If these walls could talk: Automated performance measurement for building modeling decisions using data analytics
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Building information modeling (BIM) is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, high-quality building models do not happen by accident, but rather because of a managed process that involves several participants from different disciplines and backgrounds. Throughout this process, the different priorities of design modelers often result in conflicts that can negatively impact project outcomes. There is a need for effective management of the modeling process to prevent such unwanted outcomes. Effective management of this process requires an ability to closely monitor the modeling process and correctly measure the modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of BIM tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. This research improves upon previous efforts by presenting a novel application programming interface (API)-enabled approach to automatically collect detailed design development data directly from BIM software packages and efficiently calculate several modeling performance measures. The primary objective of this research is to create and examine the feasibility of a proposed automated design performance monitoring framework. The proposed framework provides the following capabilities: (a) non-intrusive and cost-effective data acquisition for capturing design development events in real time, (b) scalable and high-speed ingestion for the storage of design modeling data, (c) objective measurement of designer performance and estimating levels of effort required to complete design tasks, and (d) identifying optimal design teams using empirical performance information. In chapter 3, the utilization of modeling development information embedded in design log files that are produced by Autodesk Revit is proposed as a rich source of performance data. To this end, generalized suffix tree (GST) data structures are utilized to find common, frequent command sequences among Revit users. In addition to identifying the common command execution patterns, the average time it takes the selected modelers to execute command sequences is calculated. The obtained results demonstrate that there is a statistically significant difference between the modelers in terms of the time it takes them to conduct similar modeling tasks. Chapter 4 utilizes modeling software solution’s APIs to automatically collect and store timestamped design development information. The proposed passive data recording approach allows for the real-time capture of comprehensive user interface (UI) interaction and model element modification events. The proposed framework is also implemented as an Autodesk Revit plugin. An experiment is then conducted to verify the accuracy of this plugin. Throughout this experiment, manual recordings of model development events were compared against the automatically generated plugin output. Chapter 5 outlines the details of an approach to identify the optimal design modeling team configuration based on automatically collected performance data. To this end, an experiment is conducted to capture data using the developed Revit plugin. Experiment participants’ individual production rates are estimated to establish the validity of the proposed approach to identify the optimal design team configurations. The presented approach uses the earliest due date (EDD) sequencing rule in combination with the critical path method (CPM) to calculate the maximum lateness for different design team arrangements. The primary contributions of this study to the state of knowledge are as follows: (a) proposing a tailored string mining algorithm that is capable of extracting meaningful information from timestamped design development data, (b) developing a framework based on APIs to automatically collect design modeling data, and (c) creating a mathematical model to estimate design modeling project completion times based on individual performance data and project requirements. This study contributes to the state of practice by (a) allowing design project managers to gain an unprecedented insight into the evolution of a building model using the information embedded in design log files, (b) helping design managers to acquire progress information without the need to manually record and report data, and (c) enabling design managers to identify an optimal modeling team arrangement based on automatically captured, quantitative performance information.