During last decade, design for deconstruction (DfD) has attracted the attention of researchers and project managers as an environmentally friendly alternative to the conventional demolition of buildings. Yet, the intensity of raw materials consumption, waste generation and greenhouse gas (GhG) emission in the construction industry proves that current methods of selecting building components have failed to make the deconstruction effectively feasible. Specifically, in the material selection process, most research studies concentrate on assessing environmental and economic aspects while in selecting material for DfD various factors must be considered. To overcome this gap, this study aims to propose a DfD-based material selection model which enables designers to choose materials that make the recyclability and reusability of building components feasible. To this end, the Kano model is first applied to categorize selection criteria identified via a questionnaire. After extracting the weights of criteria by using Fuzzy-Analytical Hierarchy Process, a Technique for Order Preference by Similarity to the Ideal Solution-based multi-criteria decision-making framework is proposed for choosing the best possible alternatives. Based on the research results, the framework enables designers to find decent materials in terms of DfD requirements. A numerical example is also provided to examine the proposed framework for selecting the most appropriate materials for walls.
An accurate estimation of generated electronic waste (e-waste) plays a pivotal role in the development of any appropriate e-waste management plan. The present study aimed to exploit modified adaptive neuro-fuzzy inference system (MANFIS) for the estimation of generated e-waste. There are different parameters affecting e-waste generation, the most important of which need to be identified to achieve the accurate estimation. The MANFIS used for parameter selection involves evaluating multiple choices between twelve initially specified parameters. The MANFIS models with five inputs have the highest mean R2(train) and R2(test) (0.978 and 0.952, respectively, in training and testing stages). According to the results, the best combination of parameters was related to legal imports of electrical and electronic equipment (EEE), smuggling (illegal) imports of EEE, exports of EEE, accumulation of EEE in Tehran, and accumulation of EEE in Iran with RMSE(train) and RMSE(test) of 0.221 and 2.221, respectively. The findings showed that the model with three triangular membership functions had the best performance; R2(train) and RMSE(train) values were 0.981 and 1.371, as well as R2(test) and RMSE(test) values were 0.971 and 1.678, respectively. Finally, the developed model was successfully applied for prediction of monthly e-waste generation in Tehran for thirteen selected electronic items. The obtained consistent results emphasized that appropriate selection of the number of input parameters and their combination, along with identifying optimal structure of MANFIS, provides a proper, simple and accurate prediction of e-waste.
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