Deep learning-based forecasting for construction project duration at completion

Project duration-at-completion (DAC) forecasting is a significant challenge in construction, where inaccuracies can lead to inefficient resource allocation, poor risk management, cost overruns, liquidated damages, and unrealistic stakeholder expectations. Especially during the construction phase, which manages the largest project budget and meets contractual milestones. This research aims to enhance DAC forecast accuracy by leveraging historical data using Deep Learning (DL), providing weekly work packages-level and project-level predictions. A Data Acquisition Model (DAM) collected duration-influencing factors per work package in a time series format, to then apply Long Short-Term Memory (LSTM), One-Dimensional Convolutional Neural Network (CONV-1D), and Multilayer Perceptron (MLP) algorithms. Once the optimal was selected, the overall DAC was computed by consolidating individual predictions and using the current project schedule, Precedence Diagramming and Critical Path Methods. By doing so, LSTM outperformed MLP and CONV-1D, with MASE 0.27, 0.29, and 0.54 for Concrete, Excavation and Backfill work packages. The LSTM-based model surpassed the widely used EVM and ESM, while a Monte Carlo-based sensitivity analysis verified its robustness. This deep-learning model was automated through a GUI, delivering forecasting Gantt charts, performance curves, critical path charts, interacting with Primavera P6 to capture data. This model aims to leverage Artificial Intelligence capabilities in construction.