Modeling Performance and Uncertainty of Construction Planning under Deep Uncertainty: A Prediction Interval Approach

Abstract

In construction planning, decision making has a great impact on final project performance. Hence, it is essential for project managers to assess the construction planning and make informed decisions. However, disproportionately large uncertainties occur during the construction planning stage; in the worst case, reliable probability distributions of uncertainties are sometimes unavailable due to a lack of information before construction implementation. This situation constitutes a deep uncertainty problem, making it a challenge to perform a probability-based uncertainty assessment. The current study proposes a modeling approach that applies prediction intervals for construction planning via the integration of discrete-event simulation (DES), fuzzy C-means clustering (FCM), Bayesian regularization backpropagation neural networks (BRBNNs), and particle swarm optimization (PSO). The DES is used to perform data sampling of the construction alternatives and assess their performances under uncertainty. Based on the generated samples, the FCM, BRBNN, and PSO are integrated in a machine learning algorithm to model the prediction intervals that represent relationships between construction planning schemes, performances, and the corresponding uncertainties. The proposed approach was applied to a case project, with the results indicating that it is capable of modeling construction performance and deep uncertainties with a defined 95% confidence level and fluctuation within 1~9%. The presented research contributes a new and innovative option, using prediction intervals to solve deep uncertainty problems, without relying on the probability of the uncertainty. This study demonstrates the effectiveness of the proposed approach in construction planning.