Cost prediction model for building deconstruction in urban areas

Many U.S. cities have suffered economic decline and contain widespread residential, commercial, and industrial property abandonment. Such abandonment results in negative economic, social, and environmental consequences in urban areas. Demolition and landfilling are prevalent methods to remove these abandonments while generating large amounts of construction and demolition (C&D) debris. Unlike demolition, deconstruction is a sustainable approach to systematically disassembling buildings which allows for over 80% material reuse and recycling. Increased costs are a prevailing concern that prevents decision makers from implementing deconstruction; however, this concern is problematic because current cost prediction methods for deconstruction are insufficiently accurate. To address this problem, the authors have developed a novel cost prediction model using case-based reasoning, an artificial intelligence based technique. The paper elaborates on the model development and demonstrates the model application through a real world deconstruction case in the state of Michigan. Results indicate the accuracy of the new prediction model is greater than 95% and show a lower net cost of deconstruction than demolition. Findings suggest an additional option of design for deconstruction for green buildings in the construction industry. Findings implicate a broader construct of sustainable urban transformation where a full supply chain of deconstructed materials emerges. (C) 2017 Elsevier Ltd. All rights reserved.