Construction and demolition waste generation prediction and spatiotemporal analysis: a case study in Sichuan, China

A comprehensive model for quantifying construction waste in high-rise buildings in India

The construction industry plays a vital role in the economic development of any country. Concurrently, the sector also generates enormous quantities of construction and demolition waste (CDW) that damages the ecology causing environmental pollution and deteriorating human health. Recently, various governments and other organizations realized the importance of implementing construction waste management (CWM) practices to attain sustainability in construction. The current decade can be called a pathway for achieving the 2030 agenda for sustainable development goals in which CWM plays an inevitable role. However, accurately quantifying construction waste is necessary to successfully implement any CDW management plan. A detailed literature review for the current research revealed that limited information on the magnitude of construction waste is available in India. Therefore, the current paper proposes a practically viable model to estimate the waste generation index (construction waste generated per total floor area) of high-rise residential buildings in India. The waste quantification is being done based on the project documents and expert interviews. The methodology is later validated through a high-rise building with G + 18 stories located in Kerala, India. The study indicated that a high-rise concrete framed structure generates 122.3 kg m-2 of waste during construction. It was also noted that, concrete, aggregates and blocks constitute 92% of the total waste generated in the project. The developed model can also be used as a cornerstone for establishing a construction waste database at the regional level.

Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks

A waste management strategy needs accurate data on the generation rates of construction and demolition waste (CDW). The objective of this study is to provide a robust methodology for predicting CDW generation in Tanta City, one of the largest and most civilized cities in Egypt, based on socioeconomic and waste generation statistics from 1965 to 2021. The main contribution of this research involves the fusion of remote sensing and geographic information systems to construct a geographical database, which is employed using machine learning for modeling and predicting the quantities of generated waste. The land use/land cover map is determined by integrating topographic maps and remotely sensed data to extract the built-up, vacant, and agricultural areas. The application of a self-organizing fuzzy neural network (SOFNN) based on an adaptive quantum particle swarm optimization algorithm and a hierarchical pruning scheme is introduced to predict the waste quantities. The performance of the proposed models is compared against that of the FNN with error backpropagation and the group method of data handling using five evaluation measures. The results of the proposed models are satisfactory, with mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), determination coefficient, Kling-Gupta efficiency, and index of agreement ranging between 0.70 and 1.56%, 0.01 and 0.03, 0.99 and 1.00, 0.99, and 1.00. Compared to other models, the proposed models reduce the MAPE and NRMSE by more than 92.90% and 90.64% based on fivefold cross-validation. The research findings are beneficial for utilizing limited data in developing effective strategies for quantifying waste generation. The simulation outcomes can be applied to monitor the urban metabolism, measure carbon emissions from the generated waste, develop waste management facilities, and build a circular economy in the study area.

Technology adoption path of construction and demolition waste recycling under governmental subsidy: a theoretical model

To achieve sustainable development, waste recycling is regarded as an ideal method to dispose of construction and demolition (C&D) waste. The economy is seen as the priority factor influencing recycling technology adoption. Hence, the subsidy is generally used to cross the economic barrier. To illustrate the recycling technology adoption path under governmental subsidy, this paper constructs a non-cooperative game model to investigate the impact of governmental subsidy on the C&D waste recycling technology adoption. By taking adoption profit, opportunity cost, and initial adoption marginal cost into consideration, the best time to adopt recycling technology and adoption behavior is discussed in detail in four scenarios. Results show that the governmental subsidy has a positive impact on C&D waste recycling technology adoption, and the subsidy could advance the adoption time of recyclers. If the subsidy proportion can reach 70% of the cost, recyclers will adopt recycling technology at the initial time. The results could contribute to a deeper understanding of C&D waste management by promoting the development of C&D waste recycling projects and also provide references to governments.