Details

Realizing the Potential of AI in Construction Delay Analysis: An Examination of the Issues and Improvement Needs in Delay Analysis Techniques at SMDC Projects

Darwin L. Uy

Student, Master of Engineering Management, Graduate School, Nueva Ecija University of Science & Technology (NEUST), Cabanatuan, Nueva Ecija, Philippines

Noel T. Florencondia

Professor, Master of Engineering Management, Graduate School, Nueva Ecija University of Science & Technology (NEUST), Cabanatuan, Nueva Ecija, Philippines

19-37

Vol: 15, Issue: 2, 2025

Receiving Date: 2025-03-11 Acceptance Date:

2025-05-16

Publication Date:

2025-05-23

Download PDF

http://doi.org/10.37648/ijrst.v15i02.003

Abstract

This research investigates the potential of Artificial Intelligence (AI) to improve delay analysis techniques within the construction industry, specifically focusing on SM Development Corporation (SMDC) projects. The study aims to identify the limitations of traditional delay analysis methods (Balli & Güven, 2022) and explore how AI can address these shortcomings to enhance project outcomes (Egwim et al., 2021).

Keywords: Causes of Delay; Delay Analysis Techniques; Artificial Intelligence; Machine Learning; SM Development Corporation; Project Management

References

  1. Abioye, S., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., Akinadé, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. Elsevier BV. https://doi.org/10.1016/j.jobe.2021.103299
  2. Al-Gahtani, K. S., & Mohan, S. (2011). Delay analysis techniques comparison. Journal of Civil Engineering and Architecture, 5(8). https://doi.org/10.17265/1934-7359/2011.08.008
  3. Almén, I. (2024). Usage of AI in preventing delay within construction management.
  4. Alnaas, K. A. A., Khall, A. H. H., & Nassar, G. E. (2014). Guideline for preparing comprehensive extension of time (EoT) claim. HBRC Journal, 10(3), 308–316. https://doi.org/10.1016/j.hbrcj.2014.01.005
  5. Arun, A. (2013). Statistical methods for construction delay analysis. IOSR Journal of Mechanical and Civil Engineering, 9(2), 58–62. https://doi.org/10.9790/1684-0925862
  6. Assaf, S. A., & Al-Hejji, S. (2006). Causes of delay in large construction projects. International Journal of Project Management, 24(4), 349–357. https://doi.org/10.1016/j.ijproman.2005.11.010
  7. Balli, O., & Güven, İ. (2022). Comparison of artificial intelligence approaches used for construction project delay.
  8. Boyacioglu, S. E., Greenwood, D., Rogage, K., Gledson, B., Parry, A., & Hinds, M. (2022). Developing an improved process model for forensic analysis of construction project delays.
  9. Braimah, N. (2013). Construction delay analysis techniques—A review of application issues and improvement needs. Buildings, 3(3), 506–531. https://doi.org/10.3390/buildings3030506
  10. Dinakar, A. (2014). Delay analysis in construction project. International Journal of Emerging Technology and Advanced Engineering, 4(5), 225–230. http://ijetae.com/files/Volume4Issue5/IJETAE_0514_119.pdf
  11. Egwim, C. N., Alaka, H., Demir, E., Balogun, H., Olu-Ajayi, R., Sulaimon, I., Wusu, G., Yusuf, W., & Adegoke, M. (2023). Artificial intelligence in the construction industry: A systematic review of the entire construction value chain lifecycle. Energies, 17(1), 182. https://doi.org/10.3390/en17010182
  12. Egwim, C. N., Alaka, H., Toriola-Coker, L. O., Balogun, H., & Sunmola, F. (2021). Applied artificial intelligence for predicting construction projects delay. Machine Learning with Applications, 6, 100166. https://doi.org/10.1016/j.mlwa.2021.100166
  13. Espiritu, P. G. G., Manzon, R. D., & Florencondia, N. (2023). Digital transformation adoption in the local government unit (LGU) of the Science City of Muñoz, Nueva Ecija: The challenges and best practices. The QUEST, 2(3). Nueva Ecija University of Science and Technology, Graduate School.
  14. Ghaithi, A. A., & Prasad, M. (2020). Machine learning with artificial neural networks for shear log predictions in the Volve field Norwegian North Sea. SEG Technical Program Expanded Abstracts 2020, 450. https://doi.org/10.1190/segam2020-3427540.1
  15. Hegazy, S. (2012). Delay analysis methodology in UAE construction projects: Delay claims, literature review.
  16. Ivanović, M. Z., Nedeljković, Đ., Stojadinović, Z., Marinković, D., Ivanišević, N., & Simić, N. (2022). Detection and in-depth analysis of causes of delay in construction projects: Synergy between machine learning and expert knowledge. Sustainability, 14(22), 14927. https://doi.org/10.3390/su142214927
  17. Meena, & Babu, A. K. S. (2015). Study on time delay analysis for construction project delay analysis. International Journal of Engineering Research & Technology, 4(3). https://doi.org/10.17577/ijertv4is031166
  18. Ndekugri, I., Braimah, N., & Gameson, R. (2008). Delay analysis within construction contracting organizations. Journal of Construction Engineering and Management, 134(9), 692–700. https://doi.org/10.1061/(ASCE)0733- 9364(2008)134:9(692)
  19. Radman, K., Jelodar, M. B., Lovreglio, R., Ghazizadeh, E., & Wilkinson, S. (2022). Digital technologies and data-driven delay management process for construction projects. Frontiers in Built Environment, 8. https://doi.org/10.3389/fbuil.2022.1029586
  20. Sanni-Anibire, M., Zin, M., & Olatunji, S. (2020). Forecasting construction delay times in high-rise building projects.
  21. Shiboldenkov, V. A., & Nesterova, K. (2020). The smart technologies application for the product life-cycle management in modern manufacturing systems. MATEC Web of Conferences, 311, 2020. https://doi.org/10.1051/matecconf/202031102020
  22. Türkakın, O. H., Manisalı, E., & Arditi, D. (2020). Delay analysis in construction projects with no updated work schedules. Engineering, Construction and Architectural Management, 27(10), 2893–2912. https://doi.org/10.1108/ecam-09-2019-0470
  23. Yaseen, Z. M., Ali, Z. H., Salih, S. Q., & Al‐Ansari, N. (2020). Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability, 12(4), 1514. https://doi.org/10.3390/su12041514
Back

Disclaimer: Indexing of published papers is subject to the evaluation and acceptance criteria of the respective indexing agencies. While we strive to maintain high academic and editorial standards, International Journal of Research in Science and Technology does not guarantee the indexing of any published paper. Acceptance and inclusion in indexing databases are determined by the quality, originality, and relevance of the paper, and are at the sole discretion of the indexing bodies.

We are one of the best in the field of watches and we take care of the needs of our customers and produce replica watches of very good quality as per their demands.
BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP SUPERJP ELANG212 ELANG212 GORI77 GORI77 GORI77 GORI77 data HK WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 CLAN4D CLAN4D DINAMIT4D DINAMIT4D VIRAL88 VIRAL88 SAMSONBET86 PAKONG86 JAGOAN86 LINABET69 KAPTENJACKPOT KAPTENJACKPOT GILAJP boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp boosterjp BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 GORI77 BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP BOOSTERJP VIRAL88 VIRAL88 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 WINSTRIKE69 GOJEKPOT WINSTRIKE69 WINSTRIKE69 boosterjp akun pro thailand Paito SDY Lotto SLOT GACOR VIRAL4D WINSTRIKE69 BOOSTERJP BOOSTERJP GORI77 GORI77 GORI77 GORI77 VIRAL88