Assessment of Time-Cost Relation for Predicting
Construction Time of Trail Bridge Using
Bromilow Time Cost Model and General
Regression Neural Network
Student: Roshan Kumar Das
Supervisor: Er. Sujan Nepal/Er. Sudeep Pokhrel
Submitted Date:
September, 2024
Abstract
The success of construction companies relies on completing projects within the agreed
cost and time limits, with construction time playing a crucial role in achieving this
success. Accurate forecasting of the final construction duration early in the project is vital,
as errors in prediction can result in significant losses. Considering the several aspects that
influence construction time, precise duration estimates are essential for ensuring project
success.
This research investigates the relationship between the final cost and construction time of
trail bridges in Nepal, utilizing both the Bromilow Time Cost (BTC) Model and General
Regression Neural Network (GRNN). The primary objectives were to analyze the time-
cost relationship using these models and to evaluate their predictive accuracy for
estimating construction durations.
In this study, KII was conducted to understand the existing methods for calculating
contract duration and the viewpoints on the effectiveness of the approach employed.
Secondary data for the study was collected from a total of 254 trail bridge projects
categorized into D, N, MN, and combined types. The data was refined using MS Excel and
analyzed with DTREG software for GRNN, while regression analysis was performed for
the BTC model.
The findings reveal that neither the BTC nor the GRNN models were statistically
significant in explaining the variability in construction time based on project costs, with
both models displaying low R-squared values across all bridge types. However, the GRNN
model demonstrated slightly better predictive accuracy than the BTC model. The GRNN
model outperformed the BTC model in predictive accuracy for some bridge types but still
exhibited low overall R2 values, indicating that neither model effectively predicts
construction time.
The study concludes that both the BTC and GRNN models are insufficient for accurately
predicting construction time based on final cost for trail bridges. It recommends further
research incorporating additional variables, such as project location, labor availability,
weather conditions, and material supply, to enhance predictive accuracy. Future studies
should also focus on alternative modeling approaches and the collection of more detailed
project data to improve forecasting precision.
Keywords
Bromilow’s time cost model (BTC); General Regression Neural Network
(GRNN); Time Cost Model; Duration Prediction; Trail Bridge