Accurate as-built information is required to operate, maintain, and adapt existing buildings. Scan-to-BIM has become a feasible approach for collecting and modelling 3D as-built information and has three phases: (1) scanning, (2) registration, and (3) modelling. This paper focuses on the modelling phase, which can currently be conducted either manually or semi-automatically. As-built conditions of a building are surveyed, and the geometry is modeled in a series of modelling scenarios. For each trial, geometric dimensions of the BIMs are compared to ground truth dimensions. This paper assesses the impact of levels of automation and modeller training on the accuracy and precision of generated BIMs. Quantitative models are developed for modelling scenarios using empirical datasets. Lastly, the impacts of degrees of accuracy are discussed. This study provides insight into the dimensional certainty of BIMs generated by Scan-to-BIM and helps decision-makers assess the risk of decisions made based on this information.

This paper presents a case study of a hands-on exercise to improve students' project management skills on repetitive infrastructure/modular projects. The exercise was to construct a large foam-board model of the University of Waterloo campus, involving over 40 buildings. The exercise design involved training on the integrated life-cycle decisions of a project, including quantity take-off, bidding, repetitive scheduling, assigning workers to single-skill trades, site organization, coordination, progress follow-up, and quality control. Student groups bid against each other in developing least-cost and least-waste execution plans under time and resource constraints. After execution, students gained firsthand experience on reasons for deviations and areas for improvement. Overall the exercise was fun, complemented theoretical concepts, and closely simulated how trades interact. Students also gained a better understanding of the challenges in managing repetitive projects and the role of efficient planning to reduce execution problems. The paper presents the design and implementation of this exercise, discusses its contributions to student learning, and provides guidelines to make hands-on exercises a success, particularly when a large number of participants are involved.

Use of computational algorithms in building information modelling (BIM) software such as Autodesk Revit and Rhinoceros comprise rule-based logical data flows that automate processes and computationally explore large decision space domains. The use of computational algorithms has traditionally been used by architecture firms but is starting to also grow in popularity throughout mainstream construction for automation of design tasks, as-built analysis, and for creation of parametric models. This paper demonstrates how computational algorithms can be used to support digital twins in construction and how these workflows can be used to converge on optimal and heuristic solutions to challenging problems. Two examples are explored in this paper. The first is a computational algorithm addressed at a complex geometric challenge in the processing of architectural cladding panels for manufacturing. The second example is a computational workflow comprised of algorithms that extract and process metadata in BIM for automated egress path compliance checking. Lessons learned from these examples demonstrate the power of computational algorithms for complex and highly repetitive design processes. Computational algorithms are proven herein to explore large decision space domains efficiently, and pre-emptively catch costly design/analysis errors.

Effective pre-project planning is critical for maximizing the success of a project. The effectiveness of pre-project planning is directly tied to the completeness of project scope definition. Ideally, the most effective pre-project planning happens when the definition of all project scope elements is refined to a high level of granularity, accuracy, and prescience. This is not always possible however, since scope definition improvement is achieved through auxiliary inspections and experiments to collect additional information, which can be costly. It is also not valuable to improve scope definition for all project scope elements, since expected benefits of this knowledge will not outweigh costs for certain elements. The objective of this paper is to develop a decision making methodology to optimize the process of project scope definition improvement. This methodology is developed based on the concept of value of information, which functions as the metric for investigating whether scope definition improvement is valuable for a given scope element. For cases where scope definition improvement should be made, optimization of required information is explored along with selection of the most valuable information collection option, which provides the optimum required amount of information. This methodology is demonstrated on a building adaptive reuse case study for improving the definition of contamination and hazardous materials (specifically asbestos-containing materials) as a scope element.

Preproject planning is becoming a widespread best management practice. Potentially, it can be an extremely time and resource intensive practice and, as such, presents challenges in the management, allocation, and prioritization of the resources applied to it. This research presents a novel solution to this challenge. The solution prioritizes preproject planning activities using the value-of-information analysis and simple optimization methods applied to a modified project definition rating index (PDRI). First, scope definition elements are identified from a PDRI, and expected cost-to-benefit ratios for each element are quantified. Then, an optimized resource allocation is performed to prioritize the elements in the scope definition improvement process. We demonstrate this framework in a case study for adaptive building reuse because these are complex projects whose overall success can be directly linked to effective preproject planning using constrained resources. Results of this case study find that optimizing preproject planning using the proposed methodology resulted in approximately $127,000 of cost-savings, representing 5% of the total project cost. (C) 2020 American Society of Civil Engineers.

With increased computing power to render 3D models and affordability of as-built data acquisition technologies, new techniques for enhancing the quality of pre-project planning of adaptive reuse projects can be investigated. The main objective of this research is to present a decision making methodology to select the optimum effort using 3D asbuilt point clouds to develop a BIM of an existing building. Three value proposition and risk reduction areas are investigated: (1) dimensional, (2) material, and (3) disassembly. To measure the cost and value of developing models with corresponding value propositions, a small case study is conducted. Three different Model Detail Levels (MDL) are defined for adaptive reuse projects, and corresponding models are developed for each of them. The value of each model is considered based on its ability to provide information about dimension, materials, and fixtures within an existing building. The cost of the scan-toBIM process includes costs of purchasing 3D acquisition device, buying BIM modeling software license, scanning and registration, and developing BIM using scan-to-BIM techniques.

Adaptive reuse of buildings is considered a superior alternative for the renewal of today’s built environment. However, little research has been done for assessing adaptive reuse building projects in terms of life cycle and Circular Economy. Because of the great impact that the building industry has on the environment, failing to optimize buildings’ useful life can result in their residual life cycle expectancy not being fully exploited, and with it, wasting the resources embedded therein, such as Primary Energy Demand. The aim of this study is to develop and test a methodology to analyze the net environmental impacts as well as the building’s cost performance of an adaptive reuse project. This paper focuses on the analysis of the structural system. Results show that the adaptive reuse of the building structure produces a considerable decrease in the environmental impacts and the construction building cost. Distribution of cost among materials and equipment is different from those for a new building, while the distribution cost for labor remains the similar. This study objectively demonstrates the considerable benefits of the adaptive reuse of the structure of an existing asset. In contrast, the non-structural building subsystems have been identified as an area with high potential for improving the existing inefficiencies during the adaptive reuse process.