This thesis presents a framework to conduct a quantity take-off (QTO) and cost estimate within the Building Information Modeling (BIM) Environment. The product of this framework is a model-based cost estimating tool. The framework addresses the cost uncertainty associated with the detailed information defining BIM model element properties. This cost uncertainty is due to the lack of available tools that address detailed QTO and cost estimation using solely a BIM platform. In addition, cost estimators have little experience in leveraging and managing information within semantic-rich BIM models. Unmanaged BIM element parameters are considered a source of uncertainty in a model-based cost estimate, therefore they should be managed and quantified as work items. A model-based system, which assists the estimators to conduct a QTO and cost estimate within the BIM environment, is developed. This system harnesses BIM element parameters to drive work items associated with the parameter’s host element. The system also captures the cost of scope not modeled in the design team’s BIM models. The system consists of four modules 1) establishing estimate requirements, 2) planning and structuring the estimate, 3) quantification and costing, and 4) model-based historical cost data collection. The complete system can produce a project cost estimate based on the 3D BIM Model. This framework is supported by a computation engine built within an existing virtual design and construction (VDC) model review software. The computation engine supports BIM authoring and reviewing BIM data. The Framework’s quantification and costing module was compared to existing methods in a case study. The outcomes of the model-based system demonstrated improved cost estimate accuracy in comparison to the BIM QTO method and improved speed compared to the traditional methods. The framework provides a systematic workflow for conducting a detailed cost estimate leveraging the parameters stored in the BIM models.

Adoption of a streamlined BIM workflow throughout the AEC projects’ lifecycle will provide the project stakeholders with the rich information embedded in the parametric design models. Users can incorporate this rich information in various activities, improving efficiency and productivity of project activities and potentially enhancing accuracy and reducing errors and reworks. Two main challenges for such a streamlined information flow throughout the AEC projects that haven’t been sufficiently addressed by previous research efforts include lack of semantic interoperability and a large gap and misalignment of information between available BIM information provided by design activities and the required information for performing preconstruction and construction activities. This research effort proposes a framework for a knowledge-based system (KBS) that encapsulates domain experts’ knowledge and represents it through modularized rule set libraries as well as connected design automation and optimization solutions. The research attempts to provide a methodology for automatic semantic enrichment of design models as well as automated detailed design to fill the information gap between design and preconstruction project activities, streamlining BIM workflow and enhancing its value in the AEC projects.

Building Information Modeling (BIM) plays an important role in today's construction industry. Models are tools that help stakeholders communicate, visualize building geometry, perform trade coordination and clash detection among others. A less popular aspect of BIM that shows high potential is the quantity take-off (QTO) feature. Yet, its implementation among commercial general contractors (GC) has not received as much attention. The purpose of this study was to identify how the BIM QTO features were being implemented among commercial general contractors, what challenges they faced and how they worked to overcome those challenges. Through a three-step process including semi structured interviews with estimators, preconstruction, BIM and Virtual Design Construction (VDC) managers, valuable insights on the BIM QTO implementation state among general contractors were gathered and analyzed. Links between BIM QTO benefits, project design phases and delivery methods, software, training, leadership and jurisdictions were discussed. The data indicated that BIM QTO's benefits were best leveraged through early general contractor involvement, the adequate contract framework, trained BIM QTO estimators, and early and strategic communication between owners, designers and estimators. The conditions for increased efficiency were discussed along with the solutions to the common BIM-based QTO challenges.

<div>Cost estimation is an integral part of any project, and accuracy in the cost estimation process is critical in achieving a successful project. Manually computing cost estimates is mentally draining, difficult to compute, and error-prone. Manual cost estimate computation is a task that requires experience. The use of automated techniques can improve the accuracy of estimates and vastly improve the cost estimation process. Two main gaps in the automation of construction cost estimation are: (1) the lack of interoperability between different software platforms, and (2) the need for manual inputs to complete quantity take-off (QTO) and cost estimation. To address these gaps, this research proposed a new systems to support the computing of cost estimation using Model View Definition (MVD)-based checking, industry foundation classes (IFC) geometric analysis, logic-based reasoning, natural language processing (NLP), and automated 3D image generation to reduce/eliminate the labor-intensive, tedious, manual efforts needed in completing construction cost estimation. In this research, new IFC-based systems were developed: (1) Modeling – an automated IFC-based system for generating 3D information models from 2D PDF plans; (2) QTO - a construction MVD specification for IFC model checking to prepare for cost estimation analysis and a new algorithm development method that computes quantities using the geometric analysis of wooden building objects in an IFC-based building information modeling (BIM) and extracts the material variables needed for cost estimation through item matching based on natural language processing; and (3) Costing – an ontology-based cost model for extracting design information from construction specifications and using the extracted information to retrieve the pricing of the materials for a robust cost information provision.</div><div><br></div><div>These systems developed were tested on different projects. Compared with the industry’s current practices, the developed systems were more robust in the automated processing of drawings, specifications, and IFC models to compute material quantities and generate cost estimates. Experimental results showed that: (1) Modeling - the developed component can be utilized in developing algorithms that can generate 3D models and IFC output files from Portable Document Format (PDF) bridge drawings in a semi-automated fashion. The developed algorithms utilized 3.33% of the time it took using the current state-of-the-art method to generate a 3D model, and the generated models were of comparative quality; (2) QTO – the results obtained using the developed component were consistent with the state-of-the-art commercial software. However, the results generated using the proposed component were more robust about the different BIM authoring tools and workflows used; (3) Extraction – the algorithms developed in the extraction component achieved 99.2% precision and 99.2% recall (i.e., 99.2% F1-measure) for extracted design information instances; 100% precision and 96.5% recall (i.e., 98.2% F1-measure) for extracted materials from the database; and (4) Costing - the developed algorithms in the costing component successfully computed the cost estimates and reduced the need for manual input in matching building components with cost items.</div>