Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia

Yazdani, Maziar; Kabirifar, Kamyar; Frimpong, Boadu Elijah; Shariati, Mahdi; Mirmozaffari, Mirpouya; Boskabadi, Azam

doi:10.1016/j.jclepro.2020.124138

Cited by 220 publications

(78 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to recent research studies, the world’s climate is experiencing a significant increase in average annual temperatures (Mahmoudi et al , 2019a). Meanwhile, the atmospheric concentrations of some greenhouse gases (GHGs), particularly carbon dioxide (CO 2 ), engendered by construction activities, exacerbate climate change (Kabirifar et al , 2020; Yazdani et al , 2020a).…”

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

Self Cite

View full text Add to dashboard Cite

Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering that up to 95% of non-hazardous CDW materials are reusable and recyclable [12][13][14], the loss for the circular economy is tremendously high. Despite the magni-tude of the problem, CDW materials are not effectively managed around the world [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Conceptualising the Circular Economy Potential of Construction and Demolition Waste: An Integrative Literature Review

et al. 2021

View full text Add to dashboard Cite

Traditionally, construction and demolition waste (CDW) materials have been considered to be unwanted, surplus, or wastage materials or materials with zero value. Such a conceptualisation only embraces a negative aspect, which underpins the disposal of reusable and recyclable CDW materials in landfills, thus damaging the circular economy and the environment. The scope of this research was to conceptualise the circular economy potential of non-hazardous construction and demolition waste, which can be used as a resource for advancing the circular economy and sustainability in the built environment. Thus, the abbreviation ‘CEPCDR’ is used for this purpose. The study employs an integrative literature review to understand in depth whether the rationale in the existing CDW definitions advocates for the circular economy. Instead, the literature showed that the current definitions mainly support quantitative, economic, or classification needs, respectively. That is because they lack consideration of the dynamic nature of CDW materials, which embraces the spatial and temporal dimensions. The former involves the geographic context in which the CDW phenomenon eventuates, while the latter concerns the lifecycle of materials. This study contributes to the body of knowledge by conceptualising the CEPCDR using a holistic approach that includes five dimensions: the social, economic, environmental, spatial, and temporal perspectives. Furthermore, the study seeks to drive future research in measuring the CEPCDR.

show abstract

“…With the rapid urbanization and the ever-increasing population worldwide in recent years, an enormous amount of construction and demolition waste has been produced due to the ongoing civil engineering activities [ 1 , 2 , 3 , 4 ], which is causing serious environmental problems and threatening the sustainability of the modern society [ 5 , 6 , 7 ]. Therefore, the reuse of construction and demolition waste has attracted increasing attention globally.…”

Section: Introductionmentioning

confidence: 99%

“…Recycled concrete aggregate (RCA) is one of the most favorable and commonly implemented reutilization forms of construction and demolition waste [ 8 ]. Recycled concrete aggregate (RCA) is obtained by crushing the demolition waste of aged concrete buildings [ 7 , 9 ]. Previous research shows that the engineering properties of RCA are quite different from those of natural aggregates [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Studies on the Direct Shear Behavior of Sand–RCA (Recycled Concrete Aggregates) Mixtures with Different Contents of RCA

Liu

Huang

et al. 2021

Materials

View full text Add to dashboard Cite

Recycled concrete aggregate (RCA) is a typical construction and demolition (C&D) material generated in civil engineering activities and has been widely used as the coarse-grained filler added to sand for roadbed fillings. The effect of RCA content on the mechanical behavior of sand–RCA mixtures is complicated and still not fully understood. To explore the effect of RCA content on the macroscale and microscopic behavior of the sand–RCA mixtures with various RCA contents, laboratory direct shear tests and numerical simulations using the 3D discrete element method were performed. Experimental direct shear tests on sand–RCA mixtures with different contents of RCA were first carried out. Numerical direct shear models were then established to represent the experimental results. The particle shape effect was also considered using a new realistic shape modeling method to model the RCA particles. Good agreement was observed between the DEM simulation and experimental results, verifying the ability of the numerical direct shear models to represent the direct shear behavior of sand–RCA mixtures. The macroscopic responses of both experimental and numerical tests showed that all samples presented an initial hardening followed by a post-peak strain softening. The peak-state friction angles increased with the RCA content for samples under the same vertical stress. The effect of RCA content on the microscopic behavior based on DEM simulation was also found. The microscopic properties of RCA–sand mixtures, such as coordination numbers, PDFs and contact force transformation features, were analyzed and related to the macroscopic results.

show abstract

Improving construction and demolition waste collection service in an urban area using a simheuristic approach: A case study in Sydney, Australia

Cited by 220 publications

References 75 publications

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Conceptualising the Circular Economy Potential of Construction and Demolition Waste: An Integrative Literature Review

Experimental and Numerical Studies on the Direct Shear Behavior of Sand–RCA (Recycled Concrete Aggregates) Mixtures with Different Contents of RCA

Contact Info

Product

Resources

About