Product Lifecycle Management (PLM) solutions have been serving as the basis for collaborative product definition, manufacturing, and service management in many industries. They capture and provide access to product and process information and preserve integrity of information throughout the lifecycle of a product. Efficient growth in the role of Building Information Modeling (BIM) can benefit vastly from unifying solutions to acquire, manage and make use of information and processes from various project and enterprise level systems, selectively adapting functionality from PLM systems. However, there are important differences between PLM's target industries and the Architecture, Engineering, and Construction (AEC) industry characteristics that require modification and tailoring of some aspects of current PLM technology. In this study we examine the fundamental PLM functionalities that create synergy with the BIM-enabled AEC industry. We propose a conceptual model for the information flow and integration between BIM and PLM systems. Finally, we explore the differences between the AEC industry and traditional scope of service for PLM solutions.

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An important set of information provided through Building Information Modeling (BIM) platforms are quantitative properties of design elements and assemblies. The capability to extract or deduce such quantitative properties from explicit and implicit model information is essential for bidding, procurement, production planning, and cost control activities in the AEC projects. Current solutions for quantity take off (QTO) and cost estimation (CE) are developed based on the assumptions that the design models are suitable, contain adequate information to perform these tasks efficiently and accurately. In practice often these criteria do not exist in the models that cost estimators receive. Many estimators, engineers and managers distrust BIM operations as a result or find it difficult to adopt a BIM-based preconstruction process. This leads to a cumbersome, manual and error-prone QT and CE process currently used by most construction companies. In order to overcome these shortcomings, we have developed a framework for a knowledge-based system to perform model based QTO and CE. This framework includes domain, reasoning, task and interface layers. This paper reports on the progress on an ongoing research effort which so far mostly focused on developing a domain layer and rule libraries for the reasoning layer. The domain layer contains a knowledge base which along with rule libraries were developed by acquiring and representing domain experts' knowledge. The rule libraries include modules of rules to infer knowledge about different product features. The inferred knowledge will enable providing and representing model information in a compatible format for QTO and CE tasks. It facilitates filtering, grouping and representing feature information provided in design models based on criteria that determines their true cost behavior. Finally, this knowledge will enable forecasting the properties of product features absent from design models. Examples are drawn from various fields inside and outside of the AEC industry, with a focus on the precast concrete industry. KeywordsKnowledge based systems, knowledge inference, quantity take off, cost estimation, precast concrete

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This paper reports on the first part of an ongoing research with the goal of designing a framework and a knowledge-based system for 3D parametric modelbased quantity take-off and cost estimation in the Architecture, Engineering and Construction (AEC) industry. The authors have studied and analyzed current cost estimation methods used in both the AEC and non-AEC industries in terms of their requirements, use contexts, methodologies, limitations and strengths to lay the groundwork for selecting the most suitable problem decomposition methods and cost estimation techniques to design a new framework. We have focused on determining the underlying methodologies of different cost estimation models and not just the techniques. Both qualitative methods used in early stages of design and quantitative methods used in more mature design stages are reviewed and their structure are analyzed.