Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia
Само за регистроване кориснике
2014
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
This paper presents the development of prediction models for the unit costs of road works that could be applied to strategic planning of road works at the network level. A specialized data set was used, which was generated under a World Bank study that included 200 road work contracts from 14 countries in Europe and Central Asia (ECA) and signed between 2000 and 2010. Two techniques were used for model development: multiple regression analysis and artificial neural networks. Classification trees were used as an intermediate step to evaluate the correctness of the selected parameters. A total of 19 variables, divided into three groups (oil-price related, country-related, and project-related variables), were tested for their influence on unit cost of asphalt concrete (AC) and road rehabilitation and reconstruction (RRR) costs. The analysis results showed that the level of corruption and the economic environment in a country have a significant effect on both costs of AC and RRR. The resul...ting models could be particularly useful for the planning and optimization of work on road networks in ECA countries. However, the approach and methodology used for model developments may be applied generally.
Кључне речи:
Statistics / Rehabilitation / Regression models / Regression analysis / Reconstruction / Neural networks / Maintenance costs / Highways and roads / Cost and schedule / Correlation / Construction costsИзвор:
Journal of Construction Engineering and Management, 2014, 140, 3Издавач:
- Asce-Amer Soc Civil Engineers, Reston
DOI: 10.1061/(ASCE)CO.1943-7862.0000817
ISSN: 0733-9364
WoS: 000332659900016
Scopus: 2-s2.0-84894419416
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Cirilović, Jelena AU - Vajdić, Nevena AU - Mladenović, Goran M. AU - Queiroz, Cesar PY - 2014 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1993 AB - This paper presents the development of prediction models for the unit costs of road works that could be applied to strategic planning of road works at the network level. A specialized data set was used, which was generated under a World Bank study that included 200 road work contracts from 14 countries in Europe and Central Asia (ECA) and signed between 2000 and 2010. Two techniques were used for model development: multiple regression analysis and artificial neural networks. Classification trees were used as an intermediate step to evaluate the correctness of the selected parameters. A total of 19 variables, divided into three groups (oil-price related, country-related, and project-related variables), were tested for their influence on unit cost of asphalt concrete (AC) and road rehabilitation and reconstruction (RRR) costs. The analysis results showed that the level of corruption and the economic environment in a country have a significant effect on both costs of AC and RRR. The resulting models could be particularly useful for the planning and optimization of work on road networks in ECA countries. However, the approach and methodology used for model developments may be applied generally. PB - Asce-Amer Soc Civil Engineers, Reston T2 - Journal of Construction Engineering and Management T1 - Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia IS - 3 VL - 140 DO - 10.1061/(ASCE)CO.1943-7862.0000817 ER -
@article{ author = "Cirilović, Jelena and Vajdić, Nevena and Mladenović, Goran M. and Queiroz, Cesar", year = "2014", abstract = "This paper presents the development of prediction models for the unit costs of road works that could be applied to strategic planning of road works at the network level. A specialized data set was used, which was generated under a World Bank study that included 200 road work contracts from 14 countries in Europe and Central Asia (ECA) and signed between 2000 and 2010. Two techniques were used for model development: multiple regression analysis and artificial neural networks. Classification trees were used as an intermediate step to evaluate the correctness of the selected parameters. A total of 19 variables, divided into three groups (oil-price related, country-related, and project-related variables), were tested for their influence on unit cost of asphalt concrete (AC) and road rehabilitation and reconstruction (RRR) costs. The analysis results showed that the level of corruption and the economic environment in a country have a significant effect on both costs of AC and RRR. The resulting models could be particularly useful for the planning and optimization of work on road networks in ECA countries. However, the approach and methodology used for model developments may be applied generally.", publisher = "Asce-Amer Soc Civil Engineers, Reston", journal = "Journal of Construction Engineering and Management", title = "Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia", number = "3", volume = "140", doi = "10.1061/(ASCE)CO.1943-7862.0000817" }
Cirilović, J., Vajdić, N., Mladenović, G. M.,& Queiroz, C.. (2014). Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia. in Journal of Construction Engineering and Management Asce-Amer Soc Civil Engineers, Reston., 140(3). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000817
Cirilović J, Vajdić N, Mladenović GM, Queiroz C. Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia. in Journal of Construction Engineering and Management. 2014;140(3). doi:10.1061/(ASCE)CO.1943-7862.0000817 .
Cirilović, Jelena, Vajdić, Nevena, Mladenović, Goran M., Queiroz, Cesar, "Developing Cost Estimation Models for Road Rehabilitation and Reconstruction: Case Study of Projects in Europe and Central Asia" in Journal of Construction Engineering and Management, 140, no. 3 (2014), https://doi.org/10.1061/(ASCE)CO.1943-7862.0000817 . .