Science AI Journal
Engineering · Research topic

Open research questions in Asphalt Pavement Performance Evaluation

34 unresolved questions extracted from the limitations and future-work sections of 706 Asphalt Pavement Performance Evaluation papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • To develop a more thorough understanding, future studies should examine the cracking resistance of HMA-GO under varying asphalt binder grades, aggregate types, and testing conditions, includ- ing fatigue cracking analysis. Future research should focus on validating these promising findings with long-term field perfor- mance data, employing advanced numerical modeling techniques, and investigating the influence of environ- mental factors on the durability and performance of HMA-GO pavements.

    Experimental analysis of the structural and functional performance of asphalt concrete modified with graphene oxide · 2026 · DOI

  • Future research should focus on more realistic rubber–pavement interfacial contact mechanisms, standardization of friction measurement methods, refined texture modeling, large-scale multi-source datasets, interpretable deep learning models, and field-applicable skid resistance rehabilitation technologies.

    Evaluation approaches and enhancing strategies for airport pavement skid resistance: A state-of- the- art · 2026 · DOI

  • This simplified assumption considers only the reduction in petroleum-based binder use and does not account for additional emissions associated with WCO collection, transportation, processing, filtration, storage, and blending, as well as energy consumption for these activities.

    Hybrid Experimental–Machine Learning Framework for Environmental Optimization of Bio-Modified Bitumen · 2026 · DOI

  • As presented, the Kelvin-Voigt model consists of two ele- ments, an elastic spring and a dashpot. These two elements operate in different ways according to the condition of load- ing. During loading, both elements work together in one direction against loading, while during unloading these elements work in opposite directions: the spring tries to recover strain while the dashpot tries to hold this strain, and the difference between both represents the power index (PI) of recovery, as demonstrated in Fig. 16a. To find the differ- ence between both elements, first find the time interval of the Kevin-Voigt model. This can be determined by finding the point at which the linear part deviates from the curve in the relationship of creep strain vs. time, as shown in Fig. 16b; then, this time is multiplied by the viscous element of the Kelvin-Voigt model (ηK) by time, and then the power index (PI) can be as follows: P I l = EK + t η k × P I u = EK + t η k × (4) (5) where, PIl and PIu are power index for loading and unload- ing state, EK is elastic stiffness of spring element and ηK power viscous of dashpot element. Figure 17 demonstrates the PI of the different mixes for loading and unloading conditions. It is clear that the PI of loading is higher than unloading, and this is rational because during loading, both the spring and dashpot work together against loading, while during unloading, the spring works in the direction of recovery strain and the dashpot works in 1 3Creep Performance and Interpreting the Outputs Based on Maxwell–Kelvin–Voigt Model for HMA Modified by… Fig. 16 Kelvin-Voigt model elements: a Performance way and b Operating time Fig. 17 PI of Kelvin-Voigt model elements (a during loading and b during unloading) resistance to this movement due to viscosity. On the other hand, SWAC showed higher PI in both (loading and unload- ing than ACM and AC; this is consistent with the afore- mentioned analyses. However, PI needs to further study to develop other calculations from different perspectives to give a high simulation.

    Creep Performance and Interpreting the Outputs Based on Maxwell–Kelvin–Voigt Model for HMA Modified by Sulfur Waste · 2026 · DOI

  • The specific input parameters (mix design variables, binder properties, loading frequencies, temperature ranges) used to train the ANN model are not enumerated in the excerpt, making it impossible to determine whether the model can predict fatigue endurance limits across the full range of realistic field conditions or only within narrow laboratory testing parameters.

    Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests · 2026 · DOI

  • The paper references Isied et al. (2021) as developing a similar ANN fatigue endurance limit model based on volumetric properties and loading conditions, but comparative analysis between that model's architecture, input features, and prediction accuracy versus the current approach is absent.

    Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests · 2026 · DOI

  • The relationship between the ANN-predicted fatigue endurance limit and the viscoelastic continuum damage (VECD) model parameters has not been explicitly explored. Establishing direct correlation between the neural network outputs and continuum damage mechanics principles would enhance theoretical foundation of the predictive model.

    Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests · 2026 · DOI

  • The model's applicability to modified asphalt mixes incorporating recycled plastic waste (PET, HDPE, PVC) or polymer modifiers is unclear, as the training dataset composition regarding binder modification type and aggregate gradation parameters is not explicitly characterized. Testing the ANN model's predictive accuracy across these modified mix designs is needed.

    Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests · 2026 · DOI

  • The ANN fatigue endurance limit model was developed using uniaxial tension-compression test data, but validation against field performance data from actual pavement sections under in-service traffic loading conditions has not been conducted. This limits verification of whether laboratory-predicted fatigue endurance limits translate to real-world asphalt pavement durability.

    Artificial Neural Network Model for Predicting Fatigue Endurance Limit of Hot Mix Asphalt Using Uniaxial Tension–Compression Tests · 2026 · DOI

  • Even though this research shows positive outcomes using WEO and CF as a modifier for bitumen grade 60/70, it is important to evaluate its other characteristics thoroughly so that it can be used widely to a larger extent.

    Investigation of the Impact of Waste Engine Oil and Coconut Fiber on the Physical Characteristics of Bitumen · 2026 · DOI

  • Since no standardized recommendation for cleaning PC currently exists, water management agencies in some states do not provide stormwater management credit for using PC systems.

    Cleaning Methods for Pervious Concrete Pavements · 2013 · DOI

  • ’’ Although sufficient may be known about rock types and their properties, study of these processes is handicapped by a serious lack of data on microclimatic condi- tions at, and immediately above and below the ground surface. Whereas the latter are known gen- erally, temperature details are scarce.

    STONE PAVEMENTS IN DESERTS<sup>1</sup> · 1970 · DOI

  • This combined approach bridges a key gap in the literature and provides a practical tool for enhancing binder design and improving pavement performance.

    Experimental evaluation and predictive modeling of asphalt binders modified with ground tire rubber · 2026 · DOI

  • Although vegetable oils are proposed as viscosity-reducing additives, the optimal balance between crumb rubber content, oil content, and oil origin has not yet been fully established.

    Comparative Study of New and Residual Vegetable Oils as Viscosity-Reducing Additives in Asphalt Binders Modified with Waste-Tire Crumb Rubber · 2026 · DOI

  • However, the potential increase in stiffness due to aging may heighten the risk of low-temper- ature cracking, which constitutes a major limitation of this work, as low-temperature tests (e.

    Laboratory Simulation of Field Short-Term Aging of Asphalt Concrete and Stone Mastic Asphalt Wearing Course Mixtures · 2026 · DOI

  • Future research should focus on developing composite modification technologies, including fiber reinforcement, nanomaterial doping, and elastomer compounding, to improve low-temperature toughness and long-term fatigue resistance while maintaining high modulus.

    Performance evaluation and engineering application of high-modulus asphalt mixtures with various modifications · 2026 · DOI

  • The mechanism explaining the synergetic effects of WEO and CF on bitumen properties requires deeper theoretical investigation.

    Investigation of the Impact of Waste Engine Oil and Coconut Fiber on the Physical Characteristics of Bitumen · 2026 · DOI

  • The study used simplified linear elastic material assumptions and static loading; more constitutive models including viscoelasticity of asphalt and plasticity of soils need to be incorporated.

    Pavement Rutting and Deformation Performance Analysis through FEM · 2026 · DOI

  • Research on the microscopic mechanisms of open-graded friction courses (OGFCs) is still in its early stages, and the specific effects of various factors on the fatigue performance of OGFCs have not been fully explored.

    Multiscale Evaluation of Open-Graded Friction Course (OGFC) Asphalt Mixture Fatigue Damage · 2025 · DOI

  • New polymer modifiers have provided the stability to these mixes that was lacking initially, and the result is that pavement owners and builders are taking a second look at OGFCs.

    OPEN-GRADED MIXES: BETTER THE SECOND TIME AROUND · 1996

Most-cited papers in Asphalt Pavement Performance Evaluation

Most recent work

Find a gap in your own Asphalt Pavement Performance Evaluation sub-topic

This page shows what the Asphalt Pavement Performance Evaluation literature already flags as unresolved. To narrow it to your specific question, run the guided finder — it searches the gap library on demand and checks candidates against 250M+ OpenAlex works.

Open the Research Gap Finder →

Related topics in Engineering

Traffic and Road Safety
143 open questions
Spatial Cognition and Navigation
95 open questions
Smart Cities and Technologies
89 open questions
BIM and Construction Integration
87 open questions
Design Education and Practice
86 open questions
Engineering Education and Curriculum Development
82 open questions
Digital Transformation in Industry
81 open questions
Perovskite Materials and Applications
78 open questions
34 open questions have been extracted from the limitations and future-work passages of 706 Asphalt Pavement Performance Evaluation papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 120,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? A Tier 1–5 acceptance probability in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck prior-publication & salami-slicing risk across 8 sources.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.