Prediction of Labor Activity Recognition in Construction with Machine Learning Algorithms

Karatas, Ibrahim; Budak, Abdulkadir

doi:10.46291/icontechvol5iss3pp38-47

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…The aim of the support vector regression (svr) model is to find a function that is as straight as possible, which is the closest to the features toward the Є maximum deviation determined between the predictions obtained from the trained data and the actual values (Drucker et al, 1997;Nguyen et al, 2021). The k-Nearest Neighbors (knn) algorithm was used to predict labor productivity by recognizing labor activities (Akhavian and Behzadan, 2016;Karatas and Budak, 2021). The knn algorithm estimates the output value by training the input features, stocking up them in the featured space and comparing the new incoming inputs with this feature space according to the closest distance (Han et al, 2011).…”

Section: Ecam 313mentioning

confidence: 99%

“…, 2021). The k- Nearest Neighbors (knn) algorithm was used to predict labor productivity by recognizing labor activities (Akhavian and Behzadan, 2016; Karatas and Budak, 2021). The knn algorithm estimates the output value by training the input features, stocking up them in the featured space and comparing the new incoming inputs with this feature space according to the closest distance (Han et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development and comparative of a new meta-ensemble machine learning model in predicting construction labor productivity

Karatas

Budak²

2022

ECAM

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PurposeThe study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.Design/methodology/approachCategorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.FindingsMeta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R2, were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.Research limitations/implicationsThe study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.Originality/valueThe study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

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