<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Improving productivity in construction projects has long been a major concern, and much research has been carried out to try to ameliorate construction productivity. To this end, this study aims to improve and increase the productivity rate of flat slab formwork used in residential construction projects. A survey consisting of 150 questionnaires was undertaken to identify the factors that influence on the productivity. Based on the relative Importance Index (RII), data on eleven factors deemed to affect productivity were selected. A collection of 100 data points from various sites were utilized to develop two models. Firstly, an Artificial Neural Network (ANN) model was employed, and secondly, a parametric approach was investigated. The data were divided into two sets, with 70% of the data used for training and the remaining 30% used for testing. The models’ performance was evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. In the test phase, the artificial neural network model yielded an MSE value of 2.6610e<sup>−4</sup> and a MAPE value of 4.9227, whereas the parametric model produced an MSE of 0.040 and a MAPE of 9.525. It was found that the artificial neural network model provided reliable prediction accuracy compared to the parametric model. However, the artificial neural network approach can be selected as a robust model in predicting and controlling the productivity rate in local construction projects by using the developed model based on the identified factors.</p>
</abstract>

Improving productivity in construction projects has long been a major concern, and much research has been carried out to try to ameliorate construction productivity. To this end, this study aims to improve and increase the productivity rate of flat slab formwork used in residential construction projects. A survey consisting of 150 questionnaires was undertaken to identify the factors that influence on the productivity. Based on the relative Importance Index (RII), data on eleven factors deemed to affect productivity were selected. A collection of 100 data points from various sites were utilized to develop two models. Firstly, an Artificial Neural Network (ANN) model was employed, and secondly, a parametric approach was investigated. The data were divided into two sets, with 70% of the data used for training and the remaining 30% used for testing. The models’ performance was evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. In the test phase, the artificial neural network model yielded an MSE value of 2.6610e−4 and a MAPE value of 4.9227, whereas the parametric model produced an MSE of 0.040 and a MAPE of 9.525. It was found that the artificial neural network model provided reliable prediction accuracy compared to the parametric model. However, the artificial neural network approach can be selected as a robust model in predicting and controlling the productivity rate in local construction projects by using the developed model based on the identified factors.

Improvement of Productivity in Buildings Construction

Department of Civil Engineering, LMGHU Laboratory

Selected Scientific Papers - Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Improvement of Productivity in Buildings Construction"}

Improving productivity in construction projects has long been a major concern, and much research has been carried out to try to ameliorate construction...

Improvement of Productivity in Buildings Construction

Publié en ligne: 29 déc. 2023

Pages: -

DOI: https://doi.org/10.2478/sspjce-2023-0005

Mots clés
Artificial Intelligence, Construction Industry, Artificial Neural Network, Labour Productivity, Parametric Approach

© 2023 Chemseddine Dehchar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Improvement of Productivity in Buildings Construction

Publié en ligne: 29 déc. 2023

Pages: -

DOI: https://doi.org/10.2478/sspjce-2023-0005

Mots clésArtificial Intelligence, Construction Industry, Artificial Neural Network, Labour Productivity, Parametric Approach

© 2023 Chemseddine Dehchar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Artificial Intelligence, Construction Industry, Artificial Neural Network, Labour Productivity, Parametric Approach