Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data

Abstract

Accurate estimation of construction costs and schedules is crucial for optimizing project planning and resource allocation. Most current approaches utilize traditional statistical analysis and machine learning techniques to process the vast amounts of data regularly generated in construction environments. However, these approaches do not adequately capture the intricate patterns in either time-dependent or time-independent data. Thus, a hybrid deep learning model (NN-BiGRU), combining Neural Network (NN) for time-independent and Bidirectional Gated Recurrent Unit (BiGRU) for time-dependent, was developed in this paper to estimate the final cost and schedule to completion of projects. The Optical Microscope Algorithm (OMA) was used to fine-tune the NN-BiGRU model (OMA-NN-BiGRU). The proposed model earned Reference Index (RI) values of 0.977 for construction costs and 0.932 for completion schedules. These findings underscore the potential of the OMA-NN-BiGRU model to provide highly accurate predictions, enabling stakeholders to make informed decisions that promote project efficiency and overall success.