Structural design of pavements incorporating foamed bitumen mixtures

This paper presents a rational structural design methodology termed the ‘cumulative damage approach’ for road, port and airport pavements incorporating cold bituminous mixtures with foamed bitumen. This has been developed along with a laboratory test, the uniaxial indirect tensile test, to evaluate the fatigue characteristics of these mixtures. The test was developed with a view to addressing the limitations of conventional fatigue tests for foamed bitumen mixtures. The new design approach takes account of the actual stiffness evolution of the mixtures obtained from the fatigue test. It is compared with a traditional approach for conventional flexible pavements, which is based on pavement life as a function of computed tensile strain in the material and interpretation of fatigue data in a conventional way. The results show that the traditional design approach yields conservative outcomes for pavements with foamed bitumen mixtures if the same transfer function or shift factor used for hot mix asphalt is applied. The results also show that if all factors other than induced load influence are the same, the shift factor for foamed bitumen mixtures could be 25–35% higher than for hot mix asphalt

Probabilistic prediction of asphalt pavement performance

Variability of pavement design parameters has always been a concern to pavement designers and highway agencies. A robust pavement design should take into account the variability of the design inputs and its impact on the reliability of the design. In this study, the variability effect of thickness and stiffness of pavement layers was investigated. The variability of these parameters was described by their mean values, standard deviations and probability distribution functions. Monte Carlo Simulation method was utilised to incorporate variability of the design parameters and to construct the probability distribution function of the outputs. KENLAYER software was used to calculate pavement response at predetermined critical locations; pavement reponse was then used to predict pavement performance regarding permanent deformation, bottom-up and top-down fatigue cracking by using the mechanistic empirical pavement design guide (MEPDG) models. A Matlab code was developed to run that analysis and obtain the probability distribution function of pavement performance indicators over time. It was found that the variability of pavement layer thickness and stiffness has a significant impact on pavement performance. Also, it was found that not only the mean of the predicted performance indicators is increasing over time, but the variance of these indicators is also increasing. This means that pavement condition cannot be described by the mean values of the indicators but by the probability distribution function which can describe pavement condition at any reliability level

Development of compression pull-off test (CPOT) to assess bond strength of bitumen

The quantification of moisture susceptibility has been a major concern for researchers as it adversely affects the performance of asphalt pavements. Several methods have been developed to assess bond strength using asphalt mixtures in loose and compacted state. These tests lack in their ability to study fundamental properties that affect the bond between bitumen and aggregate. In this context, more fundamental techniques have been developed such as pull-off stub tests and direct tension tests. The first group only measures the maximum pull-off strength and second group has problems related to use of consistent binder film thickness and operational difficulties in test itself.This paper presents a new test to evaluate bond strength, in an attempt, to solve problems associated with traditional direct tension tests. The aim is achieved through a review of existing techniques, development of a gap assembly, fabrication of direct tension test moulds, development of Compression Pull-Off Test (CPOT) and evaluation of its results. The key parameters for bitumen and mastics were evaluated. The CPOT shows promising results for use of this technique to study cohesive as well as adhesive bond strength of binder

Mix design considerations of foamed bitumen mixtures with reclaimed asphalt pavement material

In the present work, a mix design parametric study was carried out with the aim of proposing a practical and consistent mix design procedure for foamed bitumen mixtures (FBMs). The mix design parameters that were adopted in the study are mixing and compaction water content (MWC), compaction effort using a gyratory compactor and aggregate temperature. This parametric study was initially carried out on FBMs with virgin limestone aggregate without reclaimed asphalt pavement (RAP) material and a mix design procedure was proposed. This proposed methodology was also found to apply to FBMs with RAP. A detailed consideration was also given to characterising the RAP material so as to understand its contribution to the mechanical properties of FBMs. Optimum MWC was achieved by optimising mechanical properties such as indirect tensile stiffness modulus and indirect tensile strength (ITS-dry and ITS-wet). A rational range of 75–85% of optimum water content obtained by the modified Proctor test was found to be the optimum range of MWC that gives optimum mechanical properties for FBMs. It was also found that the presence of RAP influenced the design foamed bitumen content, which means that treating RAP as black rock in FBM mix design is not appropriate. To study the influence of bitumen and water during compaction, modified Proctor compaction and gyratory compaction were employed on mixes with varying amounts of water and bitumen. By this, the work also evaluated the validity of the total fluid (water + bitumen) concept that is widely used in bitumen–emulsion-treated mixes, and found it not to be applicable

Behaviour of rubberised cement-bound aggregate mixtures containing different stabilisation levels under static and cyclic flexural loading

An investigation has been undertaken to investigate the influence of rubber inclusion at different levels of stabilisation on the behaviour of cemented granular mixtures, under static and cyclic flexural testing, and to compare this with mixtures without rubber. Both are intended to be used as base courses of semi-rigid pavement structure. 3%, 5%, and 7% of cement by dry weight of aggregate were used for stabilisation purposes. Rubberisation of cemented aggregate was conducted by replacing 30% of the aggregate of the 6 mm fraction size by an equivalent rubber volume. The investigated properties were flexural strength, static and dynamic stiffness moduli, toughness and fatigue life. Damage due to cyclic loading was evaluated in terms of stiffness degradation and permanent deformation accumulation. Flexural-induced cracking behaviour was also investigated. Results reveal that the rate of flexural strength increase is higher for the reference cemented mixtures. As stabiliser quantity increase, both static and dynamic stiffness moduli increased while rubberisation mitigated these two parameters at all stabiliser contents. Toughness and fatigue life were improved due to rubber modification at investigated stabiliser contents. Flexural-induced cracks always tend to propagate through rubber aggregate regardless of the quantity of cement

The HANDE-QMC project: open-source stochastic quantum chemistry from the ground state up

Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the last decade. The full configuration interaction quantum Monte Carlo (FCIQMC) method allows one to systematically approach the exact solution of such problems, for cases where very high accuracy is desired. The introduction of FCIQMC has subsequently led to the development of coupled cluster Monte Carlo (CCMC) and density matrix quantum Monte Carlo (DMQMC), allowing stochastic sampling of the coupled cluster wave function and the exact thermal density matrix, respectively. In this article we describe the HANDE-QMC code, an open-source implementation of FCIQMC, CCMC and DMQMC, including initiator and semi-stochastic adaptations. We describe our code and demonstrate its use on three example systems; a molecule (nitric oxide), a model solid (the uniform electron gas), and a real solid (diamond). An illustrative tutorial is also included

Investigation into the bond strength of bitumen-fibre mastic

The loss of bond strength in road pavement surfacing due to high traffic loads or moisture is a recurring problem, creating distresses such as ravelling, fatigue and rutting. It is, therefore, important to find a way to prevent or at least delay the loss of bond strength in asphalt mixtures. Such an improvement would lead to longer service life and a more comfortable drive for road users. This study describes how the pneumatic adhesion tensile testing instrument (PATTI) was used to examine the mechanism by which fibres influence the pull-off tensile strength of asphalt mastic. This study assesses the potential for chemical modification of the binder due to the presence of fibres, by means of work of cohesion and work of adhesion calculations, based on surface energy parameters and a binder drainage test. The study also evaluates the influence of different filler-bitumen ratios and fibre percentages on pull-off tensile strength. The test results indicate that the fibres enhance the pull-off tensile strength of the mastic, in addition to changing the failure mode from cohesive to hybrid, implying an improvement in the cohesive strength of the mastic

Physics-guided neural network for predicting international roughness index on flexible pavements considering accuracy, uncertainty and stability

An outstanding amount of funds are allocated to maintain road network conditions. To ensure the serviceability of roads, the accurate prediction of its roughness or International Roughness Index plays a pivotal role in road management. Artificial neural network, typically used in roughness prediction, is a powerful machine learning algorithm that learns complex patterns in data with non-linear relationships. However, it remains a black box solution and relies heavily on the utilized data and its internal structures, causing model's overfitting and instability. To address such issues, a physics-guided neural network modelling framework is proposed for short- and long-term predictions of roughness aimed at improving model's accuracy, uncertainty and stability. This framework fuses the output of physics-based model simulations along with field observational data acquired from the Long-Term Pavement Performance public database as inputs to develop a neural network architecture. Additionally, the framework uses a physics-based loss function in the network's learning process to ensure the predictions are consistent with the known physics. The performances are evaluated and compared to traditional artificial neural network. The comparison results indicate that the proposed modelling framework can increase the accuracy by 4%, and 26.08%, reduce the uncertainty by 4% and at least 22.15%, and improve the stability by 24.09% and by 46.34%, for one year and multi-year predictions, respectively. This framework offers great potential for accurate, reliable and stable predictions for engineering asset conditions by leveraging the complementary strengths of numerical simulations and data-driven models

Toward the Development of Load Transfer Efficiency Evaluation of Rigid Pavements by a Rolling Wheel Deflectometer

The jointed rigid pavement is currently evaluated by the Falling weight deflectometer which is rather slow for the testing of the jointed pavements. Continuous nondestructive evaluation of rigid pavements with a rolling wheel deflectometer can be used to measure the load transfer and is investigated. Load transfer is an important indicator of the rigid pavement's condition and this is the primary factor which is studied. Continuous data from experimental measurements across a joint allows for the determination of not only the load transfer efficiency provided parameters characterizing the pavement is known. A three-dimensional semi-analytical model was implemented for simulating the pavement response near a joint and used for interpretation and verification of the experimental data. Results show that this development is promising for the use of a rolling wheel deflectometer for rapid evaluation of joints

Preconditioning and perturbative estimators in full configuration interaction quantum Monte Carlo

We propose the use of preconditioning in FCIQMC which, in combination with perturbative estimators, greatly increases the efficiency of the algorithm. The use of preconditioning allows a time step close to unity to be used (without time-step errors), provided that multiple spawning attempts are made per walker. We show that this approach substantially reduces statistical noise on perturbative corrections to initiator error, which improve the accuracy of FCIQMC but which can suffer from significant noise in the original scheme. Therefore, the use of preconditioning and perturbatively-corrected estimators in combination leads to a significantly more efficient algorithm. In addition, a simpler approach to sampling variational and perturbative estimators in FCIQMC is presented, which also allows the variance of the energy to be calculated. These developments are investigated and applied to benzene (30e,108o), an example where accurate treatment is not possible with the original method.Comment: 15 pages, 7 figure