Effect of vertical stress rest period on deformation behaviour of unbound granular materials: Experimental and numerical investigations

Repeated load triaxial test is used to assess the deformation behaviour of unbound granular materials (UGMs) in flexible road pavements. Repeated load pulse characteristics (i.e. shape, loading period and rest period) are the stress configurations used in the experimental set-up to simulate the passing axle loads. Some researchers and standard testing protocols suggest a rest period of varying durations after a loading phase. A thorough review of existing literature and practises has revealed that there is no agreement about the effect of the rest period of vertical stress pulse on the deformation behaviour of the UGMs. Therefore, the main objective of this study is to investigate the effect of repeated stress rest period on the deformation behaviour of UGMs experimentally. Experiments are conducted, both with and without rest period, using basalt and granite crushed rocks from Victoria, Australia. Furthermore, in order to gain insight into the effect of the rest period, finite element modelling is also developed. Both the experimental and modelling results show that the rest period has a noticeable effect on both resilient and permanent deformation behaviours of UGMs. It is, therefore, recommended to take extra precautions while adopting a particular standard testing protocol and to supplement the results by additional tests with different loading configurations.     

Characterisation of permanent deformation behaviour of unbound granular materials under repeated triaxial loading

The present paper reports the results of a laboratory experiment that aimed to investigate the permanent deformation behaviour of two unbound granular materials for road subbase layers under repeated triaxial loading. In the first part of research the shakedown concept was used to classify the mechanical response of the granular mixtures. The obtained results confirmed the potential usefulness of this theoretical approach for ranking granular materials with regard to their rutting potential. The second part was entirely dedicated to the development of an analytical model to describe the long-term permanent deformation behaviour of these materials. The proposed model allowed permanent strain accumulation as function of the number of load applications and applied stress level to be described. The results, besides being consistent with the shakedown concept, showed the ability of the model to reflect the change in mechanical behaviour due to the different nature of materials, under specific stress and moisture condition. In addition, the model allowed the permanent deformation accumulation resistance of the material to be properly investigated through specific strain rate envelope curves defined on a Mohr-Coulomb diagram. Therefore, this study could propose an innovative and practical design approach for permanent deformation behaviour assessment of unbound granular material and consequently for evaluating its suitability in the pavement in order to avoid undesirable response.     

Development of a void ratio-moisture ratio-net stress framework for the prediction of the volumetric behavior of unsaturated granular materials

Despite extensive research on the behavior of unsaturated fine-grained materials, there is still a lack of understanding of the volumetric behavior of unsaturated granular materials. In this research, a model has been developed to predict the fundamental volumetric behavior of unsaturated granular materials through loading and wetting state paths. In this regard, a loading-wetting surface was developed in a space of void ratio-moisture ratio-net stress. A distinctive feature of the proposed model is the relative simplicity in obtaining the model parameters using conventional geotechnical testing equipment. Two types of recycled granular materials, commonly applied in unbound pavements were used, namely, recycled crushed brick (CB) and excavation waste rock (WR). The uniqueness of the developed surface was evaluated by employing a number of loading and wetting state paths. The results indicate that the developed surface is unique in its loading state paths; however, it only shows uniqueness in its wetting state paths for stress levels greater than 2000 kPa. The proposed model seeks to introduce the application of the unsaturated soil mechanics theory, for predicting the behavior of granular materials in the field, by providing a practical and cost-effective methodology.     

Illuminance-based slat angle selection model for automated control of split blinds

Venetian blinds play an important role in controlling daylight in buildings. Automated blinds overcome some limitations of manual blinds; however, the existing automated systems mainly control the direct solar radiation and glare and cannot be used for controlling innovative blind systems such as split blinds. This research developed an Illuminance-based Slat Angle Selection (ISAS) model that predicts the optimum slat angles of split blinds to achieve the designed indoor illuminance. The model was constructed based on a series of multi-layer feed-forward artificial neural networks (ANNs). The illuminance values at the sensor points used to develop the ANNs were obtained by the software EnergyPlus™. The weather determinants (such as horizontal illuminance and sun angles) were used as the input variables for the ANNs. The illuminance level at a sensor point was the output variable for the ANNs. The ISAS model was validated by evaluating the errors in the calculation of the: 1) illuminance and 2) optimum slat angles. The validation results showed that the power of the ISAS model to predict illuminance was 94.7% while its power to calculate the optimum slat angles was 98.5%. For about 90% of time in the year, the illuminance percentage errors were less than 10%, and the percentage errors in calculating the optimum slat angles were less than 5%. This research offers a new approach for the automated control of split blinds and a guide for future research to utilize the adaptive nature of ANNs to develop a more practical and applicable blind control system.     

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction (RDC), which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP) test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.