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A set of 1,900 IFC objects with their invariant signature values. 

Building Information Modelling (BIM) is an integrated informational process and plays a key role in enabling efficient planning and control of a project in the Architecture, Engineering, and Construction (AEC) domain. Industry Foundation Classes (IFC)-based BIM allows building information to be interoperable among different BIM applications. Different stakeholders take different responsibilities in a project and therefore keep different types of information to meet project requirements. In this paper, the authors proposed and adopted a six-step methodology to support BIM interoperability between architectural design and structural analysis at both AEC project level and information level, in which: (1) the intrinsic and extrinsic information transferred between architectural models and structural models were analyzed and demonstrated by a Business Process Model and Notation (BPMN) model that the authors developed; (2) the proposed technical routes with different combinations, and their applications to different project delivery methods provided new instruments to stakeholders in industry for efficient and accurate decision-making; (3) the material centered invariant signature with portability can improve information exchange between different data formats and models to support interoperable BIM applications; and (4) a developed formal material information representation and checking method was tested on a case study where its efficiency was demonstrated to outperform: (1) proprietary representations and information checking method based on a manual operation, and (2) MVD-based information checking method. The proposed invariant signatures-based material information representation and checking method brings a better efficiency for information transfer between architectural design and structural analysis, which can have significant positive effect on a project delivery, due to the frequent and iterative update of a project design. This improves the information transfer and coordination between architects and structural engineers and therefore the efficiency of the whole project. The proposed method can be extended and applied to other application phases and functions such as cost estimation, scheduling, and energy analysis. 

Building information modeling (BIM) provides a novel way of information management for all lifecycle phases of a building project. It is facilitating the processes of a construction project, such as architectural design, structural analysis, and construction management. Industry foundation classes (IFC) is an open standard for information exchange between different BIM applications in the architecture, engineering, and construction (AEC) domain. It represents project information in an interoperable way that contains geometric information, material information, and other physical and functional information needed of analyzing and managing a project. Structural analysis aims to simulate the structural performance of a building under different types of loads to make sure the structure is safe. The needed information for structural analysis mainly include geometric, material, and load information. These information come from architectural design and selected analysis scenarios. The information should be represented in an interoperable way to allow information transfer between different phases and different stakeholders. Information missing is a crucial problem during the interoperable use of BIM, which may cause misunderstandings between different stakeholders and therefore erroneous structural analysis result and misleading information to feed construction process later on. In this paper, the authors focus on analyzing the use of IFC at three stages in structural analysis, namely, intrinsic modeling stage, extrinsic modeling stage, and the analysis stage. The authors compared IFC files at these three stages with original BIM software text files in terms of information coverage, and identified information missing cases. This is the first systematic investigation of BIM interoperability at detailed work stages of structural analysis and provides insights in how BIM usage should be improved in this domain. 

In achieving full automation of construction cost estimation, the complete processes involved in computing cost estimates must be automated. The typical processes involved in achieving cost estimates are: (1) classification and matching of model elements to their various categories; (2) taking off quantities from design documents or building information models; (3) retrieving unit cost from a cost database; and (4) applying the unit costs and quantities in computing the cost estimate. Although, the level of automation in quantity takeoff has been relatively high, most commercial software programs still require manual inputs from estimators to: (1) match materials of building elements to work items; and/or (2) fulfill essential information requirements that may be missing from design models for accurate cost estimate computations. These missing information are usually obtained from the construction specifications in supplement to the design models. Automating the process of design information extraction from construction specifications can help reduce: (1) the time and cost of the estimation, (2) the manual inputs required in cost estimation computations, and (3) human errors in cost estimates. This paper explores the use of natural language processing techniques to help process construction specifications and the authors propose a new algorithmic method for extracting the needed design information from construction specifications to support wood construction cost estimation. A case study was conducted on a wood construction project to evaluate the authors’ proposed method. The results showed that the proposed method successfully searched for and found design details from construction specifications to fulfil essential information requirements for detailed wood construction cost estimation, with a 94.9% precision and a 97.4% recall. 

Building information modeling (BIM) is widely used in the architectural, engineering, and construction (AEC) domain to support different applications such as cost estimation, planning & scheduling, and structural analysis. Structural analysis is an essential way to ensure structural safety. However, different structural analysis software may not process all information from building information models (BIMs) correctly, which impedes BIM interoperability. To address this problem, the authors proposed a new method for automatically checking information completeness of BIMs to support BIM usage in structural analysis in an interoperable manner. The method was tested in an experimental implementation using python programs and a structural analysis software. The checking results using the proposed method was compared with results from a manual checking and a Model View Definition (MVD)-based checking, respectively. The experiment showed a comparable or better performance of the proposed method in accuracy and efficiency than manual checking and MVD-based checking. Furthermore, the proposed method overcomes the scope limitation possessed by MVD-based checking. Therefore, the proposed information checking method is expected to support BIM interoperability by helping people identify missing information from IFC-based BIMs. The authors also proposed a new system model for the BIM information checking domain [i.e., information, model, application, and application context (IMAAC) model]. 

Object signatures have been widely used in object detection and classification. Following a similar idea, the authors developed geometric signatures for architecture, engineering, and construction (AEC) objects such as footings, slabs, walls, beams, and columns. The signatures were developed both scientifically and empirically, by following a data-driven approach based on analysis of collected building information modeling (BIM) data using geometric theories. Rigorous geometric properties and statistical information were included in the developed geometric signatures. To enable an open access to BIM data using these signatures, the authors also initiated a BIM data repository with a preliminary collection of AEC objects and their geometric signatures. The developed geometric signatures were preliminarily tested by a small object classification experiment where 389 object instances from an architectural model were used. A rule-based algorithm developed using all parameter values of 14 features from the geometric signatures of the objects successfully classified 336 object instances into the correct categories of beams, columns, slabs, and walls. This higher than 85% accuracy showed the developed geometric signatures are promising. The collected and processed data were deposited into the Purdue University Research Repository (PURR) for sharing. 

A major gap in the automation of construction cost estimation is the need of manual inputs to complete cost estimation processes. To address this gap, the authors propose a new method for matching wood building elements from a Building Information Modeling (BIM)-based design to cost data entries in a cost database. The proposed method uses a java constructor and HashMap to create objects, and store and retrieve the created values of the objects. Term matching and natural language processing (NLP) techniques are used in the method to match items from a design model and automatically extract their unit costs from a cost database. These unit costs retrieved are then used in generating the cost estimates. The proposed method was tested on estimating a wood construction model retrieved online. A cost estimate was successfully generated. Comparison of the experimental results with results from the state-of-the-art commercial software showed that the algorithms developed from the proposed method reduced the manual inputs required in generating wood construction cost estimates.