Emil L. Jacobsen, Jochen Teizer, Søren Wandahl, Ioannis Brilakis
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引用次数: 0
Abstract
This study investigates the possibility of doing probabilistic forecasting of construction labor productivity metrics for both long-term and short-term estimates. The research aims to evaluate autoregressive forecasting models, which may help decision-makers with information currently unavailable in construction projects. Unlike point forecasts, the proposed method employs probabilistic forecasting, offering additional valuable insights for decision-makers. The distributional information is obtained by updating the moments of the distribution during training. Two datasets are used to evaluate the models: one collected from an entire construction site for long-term forecasting and one from an individual worker for short-term forecasting. The models aim to predict the state of direct work, indirect work, and waste. Several models are trained using different hyperparameters. The models are tuned on the number of trees and the regularization used. The presented method gives estimates of future levels of direct work, indirect work, and waste, which will add value to future processes.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
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