Novel Approach to Estimating Schedule to Completion in Construction Projects Using Sequence and Nonsequence Learning
Publication: Journal of Construction Engineering and Management
Volume 145, Issue 11
Abstract
Estimate schedule to completion (ESTC) is a difficult variable to determine accurately during the various phases of construction projects due to the complex, uncertain, and limited nature of currently available information. Currently, most construction managers estimate this variable using their subjective prior experience with budget planning. However, this method is costly and highly prone to inaccuracy. The study developed a novel inference model, the neural network–long short-term memory (NN-LSTM) model, to estimate ESTC accurately by factoring in the sequence and nonsequence factors that significantly influence project duration and that capture the inherent uncertainties in the construction field. NN-LSTM fuses two characteristics of artificial neural networks (ANNs): feedforward neural networks (FNNs) for nonsequential issue, and recurrent neural networks (RNNs) for sequential issue. After a systematic review of the literature, brainstorming, and in-depth interviews with domain experts, a database of 226 historical cases collected from 11 construction projects with 14 influencing factors was created for learning purposes, with 10-fold cross validation used to partition the training and testing data sets. NN-LSTM was then applied to estimate the schedule to completion for the historical cases, with FNN capturing the impact of time-independent factors on project duration and LSTM capturing the long temporal dependency for sequential inputs. The learning results indicated good performance, with a mean absolute percentage error (MAPE) of less than 5% and a mean absolute error (MAE) of 2%, proving the NN-LSTM model to be more reliable than the currently prevailing earned value management (EVM) formulas. Moreover, in subsequent comparison testing NN-LSTM proved to be superior to other artificial intelligence (AI) prediction models. The model has the ability to generate reliable scheduling estimates for project managers in order to facilitate their accurate planning and the monitoring of the time performance of projects, allowing timely action to be taken to remedy foreseen delays and facilitating informed decision-making.
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Data Availability Statement
Data generated and analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
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Journal of Construction Engineering and Management
Volume 145 • Issue 11 • November 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jun 12, 2018
Accepted: Feb 15, 2019
Published online: Sep 12, 2019
Published in print: Nov 1, 2019
Discussion open until: Feb 12, 2020
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