Estimating contract value using structural parameters: a machine learning approach with data preprocessing

Year 2024, Volume: 15 Issue: 3, 755 - 765, 30.09.2024

Semi Emrah Aslay

https://doi.org/10.24012/dumf.1515160

Abstract

In this study, contract price in public construction tenders are predicted using structural project parameters. The variables applied in the study are created by adding the quantities of columns, shear walls, and beams to variables commonly used in the literature for cost estimations. Six different machine learning algorithms are employed as machine learning algorithms. Preprocessing methods and a series of parameter optimizations are applied to enhance the predictive capability on datasets. These processes and the applied algorithms are evaluated with five different performance metrics. The SVM algorithm produced the best results, achieving an value of 0.8966, MAPE of 23.70, NSE of 0.8956, MAE of 0.4849, and RMSE of 0.6989. This study contributes to the literature by developing machine learning models and data analysis processes for contract price approaches.

Keywords

SVM, machine learning, structural parameters, data preprocessing, feature importance.

Yapısal parametreler kullanarak sözleşme değerinin tahmin edilmesi: veri ön işleme ile bir makine öğrenimi yaklaşımı

Abstract

Bu çalışmada, kamu inşaat ihalelerinde sözleşme bedeli yapısal proje parametreleri kullanılarak tahmin edilmektedir. Çalışmada uygulanan değişkenler, literatürde maliyet tahminleri için yaygın olarak kullanılan değişkenlere kolon, perde duvar ve kiriş miktarlarının eklenmesiyle oluşturulmuştur. Makine öğrenmesi algoritmaları olarak altı farklı makine öğrenmesi algoritması kullanılmıştır. Veri kümeleri üzerinde tahmin kabiliyetini artırmak için ön işleme yöntemleri ve bir dizi parametre optimizasyonu uygulanmaktadır. Bu işlemler ve uygulanan algoritmalar beş farklı performans metriği ile değerlendirilmiştir. SVM algoritması 0,8966, MAPE 23,70, NSE 0,8956, MAE 0,4849 ve RMSE 0,6989 değerlerine ulaşarak en iyi sonuçları vermiştir. Bu çalışma sözleşme fiyatı yaklaşımları için makine öğrenimi modellerini ve veri analizi süreçlerini geliştirerek literatüre katkı sunmaktadır.

Keywords

SVM, makine öğrenmesi, yapısal parametreler, veri ön işleme, özellik önemi.

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