Data-Driven Analysis of Claim-Causing Factors in Water and Wastewater Projects Using a Decision Support System Approach

Shakibazahed, Hadi; Rahimi, Vahid

doi:10.22065/jsce.2026.538327.3785

Data-Driven Analysis of Claim-Causing Factors in Water and Wastewater Projects Using a Decision Support System Approach

Document Type : Original Article

Authors

hadi shakibazahed ¹

Vahid Rahimi ²

¹ Assistant Professor, Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran. E-mail: h.shakiba@hsu.ac.ir

² Master's student, Department of Civil Engineering, Hakim Sabzevari University, Sabzevar, Iran

10.22065/jsce.2026.538327.3785

Abstract

Contractual claims are among the most critical challenges in construction projects, often leading to cost overruns, time delays, and quality deterioration. This study aims to identify and analyze the key factors causing claims in water and wastewater projects in Mashhad using a combined data-driven and analytical approach. In the first stage, the influencing factors were identified through document review and expert interviews. Based on these results, a questionnaire was developed and the collected data were analyzed using SPSS software and the Friedman test to obtain an initial ranking of the claim-causing factors. In the next stage, a predictive model for claim occurrence was developed using the Random Forest algorithm. The results demonstrated that the model achieved an accuracy of 87%, indicating a high level of reliability. In addition, structural analysis based on Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that ambiguity in contract terms, unforeseen site conditions, and weak resource management are among the most influential causes of claims. Furthermore, the TOPSIS method was applied to grade and prioritize projects in order to support effective and structured decision-making. The results confirm that integrating data-driven approaches with decision support systems can significantly reduce disputes and costs, enhance transparency, strengthen trust between contractual parties, and improve the overall efficiency of construction projects. The proposed framework provides a practical foundation for the development of intelligent decision support systems aimed at proactive and systematic claim management in project-oriented organizations.

Keywords

Contractual Claims

Machine Learning

Structural Equation Modelling

Random Forest

DSS

Subjects

Project and Construction Management

[1] Emami, H. (2012). Civil Law, Volume I. Tehran: Eslamieh Press, pp. 45–67.

[2] Shadid, M. S. R. (2015). Construction claims management in united Arab Emirates construction industry (Master's thesis, Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ)).

[3] Jergeas, G. F. (2001). Claims and disputes in the construction industry. AACE International Transactions, CD31.

[4] Bakhary, N. A., Adnan, H., & Ibrahim, A. (2015). A study of construction claim management problems in Malaysia. Procedia economics and finance, 23, 63-70.

[5] Bhangwar, S.N. (2022). Risk Level of the Types and Causes of Claims in Construction Projects of Pakistan. Quaid-e-Awam University Research Journal of Engineering Science and Technology, Vol.20 No.1, pp. … https://doi.org/10.52584/QRJ.2001.03

[6] Mohamed, H. H., Ibrahim, A. H., & Soliman, A. A. (2014). Reducing construction disputes through effective claims management. American Journal of Civil Engineering and Architecture, 2(6), 186-196.

[7] Nosheen, A., Arewa, A., & Akhtar, H. M. (2020, April). Preventive approach to unsubstantiated claims and disputes in the UK construction industry. In International Conference on Civil Infrastructure and Construction (pp. 98-107). Qatar University Press.

[8] Kumaraswamy, M. M. (1997). Conflicts, claims and disputes in construction. Engineering Construction and Architectural Management, 4(2), 95-111.

[9] Hashem M. Mehany, M. S., & Grigg, N. (2015). Causes of road and bridge construction claims: Analysis of Colorado department of transportation projects. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 7(2), 04514006.

[10] Piyumra, R. A. D., Disaratna, V., & Perera, B. A. K. S. (2025). Framework for Integrating Experiential Knowledge, Personal Constructs, and Knowledge Transfer toward Successful Construction Claims Management. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 17(1), 04524031.

[11] Jalal, M. P., & Moradi, S. (2024). Journal of Structural and Construction Engineering. Journal of Structural and Construction Engineering, 10(10), 131-150.

[12] Ansari, R., & Banihashemi, S. A. (2023). A Hybrid Method for Analyzing Key Performance Indicators Based on the Causes of Claims in Construction Industry Projects with the Fuzzy set Theory Approach. Journal of Structural and Construction Engineering, 10(2), 120-137.

[13] Amani, N., & Derafshi, M. (2021). Optimal Implementation of Claim Management Framework on Rubber Dam Projects with Price List Contract. Journal of Structural and Construction Engineering, 8(7), 124-145.

[14] Parchami Jalal, M., & Farshad, S. (2018). The claim orientation dissection in Iran and FIDIC consultant's contracts. Journal of Structural and Construction Engineering, 5(Special Issue 3), 16-36.

[15] Sajedi, F., & Sarmast Shoushtari, F. (2017). Analysis of claims and disputes in contracts for oil and gas development projects in Iran with solutions. Journal of Structural and Construction Engineering, 4(2), 173-192.

[16] Banwo, O. (2016). The development of a procurement decision support system to enhance construction claims management practice (Doctoral dissertation, Coventry University).

[17] Mamboleo, H. G. (2023). A Study of the Role of Contracting Parties in Preventing Escalation of Construction Claims Into Disputes Through the Claim Management Processes (Doctoral dissertation, University of Nairobi).

[18] Kalogeraki, M., & Antoniou, F. (2024). Claim management and dispute resolution in the construction industry: Current research trends using novel technologies. Buildings, 14(4), 967.

[19] Galjanić, K., Marović, I., & Jajac, N. (2022). Decision support systems for managing construction projects: A scientific evolution analysis. Sustainability, 14(9), 4977.

[20] Alqaisi, A. S., Ataei, H., Seyrfar, A., & Al Omari, M. (2024). Predicting the outcome of construction change disputes using machine-learning algorithms. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 16(1), 04523051.

[21] Waqar, A. (2024). Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches. Expert Systems with Applications, 249, 123503.

[22] Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827.

[23] Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2019). Multivariate Data Analysis. 8th ed. Boston: Cengage Learning, pp.1–800.