finishes. This work is intended as a contribution and reference for future developments within the subject of durability in the construction industry as knowledge on the performance of given materials or constructive systems over time allows estimations of when to invest in maintenance/repair and of the amount of resources that will be required. [ABSTRACT FROM AUTHOR]

This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both accelerated life tests (ALT) and design of experiments (DOE) technology are used to determine how each critical factor affects the prediction of performance. With the Bayesian semi-parametric approach, a piecewise constant hazard regression model is used to establish the lifetime using degradation data. Gamma frailties are included to explore the influence of unobserved covariates within the same group. Ideally, results from the classical and Bayesian approaches will complement each other. To demonstrate these approaches, this paper considers a case study of locomotive wheel-set reliability. The degradation data are prepared by considering an Exponential and a Power degradation path separately. The results show that both classical and Bayesian semi-parametric approaches are useful tools to analyse degradation data and can, therefore, support a company in decision making for preventive maintenance. The approach can be applied to other technical problems (e.g. other industries, other components). [ABSTRACT FROM AUTHOR]

The determination of the strategy to ensure that the geometry for railway track is kept within acceptable limits, in a cost effective manner, is a complex process. It requires the simultaneous consideration of the activities which govern inspection, maintenance and renewal. In addition to this the geometry degradation process is dependent upon the maintenance history. The condition where the track geometry is shown to have deteriorated to a level where intervention is required can be improved using a tamping machine. Tamping is carried out by a special train which measures the geometry of the rails, predicts the correction needed, lifts the rails to the required position, inserts tines into the ballast either side of the sleepers and packs the ballast such that the correct rail position is attained. Whilst improving the geometry this process has the disadvantage that it also breaks the ballast which accelerates the track geometry degradation and reduces the time between interventions. This paper describes a modelling process to predict the state of the track geometry given any specified asset management strategy. It is based on the Petri net method and in addition to predicting the track condition over time it can also compute the expected whole life costs. By varying the parameters which govern the inspection, maintenance and renewal of the ballast as the most cost effective means to achieve the required level of performance can be predicted. [ABSTRACT FROM AUTHOR]

This paper develops a new degradation model of the PV module performance based on two degradation processes. The first is the initial photon degradation related to physical process in the solar cell itself, where the function of the performance loss caused by this mechanism is exponentially distributed. The second is the continuous degradation process; it is related to the long term weathering and to the degradation of the module package resulting in a degradation of the module performance. In this case, the function of the performance loss caused by this mechanism follows the Weibull distribution. The obtained results by simulation are discussed and corroborated by mathematical analysis of a simpler equivalent model. The analysis covers lifetime energy loss, Cumulative loss of energy (kWh, %), annual cost of energy loss, and the total cost of energy loss. Finally, the objective reached is the reliability evaluation of the PV module. [ABSTRACT FROM AUTHOR]

Highlights: [•] Corrosion, discoloration, delamination and breakage are the main modes PV modules degradation. [•] Corrosion and discoloration are the predominant modes of silicon PV module degradation. [•] Temperature, humidity and UV radiation are the main factors of silicon PV module degradation. [•] Modeling of PV module degradation is still poorly studied in literature. [•] Accelerated tests are an alternative for investigating PV module degradation. [ABSTRACT FROM AUTHOR]

The UK rail industry remains a safe, reliable and competitive alternative to other forms of transport. Maintaining such an ageing, increasingly utilized, railway network with limited financial resources is a major challenge. By utilizing data on the UK network track geometry collected over six years, along with available records of maintenance performed during this period, the effects of tamping on track geometry degradation have been analysed. This paper presents a Weibull approach to analyse the distributions of times for the track geometry to degrade to specified states following maintenance. Track quality is given by the standard deviation of the averaged vertical position of the rails taken over a 35 m wavelength. The quality of the track geometry is also investigated after tamping has been performed. The results of this study show that traffic speed and the maintenance history of any line have an effect on geometry deterioration. Furthermore, support is provided to the argument that tamping damages track ballast. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents an experimental and analytical study about the mechanical response at elevated temperature of glass fibre reinforced polymer (GFRP) pultruded profiles. The paper first describes results of DMA and DSC tests that were used to evaluate the glass transition and decomposition processes of the GFRP pultruded material. The paper then describes an extensive study about the tensile, shear and compressive responses of the GFRP material at temperatures varying from 20°C to 250°C. In these tests the mechanical responses of the GFRP material as a function of temperature were assessed, namely the load–deflection curves, the stiffness, the failure modes and the ultimate strength. Results obtained in these experiments confirmed that the mechanical performance of GFRP is severely deteriorated at moderately high temperatures, particularly when loaded in shear and compression, owing to the glass transition of the resin. The final part of this paper assesses the accuracy of different empirical models and one phenomenological model to estimate the tensile, shear and compressive strengths of GFRP pultruded material as a function of temperature. All empirical models, including a function based on Gompertz statistical distribution suggested in the present paper, provided accurate estimates of tensile, shear and compressive strengths as a function of temperature. The phenomenological model was less accurate and in general provided non conservative estimates of material strength. [Copyright &y& Elsevier]

The UK railway network contains around 20,000 miles of track and is now carrying more, faster and heavier trains than previously. The effective management of this ageing and increasingly utilised system with limited financial resources is a significant challenge. Modelling tools are required to investigate the alternative strategies to manage such assets. The models available to date are very limited in their capability. A fundamental problem is their inability to adequately account for the underlying degradation process which is strongly dependent upon the history of the previous maintenance carried out. They also lack the complexity to enable the detailed maintenance and renewal options to be explored. This paper describes a modelling approach which first of all analyses the available data on the track geometry, and by implication the condition of the ballast, gathered at regular intervals by a measurement train. The analysis produces distributions of times for the track geometry to degrade to a specified state. Degradation distributions are therefore of relevance to the railway network under consideration, avoiding the need to use generic and possibly inappropriate data. With the degradation distributions available, a track section model is formed which incorporates the maintenance and renewal processes and provides a means of predicting the condition of the ballast section over time. The model uses a Petri net formulation with a Monte Carlo solution routine and enables the effectiveness of different maintenance strategies to be investigated. Ways in which the complexity of the model can be increased if desired is discussed and a case study is provided to indicate the use of the model. [ABSTRACT FROM AUTHOR]

Thermal decomposition of an agrowaste, namely banana trunk fibers (BTF) were investigated by thermogravimetry (TG) and derivative thermogravimetry (DTG) up to 900 °C at different heating rates (from 5 to 100 °C/min). The BTF was subjected to modification by means of various known chemical methods (mercerization, acetylation, peroxide treatment, esterification, and sulfuric acid treatment). Various degradation models, such as the Kissinger, Friedman, and Flynn-Wall-Ozawa were used to determine the apparent activation energy. The obtained apparent activation energy values (149-210 kJ/mol) allow in developing a simplified approach to understand the thermal decomposition behavior of natural fibers as a function of polymer composite processing. [ABSTRACT FROM AUTHOR]

Abstract: The damaged response of a composite lamina depends on various mechanisms that take place at the microlevel, i.e., at the level of the fiber and matrix. The present work focuses on developing a ply level continuum damage model for point-wise stiffness degradation through simplified representation of the microlevel damage. A three dimensional micromechanical analysis of a single cell representative volume element is carried out for various volume fraction, and levels of damage. The model brings out the coupled effect of damage on the effective point-wise ply level stiffness. Further, the numerical results are employed to develop a functional continuum representation of stiffness degradation as a function of the damage parameters and fiber volume fraction perturbations. The micromechanics model is consistent with experimentally observed stiffness degradation, i.e., a strong influence of fiber breakage and fiber matrix debond, and a weak influence of normal cracking of matrix. The proposed model can be considered as an improved version of the widely accepted diffused (meso) damage models, i.e., DML. The study also gives a generalized and consistent definition for the free energy, which can be used for modeling growth of damage. [Copyright &y& Elsevier]