Supporting Deconstruction Waste Management through 3D Imaging: A Case Study

Abstract

Conventional demolition approaches of razing a building at the end of its life-cycle generate a large amount of mingled debris, which is difficult to reuse and recycle. Compared to demolition, deconstruction involves disassembling a building systematically and it is a more environmentally friendly alternative. Recent research studies have focused on the transition from demolition to deconstruction to minimize the amount of generated waste and maximize the amount of recycling and reusing material. However, due to tight schedule requirements, extra labor cost, and the lack of drawings and design information, it is difficult for an owner to estimate the cost and duration of deconstruction ahead of time. 3D imaging technologies, such as laser scanning and image-based 3D reconstruction, provide an opportunity to obtain data about as-is conditions at a job site and hence can potentially help in identifying quantities of materials that will be recycled. Existing 3D imaging workflows have two primary limitations: visibility and appearance ambiguity. First, 3D imaging can only capture visible objects before a deconstruction process starts. Also, data captured before deconstruction or at different times during deconstruction can only include a subset of all building components. Second, building components with similar appearances can be made from different materials, resulting in misclassification and errors in quantity estimation. Only a few case studies have discussed how visibility and appearance ambiguity can affect the usage of 3D imaging in deconstruction waste management. In this paper, the authors aim to illustrate the application of 3D imaging during a small-scale deconstruction project in Pittsburgh. Specifically, the authors documented the waste generated during deconstruction manually and by using two different 3D imaging technologies: laser scanning and image-based registration. We then quantified the number of invisible objects and objects with ambiguous appearances at different stages of deconstruction. Through the comparison between the quantity takeoffs from 3D imagery and the ground truth, the paper aims at providing insights on the following questions: 1) How accurate are the quantity estimation and documentation of two 3D imaging technologies (laser scanning and imagery) compared to the actual waste generated? 2) Does 3D imaging capture all components of interest during deconstruction?

A performance evaluation framework for construction and demolition waste management: stakeholder perspectives

“…Although the construction industry had a slow adoption of new and advanced technologies (Mzyece et al , 2019), technology and innovations were expected to improve both the quality of the construction sectors (Maskuriy et al , 2019). New tools such as three-dimensional (3D) imaging (Wei et al , 2019), 3D printing (Tay et al , 2017), four-dimensional (4D) simulation (Kunieda et al , 2019), building information modeling (BIM) (Mzyece et al , 2019), drones, virtual reality and augmented reality were all potential and applied for CDWM practices. These high technologies enabled the project stakeholders to simulate the generation of CDW, resulting in proper understanding and minimizing CDW.…”

“…Based on different understandings of CDW in divergent project contexts, researchers have recommended various strategies to improve the management of CDW. A review of the literature on CDWM strategies shows five main streams, which are raise CDWM awareness among construction stakeholders (Kulatunga et al , 2006; Bourne and Walker, 2008; Ling and Nguyen, 2013; Yuan et al , 2013), supervise the performance of CDWM in the site (Lu and Yuan, 2010; Huang et al , 2018; Mahpour, 2018), apply penalty methods (Ling and Nguyen, 2013; Yuan, 2013b; Tam et al , 2014), integrate value management throughout the project life cycle (Karunasena et al , 2016; Othman and Abdelrahim, 2019) and apply new technologies in CDWM (Tay et al , 2017; Kunieda et al , 2019; Mzyece et al , 2019; Wei et al , 2019).…”