Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach
Chen, D., Hajderanj, L. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.33965/bigdaci2019_201907l011 Abstract/SummaryIn this paper, a novel machine learning based approach is proposed for automated cost analysis on priced bill of quantities prepared by tenders in the construction industry. The proposed approach features: 1) An effective integration of structured project-specific information with surveyor's domain knowledge in order to model the complex interrelationships between the specifications and descriptions of an item and its trade category; 2) An effective transformation by supervised t-SNE to map the original data into a 2-dimensional space to tackle issues of high dimensionality in modelling and creating classifiers, and 3) Simple classifiers with a high classification accuracy and a good generalization capability. Relevant comparative experimental results have demonstrated the effectiveness of the proposed approach.
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