Handling missing data for construction waste management: machine learning based on aggregated waste generation behaviors

Abstract

In the era of big data, data is increasingly driving the construction waste management (CWM) for minimizing the impacts on the environment and recycling construction materials. However, missing data, led by various information barriers, often undermines the decision-making and hinders effective CWM. This paper applies aggregated behavior-based Machine Learning (ML) methods to handling the project-level ‘Missing Not At Random’ (MNAR) data by using aggregated waste generation behaviors as a case study. First, we define a set of 821 waste generation behavioral features based on waste big data, then screen the indicative and decisive behaviors using automatic feature selection. Then, the most predictive ML method, trained via data of 2,451 construction projects in 2011–2016 in Hong Kong, is selected for handling the MNAR data. The experiments showed that the prediction of project missing data was satisfactory (validation F1 = 0.87, test F1 = 0.80). The contribution of this paper is to pinpoint the potential of waste big data in portraying project behaviors for more value-added applications, at the same time, to present a handling method for MNAR data that is automatic, fast, and low-cost from the CWM practitioner's perspective.