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Moment balanced machine: a new supervised inference engine for on-site construction productivity prediction

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Abstract

Predicting construction productivity is challenging because of the complexity involved in the construction process and the variability in factors that regularly affect these projects. Machine learning models have the potential to improve the accuracy of construction productivity predictions. This study introduces a model for generating accurate predictions of construction productivity using an AI-based inference engine called Moment Balanced Machine (MBM). Instead of identifying the hyperplane of SVM, MBM considers moments to determine the optimal moment hyperplane. MBM balances the moments, which are the product of force and distance, with force representing the weight assigned to a datapoint and distance indicating its position relative to the moment hyperplane. To obtain the weights for each datapoint within the MBM framework, Backpropagation Neural Network (BPNN) is employed. Moreover, the performance of MBM is benchmarked against five other machine learning models, including SVM, BPNN, K-Nearest Neighbor (KNN), Decision Tree (DT), and Linear Regression (LR). According to the results of the 10-fold cross-validation, MBM consistently outperformed the other models across five evaluation metrics, including RMSE (0.068), MAE (0.054), MAPE (3.42%), R (0.982), and R2 (0.965). The comprehensive assessment, summarized by the Reference Index (RI), indicates that MBM achieved the highest RI score of 1.000, emphasizing its superior performance. Furthermore, MBM exhibits robustness against data imperfections, including incomplete and noisy datasets. Given these findings, the proposed model could serve as an advanced machine learning decision-support system that improves the prediction accuracy of construction productivity. This reinforces the data-driven approach for improving the efficiency of construction projects.

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Data availability

The code and dataset for this study is available at:    https://github.com/MomentBalancedMachine/MBM.

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  1. National Taiwan University of Science and Technology, Taipei, Taiwan

    Min-Yuan Cheng & Riqi Radian Khasani

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Cheng, MY., Khasani, R.R. Moment balanced machine: a new supervised inference engine for on-site construction productivity prediction. Appl Intell 54, 5441–5464 (2024). https://doi.org/10.1007/s10489-024-05419-9

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