TY - JOUR
T1 - Audio-Based Bayesian Model for Productivity Estimation of Cyclic Construction Activities
AU - Sabillon, Chris
AU - Rashidi, Abbas
AU - Samanta, Biswanath
AU - Davenport, Mark A.
AU - Anderson, David V.
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Construction heavy machines often perform their routine tasks in the form of cyclic operations (e.g., cycles of digging, swinging, and dumping for a hydraulic excavator), and forecasting those cycle times is an important step toward scheduling, cost estimation, and productivity analysis of construction projects. The current state of research for automated cycle time prediction for construction operations is based on processing kinematic data (e.g., acceleration) or implementing computer vision algorithms. Both methods have certain limitations (e.g., it is necessary to directly attach kinematic sensors to machines, and computer vision algorithms are very sensitive to lighting conditions and occlusions). In addition, current methods predict cycle time values once (usually at the beginning of projects) and do not include an "auto-update" component capable of adjusting results over time and due to variations, such as changes in jobsite conditions or impacts of learning curve, for example. To address these two important issues, the authors propose an audio-based Bayesian system for estimating cycle times of cyclic construction activities. The sounds generated during routine operations of construction equipment and machines are treated as the main source of data for this project, and efficient signal processing and machine learning algorithms are implemented to process recorded audio files at construction jobsites and extract useful information. A robust denoising algorithm was developed to improve the quality of audio files, and Bayesian statistical models are utilized to include historical data for cycle time estimation enhancement. Case studies illustrate that implementing robust audio signal processing techniques, along with a Markov chain-based filter, enable the system to accurately forecast cycle times of construction activities for multiple days of operation.
AB - Construction heavy machines often perform their routine tasks in the form of cyclic operations (e.g., cycles of digging, swinging, and dumping for a hydraulic excavator), and forecasting those cycle times is an important step toward scheduling, cost estimation, and productivity analysis of construction projects. The current state of research for automated cycle time prediction for construction operations is based on processing kinematic data (e.g., acceleration) or implementing computer vision algorithms. Both methods have certain limitations (e.g., it is necessary to directly attach kinematic sensors to machines, and computer vision algorithms are very sensitive to lighting conditions and occlusions). In addition, current methods predict cycle time values once (usually at the beginning of projects) and do not include an "auto-update" component capable of adjusting results over time and due to variations, such as changes in jobsite conditions or impacts of learning curve, for example. To address these two important issues, the authors propose an audio-based Bayesian system for estimating cycle times of cyclic construction activities. The sounds generated during routine operations of construction equipment and machines are treated as the main source of data for this project, and efficient signal processing and machine learning algorithms are implemented to process recorded audio files at construction jobsites and extract useful information. A robust denoising algorithm was developed to improve the quality of audio files, and Bayesian statistical models are utilized to include historical data for cycle time estimation enhancement. Case studies illustrate that implementing robust audio signal processing techniques, along with a Markov chain-based filter, enable the system to accurately forecast cycle times of construction activities for multiple days of operation.
KW - Adaptive Butterworth filter
KW - Audio
KW - Bayesian model
KW - Construction
KW - Cyclic activity
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85074428676&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)CP.1943-5487.0000863
DO - 10.1061/(ASCE)CP.1943-5487.0000863
M3 - Article
AN - SCOPUS:85074428676
SN - 0887-3801
VL - 34
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
IS - 1
M1 - 04019048
ER -