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Title:

Determination of On-site Construction Labor Productivity Using Artificial Neural Networks

Accession Number:

01151222

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

To enhance the capability of highway bridge construction, an automated on-site construction labor productivity measurement system was developed. Utilizing the advanced technologies of computer vision and the artificial neural network, the developed system first wirelessly acquired a sequence of images of construction labor activities. Then, the human pose analyzing algorithm processed these images in real-time to generate human poses associated with the construction workers at the project site. Next, a portion of the human poses were manually classified into three categories as effective work, ineffective work, and contributory work and were used to train a built-in artificial neural network. Finally, the trained neural network was employed to decide the ongoing worker’s working status by comparing the in-coming images to the developed human poses. As a result, the construction labor productivity was determined from these comparison statistics. The developed system was tested for accuracy on a bridge construction project. The results of the test indicated that the productivity measurements by the neural network were reasonable accurate when compared to the measurements produced by the manual method. This research project made two major contributions to the advancement of construction industry. First, it applied advanced technologies such as computer vision and artificial neural network for analyzing construction operations. Second, the results of this research project made it possible to automatically determine the on-site construction labor productivity in real-time. Thus, engineers and project managers were able to quickly identify on-site labor productivity problems and to take actions immediately to address these problems. Therefore, the success of this research project enhanced the contractors’ capability of managing bridge construction projects.

Monograph Accession #:

01147878

Report/Paper Numbers:

10-2481

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Bai, Yong
Huan, Luke
Peddi, Abhinav
Kim, Seonghoon

Pagination:

14p

Publication Date:

2010

Conference:

Transportation Research Board 89th Annual Meeting

Location: Washington DC, United States
Date: 2010-1-10 to 2010-1-14
Sponsors: Transportation Research Board

Media Type:

DVD

Features:

Figures; Photos; References (26) ; Tables (2)

Subject Areas:

Construction; Highways; I53: Construction of Bridges and Retaining Walls

Source Data:

Transportation Research Board Annual Meeting 2010 Paper #10-2481

Files:

TRIS, TRB

Created Date:

Jan 25 2010 11:10AM