A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)
Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi:10.28991/cej-2020-03091612 Full Text: PDF
Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)
Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi:10.28991/cej-2020-03091612 Full Text: PDF
Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)
Youneszadeh, Hessam (author) / Ardeshir, Abdollah (author) / Sebt, Mohammad Hassan (author)
2020-11-01
doi:10.28991/cej-2020-03091612
Civil Engineering Journal; Vol 6, No 11 (2020): November; 2203-2219 ; 2476-3055 ; 2676-6957
Article (Journal)
Electronic Resource
English
DDC:
690
Prediction of fracture toughness using artificial neural networks (ANNs)
| British Library Online Contents | 1997
Using Artificial Neural Networks (ANNs) for Hot Mix Asphalt E* Predictions
| British Library Conference Proceedings | 2016
Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)
| DOAJ | 2021