A productivity forecasting system for construction cyclic operations using audio signals and a Bayesian approach

A large portion of the expenses in a construction project are allocated towards the capital and operating costs of heavy equipment. Most of construction heavy equipment and tools carry out activities in the form of repetitive cycles (e.g., a cycle of digging, swinging, loading). Precisely estimating cycle times for those operations is a crucial step toward productivity analysis, cost estimation, and scheduling of a construction project. The traditional approaches for estimating cycle times of construction cyclic activities are twofold: (1) based on direct observations and recordings; and (2) using available graphs and approximate formulas for estimations. The first approach is time consuming and labor intensive and the second one might not be sufficiently accurate and realistic. To tackle the above-mentioned issues, this paper proposes an automated, Bayesian system for estimating cycle times of construction heavy equipment. Considering that construction equipment usually produces distinct acoustic patterns while performing various tasks, the main input for the system is recorded audio data. The presented system includes a de-noising algorithm for enhancing the quality of audio data as well as a short-time Fourier transform (STFT) and support vector machines (SVM) for classifying various activities in a primary stage. A Markov chain model for activity transitions is calculated from ground truth data and used to code an adaptive filter that converts SVM-labeled time-frequency bins into higher-level labels of the full period for each activity. Preliminary results show that, through this system, the accuracy of predicting cycle times could be as high as 90%.