Publicado

2026-02-11

Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq

Desarrollo de un modelo predictivo basado en máquinas de soporte vectorial para la productividad de pilotes perforados en proyectos de construcción residencial en Irak

DOI:

https://doi.org/10.15446/dyna.v93n240.121949

Palabras clave:

support vector machine, construction productivity, bored piles, predictive modeling, Iraq, residential towers (en)
máquina de vectores de soporte, productividad en la construcción, pilotes perforados, modelado predictivo, Irak, torres residenciales (es)

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Accurate prediction of construction productivity remains a critical challenge in the civil engineering sector, particularly for deep foundation works such as bored piles in residential projects. This study proposes a data-driven predictive model based on Support Vector Machine (SVM) to estimate the productivity of bored piles in high-rise residential construction projects in Iraq. Real-world data were collected from the Iraq Gate Residential Complex and used to train and validate the model. Key influencing factors included pile geometry, soil type, equipment specifications, crew size, and working hours. The model achieved a mean prediction accuracy of 99.89% and a correlation coefficient (R) of 97.02%, demonstrating superior performance over conventional estimation methods. These findings highlight the practical value of machine learning approaches in enhancing resource planning and decision-making during early project phases. The proposed SVM-based model can support contractors and engineers in forecasting performance outcomes and minimizing scheduling uncertainties in similar construction settings.

La predicción precisa de la productividad de la construcción sigue siendo un desafío crítico en el sector de la ingeniería civil, particularmente para obras de cimentación profunda como pilotes perforados en proyectos residenciales. Este estudio propone un modelo predictivo basado en datos, basado en Máquinas de Vectores de Soporte (MVS), para estimar la productividad de pilotes perforados en proyectos de construcción residencial de gran altura en Irak. Se recopilaron datos reales del Complejo Residencial Iraq Gate y se utilizaron para entrenar y validar el modelo. Los factores clave de influencia incluyeron la geometría de los pilotes, el tipo de suelo, las especificaciones del equipo, el tamaño de la cuadrilla y las horas de trabajo. El modelo alcanzó una precisión de predicción media del 99,89 % y un coeficiente de correlación (R) del 97,02 %, lo que demuestra un rendimiento superior al de los métodos de estimación convencionales. Estos hallazgos resaltan el valor práctico de los enfoques de aprendizaje automático para mejorar la planificación de recursos y la toma de decisiones durante las fases iniciales del proyecto. El modelo propuesto, basado en MVS, puede ayudar a contratistas e ingenieros a pronosticar los resultados de rendimiento y minimizar las incertidumbres de programación en entornos de construcción similares.

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Cómo citar

IEEE

[1]
L. N. Ali, «Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq», DYNA, vol. 93, n.º 240, pp. 81–88, ene. 2026.

ACM

[1]
Ali, L.N., Raheem Al-Dhamad, S.H., Al-Zwainy, F.M.S., Zaki, R.I.K., Varouqa, I.F., Al-Dulaimi, S.D.S., Obaid, A.H., Sarhan, M.M., Chan, A.P., Maya, R.A. y Hayder, G. 2026. Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq. DYNA. 93, 240 (ene. 2026), 81–88. DOI:https://doi.org/10.15446/dyna.v93n240.121949.

ACS

(1)
Ali, L. N.; Raheem Al-Dhamad, S. H.; Al-Zwainy, F. M. S.; Zaki, R. I. K.; Varouqa, I. F.; Al-Dulaimi, S. D. S.; Obaid, A. H.; Sarhan, M. M.; Chan, A. P.; Maya, R. A.; Hayder, G. Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq. DYNA 2026, 93, 81-88.

APA

Ali, L. N., Raheem Al-Dhamad, S. H., Al-Zwainy, F. M. S., Zaki, R. I. K., Varouqa, I. F., Al-Dulaimi, S. D. S., Obaid, A. H., Sarhan, M. M., Chan, A. P., Maya, R. A. & Hayder, G. (2026). Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq. DYNA, 93(240), 81–88. https://doi.org/10.15446/dyna.v93n240.121949

ABNT

ALI, L. N.; RAHEEM AL-DHAMAD, S. H.; AL-ZWAINY, F. M. S.; ZAKI, R. I. K.; VAROUQA, I. F.; AL-DULAIMI, S. D. S.; OBAID, A. H.; SARHAN, M. M.; CHAN, A. P.; MAYA, R. A.; HAYDER, G. Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq. DYNA, [S. l.], v. 93, n. 240, p. 81–88, 2026. DOI: 10.15446/dyna.v93n240.121949. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/121949. Acesso em: 4 mar. 2026.

Chicago

Ali, Laith N., Saja Hadi Raheem Al-Dhamad, Faiq M. S. Al-Zwainy, Rana I. K. Zaki, Ibrahim Farouq Varouqa, Salman Dawood Salman Al-Dulaimi, Aseel H. Obaid, Mazin M. Sarhan, Albert P.C. Chan, Rana A. Maya, y Gasim Hayder. 2026. «Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq». DYNA 93 (240):81-88. https://doi.org/10.15446/dyna.v93n240.121949.

Harvard

Ali, L. N., Raheem Al-Dhamad, S. H., Al-Zwainy, F. M. S., Zaki, R. I. K., Varouqa, I. F., Al-Dulaimi, S. D. S., Obaid, A. H., Sarhan, M. M., Chan, A. P., Maya, R. A. y Hayder, G. (2026) «Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq», DYNA, 93(240), pp. 81–88. doi: 10.15446/dyna.v93n240.121949.

MLA

Ali, L. N., S. H. Raheem Al-Dhamad, F. M. S. Al-Zwainy, R. I. K. Zaki, I. F. Varouqa, S. D. S. Al-Dulaimi, A. H. Obaid, M. M. Sarhan, A. P. Chan, R. A. Maya, y G. Hayder. «Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq». DYNA, vol. 93, n.º 240, enero de 2026, pp. 81-88, doi:10.15446/dyna.v93n240.121949.

Turabian

Ali, Laith N., Saja Hadi Raheem Al-Dhamad, Faiq M. S. Al-Zwainy, Rana I. K. Zaki, Ibrahim Farouq Varouqa, Salman Dawood Salman Al-Dulaimi, Aseel H. Obaid, Mazin M. Sarhan, Albert P.C. Chan, Rana A. Maya, y Gasim Hayder. «Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq». DYNA 93, no. 240 (enero 19, 2026): 81–88. Accedido marzo 4, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/121949.

Vancouver

1.
Ali LN, Raheem Al-Dhamad SH, Al-Zwainy FMS, Zaki RIK, Varouqa IF, Al-Dulaimi SDS, Obaid AH, Sarhan MM, Chan AP, Maya RA, Hayder G. Development of a support vector machine-based predictive model for bored pile productivity in residential construction projects in Iraq. DYNA [Internet]. 19 de enero de 2026 [citado 4 de marzo de 2026];93(240):81-8. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/121949

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