Enhancing Demolition Waste Management with Neural Network Forecasting

A study published in the journal Sustainability explores using artificial neural networks (ANNs) to forecast the generation of 10 common types of demolition waste from buildings. Accurately quantifying expected waste quantities by material supports more efficient logistics, handling, recycling, and regulation by waste managers. The research analyzes demolition data from 150 buildings in major South Korean redevelopment zones to develop specialized ANN prediction models for each waste category.

Study: Optimizing Demolition Waste Management with Neural Network Forecasting. Image credit: DedMityay/Shutterstock.
Study: Optimizing Demolition Waste Management with Neural Network Forecasting. Image credit: DedMityay/Shutterstock.

Construction and demolition activities produce over 10 billion tons of debris annually worldwide, accounting for 67% of all waste generation. Effective construction waste management is thus crucial for sustainability. In South Korea, large-scale urban renewal projects necessitate demolishing many outdated structures, producing substantial waste streams demanding careful management. Strategies for waste control and resource efficiency rely on accurate predictive data regarding expected volumes of specific waste types. 

Advanced AI modeling techniques like ANNs provide promising tools for demolition waste forecasting. By learning from training data, neural networks can capture complex multivariate relationships and provide accurate projections. However, previous attempts at specialized forecasting for distinct waste categories via single variable sets produced mixed results requiring refinement. This study focuses specifically on leveraging the versatility of neural networks to develop optimized models for projecting generation rates across ten common demolition waste types pertinent to the South Korean context.


The study compiled data on gross floor area, structure, wall types, and other building features alongside recorded waste generation volumes for ten critical materials (mortar, concrete, block, brick, roof tile, wood, plastic, steel, slate, soil) from demolishing 150 structures. Preprocessing standardized the multivariate data across categories. Input variables were selected uniquely for each waste type based on correlation analysis rather than assuming identical inputs. This captured category-specific relationships and ensured model relevance. 

The authors employed simple single hidden layer feedforward ANN architectures, adjusting model hyperparameters like hidden nodes and activation functions through validation to determine the optimal specifications per waste type. The neural networks could specialize based on each material's particular input variables and data trends. Rigorous leave-one-out cross-validation assessed model performance—the specialized variable selection and model tuning approach aimed to improve accuracy over conventional generalized modeling.

Key Findings  

The optimized ANN models achieved excellent predictive accuracy for all ten waste types, with validation and test R2 averaging 0.970 and 0.952 across categories - a significant performance gain over past studies. The floor area most strongly correlated with higher generation universally, indicating that demolition scale links directly to waste volumes. Beyond this, the influence of additional input variables like structure and wall type differed depending on material type. For instance, unsurprisingly, brick wall prevalence strongly predicted masonry debris generation. 

The customized input data and tailored model hyperparameters for each waste enabled performance improvements by accounting for their distinct predictive factors. The capability to specialize models highlights the power of neural networks for specialized forecasting applications, given deliberate design considerations. Despite constraints like limited data and variability, the approach delivered consistent, material-specific predictions within a simple, efficient computational framework.

Broader Implications

The study results showcase the utility of AI techniques for advancing demolition waste management practices. The proposed neural network approach could equip waste planners and policymakers to understand upcoming waste streams better and plan logistics accordingly regarding storage, transport, recycling channels, etc. The models can help optimize waste diversion targets and build processing facilities by anticipating waste volumes. If implemented widely, more strategic demolition waste handling and regulation could conserve resources and landfill space.

The concept of selective input variables and model specifications for waste type specificity has broad applicability for waste analytics. It could be expanded to other materials or adapted for regional contexts. As urban renewal escalates globally, advanced modeling is instrumental for next-generation waste control. The study also demonstrates that trained ANNs require minimal data for rapid projections - a significant advantage for waste planning bodies where extensive datasets are usually unavailable. The proposed technique is up-and-coming as a convenient supplementary tool for anticipatory demolition waste governance.

Future Research

While results were excellent overall, the researchers highlight model refinement for specific debris categories and incorporating more predictive variables like demolition methods or climate as areas for improvement. Exploring ensemble approaches combining optimized neural networks could provide an additional predictive edge. However, as AI capabilities grow exponentially, advanced modeling for forecasting waste streams offers immense potential if thoughtfully nurtured. Waste management systems stand to gain immense intelligence and responsiveness from the machine learning revolution.

Journal reference:
Aryaman Pattnayak

Written by

Aryaman Pattnayak

Aryaman Pattnayak is a Tech writer based in Bhubaneswar, India. His academic background is in Computer Science and Engineering. Aryaman is passionate about leveraging technology for innovation and has a keen interest in Artificial Intelligence, Machine Learning, and Data Science.


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