Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance

Changbum R. Ahn, Sanghyun Lee, Feniosky Peña-Mora

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Monitoring operational efficiency of construction equipment, which indicates how efficiently construction equipment is utilized, provides key information in reducing air pollutant emissions from equipment use as well as improving the productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. The measurement of the operational efficiency of construction equipment is formulated as a problem that classifies second-by-second equipment activity into working, idling, and engine-off modes. We extract various features from the raw accelerometer data and classify them into three different equipment activities (working, idling, and engine-off), using supervised learning algorithms such as Logical Regression, decision trees, k-Nearest Neighbor, and Naïve Bayes. The result from the real-world experiment indicates that the use of supervised learning algorithms provides over 93% of recognition accuracies, and this level of accuracies causes less than 2% error in the measurement of equipment operating efficiency.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering
Pages565-572
Number of pages8
StatePublished - Nov 15 2013
Event2013 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2013 - Los Angeles, CA, United States
Duration: Jun 23 2013Jun 25 2013

Publication series

NameComputing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering

Other

Other2013 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2013
CountryUnited States
CityLos Angeles, CA
Period6/23/136/25/13

Fingerprint

Construction equipment
Monitoring
Supervised learning
Accelerometers
Learning algorithms
Engines
Decision trees
Productivity
Air
Costs
Experiments

ASJC Scopus subject areas

  • Civil and Structural Engineering

Cite this

Ahn, C. R., Lee, S., & Peña-Mora, F. (2013). Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance. In Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering (pp. 565-572). (Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering).

Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance. / Ahn, Changbum R.; Lee, Sanghyun; Peña-Mora, Feniosky.

Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering. 2013. p. 565-572 (Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ahn, CR, Lee, S & Peña-Mora, F 2013, Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance. in Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering. Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering, pp. 565-572, 2013 ASCE International Workshop on Computing in Civil Engineering, IWCCE 2013, Los Angeles, CA, United States, 6/23/13.
Ahn CR, Lee S, Peña-Mora F. Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance. In Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering. 2013. p. 565-572. (Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering).
Ahn, Changbum R. ; Lee, Sanghyun ; Peña-Mora, Feniosky. / Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance. Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering. 2013. pp. 565-572 (Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering).
@inproceedings{09c7cd9c614b4ee29c26f277dade6608,
title = "Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance",
abstract = "Monitoring operational efficiency of construction equipment, which indicates how efficiently construction equipment is utilized, provides key information in reducing air pollutant emissions from equipment use as well as improving the productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. The measurement of the operational efficiency of construction equipment is formulated as a problem that classifies second-by-second equipment activity into working, idling, and engine-off modes. We extract various features from the raw accelerometer data and classify them into three different equipment activities (working, idling, and engine-off), using supervised learning algorithms such as Logical Regression, decision trees, k-Nearest Neighbor, and Na{\"i}ve Bayes. The result from the real-world experiment indicates that the use of supervised learning algorithms provides over 93{\%} of recognition accuracies, and this level of accuracies causes less than 2{\%} error in the measurement of equipment operating efficiency.",
author = "Ahn, {Changbum R.} and Sanghyun Lee and Feniosky Pe{\~n}a-Mora",
year = "2013",
month = "11",
day = "15",
language = "English (US)",
isbn = "9780784477908",
series = "Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering",
pages = "565--572",
booktitle = "Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering",

}

TY - GEN

T1 - Acceleromter-based measurement of construction equipment operating efficiency for monitoring environmental performance

AU - Ahn, Changbum R.

AU - Lee, Sanghyun

AU - Peña-Mora, Feniosky

PY - 2013/11/15

Y1 - 2013/11/15

N2 - Monitoring operational efficiency of construction equipment, which indicates how efficiently construction equipment is utilized, provides key information in reducing air pollutant emissions from equipment use as well as improving the productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. The measurement of the operational efficiency of construction equipment is formulated as a problem that classifies second-by-second equipment activity into working, idling, and engine-off modes. We extract various features from the raw accelerometer data and classify them into three different equipment activities (working, idling, and engine-off), using supervised learning algorithms such as Logical Regression, decision trees, k-Nearest Neighbor, and Naïve Bayes. The result from the real-world experiment indicates that the use of supervised learning algorithms provides over 93% of recognition accuracies, and this level of accuracies causes less than 2% error in the measurement of equipment operating efficiency.

AB - Monitoring operational efficiency of construction equipment, which indicates how efficiently construction equipment is utilized, provides key information in reducing air pollutant emissions from equipment use as well as improving the productivity of construction operations. In this paper, we report our efforts to measure the operational efficiency of construction equipment, using low-cost accelerometers. The measurement of the operational efficiency of construction equipment is formulated as a problem that classifies second-by-second equipment activity into working, idling, and engine-off modes. We extract various features from the raw accelerometer data and classify them into three different equipment activities (working, idling, and engine-off), using supervised learning algorithms such as Logical Regression, decision trees, k-Nearest Neighbor, and Naïve Bayes. The result from the real-world experiment indicates that the use of supervised learning algorithms provides over 93% of recognition accuracies, and this level of accuracies causes less than 2% error in the measurement of equipment operating efficiency.

UR - http://www.scopus.com/inward/record.url?scp=84887382219&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887382219&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84887382219

SN - 9780784477908

T3 - Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering

SP - 565

EP - 572

BT - Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering

ER -