TY  - CONF
TI  - Productivity Monitoring of Construction Activities Using Digital Technologies: A Literature Review
AU  - Barbosa, Amanda da S.
AU  - Costa, Dayana B.
AD  - Civil Engineer, Master’s Student, Graduate Program in Civil Engineering, Federal University of Bahia  (UFBA), Brazil, barbosa.amanda@ufba.br, orcid.org/0000-0002-1982-6633
AD  - Associate Professor, Engineering School, Department of Structural and Construction Engineering,  Graduate Program in Civil Engineering, Federal University of Bahia (UFBA), Brazil,  dayanabcosta@ufba.br, orcid.org/0000-0002-1457-6401
PY  - 2021
DA  - 2021/07/14
T2  - Proc. 29th Annual Conference of the International Group for Lean Construction (IGLC)
C3  - Proc. 29th Annual Conference of the International Group for Lean Construction (IGLC)
CY  - Lima, Peru
SP  - 707
EP  - 716
DO  - 10.24928/2021/0141
AB  - Although the engineering and construction sector is one of the largest in the world economy, it has historically been characterized by a low level of productivity and innovation. Traditional methods for productivity assessment at construction sites, despite being effective, are time-consuming and based on manual data collection and direct observation of activities on-site, which hampers the obtaining of reliable and up-to-date information of activities productivity. To contribute to future research in this area, this study aims to identify and analyze the main existing methods for measuring, analyzing, and improving productivity at construction sites using digital technologies, based on a systematic literature review. A total of 35 papers dated from 2010 to 2021 were selected using Scopus, ASCE Library, and Web of Science databases. Results show that technologies based on computer vision and sensors are the most used by researchers, being able to automate data collection for work sampling and activity analysis, measure inputs, outputs, and cycle times, and monitor factors that can influence workers’ productivity. These technologies also have the potential to assist in the development of data collection methods for the assessment of productivity, ergonomics, and worker wellbeing. This integration, despite valuable, has been little explored in the literature
KW  - Waste
KW  - flow
KW  - time compression
KW  - construction productivity
KW  - digital technologies.
L1  - https://www.iglc.net/papers/details/1913/pdf
UR  - https://www.iglc.net/papers/details/1913
DB  - IGLC.net
LA  - English
N1  - Export date: 04 October 2026
ER  -