End of life of buildings: three alternatives, two scenarios. A case study

Martínez, Eva Iglesias; Núñez, Yolanda Rodríguez; Sobaberas, Elena

doi:10.1007/s11367-013-0566-4

Cited by 43 publications

(32 citation statements)

References 4 publications

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“…In particular, demolition waste (DW) generated from building or structure demolition has been reported to account for approximately 70–90% of the total C&D waste [ 5 , 6 ]. Building demolition in cities is required for new construction because of land shortages [ 7 ]. For C&D waste management, calculating C&D waste generation has been recognized as a useful method.…”

Section: Introductionmentioning

confidence: 99%

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Cha

Moon

Kim

et al. 2020

IJERPH

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Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson’s correlation coefficient) = 0.691–0.871, R2 (coefficient of determination) = 0.554–0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

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Section: Introductionmentioning

confidence: 99%

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Cha

Moon

Kim

et al. 2020

IJERPH

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“…The uncertainty generated by the lifespan of a building, it also complicates raised scenarios during the end of life, being this stage the greater uncertainty of all [19,20].…”

Section: Introductionmentioning

confidence: 99%

Use of LCA as a Tool for Building Ecodesign. A Case Study of a Low Energy Building in Spain

et al. 2013

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This paper demonstrates how to achieve energy savings in the construction and operation of buildings by promoting the use of life cycle assessment techniques in the design for new buildings and for refurbishment. The paper aims to draw on the application of a specific methodology for low energy consumption, integrated planning, environmental performance evaluation of buildings, and design for sustainability and LCA techniques applied to buildings. The ENergy Saving through promotion of LIfe Cycle assessment in buildings (ENSLIC) methodology based on LCA for use in an integral planning process has been promoted to stakeholders who require a means to optimize the environmental performance of buildings. Feedback from the stakeholders has facilitated the creation of simplified LCA guidelines, a systematic approach guiding the user through the alternative options regarding software choices, their strengths and weaknesses, the databases available, the usefulness of different indicators, aggregation, definition of limits and options for simplifying the process. As a result, this paper presents the applied results of a case study where this methodology is implemented serving as an energy savings evaluation tool for decision makers, end-users, professionals involved in the different stages of construction, etc. Finally, it is demonstrated how LCA can facilitate comparisons between different buildings, showing the influence of all variables on a building's life cycle environmental impact and showing the potential for energy savings. Removing market barriers to sustainable construction is actually stricter and this is good news for promoting higher energy efficiency in buildings.

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“…▪ To compare the impacts of various NPS, by showing the benefits of re-use and recycling with respect to disposal: (i) recycling vs. disposal (Craighill and Powell, 1999;Blengini, 2009;Butera et al, 2015;Penteado and Rosado, 2016;Vitale et al, 2017); (ii) re-use vs. recycling vs. disposal (Balazs et al, 2001;Martínez et al, 2013;Xia et al, 2020); (iii) recycling vs. incineration (Rivela et al, 2006); (iv) recycling vs. disposal vs. incineration (Jambeck et al, 2007;Ortiz et al, 2010); (v) recycling vs. downcycling vs. disposal (Di Maria et al, 2018).…”

Section: Lca and Npsmentioning

confidence: 99%

“…Many approaches address the causes and strategies for the reduction of CDW, but there re https://doi.org/10.1016/j.wasman.2021.03.047 0956-053/© 2021. mains a lack of studies that investigate the environmental consequences of preventing CDW production. Moreover, research endeavours over recent decades (Craighill and Powell, 1999;Blengini, 2009;Ortiz et al, 2010;Martínez et al, 2013) have been largely focused on showing the environmental benefits of recycling CDW rather than on its actual reduction. Consequently, there is a wide gap in the literature regarding the environmental impact related to CDW prevention, which is mainly due to the lack of quantitative environmental assessment methods that include prevention.…”

Section: Introductionmentioning

confidence: 99%

An LCA-based model for assessing prevention versus non-prevention of construction waste in buildings

et al. 2021

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Waste generated by the Construction Sector represents an environmental problem in many countries. To achieve increasingly eco-efficient waste management, Life Cycle Assessment (LCA) provides an objective method for the quantification of the potential impact that waste management exerts on the environment. Traditionally, LCA has focused on the evaluation of non-prevention scenarios once the waste is generated, mainly by showing the benefits of recycling vs. disposal. Consequently, the literature has hardly addressed the positive environmental impacts caused by waste prevention, that is, the reduction at source, which constitutes the preferred option of any waste management hierarchy. Therefore, this study proposes a model to simulate the environmental performance of the prevention vs. the non-prevention of construction waste production. The model is applied to an urban system of residential buildings in Spain. The results provide evidence of the environmental benefits achieved with the prevention scenario. The prevention scenario reduces the construction waste generated in the non-prevention scenarios by up to 57%. Furthermore, it allows a potential reduction of up to 4.6 and 171.1 times the impact caused by the disposal scenario; and up to 1.7 and 8.3 times those of the recycling scenario. The model can be implemented in other contexts with other reference buildings, and enables the environmental benefits of reduction strategies to be studied, thereby providing a tool to guide and support decision-making during the building design stage. Moreover, the results obtained can help professionals and policymakers to incorporate effective construction waste prevention measures in waste prevention plans and programs.

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End of life of buildings: three alternatives, two scenarios. A case study

Cited by 43 publications

References 4 publications

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Use of LCA as a Tool for Building Ecodesign. A Case Study of a Low Energy Building in Spain

An LCA-based model for assessing prevention versus non-prevention of construction waste in buildings

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