Ashtab, M and Ryoo, B Y (2022) Predicting construction workforce demand using a combination of feature selection and multivariate deep-learning seq2seq models. Journal of Construction Engineering and Management, 148(12): 04022136, ISSN 0733-9364
Abstract
Construction companies struggle with their hiring plans and react to economic shifts afterward, resulting in unnecessary layoffs and overhirings. A model that forecasts future construction hiring can help adjust hiring levels based on upcoming projections. This research proposes a framework for predicting the future sequence (upcoming 12 months) of hiring values instead of specific months, based on historical data between 1993 and 2022 for hiring and economic explanatory variables. Explanatory variables are categorized into the local, neighboring states, and national levels. Feature selection methods were used to filter out the initial data set to reduce data dimensionality - the output of each method trained by the recurrent neural network (RNN). Seq2seq models were evaluated based on their mean absolute error (MAE). The results of the best-performing model indicate that the multivariate seq2seq model can capture general trends and disruptions due to economic recession and natural disasters more accurately than the univariate statistical models, even though there was no feature inside the data set regarding hurricanes.
| Item Type: | Article |
|---|---|
| Index terms: | neural network, selection method, construction company, natural disaster, statistical model, recession |
| Subjects: | environmental hazards, tendering, economic analysis, data science, artificial intelligence, organization |
| Topics: | Digital Applications, Research Practice, Business Strategy, Procurement, Sustainability |
| Descriptive scope: | 3 PCA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here