Application of novel hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN): construction duration estimates prediction considering preconstruction uncertainties

Abstract

Abstract Construction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns and delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) while considering construction delivery uncertainties. The first model introduces a Spatio-Temporal Attention CNN-RNN Hybrid Model with Probabilistic Uncertainty Modeling, which integrates attention mechanisms and probabilistic uncertainty modeling to provide accurate and probabilistic estimates of construction duration, offering insights into critical areas of uncertainty. The second model presents a Multi-Modal Graph CNN-RNN Hybrid Model with Bayesian Uncertainty Integration, which harnesses multi-modal data sources and graph representations to offer comprehensive estimates of construction duration while incorporating Bayesian uncertainty measures, facilitating informed decision-making and optimized resource allocation. Lastly, the third model introduces a Hierarchical Spatio-Temporal Transformer CNN-RNN Hybrid Model with Fuzzy Logic Uncertainty Handling, which addresses the inherent vagueness and imprecision in construction duration estimates by incorporating hierarchical spatio-temporal transformer architecture and fuzzy logic uncertainty handling, leading to more nuanced and adaptable project management practices. These advanced models represent significant advancements in addressing construction duration challenges, providing valuable insights and recommendations for future research and industry applications. Moreover, this review article critically examines the application of hybrid deep learning architectures, specifically the combination of CNNs RNNs, in predicting construction duration estimates at the preconstruction stage while considering uncertainties inherent in construction delivery systems.