Elsevier

Transportation Geotechnics

Volume 24, September 2020, 100365
Transportation Geotechnics

Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes

https://doi.org/10.1016/j.trgeo.2020.100365Get rights and content

Highlights

  • Permanent deformation and of RCA/RAP blends in different temperatures was investigated.

  • Most of the investigated blends exhibited stable behaviour in different temperatures.

  • Mixing up to 40% RAP with RCA mitigated the permanent strain of RAP at higher temperatures.

  • An ANN model was developed for prediction of permanent strain of blends.

  • Number of the cycles had the most influence on the permanent strain.

Abstract

The aim of this research is to study the effect of temperature and stress levels on the permanent strain of blends of two types of recycled waste materials, namely recycled concrete aggregate (RCA) and reclaimed asphalt pavement (RAP). RAP was mixed with RCA in different percentages of up to 80% by dry mass. A three-stage repeated load triaxial (RLT) testing procedure was employed for evaluation of permanent strain of RCA/RAP blends in different stress paths and temperatures. The tests were conducted at different temperatures of 5 °C, 20 °C (room temperature), and 50 °C, to assess the behavior of blends in a wide range of temperatures. The addition of RAP was found to result in an increase of permanent strain of the blends. As the RAP content increased in the blends, the accumulation of permanent strain increased at 50 °C. The specimens with up to 40% RAP exhibited stable behavior at 50 °C in all stages of the test. With increasing RAP content, the permanent strain behavior of the specimens was found to improve for the same blends at 5 °C. Based on the results of the experiments, an artificial neural network (ANN) model was developed for prediction of permanent strain of blends and investigating the impact of the test variables. Effect of RAP content, temperature, deviator stress, and number of cycles was evaluated. Although the RAP content and temperature were found to have a significant effect on the permanent strain, the number of cycles in the RLT test was found to be the more predominant influencing factor.

Introduction

The increasing demand for natural aggregates as a result of industrial developments and population growth has necessitated the recycling of waste materials for usage as construction materials [41]. Construction activities produce a large amount of waste, which is typically transferred to landfills and stockpiled. Construction and demolition (C&D) waste is referred to as the waste produced by demolition of building and road infrastructures, which accounts for approximately half of the waste generated worldwide [5], [8]. C&D materials are being widely used in different applications in civil engineering projects such as construction of roads, pavement, and footpaths. Reusing C&D materials in the construction industry is considered a sustainable strategy, with several beneficial effects such as reducing the volume of landfills and mitigating the environmental concerns associated with landfilling [41].

Recycled concrete aggregate (RCA) is a major source of C&D waste, which is obtained from the demolition of concrete structures. Approximately 8.7 million tons of RCA are stockpiled annually in Australia [6]. RCA is widely being used in pavement applications such as construction of pavement base and subbase layers [7], [10], [25]. The strength properties of RCA have been found to be superior than other forms of C&D aggregates and natural aggregates [9].

Reclaimed asphalt pavement (RAP) is another type of C&D materials that is generated as a result of and rehabilitation and reconstruction of pavements [39]. More than 1.2 million tons of RAP is stockpiled annually in Australia [9]. Most of the removed asphalt from pavements are currently used in the production of hot-mix asphalt [13]. In some cases, up to 80% of RAP has been used for production of hot-mix asphalt [4]. The recommended range of RAP for usage in the hot-mix asphalt is about 20–50% [23]. The remaining RAP is usually stockpiled in landfills which results in several environmental and economic issues. To mitigate the environmental and economic concerns associated with stockpiling of RAP, attempts have been made to incorporate the RAP as granular materials in the pavement base and subbase layers [9], [14], [17], [24]. The usage of RAP in different applications could reduce the high demand for virgin aggregates and results in significant economic savings. However, results of several research studies have indicated that RAP does not fulfill the structural and strength requirements to be used solely in the pavement base and subbase applications [9]. Therefore, extensive research has been conducted to improve the strength properties of RAP by blending it with other materials or by stabilizing the RAP using different binders [22], [32], [39].

As a sustainable solution for re-using the stockpiled RAP, several studies have considered the replacement of natural aggregates with RAP for use in pavement layers [27], [29], [31]. In this regard, Maher et al. [27] conducted laboratory and field investigations to examine the usage of RAP in the pavement base/subbase layer and stated that RAP had higher resilient modulus compared to natural aggregates. Bennert et al. [12] concluded that mixing RAP with natural aggregates increased the permanent deformation of blends. Arshad and Ahmed [4] investigated the resilient modulus of RCA and natural aggregates mixed with 50 and 75% RAP. Results of their experiments indicated that mixing RAP with natural aggregates increased the resilient modulus of the blends. It was also concluded that increasing the percentage of RAP resulted in an increase in the accumulated permanent strain of the blends. Arulrajah et al. [9] examined the potential of using RCA and RAP blends as for construction of pavement base and subbase, and concluded that the blends with 15% of RAP only satisfied the requirements for usage in pavement subbase layer.

The permanent deformation or rutting of pavement layers is an important concern in transportation geotechnics. The materials used in the pavement layers should have an acceptable level of deformation to ensure the long-lasting and efficient design of pavement layers. Permanent deformation principally occurs due to the repetitive loading of traffic over a long period of time. The repeated load triaxial (RLT) test is generally conducted for evaluating the permanent deformation behavior of pavement materials. In RLT test, a large number of load cycles with a constant magnitude are applied to the specimens, and the vertical permanent deformation is measured.

There are several factors such as material type, gradation, moisture content, dry density, stress level, and environmental factors such as freeze-thaw cycles and temperature that could affect the permanent deformation properties of pavement materials. Among these factors, less attention has been devoted to environmental factors and their impact on permanent deformation. RAP particles contain an asphalt coating influencing their characteristics in different temperatures. Soleimanbeigi et al. [37] observed an increase of 0.08% in plastic strain of RAP per 1 °C increase in temperature. Miao et al. [30] investigated the effect of temperature on the permanent deformation of natural aggregates and a combination of RAP with natural aggregates with different gradations (gap graded, continuous graded, and coarse graded). Miao et al. [30] noted that the addition of RAP decreased the rutting potential of RAP and natural aggregate blends. In addition, they stated that temperature had an influence on the rutting potential of both natural aggregates and natural aggregates mixed with RAP. By conducting an experimental study, Domitrović et al. [16] evaluated the effect of fourteen freeze-thaw cycles on the resilient modulus and permanent deformation of crushed limestone mixed with 0, 20%, 35%, and 50% of RAP, and found that freeze-thaw conditioning decreased the resilient modulus and increased the permanent deformation of blends with varying rates. Considering the literature, limited studies have been conducted to evaluate the effect of temperature on the permanent deformation of C&D materials. An extensive study is lacking to evaluate the permanent deformation of RAP blends in different temperatures.

Due to the cost and time associated with performing experimental tests, several attempts have been made to employ numerical approaches for explaining the permanent deformation and rutting potential of pavement materials. Pavement materials exhibit complicated behavior under different loading conditions and environmental conditions. Explaining the rutting behavior of pavement materials is a complex problem in transportations geotechnics due to inclusion of several factors affecting the system behavior. Recently, artificial intelligence techniques such as artificial neural network (ANN), support vector machine, and adaptive neuro-fuzzy inference system have been used for solving several complicated problems in geotechnical engineering [11], [20], [26], [28], [36], [40]. Artificial intelligence approaches learn from the data and identify the pattern in the data to generate a prediction model without requiring any information about the problem investigated.

This study aims at investigating the permanent deformation behavior of RCA/RAP blends at the temperature of 5 °C, 20 °C, and 50 °C. A three-stage repeated load triaxial (RLT) testing procedure has been employed for evaluating the rutting potential of RCA and RAP blends. The behavior of blends has been classified based on the shakedown criteria considering different RAP contents and temperatures. Based on the results of experiments, an ANN model has been developed for prediction of permanent strain of RCA/RAP blends and performing sensitivity analysis.

Section snippets

Materials and methods

In this study, an experimental investigation was conducted to evaluate the permanent deformation of RCA/RAP blends. RCA and RAP were collected from recycling sites in the state of Victoria, Australia. The utilized RCA and RAP had a maximum particle size of 20 mm and 10 mm, respectively. RAP was mixed with RCA in different percentages of 0, 20%, 40%, 60%, and 80% by dry mass. Table 1 summarizes the blends of the materials used in this research. A series of laboratory tests were undertaken to

Permanent deformation behavior of RCA/RAP blends

The relationship between permanent strain and number of load cycles for different blends is presented in Fig. 4. As expected, the RCA had the lowest permanent strain among all blends. RCA experienced accumulation of permanent strain in initial cycles, and then the permanent strain became almost constant in the rest of the cycles. Increasing the RAP content increased the permanent strain of RCA/RAP blends. For blends with 20% RAP, the permanent strain was the lowest at the temperature of 20 °C.

Designing ANN predictive model

This section presents an ANN predictive technique for prediction of the permanent strain of RCA/RAP blends. ANNs transfer the data in different layers to form a relationship between the input variables and the output. Multi-layer perceptron neural network is a form of ANN with three layers, namely, input layer, hidden layer, and output layer [19]. Each of the layers contain processing elements known as neurons or nodes. The information is transferred between the layers through inter-connection

Conclusion

In this research, the permanent strain behavior of different combinations of RCA and RAP was investigated at different temperature. Effect of different temperatures, i.e., 5 °C, 20 °C, and 50 °C, on the permanent strain of blends was examined. A three-stage RLT test was proposed to investigate the permanent strain of blends in different stress levels. In addition, an ANN model was developed for prediction of the permanent strain of RCA and RAP blends and examining the influence of each input

CRediT authorship contribution statement

Behnam Ghorbani: Conceptualization, Project administration, Methodology, Formal analysis, Investigation, Writing - original draft. Arul Arulrajah: Conceptualization, Supervision, Funding acquisition, Methodology, Writing - review & editing. Guillermo Narsilio: Conceptualization, Funding acquisition, Writing - review & editing. Suksun Horpibulsuk: Conceptualization, Funding acquisition, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported under Australian Research Council’s Linkage Projects funding scheme (project number LP170100072). The second and fourth authors would also like to acknowledge the support from National Science and Technology Development Agency (NSTDA), Thailand under Chair Professor program (P-19-52303).

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