Unbound granular material specifications for road pavements in Australia are primarily based on physical material specification rather than mechanical characterisation. This simplified approach does not reflect the ac...Unbound granular material specifications for road pavements in Australia are primarily based on physical material specification rather than mechanical characterisation. This simplified approach does not reflect the actual material performance under repeated dynamic traffic loads. There is a little information available on the influence of the local crushed rock properties and compacted layer properties on permanent deformation (PD). This study aims to characterise the local unbound granular materials in Victoria according to their PD behaviour under repeated loads and to develop a suitable shakedown criterion that could describe the PD of the tested materials to simplify the flexible pavement design. Repeated-load triaxial tests were conducted over several samples with a range of moisture contents, gradations, densities, and stress conditions. The laboratory test results showed that PD behaviour was influenced by several factors. In addition, the tested subbase-specified unbound granular materials reflect high PD resistance that is almost equivalent to basequality unbound granular materials. This may indicate that current requirements for the subbase-quality unbound granular materials are over-prescribe. Moreover, as the existing shakedown criterion was not applicable for the multi-stage repeated-load triaxial test and the local tested materials, a new shakedown criterion and new boundaries are proposed based on the PD behaviour. In the proposed criterion, the shakedown ranges are identified based on the curve angle of the PD vs. logarithm of the number of loading cycles, and this new criterion was validated using several materials from existing literature. The local tested base and subbase materials can be assigned as Range A when PD\1%, Range B when 1%\PD\3%, and Range C when PD[3%. The proposed criterion could provide a useful and quick approach to assess the PD of the unbound granular materials with both single and multistages of stresses.展开更多
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) a...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.展开更多
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,...Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.展开更多
Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have be...Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.展开更多
文摘Unbound granular material specifications for road pavements in Australia are primarily based on physical material specification rather than mechanical characterisation. This simplified approach does not reflect the actual material performance under repeated dynamic traffic loads. There is a little information available on the influence of the local crushed rock properties and compacted layer properties on permanent deformation (PD). This study aims to characterise the local unbound granular materials in Victoria according to their PD behaviour under repeated loads and to develop a suitable shakedown criterion that could describe the PD of the tested materials to simplify the flexible pavement design. Repeated-load triaxial tests were conducted over several samples with a range of moisture contents, gradations, densities, and stress conditions. The laboratory test results showed that PD behaviour was influenced by several factors. In addition, the tested subbase-specified unbound granular materials reflect high PD resistance that is almost equivalent to basequality unbound granular materials. This may indicate that current requirements for the subbase-quality unbound granular materials are over-prescribe. Moreover, as the existing shakedown criterion was not applicable for the multi-stage repeated-load triaxial test and the local tested materials, a new shakedown criterion and new boundaries are proposed based on the PD behaviour. In the proposed criterion, the shakedown ranges are identified based on the curve angle of the PD vs. logarithm of the number of loading cycles, and this new criterion was validated using several materials from existing literature. The local tested base and subbase materials can be assigned as Range A when PD\1%, Range B when 1%\PD\3%, and Range C when PD[3%. The proposed criterion could provide a useful and quick approach to assess the PD of the unbound granular materials with both single and multistages of stresses.
文摘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.
文摘Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.
文摘Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.
