Modeling dynamic construction work template from existing scheduling records via sequential machine learning. (January 2021)

• Record Type: Journal Article
• Authors: Amer, Fouad; Golparvar-Fard, Mani
• Is Part Of: Advanced engineering informatics. Volume 47(2021)
• Publication Date: 2021-01
• Subjects: Construction planning -- Data mining -- Natural language processing -- Machine learning -- Neural networks; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2020.101198
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Highlights: A method to learn planning & scheduling knowledge as Dynamic Process Templates (DPT). A sequential machine learning learns planning and scheduling knowledge for the DPTs. For an input sequence of activities, DPTs generate predecessor & successor activities. The DPTs predict previously seen & unseen activity dependencies at 98% & 72% accuracy. Abstract: Today, construction planning and scheduling is almost always performed manually, by experienced practitioners. The knowledge of those individuals is materialized, maintained, and propagated through master schedules and look-ahead plans. While historical project schedules are available, manually mining their embedded knowledge to create generic work templates for future projects or revising look-ahead schedules is very difficult, time-consuming and error-prone. The rigid work templates from prior research are also not scalable to cover the inter and intra-class variability in historical schedule activities. This paper aims at fulfilling these needs via a new method to automatically learn construction knowledge from historical project planning and scheduling records and digitize such knowledge in a flexible and generalizable data schema. Specifically, we present Dynamic Process Templates (DPTs) based on a novel vector representation for construction activities where the sequencing knowledge is modeled with generative Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Our machine learning models are exhaustively tested and validated on a diverse dataset of 32 schedules obtained from real-world projects. The experimental results show our method is capable of learning planning and sequencing knowledge at high accuracy across different projects. The benefits for automated project planning and scheduling, schedule quality control, and automated generation of project look-aheads are discussed in detail.