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A Comparative Study of Models for the Construction Duration Prediction in Highway Road Projects of India
| Content Provider | MDPI |
|---|---|
| Author | Urbański, M. Velumani, P. Nampoothiri, N. |
| Copyright Year | 2021 |
| Description | Predicting the duration of construction projects with acceptable accuracy is a problem for contractors and researchers. Numerous researchers and tools are involved in sorting out this problem. The aim of the study is to predict the construction duration using four analytical tools as an approach. The success of construction projects in regard to time depends on various factors such as selection of contractors, consultants, cost of the projects, quality of the projects, the quantity of the projects, environmental factors, etc. Presently available commercial tools in the market are not designed as universally common and concerned. Every tool performs well in a particular situation. The prediction of India’s highway road projects duration is the biggest construction issue in the country due to various reasons. To overcome this problem, the methodology of the paper adopts various strategies to find suitable tools to predict the highway road projects’ duration, in which it classifies and analyzes the collected data. As a part of this work, the details of 363 government infrastructure projects (traditional procurement) were collected from 2000 to 2018. The present study also adopts various tools for duration prediction such as artificial neural networks (ANNs), smoothing techniques, time series analysis, and Bromilow’s time–cost (BTC) model. The results of the study recommend smoothing techniques with a constant value of 0.3, which gave the remarkable very small error of 1.2%, and its outcomes become even better when compared to other techniques. |
| Starting Page | 4552 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su13084552 |
| Journal | Sustainability |
| Issue Number | 8 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-04-20 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Transportation Science and Technology Construction Duration Artificial Neural Network Smoothing Techniques Time Series Analysis Bromilow's Time–cost Model Prediction and Infrastructure Projects |
| Content Type | Text |
| Resource Type | Article |
