Graph‐based deep learning model for knowledge base completion in constraint management of construction projects

Abstract

Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Packagebased constraint management (PCM) is a state-of-the-art graph-based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuring smooth construction and minimizing delay and waste. PCM relies on exploring and investigating project knowledge bases (KBs), formed by entity-relation-entity triples of constraints.However, most PCM KBs are incomplete and suffer from poor semantics, which hinders the PCM functions. Although many KB completion (KBC) methods exist in the field of artificial intelligence, they primarily focus on general knowledge and exclude the features of specific domains. Therefore, they cannot be directly applied to complete PCM KBs. To address the issue, this study proposes a novel deep learning model, referred to as the domain information enhanced graph neural network (D-GNN). The features of the developed D-GNN include (1) building a domain ontology to enrich semantics with rule reasoning, (2) applying the GNN to learn and encode embeddings of constraint entities and relations, and (3) employing a convolution neural network (CNN) for decoding and identifying missing triples. D-GNN improves the existing KBC methods by integrating two types of domain information, namely, the ontological classes and working contexts into GNN and CNN, respectively. The experimental results verified that the D-GNN reached an accuracy of 0.848-0.951, and the domain information integration increased the performance by up to 0.263. In practical testing, the D-GNN significantly reduced the KBC time to 1/6-1/35 of the manual approach and reached higher accuracy. Therefore, the proposed D-GNN can facilitate PCM by providing complete KBs and supporting downstream constraint monitoring and removal.

Lean Construction Strategies Supported by Artificial Intelligence Techniques for Construction Project Management—A Review

“…[34], [36], [47], [58], [73], [75], [76], [82], [83] 2 Quality and Safety Real-time remote monitoring support. High accuracy of generating intelligent reports in time on the development of the construction.…”

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

“…In recent years, variants of GNNs such as graph convolutional networks (GCN; Kipf & Welling, 2016) and Graph‐SAGE (Hamilton et al., 2018) have exhibited excellent performances on many deep learning tasks. Nowadays, the applications of GNN have been extended to various tasks with graph‐like data structures, such as building structures (Song et al., 2022; P. Zhao et al., 2023a), water distribution networks (Fan et al., 2022), package‐based constraint management (Wu et al., 2023), autonomous vehicle networks (Chen et al., 2021), neural network architectures (Xue et al., 2021), neural signals (Feng et al., 2021), and electroencephalogram signals (Che et al., 2022; Lian & Xu, 2022; Y. Zhao et al., 2021).…”

Integration of Industry Foundation Classes and Ontology: Data, Applications, Modes, Challenges, and Opportunities

“…Ontology has good adaptability and has been widely used in medicine [13], biology [14], engineering [15], agriculture [16], and other fields. In the AEC/FM field, ontology is proven to have applicative value in various tasks, such as knowledge representation [17,18], data interoperability [19][20][21], and rule-based reasoning [22,23].…”