Managing demolition wastes using GIS and optimization techniques
Egypt has experienced vast urbanization and expansion in existing highways, leading to much demolition waste. Construction and demolition waste constitutes around half of the total municipal waste. So, these wastes must be appropriately managed to decrease their negative impacts. As a result, this r...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Cleaner Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666790824001320 |
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| Summary: | Egypt has experienced vast urbanization and expansion in existing highways, leading to much demolition waste. Construction and demolition waste constitutes around half of the total municipal waste. So, these wastes must be appropriately managed to decrease their negative impacts. As a result, this research presents a framework that automatically detects demolishing wastes' location and optimizes utilized resources in demolition and transportation processes. It consists of three main components: the GIS module, the optimization module, and the decision-making module. Based on the raster image of the study region, the framework detects existing buildings that should be demolished to enable highways’ expansion. The GIS module is designated to quantify the volume of demolition waste in the studied area using ArcGIS Pro software. The optimization module determines the near-optimum combination of resources involved in the demolition process and waste transportation. These resources include labor crews, excavators, and trucks. The module performs multi-objective optimization using a non-dominated sorting genetic algorithm (NSGA-II). The optimization module considers three objectives in demolition and transportation processes: time, cost, and energy consumption. Finally, the decision-making module is developed to rank the Pareto front solutions. The Entropy Weight Method (EWM) is used to identify the weights of the three criteria. The estimated weights for time, cost, and energy consumption are 38.6%, 17.3%, and 44.1%, respectively. Subsequently, the TOPSIS technique is utilized to normalize, rank, and select the best solution. The proposed framework is applied to an actual case study that involves expanding the ring road project in Cairo to demonstrate its main features. |
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| ISSN: | 2666-7908 |