Mechanical characteristics and durability of HMA made of recycled aggregates

The application of recycled aggregates in the asphalt industry has been investigated in recent decades. However, low percentages of these materials have practically been used in asphalt mixtures because of the limitations set by the relevant specifications due to their performance uncertainties. This research investigates the feasibility of increasing the percentage of recycled aggregates to 100% in hot mix asphalt (HMA). Recycled concrete aggregate (RCA), recycled glass (RG), and reclaimed asphalt pavement (RAP) were used to develop HMAs suitable for roads with light to medium traffic. First, potential mix designs were proposed using an innovative approach considering the industry’s needs. Next, the volumetric properties, tensile strength, moisture sensitivity and resilient modulus response of the mixtures under different temperature conditions were determined and compared. In general, the proposed recycled material HMA exhibited superior mechanical and resilient modulus performances, i.e., 45 to 145% increase in stiffness, and up to 99% higher in Marshall stability. Furthermore, higher tensile strength ratios of the recycled material mixtures indicated a greater resistance to water damage, and hence greater durability. The findings of this research provide evidence-based insights into the increased proportion of recycled materials in the construction of asphalt pavements, thereby promoting sustainable pavement construction materials

Prediction of residential slab foundation movement through a finite element-based deep learning algorithm

Deep learning networks were employed to predict the maximum differential deflection of stiffened and waffle rafts due to reactive soil movements, Δmax. Four deep learning networks were used to predict Δmax, these are (1) stiffened rafts on shrinking soil, (2) stiffened rafts on swelling soil, (3) waffle rafts on shrinking soil, and (4) waffle rafts on swelling soil. The deep learning models were used to create design lines, which showed that both soil and structural features strongly influence the stiffened rafts. In contrast, waffle rafts showed a strong dependence on soil features in shrinking soils and beam depth in swelling soils. This demonstrates that the finite element-based deep learning networks captured the effect of the embedment of the beams. The results of the deep learning models led to non-linear design curves, which are disparate from the suggested standard Australian design. These results suggest that increasing the value of beam depth can have a positive or negative impact on the global residential slab depending on the type of substructure and whether the founding reactive soil is shrinking or swelling. Global sensitivity analyses of the deep learning models showed that for stiffened rafts on shrinking soil, the slab length, slab width and active depth zone of reactive soil had the most significant influence on Δmax, whilst for stiffened rafts on swelling soil, the primary drivers are ground movement, beam depth, and slab width. The prediction of Δmax for waffle rafts on shrinking soil was driven by the surface characteristic and mound movements, and the active depth zone, whilst waffle rafts on swelling soil was driven by the beam depth. Overall, the finite element-based deep learning showed the capacity to estimate Δmax in both shrinking and swelling design scenarios for different types of residential footing systems to further understand the characteristic behaviour of shallow residential slab foundations on reactive soils leading to improved designs

Shrink–swell index prediction through deep learning

Growing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices

Mechanical and physical properties and cyclic swell-shrink behaviour of expansive clay improved by recycled glass

The stabilisation of expansive clay subgrades using recycled glass (RG) was proposed, as a sustainable ground improvement technique. Previous studies mainly focused on using RG powder with contents up to 10%, while the current study utilised sand-size particles and up to 40% RG content. Physical properties, compressibility, strength characteristics, and long-term climatic effects on the volumetric behaviour of stabilised clay were investigated. Volumetric responses of stabilised clay were analysed through a constitutive model developed for environmentally stabilised clay. The experimental results revealed that the plasticity of mixtures decreased by 30% as RG content increased to 40%. By using larger RG particles, the strength and bearing capacity increased by about 45% and 130% with the addition of 25% RG. However, adding about 6% of glass powder was sufficient to increase the strength and bearing capacity to about 100% and 200%, respectively. The swell-shrink results suggested that the maximum swelling was achieved in the second cycle in which the clay classification was converted from medium to high expansive clay. The experimental results were also compared and discussed with corresponding data collected from the literature. The outcomes of this study advance the prediction and understanding of the mechanical behaviour of RG-stabilised clay

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems