Converting detailed estimates to primary estimates with data augmentation. (August 2021)

• Record Type: Journal Article
• Authors: Takefuji, Yoshiyasu
• Is Part Of: Advanced engineering informatics. Volume 49(2021)
• Publication Date: 2021-08
• Subjects: Data augmentation -- Primary estimate -- Detailed estimate -- Super-skilled engineers; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101354
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: This paper shows automated conversion from detailed cost estimates to primary cost estimates without human experts involved. The primary cost estimate suggests which parameters are major contributors to the total cost. The primary cost estimate plays a key role in decision-making between clients and constructors. The proposed method can improve the prediction accuracy using converted primary estimates. Abstract: In general, preliminary or primary cost estimates are used to select contractors from among bidders in Japan. The primary cost estimate must be accurate, otherwise the contractor selected from the bidding process will lose profit. A general contractor in the world does not have a super-skilled engineer who can achieve the accurate primary cost estimates. The conventional primary estimate has a high error range and low reliability. An automated system converting detailed estimates to primary estimates has been highly demanded in the world. This paper presents a prototype AI converter that can accurately and automatically convert detailed cost estimates into primary estimates. Converting detailed cost estimates to primary estimates lies in a regression problem. This paper proposes a feature-elimination based data augmentation method for regression problems. The empirical experiment shows that the proposed data augmentation method is quite effective with an Extra-Trees ensemble method. The proposed method was empirically examined by using Colorado Department of Transportation (CDOT) dataset for accurately predicting constructions costs with the Extra-Trees algorithm and random forest algorithm respectively. The CDOT dataset is one and only one of the largest datasets available in public for constructions costs quotation/estimation of roads, bridges and buildings.