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Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.

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 base-quality 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 multi-stages of stresses.

Vertical load characteristics used in laboratory repeated load triaxial tests (RLTT) have a significant impact on evaluating unbound granular materials (UGMs) for the flexible road pavements. Many studies and standard testing protocols suggest a diverse range of these characteristics (i.e. stress magnitude, pulse shape type, loading period and rest period). Several studies have been conducted to identify the factors that affect the permanent deformation (PD) and resilient modulus (Mr) of UGMs. However, the effect of the pulse shape types has not yet been investigated. The aim of this study is to experimentally investigate the effect of vertical stress pulse shape type on the PD and Mr behaviour of the UGMs using RLTT. For further assessment, a parametric analysis was also conducted using eight existing PD and Mr constitutive models. Three typical vertical stress pulse shape types were investigated, namely trapezoidal, haversine and triangular, as suggested by several international testing protocols. The results show that tested UGMs, using trapezoidal stress pulse, produced higher PD and lower Mr than haversine and triangular pulses under controlled experimental conditions. As the loading span of the pulse increased, the amount of PD also increased, and Mr decreased. Some of the regression parameters of the investigated constitutive models for both PD and Mr showed correlations with the type of applied stress pulses. Moreover, it was found that the PD and Mr models were material dependent, as a better statistical fit was achieved for the granite than the basalt samples. It is recommended to take extra precautions while adopting a particular type of vertical stress pulse shape as the results may vary widely.

Constitutive prediction models in flexible pavement are used to predict the permanent deformation (PD) of the unbound granular materials (UGMs). The most recent flexible pavement structural design guide in Australia does not consider the response of the UGMs layers as a design criterion. Additionally, it referred to the absence of an appropriate prediction model. The available prediction models have complex equations with many variables; the use of some of which has been found to be unsuitable in Australia. Thus, the aim of this study was to develop a new simple constitutive model based on an empirical regression equation, which has two predictor variables and four regression parameters. Repeated load triaxial test data covering many loading cycles and stages of stresses were used in order to validate the proposed model by calibration. The tested materials, specified by VicRoads as high-quality base and subbase crushed rocks, were used to prepare 46 samples with a range of moisture contents, gradations, densities, and stress conditions. Additional validation was conducted by predicting the PD response of recycled crushed concrete for different stress conditions. The proposed model excellently matches the experimental PDs with the sum of squared error less than 0.060 and R2 more than 0.96 over the tested matrix for both the crushed rocks and recycled concrete. This high prediction accuracy could lead to a better design of pavement materials and their thicknesses. A new update is proposed for the current Austroads flexible pavements design procedure by involving two more critical locations as performance criteria in the base and subbase layers based on the developed constitutive model. Adopting the proposed model can better generalise the design procedure to determine the accumulated vertical PD for the whole layer system.