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Journals / Karaelmas Fen ve Mühendislik Dergisi / 2024 / Cilt: 14 Sayı: 3

Construction Contract Price Prediction Using Machine Learning with Bidding Parameters

Yapım İşlerinde İhale Parametreleri Kullanılarak Makine Öğrenmesi ile Sözleşme Bedeli Tahmini

Semi Emrah Aslay(ERZİNCAN ÜNİVERSİTESİ)0000-0002-0127-5474
Pages
64–73
Publication Date
2024-11-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study contributes to the literature by introducing novel parameters and implementing hyperparameter optimizations in the machine learning model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In public tenders, contract values are primarily composed of quantities and unit prices, and therefore are closely related to these parameters.
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Key Findings & Data:Besides these parameters, contract values are also meaningfully associated with procurement data that can produce more straightforward numerical expressions and provide significant insights into monetary agreement values.
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Conclusion & Impact:The study contributes to the literature by introducing novel parameters and implementing hyperparameter optimizations in the machine learning model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ANN · kamu ihaleleri · makine öğrenmesi

Abstract

In public tenders, contract values are primarily composed of quantities and unit prices, and therefore are closely related to these parameters. Besides these parameters, contract values are also meaningfully associated with procurement data that can produce more straightforward numerical expressions and provide significant insights into monetary agreement values. Therefore, evaluating the meaningful relationship between procurement data and contract value becomes crucial. This study aims to predict contract amounts in construction works using machine learning methods with bidding variables. Basic and popular algorithms were employed for this purpose. A series of hyperparameter optimizations were conducted to enhance prediction models, yielding positive results. Particularly, the parametric optimizations applied to XGBoost and ANN algorithms, which demonstrated the best results in the study, improved the performance of the existing model. XGBoost achieved the best generalization ability with an of 0.9435, MAE of 1.2988, and RMSE of 2.0621. The study contributes to the literature by introducing novel parameters and implementing hyperparameter optimizations in the machine learning model.

Özet

Kamu ihalelerinde sözleşme bedelleri, temel olarak metraj ve birim fiyatlardan oluşmaktadır ve dolayısıyla bu parametrelerle yakından ilişkilidir. Bu parametrelerden başka, daha sade sayısal ifadeler üretebilen ve parasal anlaşma bedelleri hakkında önemli ipuçları veren ihale verileriyle de anlamlı ilişkilere sahiptir. Dolayısıyla ihale verileri ile sözleşme bedeli arasındaki anlamlı ilişkinin değerlendirilmesi önem kazanmaktadır. Bu çalışmada inşaat işlerinde ihale değişkenleri kullanılarak makine öğrenmesi yöntemleri ile sözleşme bedelleri tahmin edilmeye çalışılmaktadır. Bunun için temel ve popüler algoritmalardan yararlanılmıştır. Tahmin modellerini geliştirmek amacıyla bir dizi hiper parametre optimizasyonu yapılarak olumlu sonuçlar alınmıştır. Özellikle çalışmada en iyi sonuçlara sahip olan XGBoost ve ANN algoritmalarında uygulanan parametrik optimizasyonlar, mevcut modelin daha iyi bir performansı göstermesini sağlamıştır. XGBoost 0.9435, MAE 1.2988, RMSE 2.0621 en iyi genelleme yeteneğine sahip algoritma olmuştur. Çalışma hem kullandığı parametrelerin özgünlüğü hem de makine öğrenmesi modelinde uygulanan hiper parametre optimizasyonları ile literatüre katkı sunmaktadır.

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Cite This Article

Aslay, S. E. (2024). Construction Contract Price Prediction Using Machine Learning with Bidding Parameters. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 14 Sayı: 3, 64–73. https://doi.org/10.7212/karaelmasfen.1484595

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