Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects
Yazarlar (4)
Tarq Zaed Khalaf Kastamonu University, Türkiye
Doç. Dr. Hakan ÇAĞLAR Kastamonu University, Türkiye
Doç. Dr. Arzu ÇAĞLAR Kastamonu University, Türkiye
Ammar N. Hanoon University Of Baghdad, Irak
| Makale Türü |
Özgün Makale
(ESCI dergilerinde yayınlanan tam makale)
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| Dergi Adı | Civil Engineering Journal Iran | ||
| Dergi ISSN | 2676-6957 Dergi Bilgileri (2020) | ||
| Dergi Tarandığı Indeksler | ESCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2020 |
| Cilt / Sayı / Sayfa | 6 / 2 / 384–401 | DOI | 10.28991/cej-2020-03091478 |
| Makale Linki | https://www.civilejournal.org/index.php/cej/article/download/1982/pdf | ||
| UAK Araştırma Alanları |
Mimarlıkta Malzeme ve Teknoloji
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| Özet |
| Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise. |
| Anahtar Kelimeler |
| Construction project | Cost | Decision making | Duration | Managing projects | Particle swarm optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 33 |
| Scopus | 53 |
| Google Scholar | 67 |