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Jahangiri, Hossein, Rashvand, Pooria, Shokouhfar, Ahmad, Farokhzad, Reza, Amani, Nima. (1405). Optimization-Oriented Final Construction Cost Forecasting under Price Uncertainty: An Integrated Building Information Modeling and Adaptive Neuro-Fuzzy Inference Framework. فصلنامه علمی زیست فناوری گیاهان زراعی, (), -. doi: 10.30473/coam.2026.78960.1445
Hossein Jahangiri; Pooria Rashvand; Ahmad Shokouhfar; Reza Farokhzad; Nima Amani. "Optimization-Oriented Final Construction Cost Forecasting under Price Uncertainty: An Integrated Building Information Modeling and Adaptive Neuro-Fuzzy Inference Framework". فصلنامه علمی زیست فناوری گیاهان زراعی, , , 1405, -. doi: 10.30473/coam.2026.78960.1445
Jahangiri, Hossein, Rashvand, Pooria, Shokouhfar, Ahmad, Farokhzad, Reza, Amani, Nima. (1405). 'Optimization-Oriented Final Construction Cost Forecasting under Price Uncertainty: An Integrated Building Information Modeling and Adaptive Neuro-Fuzzy Inference Framework', فصلنامه علمی زیست فناوری گیاهان زراعی, (), pp. -. doi: 10.30473/coam.2026.78960.1445
Jahangiri, Hossein, Rashvand, Pooria, Shokouhfar, Ahmad, Farokhzad, Reza, Amani, Nima. Optimization-Oriented Final Construction Cost Forecasting under Price Uncertainty: An Integrated Building Information Modeling and Adaptive Neuro-Fuzzy Inference Framework. فصلنامه علمی زیست فناوری گیاهان زراعی, 1405; (): -. doi: 10.30473/coam.2026.78960.1445

Optimization-Oriented Final Construction Cost Forecasting under Price Uncertainty: An Integrated Building Information Modeling and Adaptive Neuro-Fuzzy Inference Framework

Control and Optimization in Applied Mathematics
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 شهریور 1405    اصل مقاله (1.16 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2026.78960.1445
نویسندگان
Hossein Jahangiri1؛ Pooria Rashvand* 1؛ Ahmad Shokouhfar1؛ Reza Farokhzad1؛ Nima Amani2
1Department of Civil Engineering, Qa.C., Islamic Azad University, Qazvin, Iran
2Department of Civil Engineering, Cha.C., Islamic Azad University, Chalus, Iran
چکیده
Accurate forecasting of final construction costs is important for project planning and cost control in inflation-sensitive construction markets. This study develops a BIM-integrated artificial neural network (ANN) workflow that links item-level Fehrestbaha data and official construction adjustment indices to BIM components and forecasts unit costs at a user-specified completion date. Historical cost information for sixteen materials over 2013--2022 was used to develop the deterministic ANN predictor, and the resulting unit-cost forecasts were returned to the BIM quantity-takeoff workflow. The framework was evaluated on a single nine-story residential building in Tehran by comparing the April 2022 initial estimate, the ANN-based December 2023 forecast, and the actual final project cost. The December 2023 case lies beyond the 2013--2022 historical development horizon and is therefore used as the principal project-level temporal check, whereas the internal training/test diagnostics are interpreted only as model-fit diagnostics because the monthly resampling procedure can induce dependence among adjacent observations. For this case, the absolute project-level deviation was 11.30% for the ANN forecast compared with 23.89% for the initial no-update estimate. The contribution is the automated linkage of national item coding, time-indexed cost information, ANN forecasting, Revit plug-ins, and BIM quantity aggregation rather than a new optimization algorithm or a probabilistic uncertainty model. The results support the feasibility of the workflow as a proof of concept, subject to stricter chronological validation, improved categorical encoding, benchmark comparisons, and multi-project external validation.

تازه های تحقیق
  • Links Iran's national Fehrestbaha cost-index database directly into a BIM model via purpose-built plug-ins.
  • Trains an ANN/ANFIS model on 2013–2022 price data for 16 materials to forecast cost escalation.
  • Combines BIM quantities with forecast unit prices to estimate total cost at a target completion date.
  • Case study: forecast deviation from actual final cost (~11%) was roughly half that of the initial estimate (~24%).
  • Offers a practical, data-driven aid for cash-flow planning and design-change evaluation under inflation.
کلیدواژه‌ها
Building Information Modeling (BIM)؛ Artificial Neural Network (ANN)؛ Construction cost forecasting؛ Fehrestbaha؛ National adjustment indices؛ Quantity takeoff
مراجع

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