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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.
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Automated Design Information Extraction from Construction Specifications to Support Wood Construction Cost Estimation
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.
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- Award ID(s):
- 1745374
- PAR ID:
- 10202902
- Date Published:
- Journal Name:
- Construction Research Congress 2020
- Page Range / eLocation ID:
- 658 to 666
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1061/9780784482889.069
