Machine Learning in Hazardous Building Material Management: Research Status and Applications

Abstract

Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction.

WaRM: A Roof Material Spectral Library for Wallonia, Belgium

“…Spectral libraries provide spectral properties of materials and components in a wide range of wavelengths. In urban areas, the exploitation of surface and building spectral properties is of increasing importance for a broad range of applications: urban climatechange modeling [1]; assessment of the heating/cooling effects of urban materials in urban heat-island mitigation strategies [2,3]; or identification of surface materials such as roof materials, for mapping and inventories [4], for weathering-condition status detection [5], detection of specific/dangerous materials such as asbestos [6,7], and detection of solar panels [8] or metallic roofs [9].…”

Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings

“…The digital opportunities are expected to accelerate the circular transition in CDW concerning delivering data-driven insights for effective material management [3,15]. Various analytical applications were developed to serve the purposes of waste identification, source separation, and collection, the objectives specified by the EU CDW Management Protocol [16]. Hybrid statistical learning, machine learning, and neural networks were reported effectively for facilitating in situ material management with adequate quality input data [8,16].…”

“…Various analytical applications were developed to serve the purposes of waste identification, source separation, and collection, the objectives specified by the EU CDW Management Protocol [16]. Hybrid statistical learning, machine learning, and neural networks were reported effectively for facilitating in situ material management with adequate quality input data [8,16]. Among predictive algorithms, artificial neural networks (ANN) have wide adoption in the CDW domain given their flexibility in supervised and unsupervised learning, high performance, the capability of processing multiple data types, and representing data relationships with numerous levels of abstraction, etc., [12,17].…”

A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories

“…For example, pre-demolition audits for the certain scale of the non-residential building are mandatory in Flanders [12]. With an increasing number of emerging databases and extensive documentation, the goal of tracing in situ hazardous building materials through employing data mining on registered records could be attained [13].…”