Title:	TOWARDS AUTOMATED COST ANALYSIS, BENCHMARKING AND ESTIMATING IN CONSTRUCTION: A MACHINE LEARNING APPROACH
Author(s):	Daqing Chen, Laureta Hajderanj and James Fiske
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Construction Cost Benchmarking, Cost Analysis, Construction Data Analysis, Bill of Quantities, Dimensionality Reduction, Supervised t-SNE.
Type:	Full Paper
First Page:	85
Last Page:	91
Language:	English
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Paper Abstract:	In 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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