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Can Artificial Intelligence Transform the way we Estimate the Cost of Construction Projects?
By Anil Sawhney, Director of the Infrastructure Sector, RICS and Alan Muse, Global Director of Built Environment Standards, RICS
Broadly, AI can assist in the development of company-specific and industry-wide benchmarks that can reduce optimism bias and strategic misrepresentation. Specifically, we envision that AI can help the estimating process in the following ways:1.Classify and categorize unclassified cost data 2.Develop statistical models for parametric estimating 3.Identify reference class projects Classify and categorize unclassified cost data Organizations have an abundance of granular cost data that is generally unclassified and unstructured rendering it unusable. To utilize this type of historical data, it is essential that the data is classified using a high-level cost classification system such as the International Construction Measurement Standards (ICMS). Unclassified cost data is generally available in a variety of electronic formats. AI tools that perform natural language processing can be used to classify this type of data into a predefined cost classification system such as ICMS. A natural language classifier can be first trained and tested using historical cost data. Once the system is trained, it can process large volumes of cost data from historical projects and classify it into cost categories and cost groups (as shown in the figure below). After the data is classified, several important analyses including cost benchmarking can be performed.
Broadly, AI can assist in the development of company-specific and industry-wide benchmarks that can reduce optimism bias and strategic misrepresentation