References
- Gerek, I. H., Erdis, E., Mistikoglu, G. & Usmen, M. A. (2014). Evaluation of plastering crew performance in building projects using data envelopment analysis. Technological and Economic Development of Economy, 22(6), 926–940.
- Thomas, H. R., Maloney, W. F., Horner, R. M. W., Smith, G. R., Handa, V. K. & Sanders, S. R. (1990). Modeling construction labor productivity. Journal of Construction Engineering and Management, 116(4), 705–726.
- Yi, W. & Chan, A. P. C. ( 2014). Critical review of labor productivity research in construction journals. Journal of Management in Engineering, 30(2), 214–225.
- Boughaba, A. & Bouabaz, M. (2020). Identification and risk management related to construction projects. Advances in Computational Design, 5(4), 445–465.
- Boulkenafet, N., Boudjellal, K. & Bouabaz, M. (2023). Optimization of productivity in the rehabilitation of building linked to BIM. Advances in Computational Design, 8(2), 179–190.
- Olomolaiye, P. O., Jayawardane, A. K. W. & Harris, F. C. (1998). Construction Productivity Management. England: Addison Wesley Longman.
- Florez-Perez, L. Song, Z. & Cortissoz, J. C. (2022). Predicting construction productivity with machine learning approaches. Proceedings of the International Symposium on Automation and Robotics in Construction. 2022-July, (107–114).
- Jarkas, A. M. & Bitar, C. G. (2012). Factors affecting construction labor productivity in Kuwait. Journal of Construction Engineering and Management. 138(7), 811–820.
- Karim, N. A., Hassan, S., Yunus, J. & Hashim, M. (2013). Factors Influence Labour Productivity and the Impacts on Construction Industry. Caspian Journal of Applied Sciences Research, 2(August), 349–354.
- El-Gohary, K. M. & Aziz, R. F. (2013). Factors influencing construction labor productivity in Egypt. Journal of Management in Engineering, 30(1), 1–9.
- Hickson, B. G. & Ellis, L. A. (2014). Factors affecting Construction Labour Productivity in Trinidad and Tobago. The Journal of the Association of Professional Engineers of Trinidad and Tobago, 42(1), 4–11.
- Alaghbari, W., Al-Sakkaf, A. A. & Sultan. B., (2019). Factors affecting construction labour productivity in Yemen. International Journal of Construction Management, 19(1), 79–91.
- Shoar, S. & Banaitis, A. (2018). Application of fuzzy fault tree analysis to identify factors influencing construction labor productivity: A high-rise building case study. Journal of Civil Engineering and Management, 25(1), 41–52.
- Adebowale, O. & Smallwood, J. (2020). Contractors’ perceptions of factors affecting construction labour productivity in South Africa. Journal of Contemporary Managemen, 17(2), 326–346.
- Nasirzadeh, F., Rostamnezhad, M., Carmichael, D. G., Khosravi, A. & Aisbett, B. (2022). Labour productivity in Australian building construction projects: a roadmap for improvement. International Journal of Construction Management, 22(11), 2079–2088.
- Roberts, D., Park, K., Ankrah, N. & Kim, K. P. (2023). The effect on cost when utilising modular construction techniques-The impact of weather on labour productivity. International Journal of Sustainable Building Technology and Urban Development, 14(1), 77–95.
- Bouabaz, M. & Hamami, M. (2008). A cost estimation model for repair bridges based on artificial neural network. American Journal of Applied Sciences, 5(4), 334–339.
- Otmani, A., Bouabaz, M. & Al-Hajj, A. (2020). Predicting Maintenance and Rehabilitation Cost for Buildings Based on Artificial Neural Network and Fuzzy Logic. International Journal of Computational Intelligence and Applications, 19(1), 1–13.
- Peško, I. et al. (2017). Estimation of costs and durations of construction of urban roads using ANN and SVM. Complexity. 2450370.
- Umit Dikmen, S. & Sonmez, M. (2011). An artificial neural networks model for the estimation of formwork labour. Journal of Civil Engineering and Management, 17(3), 340–347.
- Al-Zwainy, F. M. S., Rasheed, H. A. & Ibraheem, H. F. (2012). Development of the construction productivity estimation model using artificial neural network for finishing works for floors with marble. ARPN Journal of Engineering and Applied Sciences, 7(6), 714–722.
- Gerek, I. H., Erdis, E., Mistikoglu, G. & Usmen, M. (2015). Modelling masonry crew productivity using two artificial neural network techniques. Journal of Civil Engineering and Management, 21(2), 231–238.
- Aswed, G. K. (2016). Productivity Estimation Model for Bracklayer in Construction Projects Using Neural Network. Al-Qadisiyah Journal For Engineering Sciences, 9(2), 183–199.
- Golnaraghi, S., Zangenehmadar, Z., Moselhi, O. & Alkass, S. (2019). Application of Artificial Neural Network ( s ) in Predicting Formwork Labour Productivity. Advances in Civil Engineering, 2019, 1–11.
- Mlybari, E. A. (2020). Application of soft computing techniques to predict construction labour productivity in Saudi Arabia. International Journal of GEOMATE, 19(71), 203–210.
- Bokor, O., Florez-Perez, L., Pesce, G. & Gerami Seresht, N. (2021). Using Artificial Neural Networks to Model Bricklaying Productivity. Proceedings of the 2021 European Conference on Computing in Construction, 2021, July (52–58).
- Juszczyk, M. (2020). Analysis of labour efficiency supported by the ensembles of neural networks on the example of steel reinforcement works. Archives of Civil Engineering, 66(1), 97–111.
- Sarmento, M., Sc, M., Luiz, M., Ferreira, R. & Sc, D. (2021). Sizing an Artificial Neural Network for Modelling Piping Assembly Labor Productivity Under Different Conditions. International Journal of Engineering Research and Applications, 11(2), 16–22.
- Goodarzizad, PP., Mohammadi Golafshani, E. & Arashpour, M. (2021). Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm. International Journal of Construction Management, 1–28.
- Lawaju, N., Parajuli, N. & Shrestha, S. K. (2021). Analysis of Labor Productivity of Brick Masonry Work in Building Construction in Kathmandu Valley. Journal of Advanced College of Engineering and Management, 6, 159–175.
- Phaobunjong, K. (2002). Parametric Cost Estimating Model for Conceptual Cost Estimating of Building Construction Projects. University of Texas at Austin.
- Al-Zwainy, F., Abed - Alla Eiada, A. & Abed - Almajed Khaleel, T. (2016). Application Intelligent Predicting Technologies in Construction Productivity. American Journal of Engineering and Technology Management, 1(3), 39–48.
- El-Kholy, A. M. (2010). Productivity performance estimation in Egyptian construction projects based on regression analysis. Mansoura Engineering Journal (MEJ), 35(01), 44–54.
- Narayanan, K. M. A. & Aswathi, M. P. (2018). Labour Productivity Model for Structural Elements By Varying Buildability Factors using Multiple Regression Analysis. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2), 139–146.
- Khanh, H. D., Kim, S. Y., Van Khoa, N. & Tu, N. T. (2021). The relationship between workers’ experience and productivity: a case study of brick masonry construction. International Journal of Construction Management, 1–10.
- Paikun, P., Fatimah, C., Nugroho, N. S. & Kusmana, D. (2021). Labor Productivity Rating Model for Light Brick Wall Installation in Residential Projects. ASTONJADRO, 10(2), 260–270.
- Matejević-Nikolić, B. & Živković, L. (2022). Comparative Representation of Two Models for Predicting the Productivity of Column and Wall Concreting Process. Buildings, 12(11), 1809.
- Al-Zwainy, F. M. S., Abdulmajeed, M. H. & Aljumaily, H. S. M. (2013). Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq. Open Journal of Civil Engineering, 03(03), 127–135.
- Enshassi, A., Mohamed, S., Mustafa, Z. A. & Mayer, P. E. (2007). Factors affecting labour productivity in building projects in the Gaza strip. Journal of Civil Engineering and Management. 13(4), 245–254.
- Fagbenle Olabosipo, I., Ogunde Ayodeji, O. & Owolabi James, D. (2011). Factors Affecting the Performance of Labour in Nigerian Construction Sites. Mediterranean Journal of Social Sciences, 2(2), 251–257.
- El-Gohary, K. M., Aziz, R. F. & Abdel-Khalek, H. A. (2017). Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences. Journal of Construction Engineering and Management, 143(8), 1–10.
- Hafez, S. M. (2014). Critical Factors Affecting Construction Labor Productivity in Egypt. American Journal of Civil Engineering, 2(2), 35.
- Kaming, P. F., Olomolaiye, P. O., Holt, G. D. & Harris, F. C. (1997). Factors influencing craftsmen’s productivity in Indonesia. International Journal of Project Management, 15(1), 21–30.
- Hogg, R. V. & Tanis, E. a. (2009). Instructor ’ s Solutions Manual Probability and Statistical Inference Eighth Edition. Upper Saddle River, NJ(USA): Prentice Hall.
- Sincich, T., Stephan, D. & Levine, D. M. (2002). Practical statistics by example using Microsoft Excel and Minitab. Prentice Hall Professional Technical Reference.
- Gopal, T. G. S. R. & Murali, K. (2015). A Critical Review on Factors Influencing Labour Productivity in Construction. Journal of Mechanical and Civil Engineering (IOSR-JMCE), 12(5), 47–51.
- Heravi, G. & Eslamdoost, E. (2015). Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity. American Society of Civil Engineers, 141(10), 04015032.
- Duch, W. & Jankowski, N. (1999). Survey of neural transfer functions,” Neural Computing Surveys, 2, 163–212.
- Nassar, Y. S. & Khaleel, T. A. (2019). Building Model to Predict Labour Productivity Using Multiple Linear Regression Technique for “ Formwork Concrete Columns “. Kufa Journal of Engineering, 10(2), 12–26.