Rut Depth Research Articles

Performance evaluation of flexible pavement using polyethylene terephthalate (PET)

In this study, the benefits and feasibility of incorporating Polyethylene Terephthalate (PET) into Flexible pavement were evaluated. The study aims to identify new ideas on recycling plastic wastes and thereby improving overall sustainability. The Samples were prepared by adding PET with an increasing concentration level along with optimum asphalt content with a percentage by weight of 3.5%, 4.0%, 4.5%, 5.0%, 5.5% and 6%. Multiple tests were conducted on Natural aggregates including Resistance to degradation of aggregates by Abrasion and Impact, Specific Gravity and Abrasion of coarse aggregates, and Impact Value of Aggregates. Similarly, the tests conducted on asphalt include the specific gravity of semi-solid bituminous material, the ductility of bituminous material, the flash point and softening point of asphalt, and the penetration grade of bituminous material. Rutting potential of the optimum asphalt, Plastic mix Was evaluated by increasing the frequency of load cycles from 0 to 5000 cycles. Furthermore, the effect of asphalt and plastic content on the density, Flow, voids, and stability were also analysed. It was found that the addition of PET resulted in improved stability by more than 50% specifically 52%, while 70% improvement was observed in flow characteristics of Hot Mix Asphalt. However, a slight change in density was observed. The sample also showed a reduction in rut depth by more than 40% of the mix.

Influence of Carbon Fibers on the Rutting Susceptibility of Sustainable HMA Mixtures with Untreated Recycled Concrete Aggregates

This paper focuses on achieving sustainability to reduce the detrimental effect on the environment and the economic aspects by including several ratios of coarse recycled concrete aggregates (RCA) (25, 50, 75, and 100%) in asphalt mixtures. The methodology included testing all raw materials, the wheel tracking test to assess mechanical performance, and the Marshall design approach to determine the appropriate asphalt content. The outcomes demonstrated no discernible difference between the volumetric characteristics of the asphalt mixtures containing RCA and the control mix. Marshall's stability rose by 14.2% when 50% of the mixture contained RCA compared to the control combination. All combinations containing RCA were performed inferiorly to the control mixture regarding rutting. 19.63% was the greatest increase in rut depth for combinations made entirely of recycled concrete aggregate. Several percentages of 0.2, 0.25, and 0.3% carbon fibers were added to the total weight of the asphalt mixture to enhance rutting performance. Marshall's stability and resistance to rutting have significantly increased, attributable to the carbon fibers; nonetheless, the volumetric properties of the asphalt mixture have only slightly altered. The combinations with 0.3% carbon fiber reinforcement and 50% RCA showed the largest gain in Marshall stability, up 34.6% above the control mixture. The same combination had the strongest resistance to rutting, which was —39.08% higher than the control mixture.

Probabilistic rutting model using NGBoost and SHAP: Incorporating other performance indicators

This study focuses on accurately predicting rutting depth (RD) through a probabilistic prediction model, which is crucial for timely road maintenance and traffic safety. Natural Gradient Boosting (NGBoost) technology is employed to achieve this goal as it provides accurate predictions and estimates the uncertainty of predictions. Furthermore, to explain the decision-making process of the proposed prediction model, Shapley Additive Explanations (SHAP) analysis is applied to identify and quantify significant factors affecting RD. Research results show that the NGBoost model surpasses the traditional Gaussian Process and Random Forest model regarding prediction accuracy and performs better regarding certainty of prediction intervals. Meanwhile, SHAP analysis improves the transparency of model decision-making and further validates the model. Explanatory ability elucidates the impact of other characteristics on RD. The two-stage probabilistic prediction model not only enhances the reliability of predictions but also provides an in-depth understanding of the key influencing factors of pavement performance, improving the efficiency and effectiveness of maintenance measures.

Assessment of stability and operability of skidder on slopes of thawing soil mass during its interaction with a wheeled forest machine

In the Russian Federation, a significant part of the forest fund is located on permafrost. Permafrost forests play a global ecological role by de-populating large amounts of carbon. However, when a forest reaches the age of obsolescence, this carbon is released into the atmosphere when the plantation decays. Therefore, from the ecological point of view, permafrost forests should be cut down when they reach the age of ripeness, in compliance with the requirements for their restoration. Many of the above-mentioned regions have large reserves of mature and overmature production forests, and a significant part of these reserves are located in hard-to-reach places, not only in relation to the development of the road network, but also by terrain relief. Currently, the vast majority of timber harvesting in Russia is done with modern machinery complexes, mainly including wheeled forest machines of different layout and purpose (harvesters, forwarders, skidders, etc.). With the help of special technical solutions, such as winches integrated into the transmission, or self-propelled winches (T-winch, ROB, Winch Assist), these machines can be successfully operated even on fairly steep slopes. The problem of negative effect of wheeled forest machines and skidding systems based on them not only stays actual, but also becomes even more urgent, as the ecosystems of mountain forests, forests on the slopes of hills, etc. are the most vulnerable and exposed to water and wind erosion. This article presents a mathematical model for reliable forecasting of rut depth and stability of a hairpin section during dynamic interaction of a forest machine with the soil on slopes, taking into account geometric parameters of the slope, the laws of the oscillating process of a forest machine with given values of speed and amortization characteristics of wheel-pair elements, the profile of the hairpin section and physical and mechanical properties of the soil in its boundaries, the weight of forest machines and the pressure in tires of wheel-pairs.

Methods of Rut Depth Measurements on Forwarder Trails in Lowland Forest

Methods of Rut Depth Measurements on Forwarder Trails in Lowland Forest

Assessing the Extent and Severity of the Impact on Forest Soils of Two Different Fully Mechanized Timber Harvesting Operations

Recently, the use of powerful and heavy vehicles for timber harvesting on flat or slightly sloping terrains has been widely expanded to provide safe working conditions and high productivity. However, soil disturbances during ground-based mechanized operations in South Korea are not fully investigated and difficult to avoid. Therefore, we compared the soil displacement and compaction (bulk density and hydraulic conductivity) between two different operations: cut-to-length (CTL) logging with a harvester and forwarder, and whole-tree (WTH) logging with a harvester and skidder. After clear-cutting, severe visual disturbances and rut depths were more prevalent in the forwarding trails than in the skidding trails. The CTL harvesting method created larger amounts of slash (6.9 kg/m2) along the trails than the WTH harvesting did (1.8 kg/m2). We found a significant difference in the compaction between the reference and the track and a negative correlation between the slash quantity values and the percentage increase in compaction. Our results showed that using skidding extraction can cause more severe impacts than forwarding extraction. Thus, these results may be helpful in understanding the influence of ground-based CTL and WTH harvesting operations and achieving best practices to minimize the environmental impacts on soil.

Determination of Permanent Deformations of Non-Cohesive Soils in Pavement Structures under Repeated Traffic Load

One of the main types of distress in pavement structures is rutting, which may also reduce serviceability significantly. Most design methods typically attribute rutting to the asphalt layer alone, proposing that it can be managed by controlling vertical deformation or stress at the subgrade’s top. Furthermore, these methods frequently lack precise measurements for rut depth. On-site measurements show that the majority of permanent deformation occurs in the unbound layers beneath the asphalt; attention should be directed towards these layers. In recent literature, there are calculation methods that account for accumulating strains in soils. However, further investigation is needed regarding the effect of soil properties and the significance of the pavement cross-section. The literature is also somewhat contractionary regarding the origin of permanent deformations. In this research, the residual settlement of soils (base, subbase, and subgrade) under flexible pavement systems was analyzed due to the repeated traffic loads. Rut depths were calculated and analyzed using the High-Cycle Accumulation (HCA) model. The different behaviour in each course of the pavement system is discussed to reveal their contribution to total ruts. The effect of the grain size distribution of the subgrade was analyzed, and its significance on the rutting depths was demonstrated. Standardized pavement cross-sections with different asphalt thicknesses were evaluated, and the calculated settlements of the pavement originating from the ground during the design lifetime are also presented. It is shown that, with the same number of repetitions, the settlements calculated in each traffic load class are proportional to the thickness of the asphalt course. The contribution of the base, subbase, and subgrade courses to the total settlement is also presented for different subgrade types and traffic load classes.

Mechanical performance of cold mix asphalt containing cup lump rubber as a sustainable bio-modifier

The road construction industry aims to contribute to the protection of already compromised environment. Cold mix asphalt (CMA) is a measure initiated by the road industry to protect the environment and preserve energy. Despite having additional benefits, CMA has attracted little attention due to its inferior performance. CMA's performance is enhanced using a sustainable binder bio-modifier, natural cup lump rubber (CLR) is one of them. This study evaluated the tensile properties, rutting, moisture susceptibility, and adhesion properties of CLR-modified CMA (CMA-CR). The tensile property was enhanced by 26% due to CLR modification. CMA-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles, showing 70% improvement compared with conventional CMA. Moisture susceptibility evaluation indicated that CMA-CR had tensile strength ratio (TSR) value of 104%, satisfying the minimum 80% requirement of AASHTO T283. It also retained more than 96% bitumen coating. The moisture damage resistance was improved by 12% and 10% in terms of TSR and stripping, respectively. The durability results revealed that the CMA-CR mixture prevented higher mass loss, representing 14% improvement compared with conventional CMA.

Finite Element Modeling to Predicting Rutting in Flexible Pavements under Overloading

Road infrastructure is seriously threatened by heavy vehicle overloading, causing substantial damage, particularly rutting. This study uses advanced finite element analysis (FEA) techniques to improve pavement performance prediction. Six models were created using the ABAQUS/CAE program to evaluate the rutting performance of flexible pavement under overloaded conditions. These models simulated the existing pavement design and a proposed design, each subjected to three axle load configurations: single axle-single tire, tandem axle-dual tire, and tridem axle-dual tire. The research includes conducting surveys on traffic volume and axle load, to precisely evaluate the current traffic situation and identify problems associated with overloading. Core extractions enabled a thorough comparison of the existing pavement's thickness, density, and material characteristics with the proposed design. Based on these findings, a new design for a flexible pavement was developed, focusing on enhancing its ability to withstand rutting. The analysis determines that the existing pavement cannot withstand overloading vehicles, necessitating a 6.67% increase in the thickness of the surface, a 37.5% increase in the thickness of the binder, a 50% increase in the thickness of the base, and a 32% increase in the thickness of the subbase layers. Significantly, these modifications and improvements to material qualities were discovered to effectively decrease rut depth, an important factor in the durability and performance of pavement.

Modeling rutting depth on RIOHTrack asphalt pavement using Circle LSTMs

In response to the critical need for accurate pavement rutting prediction in pavement design, this study introduces a novel deep-learning model, Circle LSTM, designed for the estimation of rutting depths in asphalt pavements. Leveraging data from a full-scale field accelerated road testing facility in Tongzhou, Beijing, which spans 2.038 kilometres and features 19 distinct asphalt pavements, the Circle LSTM model utilizes variables such as temperature and load axis for its predictions. Methodologically, the Circle LSTM outperforms traditional machine learning approaches by cycling through different LSTM models to enhance short-term dependence modeling, showcasing its strength in addressing nonlinear problems inherent in road engineering. Evaluation of the model on the test dataset yielded promising results, with error metrics showing an average RMSE of 2.4170, an average MAE of 2.9454, an average MAPE of 2.61%, and an average coefficient of determination (average R2) of 0.8253 for 19 different asphalt pavements. These findings underscore the Circle LSTM model’s potential not only for fast and reliable rutting depth prediction but also for broader applications in multivariate time-series forecasting tasks in road engineering and beyond.

Influence of Sustainable Grout Material on the Moisture Damage of Semi-flexible Pavement

Semi-flexible pavement (SFP), also known as grouted pavement, is a type of pavement structure consisting of a porous asphalt skeleton with air voids between 25 and 35% injected with cementitious grout materials. The special skeleton of SFP provided enhanced durability and resilience, making it a promising option for the construction of road surfaces in high-traffic areas and severe conditions. The main aim of the current research is to investigate the rutting behavior and moisture resilience of SFP-containing sustainable grout using ceramic waste powder (CWP). This research introduced the use of CWP as a replacement for conventional grout (cement) in SFP for the first time. CWP partially replaced cement at ratios of 15, 20, 30, 40, and 50% of the cement weight. Indirect Tensile Strength (ITS) and Hamburg Wheel Tracking Tests (HWTT) were used to evaluate the performance of SFP to reduce the detrimental effect of moisture. The grout modified with CWP shows excellent results in the ITS and Tensile Strength Ratio (TSR), and all modified SFP mixtures give higher values in these tests compared with Control Mix (CM). In the HWTT, the minimum rut depth of the modified SFP was 2.8 mm and 3.10 mm. Compared to CM, rutting decreased by 73%–76% for CWP mixes with 20% and 30% replacement. This indicates the high fluidity of CWP, which enabled it to penetrate all voids in the porous pavement (PA) and form a dense microstructure due to its excellent pozzolanic interaction, making it a strong structure capable of bearing rutting.

Assessment of Rutting Resistance for Fiber-Modified Asphalt Mixtures

Rutting is one of the most complex and widespread types of distress. The rutting is frequently observed on Iraqi roads, especially at the checkpoints, forming a significant hazard on the asphalt layers. Factors such as heavy loads and high temperatures contribute to this distress. Adding fibers to a hot mix asphalt (HMA) effectively improves performance and extends the lifespan of the flexible pavement. This article used glass, steel, and basalt fibers. The wheel tracking test assessed the fibre-asphalt mixture for rutting resistance and compared it with the mix without adding fibers (control HMA). Meanwhile, the microscopic structure of fibres and asphalt mixture modified with fibers was examined using the Field Emission Scanning Electron Microscopy (FESEM) technique. Steel, glass, and basalt fibers were incorporated into HMA in proportions of 0.25%, 0.10%, and 0.15%, respectively. The incorporation of fibers in asphalt mixtures implies lower rut depths after 5000 cycles. In comparison to the control HMA, a decrease in the rut depth is observed in fiber-asphalt mixtures, about 22.14%, 15.36%, and 9.64% for basalt, glass, and steel fiber, respectively, which consequently enhances flexible pavement resistance against rutting. The microstructure analysis showed the difference in the mixture's diameters, surface properties, and random fiber dispersion. Therefore, this dispersion contributed to creating a three-dimensional network, which improved the behaviour of HMA.

Evaluation of asphalt binder and mixture properties utilizing fish scale powder as a biomodifier

Fish represents an abundant and underutilized waste product from the fishing industry. This study investigated the effects of incorporating fish scale powder (FSP) at various dosages (3%, 6%, 9%, and 12%) on the properties of asphalt binder and mixtures. Conventional tests, viscosity, storage stability, Fourier transform infrared spectroscopy, and multiple stress creep recovery tests were conducted on the binder. Mix design, wheel tracking, indirect tensile strength, fatigue, and ultrasonic pulse velocity tests were evaluated for the asphalt mixtures. The results showed that FSP increased the binder’s stiffness and reduced the temperature susceptibility but compromised the low-temperature performance and workability regardless of the dosages. The storage stability test results demonstrated the improved high-temperature storage stability. In the mixtures, the permanent deformation resistance enhanced with increasing the FSP content, decreasing the rut depth from 4.3 mm for the control sample to 2.9 mm at 12% FSP. The moisture damage resistance, indicated by the tensile strength ratio, increased from 90% for the control sample to 94.1% at 12% FSP. However, the fatigue life decreased from 14010 cycles for the control sample to 11190 cycles at 12% FSP. The dynamic and elastic modulus values before conditioning increased with higher FSP dosages, and this increasing trend was also observed after conditioning, signifying greater stiffness retention and moisture resistance of the asphalt mixtures containing higher amounts of FSP. Numerically, the 6–9% FSP range offered the optimum balance, improving the rutting resistance by 18% and the moisture resistance by 3.2% compared to those of the control sample, while limiting the fatigue life to 12% and maintaining the workability. Overall, FSP has potential for use as an asphalt biomodifier by transforming an environmental liability into a value-added sustainable paving material.

Field performance validation of the Louisiana balanced asphalt mix design framework

The balanced mix design (BMD) approach was implemented by agencies to complement the volumetric mix design method. The BMD approach addresses typical pavement distresses using mechanical tests. This study aimed to validate the Hamburg Wheel Track (HWT) rut depth and Semi Circular Bend (SCB) critical strain-energy release rate (Jc) thresholds specified in the Louisiana BMD framework. Seven field projects, including 13 pavement sections with 8–18 service years, were evaluated. Field distress data showed that the maximum HWT rut depth and minimum SCB Jc thresholds are effective parameters for assessing rutting and cracking performance, respectively.

Characterization of rutting damage based on two-dimensional image analysis of changes in mesoscopic aggregate properties of asphalt mixtures

Accurately predicting the rutting development law of asphalt pavement is of great significance for preventing and controlling pavement diseases. The aggregate skeleton of the asphalt mixture is the main load-bearing part and plays an important role in its anti-rutting performance. However, quantitative and mesoscopic evaluation indicators and standards for aggregate skeletons have not been established. This study aims to reveal the relationship between the mesostructured characteristics of asphalt mixtures and rutting damage. This paper uses two-dimensional image technology to analyze the dynamic change process of internal skeleton information of two graded asphalt mixtures under cyclic wheel load. According to the fractal theory, the fractal dimension and multifractal spectrum of the full-depth aggregate were calculated. The results indicate that the linear regression model between the mesoscopic skeleton information of asphalt mixture and the average rutting depth during the rutting test can be used to quantify the rutting performance. There is an obvious correlation between the fractal dimension of the section aggregate and the loading times, and there is a certain linear relationship between the multifractal spectrum indexes and the loading times. It indicates that the fractal theory index of aggregate can reflect the change of the macro performance of loading times.

Mathematical formulation for predicting moisture damage indices of asphalt mixtures treated with sustainable waste plastic modifiers using gene expression programming

Flexible pavements are susceptible to rutting, fatigue, and moisture damage failures. As a sustainable approach, waste plastic modifiers have been used to improve the moisture damage resistance of asphalt mixtures. However, assessing moisture damage indices such as tensile strength ratio (TSR), rut depth (RD), and loss in Marshal stability (LI) for waste plastic modified asphalt mixtures (WPMAM) via traditional lab methods is time-consuming and resource intensive. As a solution, this study suggests a gene expression programming (GEP) approach for predicting moisture damage indices of WPMAM. The models were constructed based on the outcomes from qualitative lab tests on loose coated asphalt specimens, including the static water immersion test (SWIT), rolling bottle test (RBT), and boiling water test (BWT). The proposed models demonstrated robust performance with high R values and favorable low error metrics. TSR achieved 0.99 (training) and 0.98 (testing) R values, while RD and LI displayed strong predictive accuracy with 0.95, 0.93 (training), and 0.94, 0.77 (testing) R values. Notably, lower root means square error (RMSE), mean absolute error (MAE), and relatively squared error (RSE) values reinforce the models' reliability. Sensitivity analysis identified RBT as the most influential factor for TSR, RD, and LI of WPMAM. Finally, parametric analysis verified the alignment of proposed models with underlying physical processes. Thus, these models can be successfully implemented for predicting moisture damage indices of WPMAM, offering a streamlined alternatives to labor-intensive laboratory procedures, making them valuable for optimizing material choices and implementing preventive measures in construction.

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R2 = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

Evaluation of the influence of warm additives modified by Vestenamer polymer on the performance of stone matrix asphalt containing crumb rubber

This study aimed to evaluate the impact of Vestenamer polymer and warm mix asphalt (WMA) additives (Zycotherm, Sasobit, and PAWMA) on the resilient modulus and rutting behavior of crumb rubber (CR)-modified stone matrix asphalt (SMA) using resilient modulus and wheel tracking tests. The resilient modulus test illustrated that CR-modified SMA mixtures containing WMA had a greater resilient modulus than the control mixture. Also, Sasobit had the highest effect on enhancing the resilient modulus. The wheel tracking test revealed that in each percentage of CR, WMA additives decreased the rutting depth of SMA mixtures, and Sasobit represented the lowest rutting depth among WMA additives. By adding 8, 12, and 16% CR, the rutting depth of SMA was reduced by 30.57%, 50.03%, and 64.76%, respectively, compared to the control sample. Therefore, the application of CR-modified SMA mixtures containing Sasobit can have a considerable impact on improving the rutting resistance of asphalt pavement.

Design, Placement, and Laboratory and Field Evaluation of Rejuvenated Cold Recycled Asphalt Mixtures

Cold recycled (CR) pavements, the combination of reclaimed asphalt pavement and a recycling agent (foamed or emulsified asphalt) at ambient temperatures, can provide a sustainable method to rehabilitate, reconstruct, and construct roadways. However, CR pavement is not a one-for-one replacement of typical asphalt base mixtures. To utilize CR pavements as an alternative to a typical asphalt base mixture or surface course, additional materials such as rejuvenating agents may be needed. This research focuses on the formulation, plant production, and placement on the National Center for Asphalt Technology pavement test track off-ramp, and laboratory evaluation of three rejuvenated CR mixtures, one foamed asphalt CR mixture, and an engineered emulsion CR mixture. These mixtures were subjected to strength and moisture susceptibility testing, along with the indirect tensile asphalt cracking test (IDEAL-CT) and high-temperature indirect tensile test (HT-IDT). The influence of laboratory production versus plant production and the impact of rejuvenators on mixture performance and density were investigated. International roughness index and rut depth data for the off-ramp sections indicate adequate performance, except for the anionic emulsion with bio-based rejuvenator section. Laboratory test results indicate that the addition of rejuvenators into two of the three CR mixtures increased density and decreased moisture susceptibility and increased the CTIndex by at least 35% compared to non-rejuvenated CR mixtures. HT-IDT strengths revealed that two of the three rejuvenated mixtures performed slightly below the non-rejuvenated mixtures.

Investigation on the Performance of Modified Corn Stalk Fiber AC-13 Asphalt Mixture

As an agricultural waste, a large amount of corn stalk will cause environmental pollution. In order to realize the resource utilization of waste and meet the strict requirements of modern traffic on pavement strength and durability, it was modified and applied to an AC-13 asphalt mixture to study its influence on the road performance of asphalt mixture and its mechanism. The road performances of modified corn stalk fiber, lignin fiber, and ordinary asphalt mixtures were evaluated via the wheel tracking test, low-temperature bending test, water immersion Marshall test, freeze–thaw splitting test, and fatigue test. Based on the results of three-point bending fatigue test, the viscoelastic parameters and indexes of the fiber asphalt mixture were obtained by fitting the loading specimen and deflection data with the Burgers constitutive model, and the creep strain response was analyzed by applying dynamic load, so as to explore the relationship between the viscoelastic characteristics and creep behavior of modified corn stalk fiber and AC-13 mixture. The long-term high-temperature performance test of the asphalt mixture with the best fiber content was carried out by using the long-term pavement intelligent monitoring equipment independently developed by the group of investigators. According to the findings, the ideal fiber contents for modified corn and lignin in asphalt mixture are 0.2% and 0.3%, respectively. Among them, the modified corn stalk fiber with a 0.2% content has the best effect on road performance, viscoelastic performance, and the asphalt mixture’s creep behavior under dynamic load. Compared with the 0.3% lignin fiber asphalt mixture, its dynamic stability, bending stiffness modulus, immersion residual stability, freeze–thaw splitting strength ratio, and loading times at failure increased by 19.9%, 18.28%, 4.19%, 8.6%, and 9.15%, respectively. Compared with ordinary asphalt mixture, it increased by 47.0%, 28.72%, 7.65%, 15%, and 75.81%, respectively. Moreover, when modified corn stalk fiber is added at 0.2%, the viscoelastic delay time of asphalt mixture is the longest, the strain peak value and rut depth are at a minimum, and the viscoelastic properties, creep properties, and long-term high-temperature properties are the best.