Improving the accuracy of schedule information communication between humans and data
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Publication Date
2022-08Journal Title
Advanced Engineering Informatics
ISSN
1474-0346
Publisher
Elsevier BV
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Hong, Y., Xie, H., Bhumbra, G., & Brilakis, I. (2022). Improving the accuracy of schedule information communication between humans and data. Advanced Engineering Informatics https://doi.org/10.1016/j.aei.2022.101645
Abstract
Construction schedules are written instructions of construction execution shared between stakeholders for essential project information exchange. However, construction schedules are semi-structured data that lack semantic details and coherence within and across projects. This study proposes an ontology-based Recurrent Neural Network approach to bi-directionally translate between human written language and machinery ontological language. The proposed approach is assessed in three areas: text generation accuracy, machine readability, and human understandability. This study collected 30 project schedules with 19,589 activities (sample size = 19,589) from a Tier-1 contractor in the UK. The experimental results indicate that: (1) precision and recall of text generation LSTM-RNN model is 0.991 and 0.874, respectively; (2) schedule readability improved by increasing the semantic distinctiveness, measured using the cosine similarity which was reduced from 0.995 to 0.990 (p < 0.01); (3) schedule understandability improved from 75.90% to 85.55%. The proposed approach formalises text descriptions in construction schedules and other construction documents with less labour investment. It supports contractors to establish knowledge management systems to learn from historic data and make more informed decisions in future similar scenarios.
Sponsorship
Innovate UK (104795)
Embargo Lift Date
2023-08-31
Identifiers
External DOI: https://doi.org/10.1016/j.aei.2022.101645
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337571
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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