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• W4364380999 abstract "Accurate construction cost estimation is crucial to completing projects within the planned timeframe and expenditure. The estimation process depends on multiple variables maintaining complex relationships between themselves and the target cost. As a result, an in-depth analysis from an experienced construction consultant is required to estimate construction costs accurately. Machine learning (ML) technology can learn from previous data, which is equivalent to human experience. Many project-specific ML models estimate the construction cost, which misses the generalizability. This paper addresses the gap and designs, develops, implements, and analyzes a deep learning (DL) based novel framework that maps 94.67% of the independent variables with a mean average percentage error (MAPE) of 11.60%. The proposed framework is not limited to any specific project. It estimates the construction cost of similar projects, further validated by an innovative estimator validation unit." @default.
• W4364380999 created "2023-04-12" @default.
• W4364380999 creator A5035294706 @default.
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• W4364380999 date "2023-04-11" @default.
• W4364380999 modified "2023-09-30" @default.
• W4364380999 title "A construction cost estimation framework using DNN and validation unit" @default.
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• W4364380999 doi "https://doi.org/10.1080/09613218.2023.2196388" @default.
• W4364380999 hasPublicationYear "2023" @default.
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