Proposal of a mathematical model to calculate the performance of work command in block masonry. case: Cuenca city, Cañaribamba Parish

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Micaela Geovanna Coronel García
Carlos Julio Calle Castro
Marco Ávila Calle

Abstract

Introduction.  The placement of block masonry emerges as a critical phase in the construction process, where efficiency and accuracy directly influence the duration and quality of the project. This activity, although seemingly simple, carries an inherent complexity that often results in significant delays in the execution of the work. The need to understand and address the factors contributing to these delays is evident, as their impact is not only reflected in terms of schedule and budget, but also in client satisfaction. Objective.  To propose a mathematical model to calculate the labor performance in the placement of masonry with blocks in Cañaribamba Parish, Cuenca, Ecuador. Methodology.  The methodological design adopted follows a relational and descriptive orientation, involving the collection of data from nine construction sites by means of an observation sheet that covers both external and internal factors. Using these data, a linear regression analysis was carried out using a statistical program. Results.  The results highlight that, individually, none of the factors analyzed significantly influences job performance; however, the combination of these factors allows predicting performance with an accuracy of 93.3%. Conclusion.  It is concluded that linear regression emerges as a robust tool to anticipate the performance of work crews in the Cañaribamba Parish, considering the complexity of both internal and external factors in the works.

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How to Cite
Coronel García, M. G., Calle Castro, C. J., & Ávila Calle, M. (2024). Proposal of a mathematical model to calculate the performance of work command in block masonry. case: Cuenca city, Cañaribamba Parish. ConcienciaDigital, 7(1.3), 49-68. https://doi.org/10.33262/concienciadigital.v7i1.3.2938
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References

Atencio, E., Bustos, G., y Mancini, M. (2022). Enterprise Architecture Approach for Project Management and Project-Based Organizations: A Review. Sustainability, 14. https://doi.org/10.3390/su14169801
Bartoschek, P., y Kamenov, F. (2021). Labor Productivity Influence in the Construction Industry [masterThesis, Jonkoping University]. https://www.diva-portal.org/smash/get/diva2:1560486/FULLTEXT01.pdf
Cano R; Duque A. (2000). Trabajo de investigación. SENA-CAMACOL. Medellín.
Cock, J., Prager, S., Meinke, H., y Echeverria, R. (2022). Labour productivity: The forgotten yield gap. Agricultural Systems, 201, 103452. https://doi.org/10.1016/j.agsy.2022.103452
Gata, W., Novitasari, H. B., Nurfalah, R., Hernawati, R., y Shidiq, M. J. (2019). Analysis of Regression Algorithm to Predict Administration, Production, and Delivery to Accuracy of Delivery of Products in Cosmetic Industry. IOP Conference Series: Materials Science and Engineering, 662(7), 072006. https://doi.org/10.1088/1757-899X/662/7/072006
Hai, D., y Tam, N. (2019). Application of the Regression Model for Evaluating Factors Affecting Construction Workers’ Labor Productivity in Vietnam. The Open Construction and Building Technology Journal, 13, 353-362. https://doi.org/10.2174/1874836801913010353
Hamza, M., Shahid, S., Bin Hainin, M. R., y Nashwan, M. S. (2022). Construction labour productivity: Review of factors identified. International Journal of Construction Management, 22(3), 413-425. https://doi.org/10.1080/15623599.2019.1627503
Hernández González, O. (2021). Aproximación a los distintos tipos de muestreo no probabilístico que existen. Revista Cubana de Medicina General Integral, 37(3). http://scielo.sld.cu/scielo.php?script=sci_abstractypid=S0864-21252021000300002ylng=esynrm=isoytlng=es
Jeremiah, M., Kabeyi, B., y Kabeyi, M. (2019). Evolution of Project Management, Monitoring and Evaluation, with Historical Events and Projects that Have Shaped the Development of Project Management as a Profession. International Journal of Science and Research (IJSR), 8, 63-79. https://doi.org/10.21275/ART20202078
Maulud, D., y Mohsin Abdulazeez, A. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1, 140-147. https://doi.org/10.38094/jastt1457
Ouyang, T., Liu, F., y Huang, B. (2022). Dynamic econometric analysis on influencing factors of production efficiency in construction industry of Guangxi province in China. Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/s41598-022-22374-y
Xu, C., Liu, J., Li, S., Wu, Z., y Chen, Y. F. (2021). Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling. Automation in Construction, 129, 103824. https://doi.org/10.1016/j.autcon.2021.103824
Yap, J. B. H., Goay, P. L., Woon, Y. B., y Skitmore, M. (2021). Revisiting critical delay factors for construction: Analysing projects in Malaysia. Alexandria Engineering Journal, 60(1), 1717-1729. https://doi.org/10.1016/j.aej.2020.11.021

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