Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector

Ugur L. O., Kanit R., Erdal H., Namlı E., Erdal H. I., Baykan U. N., ...Daha Fazla

COMPUTATIONAL ECONOMICS, cilt.53, ss.1403-1419, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s10614-018-9814-9
  • Dergi Adı: COMPUTATIONAL ECONOMICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.1403-1419
  • Anahtar Kelimeler: Bagging, CART, Multi-layer perceptron, Ensemble machine learning, Random subspace, Construction project, Construction cost, CONCRETE COMPRESSIVE STRENGTH, BAGGING ENSEMBLE MODELS, NEURAL-NETWORKS, PERFORMANCE, ACCURACY, REGRESSION
  • Gazi Üniversitesi Adresli: Evet

Özet

The analysis of a construction project, regarding cost, is one of the most vital problem in planning. Due to its nature, the construction sector is an area of strong competition and estimation works are of vital importance. In recent years the Turkish Republic has started a serious urban regeneration movement in parallel to its economic development. This study is based on the drawings and quantities of 63 detached multi-story reinforced concrete housing unit projects of the Housing Development Administration (TOKI) and the Turkey Residential Building Cooperative Union (TURKKONUT). TOKI is a public company and its projects are that have been applied to 282 separate projects and are being applied to a further 266. On the other side TURKKONUT is a union of 1347 private building cooperative and have been completed 200,000 residential building. The main objective of this study is to improve the estimation accuracy of individual machine learning techniques, namely multi-layer perceptron and classification and regression trees and compares the performance of two machine learning meta-algorithms (i.e., bagging and random subspace) on a real world construction cost estimation problem. The study shows that the estimation accuracy of ensemble models are better than the models that constructed by their base learners and ensemble models may improve individual machine learning models.